· Confidential: For Review Only A systematic review and critical appraisal of prognostic models...
Transcript of · Confidential: For Review Only A systematic review and critical appraisal of prognostic models...
Confidential: For Review OnlyA systematic review and critical appraisal of prognostic models for outcome prediction in patients with chronic
obstructive pulmonary disease
Journal: BMJ
Manuscript ID BMJ.2018.046909.R2
Article Type: Research
BMJ Journal: BMJ
Date Submitted by the Author: 05-Jul-2019
Complete List of Authors: Bellou, Vanesa; University of Ioannina Medical School, Department of Hygiene and Epidemiology; University of Ioannina Medical School, Department of Respiratory MedicineBelbasis, Lazaros; University of Ioannina Medical School, Department of Hygiene and EpidemiologyKonstantinidis, Athanasios; University of Ioannina Medical School, Department of Respiratory MedicineTzoulaki, Ioanna; Imperial College London, Department of Epidemiology and BiostatisticsEvangelou, Evangelos; University of Ioannina Medical School, Department of Hygiene and Epidemiology; Imperial College London, Department of Epidemiology and Biostatistics
Keywords: Systematic review, Prediction model, Chronic obstructive pulmonary disease, Prognosis, Critical appraisal, Risk of bias
https://mc.manuscriptcentral.com/bmj
BMJ
Confidential: For Review Only
1
A systematic review and critical appraisal of prognostic models for
outcome prediction in patients with chronic obstructive pulmonary disease
Vanesa Bellou1,2, Lazaros Belbasis1, Athanasios K Konstantinidis2, Ioanna Tzoulaki1,3, Evangelos
Evangelou1,3
1Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
2Department of Respiratory Medicine, University Hospital of Ioannina, University of Ioannina Medical School,
Ioannina, Greece
3Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
Corresponding author:
Evangelos Evangelou,
Department of Hygiene and Epidemiology,
University of Ioannina Medical School,
Ioannina, Greece
e-mail: [email protected]
Acknowledgements:
Vanesa Bellou is supported by a PhD scholarship funded by Greek State Scholarship Foundation.
Lazaros Belbasis is supported by a PhD scholarship funded by Greek State Scholarship Foundation.
We would like to thank Prof Karel Moons for his constructive comments on the study design and the final
draft of the manuscript.
Competing risk:
None.
Authorship:
VB, LB, IT and EE designed the study. VB and LB performed the literature search, the data extraction,
and wrote the first draft of the manuscript. All the authors wrote the final version of the manuscript. EE
Page 1 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
2
accepts full responsibility for the work and conduct of the study, had access to the data, and controlled the
decision to publish.
Page 2 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
3
Abstract
Objective: To map and assess prognostic models for outcome prediction in patients with chronic
obstructive pulmonary disease (COPD).
Design: Systematic review.
Data sources: PubMed until November 2018 and hand-searched references from eligible articles.
Eligibility criteria for study selection: Studies developing, validating or updating a prediction model in
COPD patients focusing on any potential clinical outcome.
Results: Our systematic search yielded 228 eligible articles, describing the development of 408 prognostic
models, the external validation of 39 models, and the validation of 20 prediction models derived from
other diseases. The 408 prognostic models were developed in three clinical settings: outpatients (n = 239,
59%), hospitalized patients (n = 155, 38%), and patients attending the emergency department (n = 14,
3%). Among the 408 prognostic models, the most prevalent endpoints were mortality (n = 209, 51%), risk
for acute exacerbation of COPD (n = 42, 10%), and risk for readmission after the index hospitalisation (n
= 36, 9%). Overall, the most commonly used predictors were age (n = 166, 40%), forced expiratory volume
in one second (n = 85, 21%), sex (n = 74, 18%), body mass index (n = 66, 16%), and smoking (n = 65,
16%). Out of the 408 prognostic models, 100 (25%) were internally validated, and 93 (23%) examined
the calibration of the developed model. For 285 models (70%) a model presentation was not available,
and only 56 models (14%) were presented through the full equation. Model discrimination was available
for 311 models (76%) using the c-statistic. Thirty-eight (9%) were externally validated, but in only 12 of
these the external validation was performed by a fully independent team. We identified only seven
prediction models with an overall low risk of bias using PROBAST. These models were ADO, B-AE-D,
B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al. A
meta-analysis of c-statistic was performed for 12 prognostic models and the summary estimates ranged
from 0.611 to 0.769.
Conclusions: In the present study, a detailed mapping and assessment of the prognostic models for
outcome prediction in COPD patients was performed. Overall, we identified several methodological
pitfalls in their development, and a low rate of external validation. Future research should focus on the
improvement of existing models through update and external validation, as well as the assessment of
Page 3 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
4
safety, clinical effectiveness and cost-effectiveness of the application of these prognostic models in
clinical practice through impact studies.
Page 4 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
5
Summary box
What is already known on this topic?
Historically, spirometry and age have been identified as the most important prognostic indicators
in COPD.
GOLD guidelines recommended the use of multivariable prediction models to assess the prognosis
of patients, instead of single predictors such as spirometry or history of exacerbations alone.
No systematic overview has been published to summarize and critically appraise all multivariable
prognostic models for outcome prediction in COPD patients.
What this study adds?
There are more than 400 prognostic models for outcome prediction in COPD patients in an
ambulatory, hospital or emergency department setting, with the majority focusing on mortality.
Only a minority of the developed models have been externally validated and the majority were
characterized by major issues in the statistical analysis and, particularly, in internal validation,
model calibration and presentation, and handling of missing data.
After applying PROBAST, ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated
BODE, and a model developed by Bertens et al were developed in studies assessed as low risk of
bias.
We performed meta-analysis for 12 prognostic models which have been validated in at least three
non-overlapping populations.
Page 5 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
6
Introduction
Chronic obstructive pulmonary disease (COPD) is a major public health issue. COPD accounts for at
least 2.9 million deaths annually [1] and is a leading cause of morbidity and mortality with its prevalence
projected to increase over the following years. Morbidity associated with the disease entails physician
visits, emergency department visits and hospitalizations [2], all of which lead to a substantial economic
burden. The greatest proportion of the costs is attributed to COPD exacerbations [2].
COPD is a quite heterogeneous disease and stratifying cases according to prognosis would raise the
possibility of a precision medicine approach. For many years now, forced expiratory volume in one second
(FEV1) and age have been considered to be the most important prognostic indicators in COPD [3]. More
recently, a wide variety of individual clinical factors have been also linked to COPD prognosis.
Prognostic models, in general, have two distinct uses; they classify patients in groups with different
prognosis, as well as estimate prognosis for individual patients. While these are two different ways of
looking at the same information, they differ fundamentally and the ultimate goal is to guide therapeutic
and further diagnostic choices [4]. Use of a composite index to assess prognosis in COPD patients may
provide a more comprehensive way of evaluation, incorporating a cluster of systemic manifestations of
the disease [5]. Furthermore, in patients with COPD, multivariable prognostic models for various clinical
outcomes could be used in clinical practice to assist decision making about hospitalization or admission
to intensive care units and treatment strategy [6].
Numerous prognostic models, combining multiple predictors for COPD-related outcomes, have been
developed. Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines recommend the
use of multivariable prediction models to assess the prognostic profile and facilitate follow-up of patients,
instead of single predictors such as spirometry or history of exacerbations alone. Also, in the latest GOLD
statement, BODE index is proposed as a tool to determine who needs referral for lung transplantation
consideration [7].
In this study, we aim to systematically summarize the reported multivariable prognostic models
developed for predicting subsequent outcomes in patients diagnosed with COPD, to map their
characteristics, and to examine whether they have undergone external validation. We will apply risk of
bias assessment of the methodological features of the available studies developing or validating prognostic
models using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For prognostic models
Page 6 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
7
that have multiple validation studies, we will perform a meta-analysis for model performance and model
calibration in order to obtain more accurate estimates.
Methods
We designed this systematic review following the Checklist for critical Appraisal and data extraction
for systematic Reviews of prediction Modelling Studies (CHARMS) and the recent guidance by Debray
et al [8,9]. A protocol of this article was published on PROSPERO (registration number
CRD42017069247).
Literature search
We systematically searched PubMed from inception to November 11, 2018 to capture all studies
developing and/or validating a prognostic model for outcomes in COPD patients. Based on previous
research [10,11], we created the following search algorithm: (predict* OR progn* OR "risk prediction"
OR "risk score" OR "risk calculation" OR "risk assessment" OR "c statistic" OR discrimination OR
calibration OR AUC OR "area under the curve" OR "area under the receiver operator characteristic curve")
AND ("chronic obstructive pulmonary disease" OR emphysema OR "chronic bronchitis" OR COPD). The
literature search was independently conducted by two researchers (VB, LB) and discrepancies were
resolved by a third researcher (IT). We further hand-searched the references of each eligible article for
potential additional eligible studies.
Eligibility criteria
We included all studies that reported the development or validation of at least one multivariable model
for predicting the risk for any clinical outcome in COPD patients. A detailed description of the PICOTS
[8,9] for this review is presented in Table 1. To consider a study as eligible, we followed the definition of
prognostic model studies as proposed by the Transparent Reporting of a multivariable prediction model
for Individual Prognosis Or Diagnosis (TRIPOD) statement. [12] Accordingly, it should specifically
report the development, the update or the external validation of a prognostic model used in COPD patients
for making individualized predictions, either in its objectives or conclusions. A study was also eligible if
the development or update of a prognostic model could be deduced by the available information through
the full-text (e.g., model presentation, measures of predictive performance for a multivariable model).
Page 7 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
8
Eligible outcomes included any possible clinical endpoint of COPD patients, such as mortality,
exacerbations and hospitalizations.
The eligible studies could report the development of multivariable models, the external validation of
an existing model, and/or the update of an existing model. Model updating may range from simple
adjustment of the baseline risk/hazard, to additional adjustment of predictors’ weights using the same or
different adjustment factors, to re-estimating predictor weights and adding new predictors or removing
existing predictors from the original model [13]. External validation studies aim to assess the predictive
performance of an existing model in an independent population [13]. We included external validation
studies that explicitly estimated and presented a measure of model performance. We also considered
studies validating prediction models originally developed for other diseases, in COPD population. Also,
included articles should report original research, study humans and be written in English.
We excluded studies developing or validating diagnostic models to detect or exclude COPD presence
in suspected patients, studies examining only independent prognostic factors, methodological studies, and
COPD case finding or COPD screening studies. Also, we excluded studies that developed search
algorithms to identify existing COPD cases based on administrative data. Given that prognostic models
estimate a probability of a certain outcome for an individual over a specified time horizon, we excluded
cross-sectional studies, because in this study design predictors and the outcome are measured
concurrently. However, cohort studies performing an external validation of a model derived from a cross-
sectional study were deemed eligible.
Data extraction
To facilitate the data extraction process, a standardized form was constructed by three researchers
(VB, LB, IT) following recommendations by CHARMS checklist [8]. Data extraction was independently
performed by two researchers (VB, LB). From all eligible articles, we extracted information on first
author, year and journal of publication, and model name. From articles describing model development,
we extracted the following information: study design, study population, geographical location, predicted
outcome, definition of outcome, prediction horizon, definition of COPD, modelling method, method of
internal validation, number of participants and number of events, number and type of predictors in final
model, model presentation, and measures of predictive performance (discrimination, calibration,
classification, overall performance). Potential measures of discrimination were c-statistic, D-statistic;
potential measures of classification were sensitivity, specificity, positive and negative predictive value
Page 8 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
9
and predictive accuracy; potential measures for overall performance were R2 and Brier score, and potential
measures for assessment of calibration were calibration plot, calibration-in-the-large, calibration slope,
Hosmer-Lemeshow test, Harrel’s E statistic, and calibration test [14,15]. Furthermore, we evaluated
whether the authors only reported the apparent performance of a prediction model or if they examined
overfitting using internal validation. Additionally, we examined whether a shrinkage of regression
coefficients toward zero was performed in eligible studies and which methodology was applied. We
considered that the authors adjusted for optimism sufficiently if they re-evaluated the performance of a
model in internal validation and performed shrinkage of model coefficients as well. We extracted whether
the authors performed decision curve analysis and net benefit analysis to evaluate the clinical usefulness
of a model [15,16]. Moreover, for each eligible study, we examined whether the authors reported the
presence of missing data on examined outcomes and/or variables included in prediction models; and, if
so, we recorded how missing data were treated. Furthermore, we extracted information on how continuous
variables were handled, and whether non-linear trends for continuous predictors were assessed by
applying polynomials, fractional polynomials or cubic splines. If the handling of continuous predictors
was not described explicitly, we scrutinized the full-text and the tables of the respective papers to derive
this information from the reported effect sizes. In the case that this process was inconclusive, we described
the handling of continuous predictors as unclear.
In articles examining the performance of the same prediction model on various outcomes or multiple
timepoints, we retained the prediction model referring to the outcome or timepoint mentioned as primary
analysis of the study. In cases where a primary timepoint was not specified, we considered the prediction
with the longest horizon as the primary analysis of the study, because longer follow-up duration would
lead to a larger number of events. Whenever a study described model performance both in an overall
sample and in specific subgroups of population, we extracted the analysis on the total population.
From articles describing model external validation, we extracted study population, geographic
location, number of participants and events, the model’s performance and calibration. If an article
described multiple models, we performed separate data extraction for each model. For each model
externally validated in multiple articles, we included in our analysis only external validation studies with
non-overlapping populations. Furthermore, we examined whether the research team performing the
external validation was independent to the research team developing the prediction model.
Risk of bias assessment
Page 9 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
10
We appraised the presence of bias in the studies developing or externally validating prognostic
models using PROBAST, which is a risk of bias assessment tool designed for systematic reviews of
diagnostic or prognostic prediction models [17,18]. It contains a multitude of questions in 4 different
domains: participants, predictors, outcome and statistical analysis. Questions are answered with yes,
probably yes, probably no, no and no information depending on the characteristics of the study. If a domain
contains at least one question signalled as no or probably no it is considered high risk. To be considered
low risk a domain should contain all questions answered with yes or probably yes. Overall risk of bias is
graded as low risk when all domains were considered low risk, while overall risk of bias is considered
high risk when at least one of the domains is considered high risk. Risk of bias assessment was
independently performed by two researchers (VB, LB).
PROBAST describes the assessment of both development studies and external validation studies.
Often, articles describe the development of multiple prediction models using different populations or
different statistical approaches. Hence, differences in the risk of bias assessment is expected among
different prediction models developed in the same article. For this reason, we chose to report the risk of
bias assessment per developed prediction model and not per article. Furthermore, articles may describe
the external validation of multiple prediction models in the same population or in multiple different
populations. For this reason, we refer to external validation efforts and we report the risk of bias
assessment per external validation effort.
Statistical analysis
We calculated and reported descriptive statistics to summarize the characteristics of the models; for
continuous variables, we calculated the median and interquartile range and, for binary variables, we
calculated the respective percentages.
For the prediction models that were examined in more than 2 independent datasets (excluding the
model development dataset), we performed a random-effects meta-analysis to calculate a summary
estimate for model performance and model calibration. We also considered for the meta-analysis, those
prediction models that were internally validated through bootstrapping or cross-validation and were
externally validated in only two independent datasets. We followed a recently published framework for
the meta-analysis of prediction models [9,19]. In the case that a measure of uncertainty (i.e., standard error
or 95% confidence interval) was not available for mean c-statistic, we used a formula to approximate the
standard error of mean c-statistic based on number of events and number of participants [9,19,20]. The
Page 10 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
11
statistical analysis was performed on R version 3.5.0. For the meta-analysis of prediction models, we used
the R package ‘metamisc’ [19].
Results
Out of the 17,538 screened papers, 228 papers were eligible (Figure 1). These articles described the
development of 408 prognostic models in COPD patients, the external validation of 39 prognostic models,
and the application of 20 prognostic models originally developed for other health outcomes than prognosis
of COPD patients. One of the eligible papers was identified through the hand-search of references from
eligible articles [21]. The prognostic models were mainly developed in United States of America (n = 91,
22%), Spain (n = 57, 14%), and United Kingdom (n = 34, 8%), whereas 80 models were developed in
multicentre studies from multiple countries (20%). For the derivation cohorts, the median sample size was
409 (IQR, 163 to 1033) and the median number of events was 63 (IQR, 36 to 188). For the internal
validation cohorts, the median sample size was 831 (IQR, 225 to 4192) and the median number of events
was 77 (IQR, 40 to 370).
The eligible prognostic models were developed in a variety of clinical settings. 239 (59%) prognostic
models were developed in an outpatient setting and 155 (38%) were developed on a sample of hospitalized
patients. Also, 14 (3%) prognostic models were developed for COPD patients attending the emergency
department. The developed models focused on a wide range of clinical outcomes. The most commonly
used endpoints were mortality (n = 209, 51%), exacerbation (n = 42, 10%) and re-admission (n = 36, 9%).
Supplementary Table 1 presents a summary of the predicted outcomes per clinical setting. Also, 24
prognostic scores focused on a composite outcome. The most commonly used predictors were age (n =
166, 40%), forced expiratory volume in one second (n = 85, 21%), sex (n = 74, 18%), body mass index (n
= 66, 16%), smoking (n = 65, 16%), previous exacerbations (n = 53, 13%), previous hospitalisations (n =
50, 12%), BODE index (n = 43, 10%), mMRC dyspnea scale (n = 42, 10%), and Charlson comorbidity
index (n = 35, 9%). Figure 2 presents the predictors which were used in at least 20 models, and Figure 3
presents the 10 most common predictors stratified by clinical setting.
Below, we describe the methodological and clinical characteristics for a total of 408 prognostic
models, based on clinical setting.
Prognostic models for outpatients
Page 11 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
12
The majority of the prognostic models (n = 239, 59%) were developed on a sample of COPD patients
examined in an outpatient facility (Supplementary Table 3). For the derivation cohort, the median sample
size was 431 (IQR, 244 to 1000) and the median number of events was 63 (IQR, 33 to 155). For the
internal validation cohort, the median sample size was 249 (IQR, 204 to 3468) and the median number of
events was 150 (IQR, 64 to 1642). The most common clinical endpoints examined by these models were:
mortality (n = 124, 52%), exacerbations (n = 40, 17%), spirometric indices (n = 25, 10%), hospitalization
(n = 16, 7%), treatment failure during an acute exacerbation (n = 8, 4%), composite outcome (n = 9, 4%).
The most commonly used predictors in these models were: age (n = 105, 44%), FEV1 (n = 69, 29%),
smoking (n = 54, 22%), BMI (n = 51, 22%), sex (n = 43, 18%), previous exacerbations (n = 43, 18%),
BODE index (n = 43, 18%), previous hospitalisations (n = 28, 12%), and diabetes mellitus (n = 24, 10%).
198 (83%) of these models reported a c-statistic, while the remaining 41 (17%) did not report a
discrimination metric. 172 of the ambulatory patient models only assessed apparent performance in the
development study. One prognostic model performed temporal validation, while the remaining performed
cross-validation (n = 28, 12%), bootstrapping (n = 25, 10%), random split (n = 12, 5%) or a combination
of methods (n = 1). 192 (80%) prognostic models were not calibrated. 48 (20%) prognostic models
assessed calibration and the most frequent method was Hosmer-Lemeshow test (n = 35, 15%). Various
modelling methods were applied, with the most frequent being Cox regression (n = 90, 38%), logistic
regression (n = 79, 34%), negative binomial regression (n = 21, 9%), and linear regression (n = 16, 7%).
In 12 prognostic models, shrinkage of regression coefficients was performed to reduce overfitting.
Application of a uniform shrinkage factor to all the regression coefficients was used in 9 models,
application of a penalized maximum likelihood method to estimate the regression coefficients was
described for 1 prognostic model and lasso regression was applied in 2 prognostic models to perform
shrinkage for selection of predictors. For 17 prognostic models a non-linear association between
continuous predictors and predicted outcome was examined using the following methods: polynomials (n
= 7), restricted cubic splines (n = 6), fractional polynomials (n = 2), and Box-Tidwell transformation (n =
2). A significant amount (n = 177, 73%) did not have any type of model presentation, while only 24 (10%)
reported the full regression equation. The most common type of presentation was sum score (n = 31, 13%).
Only 1 study performed decision analysis [22]. In this study, net benefit and decision curves are available
for updated ADO index. Net benefit is a category of decision analysis, comparing benefits and harms
directly after transforming them on the same scale. A detailed description of all the methodological
characteristics is presented in Table 2.
Page 12 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
13
Prognostic models for hospitalized patients
155 models were developed in patients hospitalized in medical wards, intensive care units or
rehabilitation centers (Supplementary Table 4). The median sample size of the derivation cohort was 303
(IQR, 102 to 920) and the median number of events was 67 (IQR, 37 to 311). The median sample size of
the internal validation cohort was 4131 (IQR, 731 to 4840) and the median number of events was 333
(IQR, 35 to 370). The most prevalent outcomes assessed were mortality (n = 78, 50%), re-admission after
an index hospitalisation (n = 36, 23%), failure of non-invasive ventilation (n = 14, 9%) and composite
outcomes (n = 13, 8%). The following are the predictors encountered in the majority of the prognostic
models: age (n = 56, 36%), sex (n = 30, 19%), PaCO2 (n = 24, 15%), previous hospitalisations (n = 20,
13%), length of hospital stay (n = 20, 13%), Charlson comorbidity index (n = 19, 12%), pH (n = 18, 12%),
heart failure (n = 16, 10%), BMI (n = 15, 10%), and serum albumin (n = 15, 10%).
31 of 155 prognostic models (20%) were developed for patients hospitalised in intensive care units
to predict mortality (n = 22), weaning success (n = 2), need for mechanical ventilation (n = 6), and duration
of mechanical ventilation (n = 1). The most commonly used predictors were the following: age (n = 12),
Glasgow or Japan Coma Scale (n = 9), APACHE II (n = 8), sex (n = 6), pH (n = 6), hemoglobin (n = 6),
serum albumin (n = 6), heart failure (n = 6), and hypertension (n = 6).
Only 102 prognostic models (66%) reported a c-statistic, while 53 models (34%) did not assess
discrimination. 131 prognostic models (85%) did not perform internal validation and in the few models
that this was performed, bootstrapping (n = 9, 6%), random split (n = 7, 5%), cross-validation (n = 3, 2%)
or a combination of aforementioned methods (n = 2, 1%) were used. Also 3 prognostic models (2%)
performed temporal validation. 115 prognostic tools (74%) did not assess their calibration. Hosmer-
Lemeshow test (n = 34, 22%) was the most frequently used method of calibration. Most of the prognostic
models did not have a model presentation (n = 104, 67%). The most common presentation was sum score
(n = 16, 10%), while a regression formula was available for 27 prognostic models (17%). The most
frequently used regression methods were logistic regression (n = 112, 73%) and Cox regression (n = 21,
14%). For 4 prognostic models, shrinkage was applied to reduce overfitting. Application of a uniform
shrinkage factor to all the regression coefficients was performed for 2 models, the penalized maximum
likelihood approach was used in 1 model, whereas, for 1 model, lasso regression was applied. For 3
prognostic models, the non-linear association of predictors with the predicted outcome was considered
Page 13 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
14
using polynomials (n = 1), fractional polynomials (n = 1) and Box-Tidwell transformation (n = 1). One
study after developing a prognostic model performed a decision analysis [23].
Prognostic models for patients presenting at the emergency department
Only 14 prognostic models were developed for patients that attend the emergency department
(Supplementary Table 5) with a median sample size of 1195 (IQR, 871 to 1250) and a median number of
events of 77 (IQR, 40 to 137) in the derivation cohort. The median sample size of internal validation
cohort was 1235 (IQR, 266 to 1244) and the median number of events was 52 (IQR, 29 to 66). The
outcomes that were examined were mortality (n = 7, 50%), change in physical activity (n = 2), composite
outcome (n = 2), hospitalization (n = 1), ICU admission (n = 1) and treatment failure after a visit in
emergency department for an acute exacerbation (n = 1). 5 of these models examined a long term
prediction horizon (more than 1 month). The most prevalent variables included in these models were:
long-term oxygen therapy or non-invasive ventilation at home (n = 8, 57%), age (n = 5, 36%), mMRC
dyspnea scale (n = 5, 36%), Charlson comorbidity index (n = 4, 29%), PaCO2 (n = 3, 21%), use of
inspiratory accessory muscles and paradoxical breathing (n = 3, 21%), Glasgow or Japan coma scale (n =
3, 21%).
3 of these models did not report an assessment of their discrimination, while 11 reported a c-statistic.
5 models did not perform any internal validation, while 8 models used a random split of the dataset. A
single model used bootstrapping for internal validation. The modeling methods employed by these models
were logistic regression (n = 10), linear regression (n = 2) and machine learning (n = 2). A shrinkage
procedure was not applied for any model.
External validation studies
38 of 408 prognostic models (9%) were externally validated at least once. However, only twelve of
these models (3%) were externally validated by a fully independent research team. The prognostic models
that were externally validated more than 5 times were the following: ADO (17 cohorts), BODE (13
cohorts), BODEx (8 cohorts) and CODEX (7 cohorts).
Four prognostic models (i.e., DOSE index, SAFE index, mBODE% index and COPD Severity Score)
were developed in cross-sectional studies, and these models were not described in the aforementioned
section of models. We retained only their external validation in cohort studies, which were 12 for DOSE
Page 14 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
15
index, 1 for COPD Severity Score, SAFE and mBODE% index. Table 3 presents all the external validation
studies of the prognostic models for outcome prediction in COPD patients.
Risk of bias assessment
We assessed the risk of bias of all studies developing or externally validating a prognostic model
using PROBAST. In Figure 4, we present a summary of risk of bias assessment of developed models by
domain. Seven prognostic models were assessed as low risk of bias and all these models were developed
for ambulatory COPD patients (i.e, ADO index, B-AE-D index, B-AE-D-C index, extended ADO index,
updated BODE index, updated ADO index, and a model developed by Bertens et al). Table 4 presents the
clinical setting, the predicted outcome and the time horizon, the events per variable number, the shrinkage
method and the optism-corrected C-statistic for these 7 prognostic models with low risk of bias. Also, the
predictors included in these 7 prognostic models are presented in Table 5. For one of these models (i.e.,
extended ADO index), a model presentation was not available. For the remaining 6 models, the model
equations are described in Table 6. Overall, 338 models were of low risk of bias for participants, 394
models were of low risk of bias for predictors, 402 models were of low risk of bias for outcome, but only
10 models were of low risk of bias for statistical analysis.
We additionally assessed a total of 116 validation efforts (Figure 5). Of these efforts, only 5 were
graded as low risk of bias using PROBAST. These pertained to one validation of the model developed by
Bertens et al, one validation of DECAF, one validation of BAP-65 and 2 validations of PEARL. The
remaining validation efforts were high risk of bias.
Validation of prognostic models originally developed for other diseases
Furthermore, 28 papers examined the predictive ability of 20 prognostic models originally developed
for other disease outcome or other patients (Supplementary Table 6). Specifically, these models are
APACHE II and III, CHA2DS2-VASC score, Charlson comorbidity index, CURB-65, CRB-65, CREWS,
Elixhauser comorbidity index, Framingham risk score, GRACE, HOSPITAL score, LACE, MDA,
MODS, NEWS, NRS 2002, PSI, Salford-NEWS, SAPS and SOFA. Overall, the predictive ability of these
models was examined for the following outcomes: mortality, exacerbation, hospitalization, non-invasive
ventilation failure, or high-cost patients.
Meta-analysis of prognostic models
Page 15 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
16
Overall, we performed a total of 19 meta-analyses of c-statistic for 12 prognostic models (i.e., ADO
index, APACHE II, BOD index, BODE index, BODEx index, CODEX, COTE index, CURB-65, DOSE
index, LACE index, updated ADO index, updated BODE index). For ADO index, APACHE II, BODE
index, BODEx index, and CODEX, we performed 2 different meta-analyses for 2 distinct outcomes,
whereas for DOSE index we performed 3 different meta-analyses for 3 distinct outcomes. 11 meta-
analyses examined the risk of mortality, 2 meta-analyses examined the risk of acute exacerbation of
COPD, 5 meta-analyses examined the risk of re-admission or mortality, and 1 meta-analysis was focused
on failure of non-invasive ventilation. In 12 meta-analyses of c-statistic, we observed large between-study
heterogeneity estimates (i.e., I2 >50%). Summary c-statistic estimates ranged from 0.611 for DOSE index
in prediction of a composite outcome to 0.769 for APACHE II in prediction of mortality. Figure 6 presents
a forest plot of all conducted meta-analyses. Table 7 presents the results of the meta-analyses of c-
statistics. We could not perform meta-analysis of calibration measures, because they were not adequately
reported in the external validation studies.
Discussion
Our systematic search yielded a detailed map of more than 400 prognostic models for the prediction
of clinical outcomes in COPD patients. These scores were developed on a wide range of clinical settings,
including outpatient services, emergency departments, medical wards, intensive care units and primary
care structures. We identified 7 prognostic models that were developed in low risk of bias studies as
assessed with PROBAST, and all these models were externally validated at least once. Also, we
complemented our systematic review and bias assessment with a meta-analysis of c-statistic for 12
prognostic models.
The majority of the prognostic models were developed in Western countries; more than half were
developed in USA, Spain and UK. Although COPD is a quite prevalent chronic disease in low and middle-
income countries [24], only a trivial number of prognostic models were developed or validated in Asia,
Africa or South America. In the developing world, the main risk factors for COPD are history of
tuberculosis and exposure to indoor air pollution [25]. Previous literature has shown a more favorable
prognosis in COPD inflicted by biomass fuel than in smoking-induced COPD [26,27]. We found only one
paper performing an external validation of BODEx index and COTE index on individuals with COPD
Page 16 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
17
associated with biomass fuels [28]. However, this study was conducted in Spain. Our literature search
indicates that currently developed prognostic models could not be generalized in developing countries,
given that they have not been validated in these populations, except an external validation of BODE index
in Brazilian population [29].
Our systematic review unveiled a number of methodological pitfalls in the development of the models
which is also reflected in the risk of bias assessment. Only one-fourth of the models were internally
validated and a tenth of the models were externally validated. The performance of a prognostic model is
overestimated when simply determined on the sample of subjects that was used to construct the model.
Internal validation provides a more accurate estimate of model performance in new subjects, when it is
properly performed, i.e. using bootstrapping or cross-validation techniques [30]. To ensure the
generalizability of a prognostic model in populations with different characteristics, external validation
studies are required [13]. However, independent populations with large sample sizes of COPD patients
and available COPD-specific information (used as predictors in the prognostic models) can be hard to
obtain to measure external validity. This necessitates the use of suitable internal validation techniques to
provide an optimism-adjusted performance for the population in which the model was originally
developed. Nonetheless, an evaluation of model performance in a different sample is not sufficient to
address overfitting and studies developing prognostic models should also apply shrinkage, which is a
methodology to reduce overfitting by re-adjusting the regression coefficients. Indeed, our systematic
review depicted that only a very small number of prognostic models performed shrinkage.
An important finding of our systematic review was that only one-fourth of the models assessed
calibration, which is the accuracy of absolute risk estimates, i.e. it informs clinicians how similar the
predicted absolute risk is to the true (observed) risk in groups of patients classified in different risk strata
[31]. Also, the majority of the models either did not report any method of handling missing data or
performed a complete-case analysis. Missing data often lead to biased estimates if not imputed, because
it can distort the performance of a prediction model if the missingness of values is related to other known
characteristics [32]. Additionally, in about half of the prognostic models, continuous predictors were
dichotomized or categorized and only for a small percentage of prognostic models, the non-linearity of
continuous predictors was examined. However, it is already shown that categorizing continuous predictors
into 2 or more categories leads to weaker prediction performance than analysing predictors on a
Page 17 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
18
continuous scale, due to significant loss in information [33]. Additionally, non-linear associations can be
efficiently modelled using restricted cubic splines or fractional polynomials [34].
Another key issue we should highlight is that discrimination and classification statistics that are
usually reported in prognostic model studies do not inform us on the clinical value of a model. Decision
analysis is required to evaluate whether the implementation of a prognostic model in clinical practice
would be beneficial i.e. do more good than harm [16]. However, only 2 studies performed decision
analysis. Moreover, the applicability of a prediction model in clinical practice depends on the model
presentation. In clinical practice, decision trees, sum scores, nomograms and risk charts are commonly
used in decision-making. Sum scores and decision trees are more suitable for acute care settings, while
risk charts and nomograms allow for a more detailed risk assessment and are more fitted for outpatient
settings. However, more than two-thirds of the developed models did not have any type of model
presentation. Lack of presentation of a predictive tool does not allow its use in clinical practice.
Additionally, lack of reporting of the regression formula in many of the prognostic models hinders future
efforts for validation, update and recalibration [35].
The variables most commonly used in the development of prognostic models were age, FEV1, sex,
BMI, smoking, previous exacerbations, previous hospitalisations, mMRC dyspnea scale, BODE index
and Charlson comorbidity index. The aforementioned variables are either anthropometric features,
important factors in the natural progression of the disease, or markers of disease severity. They are easily
measured so they are available in settings where limited resources are accessible (e.g. primary care) and
in acute care facilities where prompt decisions are ought to be made (e.g. emergency department). Another
advantage of these predictors is low risk for measurement bias which leads to a smaller possibility for
exposure misclassification. Finally, these variables have been identified as individual prognostic factors
in COPD [3,36–40]. However, variability in the top predictors was observed when the predictors were
stratified by clinical setting. For example, in the prognostic models designed based on COPD patients
presenting at the emergency department, the most commonly used predictor was the use of long-term
oxygen therapy or non-invasive ventilation at home, which is uncommon in other settings. Also, smoking
was a frequently used predictor only in models derived from outpatient setting, and it was only rarely used
as a predictor in hospitalized patients. Furthermore, comorbid conditions, either in the form of
multidimensional indices such as Charlson comorbidity index, or as distinct conditions (e.g., diabetes
mellitus, cardiovascular disorders), were widely used and ranked among the most common predictors to
Page 18 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
19
predict clinical outcomes in all settings. Serum albumin and arterial blood gases were used almost
exclusively as predictors in the hospitalized patients and patients visiting the emergency department.
The most extensively validated prognostic models were BODE index [41] and ADO index [42].
BODE index is the most established prognostic model in COPD and was developed to predict mortality
[41]. In GOLD statement, BODE index is used in the prediction of mortality and in clinical decision-
making for lung transplantation and post-discharge follow-up of patients. The predictors included in
BODE index are BMI, FEV1, dyspnea and exercise capacity. Despite the lack of calibration in the original
study of BODE model, it has been validated and updated extensively in medical literature. The updated
BODE index, a recalibration of BODE index, is among models with low risk of bias.
ADO index was based on the predictors used to develop BODE. It entails FEV1, dyspnea and age
[42]. The elimination of 6 minute walking distance that was used in BODE, was based on the rationale of
a more easily applicable model, even by primary care physicians in settings with limited resources, rather
than respiratory professionals alone. Despite the good predictive performance that ADO depicted in its
development study, it showed poor calibration. This led to a re-calibration of ADO index in an independent
population resulting in an updated ADO, as well as an extended version of the re-calibrated model with
the addiation of two variables. ADO index, updated ADO index, and extension of updated ADO index
had low risk of bias and have been externally validated.
Three additional prognostic models presented low risk of bias and were developed for the outpatient
setting. B-AE-D index, and its update B-AE-D-C, were developed for stable COPD patients being at
GOLD stage II to IV to predict 2-year all-cause mortality. The prognostic model developed by Bertens et
al was the only low-risk-of-bias prognostic model that was developed to predict the risk for future
exacerbation in 2 years in stable COPD patients.
An essential step before the application of prediction models in clinical practice is the external
validation in indepdendent populations with different clinical characteristics, and the comparison of
performance among different prognostic models to identify the models with the best discrimination and
calibration. A large-scale effort was recently published to externally validate and compare multiple
prognostic models for COPD patients [43]. The researchers used the methodology of network meta-
analysis to compare the performance of eight multivariable prognostic models and two different GOLD
classifications in 24 cohort studies. In this analysis, updated ADO index had the best ability to predict 3-
year mortality in patients with COPD, followed by the updated BODE index and e-BODE index. However,
Page 19 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
20
the researchers pointed out that the approach of network meta-analysis has not integrated yet the synthesis
of calibration measures [43].
Recommendations and policy implications
Based on the aforementioned methodological pitfalls, the following recommendations could be stated
to improve the research on prognostic models for outcome prediction in COPD patients. First, model
development studies should adjust for overfitting by performing internal validation(mainly through non-
random split or resampling techniques such as bootstrapping) and shrinkage techniques, and provide an
optimism-adjusted performance. Second, model calibration should be examined. If a prognostic model
presents poor calibration, efforts should be made to improve its calibration by updating it either through
re-calibration or through addition of new variables. Third, researchers should apply imputation techniques
when missing data are observed, and they should report the full equation of the prognostic model to allow
its external validation and update by independent research teams. Fourth, continuous predictors should
not be dichotomized and potential non-linear association with the outcome should be examined using
fractional polynomials or restricted cubic splines [44].
We should point out that the vast majority of prognostic models predicted the risk for mortality. Other
clinically important outcomes, such as risk for exacerbation, a very common outcome in randomized
clinical trials for COPD treatment, attracted much less attention. Also, the predictive ability of existing
models focused on European and North American populations and could not be easily generalized. Thus,
there is a need for external validation studies of existing models in other populations.
External validation studies are not sufficient to guarantee the clinical utility of a prediction model. To
select a prediction model for implementation in clinical practice impact studies are needed [13]. These are
randomized clinical trials applying a prognostic model in a clinical setting and assessing its clinical utility
for decision making. However, we found only one impact study in the literature [45]. This study concluded
that the DECAF score, a prognostic model [46] that was initially developed for patients hospitalized with
an exacerbation to predict in-hospital mortality is safe, clinically effective and cost-effective in the
selection of COPD patients with an exacerbation that could be treated at home [45,47].
Comparison with other studies
Page 20 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
21
A previously published systematic review identified 15 prognostic models (either original models or
updates of existing models) for stable COPD patients that were published until September 2010 [6]. This
systematic review mainly focused on the description of clinical characteristics of prognostic models, i.e.
population characteristics and predictors. In contrast, our systematic review included a much broader
spectrum of COPD patients by additionally detecting prognostic models for hospitalized COPD patients
and for COPD patients visiting the emergency department. As a consequence, we captured a total of 408
prognostic models from various clinical settings. Furthermore, we reported a detailed presentation of
methodological characteristics in multivariable prognostic models for COPD. We additionally performed
a meta-analysis for prognostic models with multiple external validation studies, and we assessed the risk
of bias using PROBAST.
Strenghts and limitations
The major strength of our study is that it provides an overall mapping of the available research on
prognostic models for outcome prediction in COPD patients. We collected all published prognostic
models used to forecast any clinical outcome that may occur in the course of COPD. We present a detailed
description of the characteristics of the developed models as well as updates and validation studies of
existing models. Another important aspect of our paper is the critical appraisal of prognostic models in
COPD using the PROBAST tool. We also performed a meta-analysis of c-statistics for prognostic models
that were externally validated in multiple independent populations.
A limitation of our study is the inability to perform meta-analysis of calibration measures for
prognostic models, due to poor reporting of calibration in the validation studies. Also, in the meta-analyses
of c-statistics, we observed large between-study heterogeneity. Potential sources of heterogeneity could
the differences in clinical setting, patient characteristics and time horizons across the validation studies,
but we could not perform meta-regression analyses or sensitivity analyses due to the small number of
external validation studies per prognostic model [34].
Conclusions
Our manuscript constitutes a map of the research on multivariable prognostic models for outcome
prediction in COPD patients to summarize their methodological characteristics, their calibration and
performance. There is an abundance of prognostic models for patients with COPD, so it can be challenging
for healthcare professionals to decide on which one to use in their specific setting or population. Future
Page 21 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
22
prognostic research should steer towards recalibration or update of existing prognostic models with the
addition of new predictors to enhance their prognostic performance. Studies updating existing models
should sufficiently estimate optimism-adjusted performance and calibration measures by applying
appropriate internal validation and adjust for overfitting by applying shrinkage techniques. Future studies
should also perform multiple imputation to handle missing data as well as examine non-linearity of
continuous predictors.
Moreover, to ensure the generalizability of prognostic models, validation studies in populations with
different characteristics, in regards of setting and inclusion criteria, are needed. Prognostic tools with good
calibration and external validity should inform clinical practice as well as be recommended by guidelines
after they undergo impact studies, which examine the impact of using the model for a specific outcome in
clinical practice.
Page 22 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
23
References
1 Soriano JB, Rodríguez-Roisin R. Chronic obstructive pulmonary disease overview: epidemiology,
risk factors, and clinical presentation. Proc Am Thorac Soc 2011;8:363–7.
doi:10.1513/pats.201102-017RM
2 From the Global Strategy for the Diagnosis, Management and Prevention of COPD, Global
Initiative for Chronic Obstructive Lung Disease (GOLD) 2017. 2017.http://goldcopd.org
3 Celli BR, Casanova Macario C. The use of multidimensional indices. In: Controversies in COPD.
European Respiratory Society 2015. 143–60. doi:10.1183/2312508X.10019714
4 Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med
2000;19:453–73. doi:10.1002/(SICI)1097-0258(20000229)19:4<453::AID-SIM350>3.0.CO;2-5
5 Celli BR. Predictors of mortality in COPD. Respir Med 2010;104:773–9.
doi:10.1016/j.rmed.2009.12.017
6 van Dijk WD, van den Bemt L, van den Haak-Rongen S, et al. Multidimensional prognostic
indices for use in COPD patient care. A systematic review. Respir Res 2011;12:151.
doi:10.1186/1465-9921-12-151
7 Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global Strategy for the Diagnosis, Management,
and Prevention of Chronic Obstructive Lung Disease 2017 Report: GOLD Executive Summary.
Eur Respir J 2017;49. doi:10.1183/13993003.00214-2017
8 Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical Appraisal and Data Extraction for
Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist. PLoS Med
2014;11:e1001744. doi:10.1371/journal.pmed.1001744
9 Debray TPA, Damen JAAG, Snell KIE, et al. A guide to systematic review and meta-analysis of
prediction model performance. BMJ 2017;356:i6460.
10 Geersing G-J, Bouwmeester W, Zuithoff P, et al. Search Filters for Finding Prognostic and
Diagnostic Prediction Studies in Medline to Enhance Systematic Reviews. PLoS One
2012;7:e32844. doi:10.1371/journal.pone.0032844
11 Damen JAAG, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the
Page 23 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
24
general population: systematic review. BMJ 2016;:i2416. doi:10.1136/bmj.i2416
12 Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction
model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ
2015;350:g7594.
13 Moons KGM, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation,
model updating, and impact assessment. Heart 2012;98:691–8. doi:10.1136/heartjnl-2011-301247
14 Steyerberg EW. Clinical Prediction Models. New York, NY: : Springer New York 2009.
doi:10.1007/978-0-387-77244-8
15 Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a
framework for traditional and novel measures. Epidemiology 2010;21:128–38.
doi:10.1097/EDE.0b013e3181c30fb2
16 Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction
models, molecular markers, and diagnostic tests. BMJ 2016;:i6. doi:10.1136/bmj.i6
17 Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A Tool to Assess the Risk of Bias and
Applicability of Prediction Model Studies. Ann Intern Med 2019;170:51. doi:10.7326/M18-1376
18 Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A Tool to Assess Risk of Bias and
Applicability of Prediction Model Studies: Explanation and Elaboration. Ann Intern Med
2019;170:W1. doi:10.7326/M18-1377
19 Debray TP, Damen JA, Riley RD, et al. A framework for meta-analysis of prediction model
studies with binary and time-to-event outcomes. Stat Methods Med Res 2018;:962280218785504.
doi:10.1177/0962280218785504
20 Snell KI, Ensor J, Debray TP, et al. Meta-analysis of prediction model performance across
multiple studies: Which scale helps ensure between-study normality for the C -statistic and
calibration measures? Stat Methods Med Res 2018;27:3505–22. doi:10.1177/0962280217705678
21 Madkour AM, Adly NN. Predictors of in-hospital mortality and need for invasive mechanical
ventilation in elderly COPD patients presenting with acute hypercapnic respiratory failure. Egypt
J Chest Dis Tuberc 2013;62:393–400. doi:10.1016/j.ejcdt.2013.07.003
Page 24 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
25
22 Puhan MA, Hansel NN, Sobradillo P, et al. Large-scale international validation of the ADO index
in subjects with COPD: an individual subject data analysis of 10 cohorts. BMJ Open 2012;2.
doi:10.1136/bmjopen-2012-002152
23 Zhong X, Lee S, Zhao C, et al. Reducing COPD readmissions through predictive modeling and
incentive-based interventions. Health Care Manag Sci 2019;22:121–39. doi:10.1007/s10729-017-
9426-2
24 Ntritsos G, Franek J, Belbasis L, et al. Gender-specific estimates of COPD prevalence: a
systematic review and meta-analysis. Int J Chron Obstruct Pulmon Dis 2018;13:1507–14.
doi:10.2147/COPD.S146390
25 Bellou V, Belbasis L, Konstantinidis AK, et al. Elucidating the risk factors for chronic obstructive
pulmonary disease: an umbrella review of meta-analyses. Int J Tuberc Lung Dis 2019;23:58–66.
doi:10.5588/ijtld.18.0228
26 Pérez-Padilla R, Ramirez-Venegas A, Sansores-Martinez R. Clinical Characteristics of Patients
With Biomass Smoke-Associated COPD and Chronic Bronchitis, 2004-2014. Chronic Obstr
Pulm Dis (Miami, Fla) 2014;1:23–32. doi:10.15326/jcopdf.1.1.2013.0004
27 Vestbo J, Celli B. Chronic obstructive pulmonary disease: different risk factors and different
natural histories? Am J Respir Crit Care Med 2014;190:968–70. doi:10.1164/rccm.201409-
1705ED
28 Golpe R, Mengual-Macenlle N, Sanjuán-López P, et al. Prognostic Indices and Mortality
Prediction in COPD Caused by Biomass Smoke Exposure. Lung 2015;193:497–503.
doi:10.1007/s00408-015-9731-9
29 Faganello MM, Tanni SE, Sanchez FF, et al. BODE index and GOLD staging as predictors of 1-
year exacerbation risk in chronic obstructive pulmonary disease. Am J Med Sci 2010;339:10–4.
doi:10.1097/MAJ.0b013e3181bb8111
30 Steyerberg EW, Harrell FE, Borsboom GJ, et al. Internal validation of predictive models:
efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001;54:774–81.
31 Alba AC, Agoritsas T, Walsh M, et al. Discrimination and Calibration of Clinical Prediction
Models. JAMA 2017;318:1377. doi:10.1001/jama.2017.12126
Page 25 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
26
32 Rubin DB. Inference and missing data. Biometrika 1976;63:581–92. doi:10.1093/biomet/63.3.581
33 Collins GS, Ogundimu EO, Cook JA, et al. Quantifying the impact of different approaches for
handling continuous predictors on the performance of a prognostic model. Stat Med
2016;35:4124–35. doi:10.1002/sim.6986
34 Riley RD, Ensor J, Snell KIE, et al. External validation of clinical prediction models using big
datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ
2016;:i3140. doi:10.1136/bmj.i3140
35 Bonnett LJ, Snell KIE, Collins GS, et al. Guide to presenting clinical prediction models for use in
clinical settings. BMJ 2019;:l737. doi:10.1136/bmj.l737
36 Nishimura K, Izumi T, Tsukino M, et al. Dyspnea is a better predictor of 5-year survival than
airway obstruction in patients with COPD. Chest 2002;121:1434–40.
37 Schols AM, Slangen J, Volovics L, et al. Weight loss is a reversible factor in the prognosis of
chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998;157:1791–7.
doi:10.1164/ajrccm.157.6.9705017
38 Landbo C, Prescott E, Lange P, et al. Prognostic value of nutritional status in chronic obstructive
pulmonary disease. Am J Respir Crit Care Med 1999;160:1856–61.
doi:10.1164/ajrccm.160.6.9902115
39 Yang H, Xiang P, Zhang E, et al. Is hypercapnia associated with poor prognosis in chronic
obstructive pulmonary disease? A long-term follow-up cohort study. BMJ Open 2015;5:e008909.
doi:10.1136/bmjopen-2015-008909
40 Lange P, Halpin DM, O’Donnell DE, et al. Diagnosis, assessment, and phenotyping of COPD:
beyond FEV₁. Int J Chron Obstruct Pulmon Dis 2016;11:3–12. doi:10.2147/COPD.S85976
41 Celli BR, Cote CG, Marin JM, et al. The body-mass index, airflow obstruction, dyspnea, and
exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004;350:1005–
12. doi:10.1056/NEJMoa021322
42 Puhan MA, Garcia-Aymerich J, Frey M, et al. Expansion of the prognostic assessment of patients
with chronic obstructive pulmonary disease: the updated BODE index and the ADO index. Lancet
Page 26 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
27
(London, England) 2009;374:704–11. doi:10.1016/S0140-6736(09)61301-5
43 Guerra B, Haile SR, Lamprecht B, et al. Large-scale external validation and comparison of
prognostic models: an application to chronic obstructive pulmonary disease. BMC Med
2018;16:33. doi:10.1186/s12916-018-1013-y
44 Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for
development and an ABCD for validation. Eur Heart J 2014;35:1925–31.
doi:10.1093/eurheartj/ehu207
45 Echevarria C, Gray J, Hartley T, et al. Home treatment of COPD exacerbation selected by
DECAF score: a non-inferiority, randomised controlled trial and economic evaluation. Thorax
2018;73:713–22. doi:10.1136/thoraxjnl-2017-211197
46 Steer J, Gibson J, Bourke SC. The DECAF Score: predicting hospital mortality in exacerbations
of chronic obstructive pulmonary disease. Thorax 2012;67:970–6. doi:10.1136/thoraxjnl-2012-
202103
47 Cook R, Thomas V, Martin R, et al. People with chronic obstructive pulmonary disease
exacerbations prefer early discharge, then treatment at home. BMJ 2019;364:k5339.
doi:10.1136/bmj.k5339
48 Abu Hussein N, Ter Riet G, Schoenenberger L, et al. The ADO index as a predictor of two-year
mortality in general practice-based chronic obstructive pulmonary disease cohorts. Respiration
2014;88:208–14. doi:10.1159/000363770
49 Ansari K, Keaney N, Kay A, et al. Body mass index, airflow obstruction and dyspnea and body
mass index, airflow obstruction, dyspnea scores, age and pack years-predictive properties of new
multidimensional prognostic indices of chronic obstructive pulmonary disease in primary care.
Ann Thorac Med 2016;11:261. doi:10.4103/1817-1737.191866
50 Boeck L, Soriano JB, Brusse-Keizer M, et al. Prognostic assessment in COPD without lung
function: the B-AE-D indices. Eur Respir J 2016;47:1635–44. doi:10.1183/13993003.01485-2015
51 Echevarria C, Steer J, Heslop-Marshall K, et al. The PEARL score predicts 90-day readmission or
death after hospitalisation for acute exacerbation of COPD. Thorax 2017;72:686–93.
doi:10.1136/thoraxjnl-2016-209298
Page 27 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
28
52 Esteban C, Arostegui I, Moraza J, et al. Development of a decision tree to assess the severity and
prognosis of stable COPD. Eur Respir J 2011;38:1294–300. doi:10.1183/09031936.00189010
53 Jones RC, Price D, Chavannes NH, et al. Multi-component assessment of chronic obstructive
pulmonary disease: an evaluation of the ADO and DOSE indices and the global obstructive lung
disease categories in international primary care data sets. NPJ Prim care Respir Med
2016;26:16010. doi:10.1038/npjpcrm.2016.10
54 Marin JM, Alfageme I, Almagro P, et al. Multicomponent indices to predict survival in COPD:
the COCOMICS study. Eur Respir J 2013;42:323–32. doi:10.1183/09031936.00121012
55 Morales DR, Flynn R, Zhang J, et al. External validation of ADO, DOSE, COTE and CODEX at
predicting death in primary care patients with COPD using standard and machine learning
approaches. Respir Med 2018;138:150–5. doi:10.1016/j.rmed.2018.04.003
56 Motegi T, Jones RC, Ishii T, et al. A comparison of three multidimensional indices of COPD
severity as predictors of future exacerbations. Int J Chron Obstruct Pulmon Dis 2013;8:259–71.
doi:10.2147/COPD.S42769
57 Ou C-Y, Chen C-Z, Yu C-H, et al. Discriminative and predictive properties of multidimensional
prognostic indices of chronic obstructive pulmonary disease: a validation study in Taiwanese
patients. Respirology 2014;19:694–9. doi:10.1111/resp.12313
58 Quintana JM, Esteban C, Unzurrunzaga A, et al. Predictive score for mortality in patients with
COPD exacerbations attending hospital emergency departments. BMC Med 2014;12:66.
doi:10.1186/1741-7015-12-66
59 Waschki B, Kirsten A, Holz O, et al. Physical activity is the strongest predictor of all-cause
mortality in patients with COPD: a prospective cohort study. Chest 2011;140:331–42.
doi:10.1378/chest.10-2521
60 Zhang J, Rutten FH, Cramer MJ, et al. The importance of cardiovascular disease for mortality in
patients with COPD: a prognostic cohort study. Fam Pract 2011;28:474–81.
doi:10.1093/fampra/cmr024
61 Germini F, Veronese G, Marcucci M, et al. Validation of the BAP-65 score for prediction of in-
hospital death or use of mechanical ventilation in patients presenting to the emergency
Page 28 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
29
department with an acute exacerbation of COPD: a retrospective multi-center study from the
Italian Society of Emerg. Eur J Intern Med 2019;61:62–8. doi:10.1016/j.ejim.2018.10.018
62 Sangwan V, Chaudhry D, Malik R. Dyspnea, Eosinopenia, Consolidation, Acidemia and Atrial
Fibrillation Score and BAP-65 Score, Tools for Prediction of Mortality in Acute Exacerbations of
Chronic Obstructive Pulmonary Disease: A Comparative Pilot Study. Indian J Crit Care Med
2017;21:671–7. doi:10.4103/ijccm.IJCCM_148_17
63 Chan HP, Mukhopadhyay A, Chong PLP, et al. Prognostic utility of the 2011 GOLD
classification and other multidimensional tools in Asian COPD patients: a prospective cohort
study. Int J Chron Obstruct Pulmon Dis 2016;11:823–9. doi:10.2147/COPD.S96790
64 Crook S, Frei A, Ter Riet G, et al. Prediction of long-term clinical outcomes using simple
functional exercise performance tests in patients with COPD: a 5-year prospective cohort study.
Respir Res 2017;18:112. doi:10.1186/s12931-017-0598-6
65 Stolz D, Kostikas K, Blasi F, et al. Adrenomedullin refines mortality prediction by the BODE
index in COPD: the ‘BODE-A’ index. Eur Respir J 2014;43:397–408.
doi:10.1183/09031936.00058713
66 Herer B, Chinet T. Acute exacerbation of COPD during pulmonary rehabilitation: outcomes and
risk prediction. Int J Chron Obstruct Pulmon Dis 2018;13:1767–74. doi:10.2147/COPD.S163472
67 Horita N, Koblizek V, Plutinsky M, et al. Chronic obstructive pulmonary disease prognostic
score: A new index. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2016;160:211–8.
doi:10.5507/bp.2016.030
68 Moy ML, Teylan M, Danilack VA, et al. An index of daily step count and systemic inflammation
predicts clinical outcomes in chronic obstructive pulmonary disease. Ann Am Thorac Soc
2014;11:149–57. doi:10.1513/AnnalsATS.201307-243OC
69 Neo H-Y, Xu H-Y, Wu H-Y, et al. Prediction of Poor Short-Term Prognosis and Unmet Needs in
Advanced Chronic Obstructive Pulmonary Disease: Use of the Two-Minute Walking Distance
Extracted from a Six-Minute Walk Test. J Palliat Med 2017;20:821–8.
doi:10.1089/jpm.2016.0449
70 Pedone C, Scarlata S, Forastiere F, et al. BODE index or geriatric multidimensional assessment
Page 29 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
30
for the prediction of very-long-term mortality in elderly patients with chronic obstructive
pulmonary disease? a prospective cohort study. Age Ageing 2014;43:553–8.
doi:10.1093/ageing/aft197
71 Strassmann A, Frei A, Haile SR, et al. Commonly Used Patient-Reported Outcomes Do Not
Improve Prediction of COPD Exacerbations: A Multicenter 4½ Year Prospective Cohort Study.
Chest 2017;152:1179–87. doi:10.1016/j.chest.2017.09.003
72 Golpe R, Suárez-Valor M, Veres-Racamonde A, et al. Octogenarian patients with chronic
obstructive pulmonary disease: Characteristics and usefulness of prognostic indexes. Med Clin
(Barc) 2018;151:53–8. doi:10.1016/j.medcli.2017.09.011
73 Chan HP, Mukhopadhyay A, Chong PLP, et al. Role of BMI, airflow obstruction, St George’s
Respiratory Questionnaire and age index in prognostication of Asian COPD. Respirology
2017;22:114–9. doi:10.1111/resp.12877
74 Wildman MJ, Harrison DA, Welch CA, et al. A new measure of acute physiological derangement
for patients with exacerbations of obstructive airways disease: the COPD and Asthma Physiology
Score. Respir Med 2007;101:1994–2002. doi:10.1016/j.rmed.2007.04.002
75 Almagro P, Yun S, Sangil A, et al. Palliative care and prognosis in COPD: a systematic review
with a validation cohort. Int J Chron Obstruct Pulmon Dis 2017;12:1721–9.
doi:10.2147/COPD.S135657
76 Liu D, Peng S-H, Zhang J, et al. Prediction of short term re-exacerbation in patients with acute
exacerbation of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis
2015;10:1265–73. doi:10.2147/COPD.S83378
77 Miravitlles M, Izquierdo I, Herrejón A, et al. COPD severity score as a predictor of failure in
exacerbations of COPD. The ESFERA study. Respir Med 2011;105:740–7.
doi:10.1016/j.rmed.2010.12.020
78 Villalobos N, Davidson R, Ghori UK, et al. External Validation of the COmorbidity Test. COPD
2017;14:513–7. doi:10.1080/15412555.2017.1354981
79 Hoogendoorn M, Feenstra TL, Boland M, et al. Prediction models for exacerbations in different
COPD patient populations: comparing results of five large data sources. Int J Chron Obstruct
Page 30 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
31
Pulmon Dis 2017;12:3183–94. doi:10.2147/COPD.S142378
80 Echevarria C, Steer J, Heslop-Marshall K, et al. Validation of the DECAF score to predict
hospital mortality in acute exacerbations of COPD. Thorax 2016;71:133–40.
doi:10.1136/thoraxjnl-2015-207775
81 Jones RC, Donaldson GC, Chavannes NH, et al. Derivation and validation of a composite index
of severity in chronic obstructive pulmonary disease: the DOSE Index. Am J Respir Crit Care
Med 2009;180:1189–95. doi:10.1164/rccm.200902-0271OC
82 Rolink M, van Dijk W, van den Haak-Rongen S, et al. Using the DOSE index to predict changes
in health status of patients with COPD: a prospective cohort study. Prim Care Respir J
2013;22:169–74. doi:10.4104/pcrj.2013.00033
83 Esteban C, Quintana JM, Moraza J, et al. BODE-Index vs HADO-score in chronic obstructive
pulmonary disease: Which one to use in general practice? BMC Med 2010;8:28.
doi:10.1186/1741-7015-8-28
84 Esteban C, Quintana JM, Aburto M, et al. The health, activity, dyspnea, obstruction, age, and
hospitalization: prognostic score for stable COPD patients. Respir Med 2011;105:1662–70.
doi:10.1016/j.rmed.2011.05.005
85 Cote CG, Pinto-Plata VM, Marin JM, et al. The modified BODE index: validation with mortality
in COPD. Eur Respir J 2008;32:1269–74. doi:10.1183/09031936.00138507
86 Stolz D, Meyer A, Rakic J, et al. Mortality risk prediction in COPD by a prognostic biomarker
panel. Eur Respir J 2014;44:1557–70. doi:10.1183/09031936.00043814
87 Antón A, Güell R, Gómez J, et al. Predicting the result of noninvasive ventilation in severe acute
exacerbations of patients with chronic airflow limitation. Chest 2000;117:828–33.
88 Bertens LCM, Reitsma JB, Moons KGM, et al. Development and validation of a model to predict
the risk of exacerbations in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon
Dis 2013;8:493–9. doi:10.2147/COPD.S49609
89 Bloch KE, Weder W, Bachmann LM, et al. Model-Based versus Clinical Prediction of the
Spirometric Response to Lung Volume Reduction Surgery. Respiration 2004;71:611–8.
Page 31 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
32
doi:10.1159/000081762
90 Confalonieri M, Garuti G, Cattaruzza MS, et al. A chart of failure risk for noninvasive ventilation
in patients with COPD exacerbation. Eur Respir J 2005;25:348–55.
doi:10.1183/09031936.05.00085304
91 Connors AF, Dawson N V, Thomas C, et al. Outcomes following acute exacerbation of severe
chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses
and Preferences for Outcomes and Risks of Treatments). Am J Respir Crit Care Med
1996;154:959–67. doi:10.1164/ajrccm.154.4.8887592
92 Esteban C, Castro-Acosta A, Alvarez-Martínez CJ, et al. Predictors of one-year mortality after
hospitalization for an exacerbation of COPD. BMC Pulm Med 2018;18:18. doi:10.1186/s12890-
018-0574-z
93 Kerkhof M, Freeman D, Jones R, et al. Predicting frequent COPD exacerbations using primary
care data. Int J Chron Obstruct Pulmon Dis 2015;10:2439–50. doi:10.2147/COPD.S94259
94 Lindenauer PK, Grosso LM, Wang C, et al. Development, validation, and results of a risk-
standardized measure of hospital 30-day mortality for patients with exacerbation of chronic
obstructive pulmonary disease. J Hosp Med 2013;8:428–35. doi:10.1002/jhm.2066
95 Murata GH, Gorby MS, Kapsner CO, et al. A multivariate model for the prediction of relapse
after outpatient treatment of decompensated chronic obstructive pulmonary disease. Arch Intern
Med 1992;152:73–7.
96 Stanford RH, Nag A, Mapel DW, et al. Claims-based risk model for first severe COPD
exacerbation. Am J Manag Care 2018;24:e45–53.
97 Tabak YP, Sun X, Johannes RS, et al. Development and validation of a mortality risk-adjustment
model for patients hospitalized for exacerbations of chronic obstructive pulmonary disease. Med
Care 2013;51:597–605. doi:10.1097/MLR.0b013e3182901982
98 Zafari Z, Sin DD, Postma DS, et al. Individualized prediction of lung-function decline in chronic
obstructive pulmonary disease. CMAJ 2016;188:1004–11. doi:10.1503/cmaj.151483
99 Lau CS, Siracuse BL, Chamberlain RS. Readmission After COPD Exacerbation Scale:
Page 32 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
33
determining 30-day readmission risk for COPD patients. Int J Chron Obstruct Pulmon Dis
2017;12:1891–902. doi:10.2147/COPD.S136768
100 Roche N, Chavaillon J-M, Maurer C, et al. A clinical in-hospital prognostic score for acute
exacerbations of COPD. Respir Res 2014;15:99. doi:10.1186/s12931-014-0099-9
101 Almagro P, Soriano JB, Cabrera FJ, et al. Short- and medium-term prognosis in patients
hospitalized for COPD exacerbation: the CODEX index. Chest 2014;145:972–80.
doi:10.1378/chest.13-1328
Page 33 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
34
Figure 1. Flow chart of literature search for prognostic model in COPD patients.
17,538 articles reviewed by title screening
7758 articles reviewed by abstract screening
228 eligible articles published until November 11, 2018
2868 articles reviewed by full text screening
1 eligible article was identified by hand-search
of references
Page 34 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
35
Figure 2. Predictors included in at least 20 of 408 prognostic models for COPD patients by category of predictor.
Page 35 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
36
Figure 3. The ten more frequently used predictors in the 408 prognostic models for COPD patients presented by clinical setting.
Page 36 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
37
Figure 4. Risk of bias assessment based on 4 domains across the 408 prognostic models for outcome prediction in patients with COPD using PROBAST.
Page 37 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
38
Figure 5. Risk of bias assessment based on 4 domains across external validation studies of prognostic models for outcome prediction in patients with COPD using PROBAST.
Page 38 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
39
Figure 6. Summary c-statistic for 19 meta-analyses of prognostic models for outcome prediction in patients with COPD.
Page 39 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
40
Table 1. Key items for framing the systematic review aim, search strategy, and study inclusion and exclusion criteria, following the PICOTS guidance [8,9].
Item DefinitionPopulation Patients diagnosed with COPDIntervention Any prognostic model to predict any possible clinical outcome in COPD
patients, to distinguish COPD patients with poor prognosis (i.e., who will develop any unfavorable outcome), to aid decision-making in acute care and treatment planning long term
Comparator Not applicableOutcomes Any clinical outcome reported by prognostic modelsTiming Predictors measured at any timepoint in the clinical course of COPD
and preceeding the outcome; outcome measured short-term or long-term without applying any specific limitation in the prediction horizon
Setting Patients visiting ambulatory health care facilities, patients hospitalized or patients visiting the emergency department
Page 40 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
41
Table 2. Methodological characteristics of prediction models developed for outcome prediction in COPD patients.
Emergency department (14 models)
Inpatient(155 models)
Outpatient(239 models)
Overall(408 models)
Internal validationNon-random split 0 3 (1.9 %) 1 (0.4 %) 4 (1.0 %)Random split 8 (57.2 %) 7 (4.5 %) 12 (5.0 %) 27 (6.6 %)Bootstrapping 1 (7.1 %) 9 (5.8 %) 24 (10.0 %) 34 (8.3 %)Cross-validation 0 3 (1.9 %) 28 (11.7 %) 31 (7.6 %)Combination of methods 0 2 (1.3 %) 2 (0.8 %) 4 (1.0 %)None 5 (35.7 %) 131 (84.5 %) 172 (72.0 %) 308 (75.5 %)
Modelling methodCox hazard model 0 21 (13.5 %) 90 (37.7 %) 111 (27.2 %)Logistic regression model 10 (71.4 %) 111 (71.6 %) 79 (33.1 %) 200 (49.0 %)Machine learning 2 (14.3 %) 3 (1.9 %) 2 (0.8 %) 7 (1.7 %)Linear regression model 0 1 (0.6 %) 16 (6.7 %) 17 (4.2 %)Generalized linear model 0 2 (1.3 %) 6 (2.5 %) 8 (2.0 %)Negative binomial model 0 2 (1.3 %) 21 (8.8 %) 23 (5.6 %)Weibull regression model 0 0 15 (6.3 %) 15 (3.7 %)Other methods 2 (14.3%) 4 (2.6 %) 5 (2.1 %) 11 (2.7 %)More than 1 method 0 1 (0.7 %) 3 (1.3 %) 4 (1.0 %)Not reported 0 10 (6.5 %) 2 (0.8 %) 12 (2.9 %)
Handling of missing dataImputation 4 (28.6 %) 13 (8.4 %) 18 (7.5 %) 35 (8.6 %)No missing values 0 1 (0.6 %) 17 (7.1 %) 18 (4.4 %)Exclusion of patients 7 (50.0 %) 28 (18.1 %) 47 (19.7 %) 82 (20.1 %)Not reported 3 (21.4 %) 106 (68.4 %) 153 (64.0 %) 262 (64.2 %)Inappropriate handling 0 7 (4.5 %) 4 (1.7 %) 11 (2.7 %)
Model discriminationC-statistic 11 (78.6 %) 102 (65.8 %) 198 (82.8%) 311 (76.2 %)None 3 (21.4 %) 53 (34.2 %) 41 (17.2 %) 97 (23.8)
Model presentationFull equation 5 (35.7 %) 27 (17.4 %) 24 (10.0 %) 56 (13.7 %)Sum score 3 (21.4 %) 16 (10.3 %) 30 (12.6 %) 49 (12.0 %)
Page 41 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
42
Emergency department (14 models)
Inpatient(155 models)
Outpatient(239 models)
Overall(408 models)
Decision tree 2 (14.3 %) 1 (0.6 %) 3 (1.3 %) 6 (1.5 %)Nomogram 0 1 (0.6 %) 3 (1.3 %) 4 (1.0 %)Risk chart 0 3 (1.9 %) 0 3 (0.7 %)More than 1 method 0 3 (1.9 %) 1 (0.4 %) 4 (1.0%)None 4 (28.6 %) 104 (67.1 %) 178 (74.5 %) 286 (70.1 %)
Model calibrationHosmer-Lemeshow test 5 (35.7 %) 34 (21.9 %) 35 (14.6 %) 74 (18.1 %)Calibration plot 0 1 (0.6 %) 4 (1.7 %) 5 (1.2 %)More than 1 method 1 (7.1 %) 3 (1.9 %) 4 (1.7 %) 8 (2.0 %)Other 0 1 (0.6 %) 3 (1.2 %) 4 (1.0 %)None 8 (57.1 %) 116 (74.8 %) 193 (80.8 %) 317 (77.7 %)
Handling of continuous predictorsContinuous 4 (28.6 %) 47 (30.3 %) 87 (36.4 %) 138 (33.8 %)Categorical/dichotomous 10 (71.4 %) 87 (56.1 %) 64 (26.8 %) 161 (39.5 %)Mixed handling 0 5 (3.2 %) 31 (13.0 %) 36 (8.8 %)Not included 0 15 (9.7 %) 37 (15.5 %) 52 (12.7 %)Unclear 0 1 (0.7 %) 20 (8.4 %) 21 (5.1 %)
Non-linearityPolynomials 0 1 (0.7 %) 7 (3 %) 8 (2 %)Fractional polynomials 0 1 (0.7 %) 2 (0.8 %) 3 (0.7 %)Restricted cubic splines 0 0 6 (2.5 %) 6 (1.5 %)Box-Tidwell transformation 0 1 (0.7 %) 2 2 (0.8 %) 3 (0.7 %)None 14 (100 %) 152 (98.1 %) 222 (92.9 %) 388 (95.1%)
Page 42 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
43
Table 3. External validation studies for prognostic models for outcome prediction in COPD patients.
Reference Model name Cohort name Sample size (cases/total) Outcome c-statistic (95% CI) Calibration
Abu-Hussein, 2014 [48] ADO index ICE COLD ERIC study 35/409 Mortality (2 years) 0.79
Hosmer-Lemeshow test, calibration plot,
calibration-in-the-large
Abu-Hussein, 2014 [48] ADO index Swiss COPD Cohort 17/237 Mortality (2 years) 0.76
Hosmer-Lemeshow test, calibration plot,
calibration-in-the-largeAnsari, 2016* [49] ADO index NR 154/458 Mortality (10 years) 0.70 (0.66 to 0.75) NRBoeck, 2016* [50] ADO index PROMISE study 82/229 Mortality (5 years) 0.68 (0.61 to 0.74) Hosmer-Lemeshow testEchevarria, 2017*
[51] ADO index Cohort 1 309/824 Re-admission or mortality (90 days) 0.67 (0.63 to 0.71) NR
Echevarria, 2017* [51] ADO index Cohort 2 297/802 Re-admission or mortality (90
days) 0.64 (0.60 to 0.67) NR
Echevarria, 2017* [51] ADO index Cohort 3 330/791 Re-admission or mortality (90
days) 0.58 (0.54 to 0.62) NR
Esteban, 2011* [52] ADO index NR 112/348 Mortality (5 years) 0.74 (0.69 to 0.80) Hosmer-Lemeshow test
Jones, 2016* [53] ADO index Optimum Patient Care NR/7105 Hospitalization (1 year) 0.57 (0.50 to 0.64) NRMarin, 2013* [54] ADO index COCOMICS study NR/3633 Mortality (10 years) 0.68 NR
Morales, 2018* [55] ADO index UK Clinical Practice Research Datalink 8083/52,684 Mortality (3 years) 0.732 (0.727 to 0.738) NR
Motegi, 2013* [56] ADO index NR 64/183 AECOPD (1 year) 0.64 (0.56 to 0.73) NROu, 2014* [57] ADO index NR 114/594 Mortality (33 months) 0.70 NR
Puhan, 2012 [22] ADO index
Basque study, Cardiovascular Health Study, Copenhagen City Heart Study,
Jackson Heart Study, Lung Health Study, National Emphysema Treatment Trial, Phenotype and Course of COPD PAC-
COPD Study, PLATINO, Quality of Life of COPD Study Group
1350/13,683 Mortality (3 years) 0.82 (0.81 to 0.83)Hosmer-Lemeshow test,
calibration plot, calibration-in-the-large
Quintana, 2014* [58] ADO index NR NR/2067 Mortality (in-hospital) 0.78 NR
Page 43 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
44
Reference Model name Cohort name Sample size (cases/total) Outcome c-statistic (95% CI) Calibration
Waschki, 2011* [59] ADO index NR 26/170 Mortality (4 years) 0.77 NR
Zhang, 2011* [60] ADO index NR 60/405 Mortality (5 years) 0.66 (0.59 to 0.73) NRBoeck, 2016 [50] B-AE-D index COCOMICS study 307/2047 Mortality (2 years) 0.65 (0.62 to 0.69) Hosmer-Lemeshow testBoeck, 2016 [50] B-AE-D index COMIC study 82/675 Mortality (2 years) 0.67 (0.61 to 0.72) Hosmer-Lemeshow test
Boeck, 2016 [50] † B-AE-D index ProCOLD study 36/160 Mortality (2 years) 0.60 (0.52 to 0.69) Hosmer-Lemeshow test
Boeck, 2016 [50] † B-AE-D-C index ProCOLD study 36/160 Mortality (2 years) 0.69 (0.60 to 0.78) Hosmer-Lemeshow test
Germini, 2018* [61] BAP-65 NR 165/2908 Mortality (in-hospital) 0.64 (0.59 to 0.68) Calibration plot
Sangwan, 2017* [62] BAP-65 NR 9/50 Mortality (in-hospital) 0.92 (0.83 to 1.00) NR
Chan, 2016* [63]v BOD index NR NR/1110 Mortality 0.72 NRCrook, 2017* [64] BOD index NR 77/409 Mortality (5 years) 0.60 (0.52 to 0.68) NRStolz, 2014* [65] BOD index PROMISE 43/549 Mortality (2 years) 0.65 NRZhang, 2011* [60] BOD index NR 60/405 Mortality (5 years) 0.66 (0.59 to 0.73) NRFaganello, 2010*
[29] BODE index NR 60/120 AECOPD (1 year) 0.62 NR
Herer, 2018* [66] BODE index NR 32/125 AECOPD (1 month) 0.78 (0.69 to 0.85) NRHorita, 2016* [67] BODE index NETT 287/607 Mortality (5 years) 0.62 NRMarin, 2013 [54] BODE index COCOMICS study 230/3633 Mortality (1 year) 0.682 NR
Motegi, 2013* [56] BODE index NR 64/183 AECOPD (1 year) 0.65 (0.56 to 0.73) NR
Moy, 2014* [68] BODE index NR 167 Number of AECOPD (16 months) 0.62 NR
Neo, 2017* [69] BODE index NR 17/124 Mortality (18 months) 0.72 (0.58 to 0.86) NRPedone, 2014* [70] BODE index SARA study 141/468 Mortality (5 years) 0.64 NR
Puhan, 2009* [42] BODE index Swiss Barmelweid Cohort 79/232 Mortality (3 years) 0.67 Hosmer-Lemeshow test, calibration plot
Puhan, 2009* [42] BODE index Spanish Phenotype and Course of COPD 41/342 Mortality (3 years) 0.62 Hosmer-Lemeshow test, calibration plot
Stolz, 2014*[65] BODE index PROMISE study 26/549 Mortality (1 year) 0.745 NRStrassman, 2017*
[71] BODE index ICE COLD ERIC study 408 Number of AECOPD (54 months) 0.69 (0.64 to 0.74) NR
Page 44 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
45
Reference Model name Cohort name Sample size (cases/total) Outcome c-statistic (95% CI) Calibration
Waschki, 2011* [59] BODE index NR 26/169 Mortality (4 years) 0.76 NR
Echevarria, 2017* [51] BODEx index Cohort 1 309/824 Re-admission or mortality (90
days) 0.65 (0.61 to 0.69) NR
Echevarria, 2017* [51] BODEx index Cohort 2 297/802 Re-admission or mortality (90
days) 0.64 (0.60 to 0.68) NR
Echevarria, 2017* [51] BODEx index Cohort 3 330/791 Re-admission or mortality (90
days) 0.62 (0.58 to 0.66) NR
Golpe, 2018* [72] BODEx index NR 154/594 Mortality (1 year) 0.77 (0.74 to 0.80) NRGolpe, 2015* [28] BODEx index NR 12/142 Mortality 0.81 (0.73 to 0.87) NRMarin, 2013 [54] BODEx index COCOMICS study 1225/3633 Mortality (10 years) 0.642 NROu, 2014* [57] BODEx index NR 114/594 Mortality (1 year) 0.702 NR
Strassman, 2017* [71] BODEx index ICE COLD ERIC 408 Number of AECOPD (54
months) 0.72 (0.67 to 0.77) NR
Chan, 2017 [73] BOSA NR NR/772 Mortality (5 years) 0.69 (0.64 to 0.74) NRWildman, 2007
[74] CAPS Case Mix Programme Database NR/NR Mortality (in-hospital) 0.70 (0.69 to 0.72) Hosmer-Lemeshow test
Almagro, 2017 [75] CODEX NR 122/697 Mortality (1 year) 0.68 (0.62 to 0.72) NREchevarria, 2017*
[51] CODEX Cohort 1 309/824 Re-admission or mortality (90 days) 0.69 (0.65 to 0.73) NR
Echevarria, 2017* [51] CODEX Cohort 2 297/802 Re-admission or mortality (90
days) 0.66 (0.63 to 0.70) NR
Echevarria, 2017* [51] CODEX Cohort 3 330/791 Re-admission or mortality (90
days) 0.62 (0.58 to 0.66) NR
Golpe, 2018* [72] CODEX NR 154/594 Mortality (1 year) 0.80 (0.77 to 0.83) NR
Liu, 2015* [76] CODEX NR 86/176 AECOPD or mortality (90 days) 0.57 (0.49 to 0.66) NR
Morales, 2018* [55] CODEX UK Clinical Practice Research Datalink 8083/52,684 Mortality (3 years) 0.652 (0.646 to 0.648) NR
Miravitlles, 2011* [77]
COPD Severity Score ESFERA study 97/346 Treatment failure 0.72 NR
Golpe 2018* [72] COTE index NR 154/594 Mortality (1 year) 0.63 (0.60 to 0.67) NRGolpe, 2015* [28] COTE index NR 12/142 Mortality (32 months) 0.66 (0.57 to 0.73) NR
Page 45 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
46
Reference Model name Cohort name Sample size (cases/total) Outcome c-statistic (95% CI) Calibration
Morales, 2018* [55] COTE index UK Clinical Practice Research Datalink 8083/52,684 Mortality (3 years) 0.674 (0.667 to 0.680) NR
Villalobos, 2017* [78] COTE index NR 54/317 Mortality (4 months) 0.63 (0.55 to 0.71) Cox-Snell residuals
Hoogendoorn, 2017 [79] CPI COPDGene, OLIN, RECODE, ECLIPSE,
UPLIFT 13254 N of total AECOPD NR NR
Ou, 2014* [57] CPI NR 114/594 Mortality (33 months) 0.72 NREchevarria, 2016
[80] DECAF NR NR/880 Mortality (30 days) 0.79 (0.75 to 0.83) Calibration plot
Sangwan, 2017* [62] DECAF NR 9/50 Mortality (in-hospital) 0.91 (0.79 to 1.00) NR
Boeck, 2016* [50] DOSE PROMISE study 82/229 Mortality (5 years) 0.58 (0.52 to 0.64) Hosmer-Lemeshow testEchevarria, 2017*
[51] DOSE Cohort 1 309/824 Re-admission or mortality (90 days) 0.63 (0.59 to 0.67) NR
Echevarria, 2017* [51] DOSE Cohort 2 297/802 Re-admission or mortality (90
days) 0.59 (0.55 to 0.64) NR
Echevarria, 2017* [51] DOSE Cohort 3 330/791 Re-admission or mortality (90
days) 0.61 (0.57 to 0.65) NR
Herer, 2018* [66] DOSE NR 32/125 AECOPD (1 month) 0.50 (0.41 to 0.59) NRJones, 2009 [81] DOSE London COPD cohort 50/175 Hospitalization 0.755 NRJones, 2016 [53] DOSE Optimum Patient Care NR/7105 Hospitalization (1 year) 0.64 (0.57 to 0.71) NR
Marin, 2013* [54] DOSE COCOMICS study 1225/3633 Mortality (10 years) 0.618 NRMorales, 2018*
[55] DOSE UK Clinical Practice Research Datalink 8083/52684 Mortality (3 years) 0.645 (0.638 to 0.651) NR
Motegi, 2013 [56] DOSE NR 64/183 AECOPD (1 year) 0.75 (0.67 to 0.82) NR
Rolink, 2013* [82] DOSE NR NR/209 Change in health status (2 years) 0.62 (0.50 to 0.74) NR
Strassman, 2017* [71] DOSE ICE COLD ERIC 408 Number of AECOPD (54
months) 0.68 (0.62 to 0.73) NR
Marin, 2013 [54] e-BODE index COCOMICS study 1225/3633 Mortality (10 years) 0.66 NR
Puhan, 2012 [22] Extended ADO index
Barmelweid study, CHS, Basque Study, JHS and SEPOC 338/3693 Mortality (3 years) 0.74 (0.71 to 0.77)
Hosmer-Lemeshow test, calibration plot,
calibration-in-the-largeEsteban, 2010 [83] HADO NR 71/543 Mortality (3 years) 0.81 (0.76 to 0.86) NR
Page 46 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
47
Reference Model name Cohort name Sample size (cases/total) Outcome c-statistic (95% CI) Calibration
Quintana, 2014 [58] HADO NR 36/1887
Mortality (in-hospital or within 1 week of ED
presentation)0.68 NR
Esteban, 2011 [84] HADO-AH score NR 112/348 Mortality (5 years) 0.76 (0.71 to 0.81) Hosmer-Lemeshow test,
calibration plot
Cote, 2008 [85] mBODE% index BODE cohort 206/444 Mortality 0.72 (0.66 to 0.78) NR
Stolz, 2014 [86] Model 1 NR 49/638 Mortality (3 years) 0.72 NRStolz, 2014 [86] Model 2 NR 49/638 Mortality (3 years) 0.73 NRAnton, 2000 [87] NR NR 12/15 Success of NIV NR NR
Bertens, 2013 [88] NR NR 793/222 AECOPD (2 years) 0.66 (0.61 to 0.71) Calibration plotBloch, 2004 [89] NR NR 27/60 FEV1 change (6 months) 0.75 (0.62 to 0.87) NR
Confalonieri, 2005 [90] NR NR NR/145 NPPV failure (in-hospital) 0.71 (0.63 to 0.79) Hosmer-Lemeshow test
Connors, 1996 [91] NR NR 124/416 Mortality (6 months) 0.731 Calibration plotEsteban, 2011 [52] NR NR 112/348 Mortality (5 years) 0.74 (0.69 to 0.80) Hosmer-Lemeshow testEsteban, 2018 [92] NR NR 492/3235 Mortality (1 year) 0.76 (0.74 to 0.78) Hosmer-Lemeshow testKerkhof, 2015 [93] NR NR NR/2713 Frequent exacerbations 0.74 (0.71 to 0.76) Calibration plotLindenauer, 2013
[94] NR Cohort 1 NR/259,911 Mortality (30 days) 0.73 Overfitting index
Lindenauer, 2013 [94] NR Cohort 2 NR/279,377 Mortality (30 days) 0.72 Overfitting index
Lindenauer, 2013 [94] NR Cohort 3 NR/NR Mortality (30 days) 0.74 Overfitting index
Murata, 1992 [95] NR NR 47/476 Relapse (48 hours) NR NRStanford, 2018 [96] NR NR NR/NR Hospitalisation (1 year) 0.71 NR
Tabak, 2013 [97] NR NR 957/33,327 Mortality (in-hospital) 0.84 (0.82 to 0.85) Hosmer-Lemeshow test, calibration plot
Zafari, 2016 [98] NR EUROSCOP 542 FEV1 NA NR
Zafari, 2016 [98] NR Pan-Canadian Early Detection of Lung Cancer Study 940 FEV1 NA NR
Echevarria, 2017 [51] PEARL score Cohort 1 297/802 Re-admission or mortality (90
days) 0.68 (0.64 to 0.72) Calibration plot
Page 47 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
48
Reference Model name Cohort name Sample size (cases/total) Outcome c-statistic (95% CI) Calibration
Echevarria, 2017 [51] PEARL score Cohort 2 330/791 Re-admission or mortality (90
days) 0.70 (0.66 to 0.73) Calibration plot
Lau, 2017 [99] RACE State Inpatient Database 17,294/258,113 Re-admission (30 days) NR NR
Roche, 2014 [100] Roche 2008 score NR 45/1824 Mortality (in-hospital) 0.77 (0.72 to 0.81) NR
Marin, 2013* [54] SAFE COCOMICS study 1225/3633 Mortality (10 years) 0.671 NRHerer, 2018* [66] SCOPEX NR 32/125 AECOPD (1 month) 0.74 (0.65 to 0.81) NRStrassman, 2017*
[71] SCOPEX ICE COLD ERIC 408 Number of AECOPD (54 months) 0.70 (0.65 to 0.75) NR
Almagro, 2014* [101]
Updated ADO index ESMI study NR/557 Mortality (1 years) 0.64 (0.61 to 0.67) NR
Morales, 2018* [55]
Updated ADO index UK Clinical Practice Research Datalink 8083/52,684 Mortality (3 years) 0.724 (0.719 to 0.730) NR
Neo, 2017* [69] Updated ADO index NR 17/124 Mortality (18 months) 0.685 (0.55 to 0.82) NR
Puhan, 2012 [22] Updated ADO index
Barmelweid study, CHS, Basque Study, JHS and SEPOC cohorts 338/3693 Mortality (3 years) 0.73 (0.70 to 0.76)
Hosmer-Lemeshow test, calibration plot,
calibration-in-the-large
Boeck, 2016* [50] Updated BODE index PROMISE study 82/211 Mortality (5 years) 0.62 (0.55 to 0.68) Hosmer-Lemeshow test
Puhan, 2009 [42] Updated BODE index Spanish Phenotype and Course of COPD 41/342 Mortality (3 years) 0.61 Hosmer-Lemeshow test,
calibration plotWaschki, 2011*
[59]Updated
BODE index NR 26/170 Mortality (4 years) 0.75 NR
Abbreviations: AECOPD, acute exacerbation of chronic obstructive pulmonary disease; CI, confidence interval; COPDSS, COPD
severity score; NIV, non-invasive ventilation; NR, not reported.
* External validation studies conducted by fully independent research teams.
† In ProCOLD study a measure for dyspnea was not available. B-AE-D index and B-AE-D-C index were externally validated in
Page 48 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
49
ProCOLD study without considering dyspnea.
Page 49 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
50
Table 4. Characteristics of 7 prognostic models for outcome prediction in COPD patients that presented an overall low risk of bias.
Reference Model name Clinical setting Outcome Predictors Shrinkage
methods
Handling of
continuous predictors
EPVOptimism-
corrected C-statistic (95% CI)
Puhan, 2009 [42] ADO index Outpatient Mortality
(3 years) Age, FEV1, mMRC Uniform Continuous 26.3 0.63*
Puhan, 2009 [42] Updated BODE index Outpatient Mortality
(3 years)BMI, FEV1, mMRC, 6MWD Uniform Continuous 19.8 0.61*
Puhan, 2012 [22] Updated ADO index Outpatient Mortality
(3 years) Age, FEV1, mMRC Uniform Continuous 311 0.73 (0.70 to 0.76)
Puhan, 2012 [22] Extended ADO index Outpatient Mortality
(3 years)Age, FEV1, mMRC, BMI, CVD, sex Uniform Continuous 155.5 0.74 (0.71 to 0.77)
Bertens, 2013 [88] NR Outpatient AECOPD
(2 years)
FEV1, previous exacerbations, smoking, vascular disease
Uniform Continuous 17.5 0.66 (0.61 to 0.71)
Boeck, 2016 [50] B-AE-D index Outpatient Mortality
(2 years)BMI, previous exacerbations, mMRC Lasso Continuous 18 0.63 (0.61 to 0.66)
Boeck, 2016 [50] B-AE-D-C index Outpatient Mortality
(2 years)
BMI, previous exacerbations, mMRC, serum copeptin
Lasso Continuous 13.5 0.65 (0.57 to 0.72)
Footnotes:
* Confidence intervals were not reported.
The c-statistic presented in the table is the optimism-corrected metric as reported in internal validation.
Page 50 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
51
Abbreviations: 6MWD, 6 minute walk distance test; AECOPD, acute exacerbation of chronic obstructive pulmonary disase; BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; EPV, events per variable; FEV1, forced expiratory volume in 1 second; mMRC, modified Medical Research Council dyspnea scale; NR, not reported.
Page 51 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
52
Table 5. Predictors included in the prognostic models for outcome prediction in COPD patients with low risk of bias.
mMRC FEV1 Age BMI Previous AECOPD Additional predictors
Updated BODE ✓ ✓ ✓ 6MWDADO ✓ ✓ ✓ -
Updated ADO ✓ ✓ ✓ -Extended ADO ✓ ✓ ✓ ✓ Sex, CVDBertens, 2013 ✓ ✓ Smoking, vascular disease
B-AE-D ✓ ✓ ✓ -B-AE-D-C ✓ ✓ ✓ Copeptin
Abbreviations: 6MWD, 6 minute walk distance test; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; BMI,
body mass index; CVD, cardiovascular disease; FEV1, forced expiratory volume in 1 second; mMRC, modified Medical Research
Council dyspnea scale.
Page 52 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
53
Table 6. Model equations of prognostic models for outcome prediction in COPD patients with low risk of bias.
Prediction
model
Model equation
ADO 𝑦 = ―0.012 × 𝐹𝐸𝑉1(% 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑) + 0.193 × 𝑚𝑀𝑅𝐶 + 0.027 × 𝑎𝑔𝑒 ― 3.436
Updated
ADO
𝑦 = ―0.288 × 𝐹𝐸𝑉1(% 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑) + 0.2585 × 𝑚𝑀𝑅𝐶 + 0.0703 × 𝑎𝑔𝑒 ― 5.640
Updated
BODE
𝑦 = ―0.013 × 𝐵𝑀𝐼 ― 0.005 × 𝐹𝐸𝑉1(% 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑) + 0.146 × 𝑚𝑀𝑅𝐶 ― 0.005 × 6𝑀𝑊𝐷 + 1.483
B-AE-D 𝑦= 0.97 × (18.5 ≤ 𝐵𝑀𝐼 < 21) + 1.45 × (𝐵𝑀𝐼 < 18.5) + 0.45 × (𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑠𝑒𝑣𝑒𝑟𝑒 𝑒𝑥𝑎𝑐𝑒𝑟𝑏𝑎𝑡𝑖𝑜𝑛𝑠 = 1) + 1.22 × (𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑠𝑒𝑣𝑒𝑟𝑒 𝑒𝑥𝑎𝑐𝑒𝑟𝑏𝑎𝑡𝑖𝑜𝑛𝑠 ≥ 2)+ 0.97 × (𝑚𝑀𝑅𝐶 = 3) + 1.67 × (𝑚𝑀𝑅𝐶 = 4) + 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡
B-AE-D-C 𝑦= 0.97 × (18.5 ≤ 𝐵𝑀𝐼 < 21) + 1.45 × (𝐵𝑀𝐼 < 18.5) + 0.45 × (𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑠𝑒𝑣𝑒𝑟𝑒 𝑒𝑥𝑎𝑐𝑒𝑟𝑏𝑎𝑡𝑖𝑜𝑛𝑠 = 1) + 1.22 × (𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑠𝑒𝑣𝑒𝑟𝑒 𝑒𝑥𝑎𝑐𝑒𝑟𝑏𝑎𝑡𝑖𝑜𝑛𝑠 ≥ 2)+ 0.97 × (𝑚𝑀𝑅𝐶 = 3) + 1.67 × (𝑚𝑀𝑅𝐶 = 4) + 0.50 × (20 ≤ 𝑐𝑜𝑝𝑒𝑝𝑡𝑖𝑛 < 40) + 1.58 × (𝑐𝑜𝑝𝑒𝑝𝑡𝑖𝑛 ≥ 40) + 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡
Bertens et
al.
𝑦 = 1.62 × (𝑝𝑟𝑒𝑠𝑒𝑛𝑐𝑒 𝑜𝑓 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑒𝑥𝑎𝑐𝑒𝑟𝑏𝑎𝑡𝑖𝑜𝑛) ― 0.05 × 𝐹𝐸𝑉1(% 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑, 𝑝𝑒𝑟 5% 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒) + 0.12 × (2 × log (packyears)) + 0.65× (𝑝𝑟𝑒𝑠𝑒𝑛𝑐𝑒 𝑜𝑓 𝑣𝑎𝑠𝑐𝑢𝑙𝑎𝑟 𝑑𝑖𝑠𝑒𝑎𝑠𝑒) ― 1.33
For B-AE-D and B-AE-D-C models, the constant of regression equation was not reported.
Abbreviations: 6MWD, 6 minute walk distance test; BMI, body mass index; FEV1, forced expiratory volume in 1 second; mMRC,
modified Medical Research Council dyspnea scale.
Page 53 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
54
Table 7. Results of meta-analyses of c-statistics for prognostic models in COPD patients.
Model Outcome Number of datasetsNumber of
events/participants
Summary c-statistic (95%
CI)I2
ADO index Mortality 11 11,258/72,850 0.731 (0.692 to 0.766) 95
ADO indexRe-admission or
mortality3 936/2417 0.630 (0.513 to 0.734) 81
APACHE II NIV failure 3 121/550 0.718 (0.647 to 0.780) 0
APACHE II Mortality 7 NA/NA* 0.769 (0.681 to 0.838) 84
BOD index Mortality 4 NA/NA* 0.665 (0.578 to 0.742) 63
BODE index Mortality 8 847/6124 0.663 (0.624 to 0.701) 36
BODE index AECOPD 3 156/428 0.686 (0.442 to 0.857) 71
BODEx indexRe-admission or
mortality3 936/2417 0.636 (0.598 to 0.674) 0
BODEx index Mortality 4 1505/4963 0.730 (0.597 to 0.831) 93
CODEX Mortality 3 8359/53,975 0.720 (0.500 to 0.869) 96
CODEXRe-admission or
mortality3 936/2417 0.657 (0.566 to 0.737) 67
COTE index Mortality 4 8303/53,737 0.655 (0.616 to 0.692) 57
CURB-65 Mortality 6 451/4250 0.730 (0.690 to 0.767) 24
DOSE index AECOPD 3 NA/NA* 0.615 (0.291 to 0.861) 93
DOSE indexRe-admission or
mortality3 936/2417 0.611 (0.562 to 0.658) 0
Page 54 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
55
Model Outcome Number of datasetsNumber of
events/participants
Summary c-statistic (95%
CI)I2
DOSE index Mortality 3 9390/56,546 0.624 (0.552 to 0.691) 85
LACE indexRe-admission or
mortality4 1601/5079 0.632 (0.612 to 0.652) 0
Updated ADO index Mortality 4 NA/NA* 0.699 (0.624 to 0.764) 91
Updated BODE index Mortality 3 149/723 0.647 (0.456 to 0.800) 48
Abbreviations:
CI, confidence interval; NA, not available
Footnotes:
* For at least one dataset, the number of events and/or participants was not reported.
Page 55 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review OnlyA systematic review and critical appraisal of prognostic
prediction models for patients with chronic obstructive
pulmonary disease
Supplementary material
Supplementary Table 1. Summary of predicted outcomes for the 408 prognostic models for patients with COPD.
Supplementary Table 2. Top predictors in the 408 prognostic models for COPD patients stratified by clinical setting.
Supplementary Table 3. Articles describing the development of prognostic models for patients with COPD in outpatient settings.
Supplementary Table 4. Articles describing the development of prognostic models for hospitalized COPD patients.
Supplementary Table 5. Articles describing the development of prognostic model for COPD patients attending the emergency department.
Supplementary Table 6. Articles describing the validation of prognostic models developed for diseases other than COPD.
Page 56 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review OnlySupplementary Table 1. Summary of predicted outcomes for the 408 prognostic models for patients with COPD.
OutcomeEmergency department
(n = 14 models)
Hospitalized patients
(n = 155 models)
Outpatients(n = 239)
Overall(n = 408)
Mortality 7 78 124 209Change in physical activity 2 0 0 2
Composite outcome 2 13 9 24Hospitalization 1 0 16 17ICU admission 1 0 0 1Readmission 0 36 0 36NIV failure 0 14 0 14Length of stay 0 9 0 9Weaning success 0 2 0 2Treatment failure 1 0 8 9AECOPD 0 2 40 42Prolonged MV 0 1 0 1Spirometric indices 0 0 25 25Cost 0 0 2 2Adherence to rehab 0 0 2 2Late recovery 0 0 1 1COPD-related disability 0 0 3 36-MWD decline 0 0 4 4Pneumonia 0 0 1 1Hypoxic RF 0 0 3 3BODE improvement 0 0 1 1
Abbreviations: 6-MWD, 6-minute walking distance test; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; MV, mechanical ventilation; NIV, non-invasive ventilation; RF, respiratory failure
Page 57 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review OnlySupplementary Table 2. Top predictors in the 408 prognostic models for COPD patients stratified by clinical setting.
Overall (n = 408 models) Outpatients (n = 239 models)
Hospitalized patients (n = 155 models)
Patients attending emergency department
(n = 14 models)
Predictor N of models Predictor N of
models Predictor N of models Predictor N of
models
Age 166 Age 105 Age 56 LTOT/NIV at home 8
FEV1 85 FEV1 69 Sex 30 Age 5Sex 74 Smoking 54 PaCO2 24 mMRC scale 5
BMI 66 BMI 51 Previous hospitalizations 20
Charlson comorbidity
index4
Smoking 65 Sex 43 Length of stay 20 PaCO2 3
Previous exacerbations 53 Previous
exacerbations 43Charlson
comorbidity index
19
Use of inspiratory accessory
muscle and Paradoxical breathing
3
Previous hospitalizations 50 BODE index 43 pH 18
Glasgow (or Japan) coma
scale3
BODE index 43 Previous hospitalizations 28 Heart failure 16 FEV1 2
mMRC scale 42 Diabetes mellitus 24 BMI 15 pH 2
Charlson comorbidity
index35 mMRC scale 23 Serum albumin 15 Cardiovascular
diseases 2
Serum CRP 34 Cardiovascular diseases 23 FEV1 14 Previous
hospitalizations 2
Serum CRP 23 mMRC scale 14Anemia 14
Page 58 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Supplementary Table 3. Articles describing the development of prognostic models for patients with COPD in outpatient settings.
References Number of models developedDompeling, 1992 [1] 2Campbell, 1985 [2] 1Smit, 1983 [3] 3Daughton, 1984 [4] 1Ball, 1995 [5] 1Ashutosh, 1997 [6] 1Kessler, 1999 [7] 1Dewan, 2000 [8] 1Miravitlles, 2000 [9] 2Miravitlles, 2001 [10] 1Fan, 2002 [11] 1Celli, 2004 [12] 1Bloch, 2004 [13] 1Miravitlles, 2005 [14] 2Mapel, 2005 [15] 1Miravitlles, 2006 [16] 2Man, 2006 [17] 1Esteban, 2006 [18] 1Niewoehner, 2007 [19] 2Fan, 2007 [20] 3Briggs, 2008 [21] 1Blanchette, 2008 [22] 1Fan, 2008 [23] 1Soler-Cataluña, 2009 [24] 2Omachi, 2008 [25] 2Schembri, 2009 [26] 1Puhan, 2009 [27] 2Mehrotra, 2010 [28] 2Benzo, 2010 [29] 1Eisner, 2011 [30] 3Brusse, 2011 [31] 1Simon-Tuval, 2011 [32] 1Waschki, 2011 [33] 1Lee, 2011 [34] 3Esteban, 2011 [35] 1Zhang, 2011 [36] 1
Page 59 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Esteban, 2011 [37] 1Gale, 2011 [38] 3Williams, 2012 [39] 1Yoo, 2011 [40] 3Ozgur, 2012 [41] 1Celli, 2012 [42] 9Divo, 2012 [43] 2Jensen, 2013 [44] 2Puhan, 2012 [45] 2Mannino, 2013 [46] 1Ryynanen, 2013 [47] 1Thomsen, 2013 [48] 2Stolz, 2014 [49] 8Roberts, 2013 [50] 4Heerema, 2013 [51] 1Bertens, 2013 [52] 1Moy, 2014 [53] 2Bowler, 2014 [54] 1Koskela, 2014 [55] 1Stolz, 2014 [56] 3Boutou, 2014 [57] 1Suzuki, 2014 [58] 7Make, 2015 [59] 1Wilson, 2015 [60] 2Montserrat-Capdevila, 2015 [61] 1Shin, 2015 [62] 2Abascal-Bolado, 2015 [63] 1Kerkhof, 2015 [64] 1Montserrat-Capdevila, 2015 [65] 1Miravitlles, 2016 [66] 1Ramon, 2016 [67] 3Park, 2016 [68] 5Blumenthal, 2016 [69] 1Beijers, 2016 [70] 1Boeck, 2016 [71] 12Horita, 2016 [72] 1Zafari, 2016 [73] 1Chan, 2017 [74] 1
Page 60 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Sand, 2016 [75] 17Ansari, 2016 [76] 2Barton, 2017 [77] 1Crook, 2017 [78] 4Russo, 2017 [79] 2Sundh, 2017 [80] 3Kurashima, 2016 [81] 1Villalobos, 2017 [82] 2Strassman, 2017 [83] 17Dal Negro, 2017 [84] 1Hoogendoorn, 2017 [85] 1Mendy, 2018 [86] 14Kang, 2017 [87] 6Fijačko, 2018 [88] 6Bafadhel, 2018 [89] 1Stanford, 2018 [90] 1Samp, 2018 [91] 1Miravitlles, 2018 [92] 1Vela, 2018 [93] 1Morales, 2018 [94] 2Hwang, 2019 [95] 9Annavarapu, 2018 [96] 1
Page 61 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Supplementary Table 4. Articles describing the development of prognostic models for hospitalized COPD patients.
References Number of models developedMenzies, 1989 [97] 2Fuso, 1995 [98] 2Nava, 1994 [99] 2Dubois, 1994 [100] 1Rieves, 1993 [101] 2Vitacca, 1996 [102] 1Connors, 1996 [103] 1Szekely, 1997 [104] 1Antonelli Incalzi, 1997 [105] 1Anton, 2000 [106] 1Putinati, 2000 [107] 1Antonelli Incalzi, 2001 [108] 1Roberts, 2002 [109] 3Patil, 2003 [110] 6Goel, 2003 [111] 3Scala, 2004 [112] 2Khilnani, 2004 [113] 1Confalonieri, 2005 [114] 1Ucgun, 2006 [115] 3Yohannes, 2005 [116] 2Almagro, 2006 [117] 1Chen, 2006 [118] 1Wildman, 2007 [119] 2Liu, 2007 [120] 1Ruiz-Gonzalez, 2008 [121] 1Mohan, 2008 [122] 2Wildman, 2009 [123] 1Chakrabarti, 2009 [124] 4Tsimogianni, 2009 [125] 1Tabak, 2009 [126] 1Terzano, 2010 [127] 3Roca, 2011 [128] 1Aburto, 2011 [129] 1Asiimwe, 2011 [130] 1Abrams, 2011 [131] 1Matkovic, 2012 [132] 3
Page 62 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Steer, 2012 [133] 3Slenter, 2013 [134] 1Jurado Gamez, 2013 [135] 1Tabak, 2013 [136] 1Haja Mydin, 2013 [137] 1Amalakuhan, 2012 [138] 1Lindenauer, 2013 [139] 1Almagro, 2014 [140] 1Wang, 2014 [141] 1Batzlaff, 2014 [142] 1Sharif, 2014 [143] 2Diamantea, 2014 [144] 1Cheng, 2014 [145] 2Roche, 2014 [146] 1Quintana, 2014 [147] 1Grolimund, 2015 [148] 5Crisafulli, 2015 [149] 1Fan, 2014 [150] 1Quintana, 2015 [151] 1Quintana, 2014 [152] 3Yu, 2015 [153] 1Roberts, 2015 [154] 5Liu, 2015 [155] 1Kon, 2015 [156] 2Glaser, 2015 [157] 1Crisafulli, 2016 [158] 1Garcia Sidro, 2015 [159] 1Ramaraju, 2016 [160] 2Sainaghi, 2016 [161] 1He, 2018 [162] 1Garcia-Rivero, 2017 [163] 2Echevarria, 2017 [164] 1Sakamoto, 2017 [165] 1Feng, 2017 [166] 5Almagro, 2017 [167] 1Lau, 2017 [168] 1Vanasse, 2017 [169] 1Duenk, 2017 [170] 1
Page 63 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Yao, 2017 [171] 4Bernabeu-Mora, 2017 [172] 2Winther, 2017 [173] 2Zhong, 2017 [174] 1Pavlisa, 2017 [175] 4Rezaee, 2017 [176] 3Hakim, 2018 [177] 5Esteban, 2018 [178] 1Serra-Pimacal, 2018 [179] 3Cerezo Lajas, 2018 [180] 1Epstein, 2018 [181] 2Bottle, 2018 [182] 2Schuler, 2018 [183] 2Madkour, 2013 [184] 2
Page 64 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Supplementary Table 5. Articles describing the development of prognostic model for COPD patients attending the emergency department.
References Number of modelsMurata, 1992 [185] 1Kim, 2006 [186] 1Roche, 2008 [187] 1Stiell, 2014 [188] 1Quintana, 2014 [189] 1Quintana, 2014 [147] 1Quintana, 2014 [152] 2Esteban, 2015 [190] 2Esteban, 2016 [191] 4
Page 65 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Supplementary Table 6. Articles describing the validation of prognostic models developed for diseases other than COPD.
Model name ReferencesAPACHE II [113,122,124,133,150,192–197]APACHE III [194]CHA2DS2-VASC [198,199]Charlson comorbidity index [200,201]CRB-65 [202]CREWS [203]CURB-65 [133,196,204–207]Elixhauser comorbidity index [200]GRACE [208]HOSPITAL score [209]LACE [164,177]MDA [210]MODS [194]NEWS [203]NRS 2002 [211]PSI [205]Salford-NEWS [203]SAPS II [122,184,194]SOFA [194]
Page 66 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Page 67 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Supplementary References
1 Dompeling E, van Grunsven PM, Molema J, et al. Early detection of patients with fast progressive asthma or chronic bronchitis in general practice. Scand J Prim Health Care 1992;10:143–50.
2 Campbell AH, Barter CE, O’Connell JM, et al. Factors affecting the decline of ventilatory function in chronic bronchitis. Thorax 1985;40:741–8.
3 Smit JM, Burema J, May JF, et al. Prognosis in severe chronic obstructive pulmonary disease with regard to the electrocardiogram. J Electrocardiol 1983;16:77–86.
4 Daughton DM, James Fix A, Kass I, et al. Three-year survival rates of pulmonary rehabilitation patients with chronic obstructive pulmonary disease. J Natl Med Assoc 1984;76:265–8.
5 Ball P, Harris JM, Lowson D, et al. Acute infective exacerbations of chronic bronchitis. QJM 1995;88:61–8.
6 Ashutosh K, Haldipur C, Boucher ML. Clinical and personality profiles and survival in patients with COPD. Chest 1997;111:95–8.
7 Kessler R, Faller M, Fourgaut G, et al. Predictive factors of hospitalization for acute exacerbation in a series of 64 patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1999;159:158–64. doi:10.1164/ajrccm.159.1.9803117
8 Dewan NA, Rafique S, Kanwar B, et al. Acute exacerbation of COPD: factors associated with poor treatment outcome. Chest 2000;117:662–71.
9 Miravitlles M, Guerrero T, Mayordomo C, et al. Factors associated with increased risk of exacerbation and hospital admission in a cohort of ambulatory COPD patients: a multiple logistic regression analysis. The EOLO Study Group. Respiration 2000;67:495–501. doi:10.1159/000067462
10 Miravitlles M, Murio C, Guerrero T. Factors associated with relapse after ambulatory treatment of acute exacerbations of chronic bronchitis. DAFNE Study Group. Eur Respir J 2001;17:928–33.
11 Fan VS, Curtis JR, Tu S-P, et al. Using quality of life to predict hospitalization and mortality in patients with obstructive lung diseases. Chest 2002;122:429–36.
12 Celli BR, Cote CG, Marin JM, et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004;350:1005–12. doi:10.1056/NEJMoa021322
13 Bloch KE, Weder W, Bachmann LM, et al. Model-Based versus Clinical Prediction of the Spirometric Response to Lung Volume Reduction Surgery. Respiration 2004;71:611–8. doi:10.1159/000081762
14 Miravitlles M, Llor C, Naberan K, et al. Variables associated with recovery from acute exacerbations of chronic bronchitis and chronic obstructive pulmonary disease. Respir Med 2005;99:955–65. doi:10.1016/j.rmed.2005.01.013
15 Mapel DW, McMillan GP, Frost FJ, et al. Predicting the costs of managing patients with chronic obstructive pulmonary disease. Respir Med 2005;99:1325–33. doi:10.1016/j.rmed.2005.03.001
16 Miravitlles M, Calle M, Alvarez-Gutierrez F, et al. Exacerbations, hospital admissions and impaired health status in chronic obstructive pulmonary disease. Qual Life Res 2006;15:471–80. doi:10.1007/s11136-005-3215-y
17 Man SFP, Connett JE, Anthonisen NR, et al. C-reactive protein and mortality in mild to moderate chronic obstructive pulmonary disease. Thorax 2006;61:849–53. doi:10.1136/thx.2006.059808
18 Esteban C, Quintana JM, Aburto M, et al. A simple score for assessing stable chronic obstructive pulmonary disease. QJM 2006;99:751–9. doi:10.1093/qjmed/hcl110
19 Niewoehner DE, Lokhnygina Y, Rice K, et al. Risk indexes for exacerbations and hospitalizations due to COPD. Chest 2007;131:20–8. doi:10.1378/chest.06-1316
20 Fan VS, Ramsey SD, Make BJ, et al. Physiologic variables and functional status independently predict COPD hospitalizations and emergency department visits in patients with severe COPD. COPD 2007;4:29–
Page 68 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
39. doi:10.1080/15412550601169430
21 Briggs A, Spencer M, Wang H, et al. Development and validation of a prognostic index for health outcomes in chronic obstructive pulmonary disease. Arch Intern Med 2008;168:71–9. doi:10.1001/archinternmed.2007.37
22 Blanchette CM, Gutierrez B, Ory C, et al. Economic burden in direct costs of concomitant chronic obstructive pulmonary disease and asthma in a Medicare Advantage population. J Manag Care Pharm 2008;14:176–85. doi:10.18553/jmcp.2008.14.2.176
23 Fan VS, Giardino ND, Blough DK, et al. Costs of pulmonary rehabilitation and predictors of adherence in the National Emphysema Treatment Trial. COPD 2008;5:105–16. doi:10.1080/15412550801941190
24 Soler-Cataluña JJ, Martínez-García MA, Sánchez LS, et al. Severe exacerbations and BODE index: two independent risk factors for death in male COPD patients. Respir Med 2009;103:692–9. doi:10.1016/j.rmed.2008.12.005
25 Omachi TA, Yelin EH, Katz PP, et al. The COPD severity score: a dynamic prediction tool for health-care utilization. COPD 2008;5:339–46. doi:10.1080/15412550802522700
26 Schembri S, Anderson W, Morant S, et al. A predictive model of hospitalisation and death from chronic obstructive pulmonary disease. Respir Med 2009;103:1461–7. doi:10.1016/j.rmed.2009.04.021
27 Puhan MA, Garcia-Aymerich J, Frey M, et al. Expansion of the prognostic assessment of patients with chronic obstructive pulmonary disease: the updated BODE index and the ADO index. Lancet (London, England) 2009;374:704–11. doi:10.1016/S0140-6736(09)61301-5
28 Mehrotra N, Freire AX, Bauer DC, et al. Predictors of mortality in elderly subjects with obstructive airway disease: the PILE score. Ann Epidemiol 2010;20:223–32. doi:10.1016/j.annepidem.2009.11.005
29 Benzo RP, Chang C-CH, Farrell MH, et al. Physical activity, health status and risk of hospitalization in patients with severe chronic obstructive pulmonary disease. Respiration 2010;80:10–8. doi:10.1159/000296504
30 Eisner MD, Iribarren C, Blanc PD, et al. Development of disability in chronic obstructive pulmonary disease: beyond lung function. Thorax 2011;66:108–14. doi:10.1136/thx.2010.137661
31 Brusse-Keizer M, van der Palen J, van der Valk P, et al. Clinical predictors of exacerbation frequency in chronic obstructive pulmonary disease. Clin Respir J 2011;5:227–34. doi:10.1111/j.1752-699X.2010.00234.x
32 Simon-Tuval T, Scharf SM, Maimon N, et al. Determinants of elevated healthcare utilization in patients with COPD. Respir Res 2011;12:7. doi:10.1186/1465-9921-12-7
33 Waschki B, Kirsten A, Holz O, et al. Physical activity is the strongest predictor of all-cause mortality in patients with COPD: a prospective cohort study. Chest 2011;140:331–42. doi:10.1378/chest.10-2521
34 Lee JS, Huh JW, Chae EJ, et al. Predictors of pulmonary function response to treatment with salmeterol/fluticasone in patients with chronic obstructive pulmonary disease. J Korean Med Sci 2011;26:379–85. doi:10.3346/jkms.2011.26.3.379
35 Esteban C, Arostegui I, Moraza J, et al. Development of a decision tree to assess the severity and prognosis of stable COPD. Eur Respir J 2011;38:1294–300. doi:10.1183/09031936.00189010
36 Zhang J, Rutten FH, Cramer MJ, et al. The importance of cardiovascular disease for mortality in patients with COPD: a prognostic cohort study. Fam Pract 2011;28:474–81. doi:10.1093/fampra/cmr024
37 Esteban C, Quintana JM, Aburto M, et al. The health, activity, dyspnea, obstruction, age, and hospitalization: prognostic score for stable COPD patients. Respir Med 2011;105:1662–70. doi:10.1016/j.rmed.2011.05.005
38 Gale CP, White JES, Hunter A, et al. Predicting mortality and hospital admission in patients with COPD: significance of NT pro-BNP, clinical and echocardiographic assessment. J Cardiovasc Med (Hagerstown) 2011;12:613–8. doi:10.2459/JCM.0b013e3283491780
Page 69 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
39 Williams JEA, Green RH, Warrington V, et al. Development of the i-BODE: validation of the incremental shuttle walking test within the BODE index. Respir Med 2012;106:390–6. doi:10.1016/j.rmed.2011.09.005
40 Yoo J-W, Hong Y, Seo JB, et al. Comparison of clinico-physiologic and CT imaging risk factors for COPD exacerbation. J Korean Med Sci 2011;26:1606–12. doi:10.3346/jkms.2011.26.12.1606
41 Ozgür ES, Nayci SA, Özge C, et al. An integrated index combined by dynamic hyperinflation and exercise capacity in the prediction of morbidity and mortality in COPD. Respir Care 2012;57:1452–9. doi:10.4187/respcare.01440
42 Celli BR, Locantore N, Yates J, et al. Inflammatory biomarkers improve clinical prediction of mortality in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012;185:1065–72. doi:10.1164/rccm.201110-1792OC
43 Divo M, Cote C, de Torres JP, et al. Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012;186:155–61. doi:10.1164/rccm.201201-0034OC
44 Jensen MT, Marott JL, Lange P, et al. Resting heart rate is a predictor of mortality in COPD. Eur Respir J 2013;42:341–9. doi:10.1183/09031936.00072212
45 Puhan MA, Hansel NN, Sobradillo P, et al. Large-scale international validation of the ADO index in subjects with COPD: an individual subject data analysis of 10 cohorts. BMJ Open 2012;2. doi:10.1136/bmjopen-2012-002152
46 Mannino DM, Diaz-Guzman E, Pospisil J. A new approach to classification of disease severity and progression of COPD. Chest 2013;144:1179–85. doi:10.1378/chest.12-2674
47 Ryynänen O-P, Soini EJ, Lindqvist A, et al. Bayesian predictors of very poor health related quality of life and mortality in patients with COPD. BMC Med Inform Decis Mak 2013;13:34. doi:10.1186/1472-6947-13-34
48 Thomsen M, Ingebrigtsen TS, Marott JL, et al. Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. JAMA 2013;309:2353–61. doi:10.1001/jama.2013.5732
49 Stolz D, Kostikas K, Blasi F, et al. Adrenomedullin refines mortality prediction by the BODE index in COPD: the ‘BODE-A’ index. Eur Respir J 2014;43:397–408. doi:10.1183/09031936.00058713
50 Roberts MH, Mapel DW, Bruse S, et al. Development of a modified BODE index as a mortality risk measure among older adults with and without chronic obstructive pulmonary disease. Am J Epidemiol 2013;178:1150–60. doi:10.1093/aje/kwt087
51 Heerema-Poelman A, Stuive I, Wempe JB. Adherence to a Maintenance Exercise Program 1 Year After Pulmonary Rehabilitation. J Cardiopulm Rehabil Prev 2013;33:419–26. doi:10.1097/HCR.0b013e3182a5274a
52 Bertens LCM, Reitsma JB, Moons KGM, et al. Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2013;8:493–9. doi:10.2147/COPD.S49609
53 Moy ML, Teylan M, Danilack VA, et al. An index of daily step count and systemic inflammation predicts clinical outcomes in chronic obstructive pulmonary disease. Ann Am Thorac Soc 2014;11:149–57. doi:10.1513/AnnalsATS.201307-243OC
54 Bowler RP, Kim V, Regan E, et al. Prediction of acute respiratory disease in current and former smokers with and without COPD. Chest 2014;146:941–50. doi:10.1378/chest.13-2946
55 Koskela J, Kilpeläinen M, Kupiainen H, et al. Co-morbidities are the key nominators of the health related quality of life in mild and moderate COPD. BMC Pulm Med 2014;14:102. doi:10.1186/1471-2466-14-102
56 Stolz D, Meyer A, Rakic J, et al. Mortality risk prediction in COPD by a prognostic biomarker panel. Eur Respir J 2014;44:1557–70. doi:10.1183/09031936.00043814
57 Boutou AK, Nair A, Douraghi-Zadeh D, et al. A combined pulmonary function and emphysema score prognostic index for staging in Chronic Obstructive Pulmonary Disease. PLoS One 2014;9:e111109.
Page 70 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
doi:10.1371/journal.pone.0111109
58 Suzuki M, Makita H, Östling J, et al. Lower leptin/adiponectin ratio and risk of rapid lung function decline in chronic obstructive pulmonary disease. Ann Am Thorac Soc 2014;11:1511–9. doi:10.1513/AnnalsATS.201408-351OC
59 Make BJ, Eriksson G, Calverley PM, et al. A score to predict short-term risk of COPD exacerbations (SCOPEX). Int J Chron Obstruct Pulmon Dis 2015;10:201–9. doi:10.2147/COPD.S69589
60 Wilson R, Anzueto A, Miravitlles M, et al. Prognostic factors for clinical failure of exacerbations in elderly outpatients with moderate-to-severe COPD. Int J Chron Obstruct Pulmon Dis 2015;10:985–93. doi:10.2147/COPD.S80926
61 Montserrat-Capdevila J, Godoy P, Marsal JR, et al. Predictive Model of Hospital Admission for COPD Exacerbation. Respir Care 2015;60:1288–94. doi:10.4187/respcare.04005
62 Shin TR, Oh Y-M, Park JH, et al. The Prognostic Value of Residual Volume/Total Lung Capacity in Patients with Chronic Obstructive Pulmonary Disease. J Korean Med Sci 2015;30:1459–65. doi:10.3346/jkms.2015.30.10.1459
63 Abascal-Bolado B, Novotny PJ, Sloan JA, et al. Forecasting COPD hospitalization in the clinic: optimizing the chronic respiratory questionnaire. Int J Chron Obstruct Pulmon Dis 2015;10:2295–301. doi:10.2147/COPD.S87469
64 Kerkhof M, Freeman D, Jones R, et al. Predicting frequent COPD exacerbations using primary care data. Int J Chron Obstruct Pulmon Dis 2015;10:2439–50. doi:10.2147/COPD.S94259
65 Montserrat-Capdevila J, Godoy P, Marsal JR, et al. Risk of exacerbation in chronic obstructive pulmonary disease: a primary care retrospective cohort study. BMC Fam Pract 2015;16:173. doi:10.1186/s12875-015-0387-6
66 Miravitlles M, García-Sidro P, Fernández-Nistal A, et al. The chronic obstructive pulmonary disease assessment test improves the predictive value of previous exacerbations for poor outcomes in COPD. Int J Chron Obstruct Pulmon Dis 2015;10:2571–9. doi:10.2147/COPD.S91163
67 Ramon MA, Ferrer J, Gimeno-Santos E, et al. Inspiratory capacity-to-total lung capacity ratio and dyspnoea predict exercise capacity decline in COPD. Respirology 2016;21:476–82. doi:10.1111/resp.12723
68 Park HY, Lee H, Koh W-J, et al. Association of blood eosinophils and plasma periostin with FEV1 response after 3-month inhaled corticosteroid and long-acting beta2-agonist treatment in stable COPD patients. Int J Chron Obstruct Pulmon Dis 2016;11:23–30. doi:10.2147/COPD.S94797
69 Blumenthal JA, Smith PJ, Durheim M, et al. Biobehavioral Prognostic Factors in Chronic Obstructive Pulmonary Disease. Psychosom Med 2016;78:153–62. doi:10.1097/PSY.0000000000000260
70 Beijers RJHCG, van den Borst B, Newman AB, et al. A Multidimensional Risk Score to Predict All-Cause Hospitalization in Community-Dwelling Older Individuals With Obstructive Lung Disease. J Am Med Dir Assoc 2016;17:508–13. doi:10.1016/j.jamda.2016.01.007
71 Boeck L, Soriano JB, Brusse-Keizer M, et al. Prognostic assessment in COPD without lung function: the B-AE-D indices. Eur Respir J 2016;47:1635–44. doi:10.1183/13993003.01485-2015
72 Horita N, Koblizek V, Plutinsky M, et al. Chronic obstructive pulmonary disease prognostic score: A new index. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2016;160:211–8. doi:10.5507/bp.2016.030
73 Zafari Z, Sin DD, Postma DS, et al. Individualized prediction of lung-function decline in chronic obstructive pulmonary disease. CMAJ 2016;188:1004–11. doi:10.1503/cmaj.151483
74 Chan HP, Mukhopadhyay A, Chong PLP, et al. Role of BMI, airflow obstruction, St George’s Respiratory Questionnaire and age index in prognostication of Asian COPD. Respirology 2017;22:114–9. doi:10.1111/resp.12877
75 Sand JMB, Leeming DJ, Byrjalsen I, et al. High levels of biomarkers of collagen remodeling are associated
Page 71 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
with increased mortality in COPD - results from the ECLIPSE study. Respir Res 2016;17:125. doi:10.1186/s12931-016-0440-6
76 Ansari K, Keaney N, Kay A, et al. Body mass index, airflow obstruction and dyspnea and body mass index, airflow obstruction, dyspnea scores, age and pack years-predictive properties of new multidimensional prognostic indices of chronic obstructive pulmonary disease in primary care. Ann Thorac Med 2016;11:261. doi:10.4103/1817-1737.191866
77 Barton CA, Bassett KL, Buckman J, et al. The predictive value of an adjusted COPD assessment test score on the risk of respiratory-related hospitalizations in severe COPD patients. Chron Respir Dis 2017;14:72–84. doi:10.1177/1479972316687099
78 Crook S, Frei A, Ter Riet G, et al. Prediction of long-term clinical outcomes using simple functional exercise performance tests in patients with COPD: a 5-year prospective cohort study. Respir Res 2017;18:112. doi:10.1186/s12931-017-0598-6
79 Russo AN, Sathiyamoorthy G, Lau C, et al. Impact of a Post-Discharge Integrated Disease Management Program on COPD Hospital Readmissions. Respir Care 2017;62:1396–402. doi:10.4187/respcare.05547
80 Sundh J, Ekström M. Risk factors for developing hypoxic respiratory failure in COPD. Int J Chron Obstruct Pulmon Dis 2017;12:2095–100. doi:10.2147/COPD.S140299
81 Kurashima K, Takaku Y, Nakamoto K, et al. Risk Factors for Pneumonia and the Effect of the Pneumococcal Vaccine in Patients With Chronic Airflow Obstruction. Chronic Obstr Pulm Dis (Miami, Fla) 2016;3:610–9. doi:10.15326/jcopdf.3.3.2015.0167
82 Villalobos N, Davidson R, Ghori UK, et al. External Validation of the COmorbidity Test. COPD 2017;14:513–7. doi:10.1080/15412555.2017.1354981
83 Strassmann A, Frei A, Haile SR, et al. Commonly Used Patient-Reported Outcomes Do Not Improve Prediction of COPD Exacerbations: A Multicenter 4½ Year Prospective Cohort Study. Chest 2017;152:1179–87. doi:10.1016/j.chest.2017.09.003
84 Dal Negro RW, Celli BR. Patient Related Outcomes-BODE (PRO-BODE): A composite index incorporating health utilization resources predicts mortality and economic cost of COPD in real life. Respir Med 2017;131:175–8. doi:10.1016/j.rmed.2017.08.019
85 Hoogendoorn M, Feenstra TL, Boland M, et al. Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources. Int J Chron Obstruct Pulmon Dis 2017;12:3183–94. doi:10.2147/COPD.S142378
86 Mendy A, Forno E, Niyonsenga T, et al. Blood biomarkers as predictors of long-term mortality in COPD. Clin Respir J 2018;12:1891–9. doi:10.1111/crj.12752
87 Kang J, Kim KT, Lee J-H, et al. Predicting treatable traits for long-acting bronchodilators in patients with stable COPD. Int J Chron Obstruct Pulmon Dis 2017;12:3557–65. doi:10.2147/COPD.S151909
88 Fijačko V, Labor M, Fijačko M, et al. Predictors of short-term LAMA ineffectiveness in treatment naïve patients with moderate to severe COPD. Wien Klin Wochenschr 2018;130:247–58. doi:10.1007/s00508-017-1307-7
89 Bafadhel M, Peterson S, De Blas MA, et al. Predictors of exacerbation risk and response to budesonide in patients with chronic obstructive pulmonary disease: a post-hoc analysis of three randomised trials. Lancet Respir Med 2018;6:117–26. doi:10.1016/S2213-2600(18)30006-7
90 Stanford RH, Nag A, Mapel DW, et al. Claims-based risk model for first severe COPD exacerbation. Am J Manag Care 2018;24:e45–53.
91 Samp JC, Joo MJ, Schumock GT, et al. Predicting Acute Exacerbations in Chronic Obstructive Pulmonary Disease. J Manag care Spec Pharm 2018;24:265–79. doi:10.18553/jmcp.2018.24.3.265
92 Miravitlles M, Sliwinski P, Rhee CK, et al. Evaluation of criteria for clinical control in a prospective, international, multicenter study of patients with COPD. Respir Med 2018;136:8–14. doi:10.1016/j.rmed.2018.01.019
Page 72 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
93 Vela E, Tényi Á, Cano I, et al. Population-based analysis of patients with COPD in Catalonia: a cohort study with implications for clinical management. BMJ Open 2018;8:e017283. doi:10.1136/bmjopen-2017-017283
94 Morales DR, Flynn R, Zhang J, et al. External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches. Respir Med 2018;138:150–5. doi:10.1016/j.rmed.2018.04.003
95 Hwang J, Oh Y-M, Lee M, et al. Low morphometric complexity of emphysematous lesions predicts survival in chronic obstructive pulmonary disease patients. Eur Radiol 2019;29:176–85. doi:10.1007/s00330-018-5551-7
96 Annavarapu S, Goldfarb S, Gelb M, et al. Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data. Int J Chron Obstruct Pulmon Dis 2018;13:2121–30. doi:10.2147/COPD.S155773
97 Menzies R, Gibbons W, Goldberg P. Determinants of weaning and survival among patients with COPD who require mechanical ventilation for acute respiratory failure. Chest 1989;95:398–405.
98 Fuso L, Incalzi RA, Pistelli R, et al. Predicting mortality of patients hospitalized for acutely exacerbated chronic obstructive pulmonary disease. Am J Med 1995;98:272–7.
99 Nava S, Rubini F, Zanotti E, et al. Survival and prediction of successful ventilator weaning in COPD patients requiring mechanical ventilation for more than 21 days. Eur Respir J 1994;7:1645–52.
100 Dubois P, Jamart J, Machiels J, et al. Prognosis of severely hypoxemic patients receiving long-term oxygen therapy. Chest 1994;105:469–74.
101 Rieves RD, Bass D, Carter RR, et al. Severe COPD and acute respiratory failure. Correlates for survival at the time of tracheal intubation. Chest 1993;104:854–60.
102 Vitacca M, Clini E, Porta R, et al. Acute exacerbations in patients with COPD: predictors of need for mechanical ventilation. Eur Respir J 1996;9:1487–93.
103 Connors AF, Dawson N V, Thomas C, et al. Outcomes following acute exacerbation of severe chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments). Am J Respir Crit Care Med 1996;154:959–67. doi:10.1164/ajrccm.154.4.8887592
104 Szekely LA, Oelberg DA, Wright C, et al. Preoperative predictors of operative morbidity and mortality in COPD patients undergoing bilateral lung volume reduction surgery. Chest 1997;111:550–8.
105 Antonelli Incalzi R, Fuso L, De Rosa M, et al. Co-morbidity contributes to predict mortality of patients with chronic obstructive pulmonary disease. Eur Respir J 1997;10:2794–800.
106 Antón A, Güell R, Gómez J, et al. Predicting the result of noninvasive ventilation in severe acute exacerbations of patients with chronic airflow limitation. Chest 2000;117:828–33.
107 Putinati S, Ballerin L, Piattella M, et al. Is it possible to predict the success of non-invasive positive pressure ventilation in acute respiratory failure due to COPD? Respir Med 2000;94:997–1001. doi:10.1053/rmed.2000.0883
108 Incalzi RA, Pedone C, Onder G, et al. Predicting length of stay of older patients with exacerbated chronic obstructive pulmonary disease. Aging (Milano) 2001;13:49–57.
109 Roberts CM, Lowe D, Bucknall CE, et al. Clinical audit indicators of outcome following admission to hospital with acute exacerbation of chronic obstructive pulmonary disease. Thorax 2002;57:137–41.
110 Patil SP, Krishnan JA, Lechtzin N, et al. In-hospital mortality following acute exacerbations of chronic obstructive pulmonary disease. Arch Intern Med 2003;163:1180–6. doi:10.1001/archinte.163.10.1180
111 Goel A, Pinckney RG, Littenberg B. APACHE II predicts long-term survival in COPD patients admitted to a general medical ward. J Gen Intern Med 2003;18:824–30.
112 Scala R, Bartolucci S, Naldi M, et al. Co-morbidity and acute decompensations of COPD requiring non-
Page 73 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
invasive positive-pressure ventilation. Intensive Care Med 2004;30:1747–54. doi:10.1007/s00134-004-2368-4
113 Khilnani GC, Banga A, Sharma SK. Predictors of mortality of patients with acute respiratory failure secondary to chronic obstructive pulmonary disease admitted to an intensive care unit: A one year study. BMC Pulm Med 2004;4:12. doi:10.1186/1471-2466-4-12
114 Confalonieri M, Garuti G, Cattaruzza MS, et al. A chart of failure risk for noninvasive ventilation in patients with COPD exacerbation. Eur Respir J 2005;25:348–55. doi:10.1183/09031936.05.00085304
115 Ucgun I, Metintas M, Moral H, et al. Predictors of hospital outcome and intubation in COPD patients admitted to the respiratory ICU for acute hypercapnic respiratory failure. Respir Med 2006;100:66–74. doi:10.1016/j.rmed.2005.04.005
116 Yohannes AM, Baldwin RC, Connolly MJ. Predictors of 1-year mortality in patients discharged from hospital following acute exacerbation of chronic obstructive pulmonary disease. Age Ageing 2005;34:491–6. doi:10.1093/ageing/afi163
117 Almagro P, Barreiro B, Ochoa de Echaguen A, et al. Risk factors for hospital readmission in patients with chronic obstructive pulmonary disease. Respiration 2006;73:311–7. doi:10.1159/000088092
118 Chen Y-J, Narsavage GL. Factors related to chronic obstructive pulmonary disease readmission in Taiwan. West J Nurs Res 2006;28:105–24. doi:10.1177/0193945905282354
119 Wildman MJ, Harrison DA, Welch CA, et al. A new measure of acute physiological derangement for patients with exacerbations of obstructive airways disease: the COPD and Asthma Physiology Score. Respir Med 2007;101:1994–2002. doi:10.1016/j.rmed.2007.04.002
120 Liu H, Zhang T, Ye J. Determinants of prolonged mechanical ventilation in patients with chronic obstructive pulmonary diseases and acute hypercapnic respiratory failure. Eur J Intern Med 2007;18:542–7. doi:10.1016/j.ejim.2007.04.017
121 Ruiz-González A, Lacasta D, Ibarz M, et al. C-reactive protein and other predictors of poor outcome in patients hospitalized with exacerbations of chronic obstructive pulmonary disease. Respirology 2008;13:1028–33. doi:10.1111/j.1440-1843.2008.01403.x
122 Mohan A, Bhatt SP, Mohan C, et al. Derivation of a prognostic equation to predict in-hospital mortality and requirement of invasive mechanical ventilation in patients with acute exacerbation of chronic obstructive pulmonary disease. Indian J Chest Dis Allied Sci 2008;50:335–42.
123 Wildman MJ, Sanderson C, Groves J, et al. Predicting mortality for patients with exacerbations of COPD and Asthma in the COPD and Asthma Outcome Study (CAOS). QJM 2009;102:389–99. doi:10.1093/qjmed/hcp036
124 Chakrabarti B, Angus RM, Agarwal S, et al. Hyperglycaemia as a predictor of outcome during non-invasive ventilation in decompensated COPD. Thorax 2009;64:857–62. doi:10.1136/thx.2008.106989
125 Tsimogianni AM, Papiris SA, Stathopoulos GT, et al. Predictors of outcome after exacerbation of chronic obstructive pulmonary disease. J Gen Intern Med 2009;24:1043–8. doi:10.1007/s11606-009-1061-2
126 Tabak YP, Sun X, Johannes RS, et al. Mortality and need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: development and validation of a simple risk score. Arch Intern Med 2009;169:1595–602. doi:10.1001/archinternmed.2009.270
127 Terzano C, Conti V, Di Stefano F, et al. Comorbidity, hospitalization, and mortality in COPD: results from a longitudinal study. Lung 2010;188:321–9. doi:10.1007/s00408-009-9222-y
128 Roca B, Almagro P, López F, et al. Factors associated with mortality in patients with exacerbation of chronic obstructive pulmonary disease hospitalized in General Medicine departments. Intern Emerg Med 2011;6:47–54. doi:10.1007/s11739-010-0465-7
129 Aburto M, Esteban C, Moraza FJ, et al. COPD exacerbation: mortality prognosis factors in a respiratory care unit. Arch Bronconeumol 2011;47:79–84. doi:10.1016/j.arbres.2010.10.012
Page 74 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
130 Asiimwe AC, Brims FJH, Andrews NP, et al. Routine laboratory tests can predict in-hospital mortality in acute exacerbations of COPD. Lung 2011;189:225–32. doi:10.1007/s00408-011-9298-z
131 Abrams TE, Vaughan-Sarrazin M, Vander Weg MW. Acute Exacerbations of Chronic Obstructive Pulmonary Disease and the Effect of Existing Psychiatric Comorbidity on Subsequent Mortality. Psychosomatics 2011;52:441–9. doi:10.1016/j.psym.2011.03.005
132 Matkovic Z, Huerta A, Soler N, et al. Predictors of adverse outcome in patients hospitalised for exacerbation of chronic obstructive pulmonary disease. Respiration 2012;84:17–26. doi:10.1159/000335467
133 Steer J, Gibson J, Bourke SC. The DECAF Score: predicting hospital mortality in exacerbations of chronic obstructive pulmonary disease. Thorax 2012;67:970–6. doi:10.1136/thoraxjnl-2012-202103
134 Slenter RHJ, Sprooten RTM, Kotz D, et al. Predictors of 1-year mortality at hospital admission for acute exacerbations of chronic obstructive pulmonary disease. Respiration 2013;85:15–26. doi:10.1159/000342036
135 Jurado Gámez B, Feu Collado N, Jurado García JC, et al. Home intervention and predictor variables for rehospitalization in chronic obstructive pulmonary disease exacerbations. Arch Bronconeumol 2013;49:10–4. doi:10.1016/j.arbres.2012.08.003
136 Tabak YP, Sun X, Johannes RS, et al. Development and validation of a mortality risk-adjustment model for patients hospitalized for exacerbations of chronic obstructive pulmonary disease. Med Care 2013;51:597–605. doi:10.1097/MLR.0b013e3182901982
137 Haja Mydin H, Murphy S, Clague H, et al. Anemia and performance status as prognostic markers in acute hypercapnic respiratory failure due to chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2013;8:151–7. doi:10.2147/COPD.S39403
138 Amalakuhan B, Kiljanek L, Parvathaneni A, et al. A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem. J Community Hosp Intern Med Perspect 2012;2:9915. doi:10.3402/jchimp.v2i1.9915
139 Lindenauer PK, Grosso LM, Wang C, et al. Development, validation, and results of a risk-standardized measure of hospital 30-day mortality for patients with exacerbation of chronic obstructive pulmonary disease. J Hosp Med 2013;8:428–35. doi:10.1002/jhm.2066
140 Almagro P, Soriano JB, Cabrera FJ, et al. Short- and medium-term prognosis in patients hospitalized for COPD exacerbation: the CODEX index. Chest 2014;145:972–80. doi:10.1378/chest.13-1328
141 Wang Y, Stavem K, Dahl FA, et al. Factors associated with a prolonged length of stay after acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Int J Chron Obstruct Pulmon Dis 2014;9:99–105. doi:10.2147/COPD.S51467
142 Batzlaff CM, Karpman C, Afessa B, et al. Predicting 1-year mortality rate for patients admitted with an acute exacerbation of chronic obstructive pulmonary disease to an intensive care unit: an opportunity for palliative care. Mayo Clin Proc 2014;89:638–43. doi:10.1016/j.mayocp.2013.12.004
143 Sharif R, Parekh TM, Pierson KS, et al. Predictors of early readmission among patients 40 to 64 years of age hospitalized for chronic obstructive pulmonary disease. Ann Am Thorac Soc 2014;11:685–94. doi:10.1513/AnnalsATS.201310-358OC
144 Diamantea F, Kostikas K, Bartziokas K, et al. Prediction of hospitalization stay in COPD exacerbations: the AECOPD-F score. Respir Care 2014;59:1679–86. doi:10.4187/respcare.03171
145 Cheng Y, Borrego ME, Frost FJ, et al. Predictors for mortality in hospitalized patients with chronic obstructive pulmonary disease. Springerplus 2014;3:359. doi:10.1186/2193-1801-3-359
146 Roche N, Chavaillon J-M, Maurer C, et al. A clinical in-hospital prognostic score for acute exacerbations of COPD. Respir Res 2014;15:99. doi:10.1186/s12931-014-0099-9
147 Quintana JM, Esteban C, Garcia-Gutierrez S, et al. Predictors of hospital admission two months after emergency department evaluation of COPD exacerbation. Respiration 2014;88:298–306. doi:10.1159/000365996
Page 75 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
148 Grolimund E, Kutz A, Marlowe RJ, et al. Long-term Prognosis in COPD Exacerbation: Role of Biomarkers, Clinical Variables and Exacerbation Type. COPD 2015;12:295–305. doi:10.3109/15412555.2014.949002
149 Crisafulli E, Torres A, Huerta A, et al. C-Reactive Protein at Discharge, Diabetes Mellitus and ≥ 1 Hospitalization During Previous Year Predict Early Readmission in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. COPD 2015;12:306–14. doi:10.3109/15412555.2014.933954
150 Fan L, Zhao Q, Liu Y, et al. Semiquantitative cough strength score and associated outcomes in noninvasive positive pressure ventilation patients with acute exacerbation of chronic obstructive pulmonary disease. Respir Med 2014;108:1801–7. doi:10.1016/j.rmed.2014.10.001
151 Quintana JM, Unzurrunzaga A, Garcia-Gutierrez S, et al. Predictors of Hospital Length of Stay in Patients with Exacerbations of COPD: A Cohort Study. J Gen Intern Med 2015;30:824–31. doi:10.1007/s11606-014-3129-x
152 Quintana JM, Esteban C, Unzurrunzaga A, et al. Prognostic severity scores for patients with COPD exacerbations attending emergency departments. Int J Tuberc Lung Dis 2014;18:1415–20. doi:10.5588/ijtld.14.0312
153 Yu T-C, Zhou H, Suh K, et al. Assessing the importance of predictors in unplanned hospital readmissions for chronic obstructive pulmonary disease. Clinicoecon Outcomes Res 2015;7:37–51. doi:10.2147/CEOR.S74181
154 Roberts M, Mapel D, Von Worley A, et al. Clinical factors, including All Patient Refined Diagnosis Related Group severity, as predictors of early rehospitalization after COPD exacerbation. Drugs Context 2015;4:1–15. doi:10.7573/dic.212278
155 Liu D, Peng S-H, Zhang J, et al. Prediction of short term re-exacerbation in patients with acute exacerbation of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2015;10:1265–73. doi:10.2147/COPD.S83378
156 Kon SSC, Jones SE, Schofield SJ, et al. Gait speed and readmission following hospitalisation for acute exacerbations of COPD: a prospective study. Thorax 2015;70:1131–7. doi:10.1136/thoraxjnl-2015-207046
157 Glaser JB, El-Haddad H. Exploring Novel Medicare Readmission Risk Variables in Chronic Obstructive Pulmonary Disease Patients at High Risk of Readmission within 30 Days of Hospital Discharge. Ann Am Thorac Soc 2015;12:1288–93. doi:10.1513/AnnalsATS.201504-228OC
158 Crisafulli E, Torres A, Huerta A, et al. Predicting In-Hospital Treatment Failure (≤ 7 days) in Patients with COPD Exacerbation Using Antibiotics and Systemic Steroids. COPD 2016;13:82–92. doi:10.3109/15412555.2015.1057276
159 García-Sidro P, Naval E, Martinez Rivera C, et al. The CAT (COPD Assessment Test) questionnaire as a predictor of the evolution of severe COPD exacerbations. Respir Med 2015;109:1546–52. doi:10.1016/j.rmed.2015.10.011
160 Ramaraju K, Kaza AM, Balasubramanian N, et al. Predicting Healthcare Utilization by Patients Admitted for COPD Exacerbation. J Clin Diagn Res 2016;10:OC13-7. doi:10.7860/JCDR/2016/17721.7216
161 Sainaghi PP, Colombo D, Re A, et al. Natural history and risk stratification of patients undergoing non-invasive ventilation in a non-ICU setting for severe COPD exacerbations. Intern Emerg Med 2016;11:969–75. doi:10.1007/s11739-016-1473-z
162 He H, Sun Y, Sun B, et al. Application of a parametric model in the mortality risk analysis of ICU patients with severe COPD. Clin Respir J 2018;12:491–8. doi:10.1111/crj.12549
163 García-Rivero JL, Esquinas C, Barrecheguren M, et al. Risk Factors of Poor Outcomes after Admission for a COPD Exacerbation: Multivariate Logistic Predictive Models. COPD 2017;14:164–9. doi:10.1080/15412555.2016.1260538
164 Echevarria C, Steer J, Heslop-Marshall K, et al. The PEARL score predicts 90-day readmission or death after hospitalisation for acute exacerbation of COPD. Thorax 2017;72:686–93. doi:10.1136/thoraxjnl-2016-209298
Page 76 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
165 Sakamoto Y, Yamauchi Y, Yasunaga H, et al. Development of a nomogram for predicting in-hospital mortality of patients with exacerbation of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2017;12:1605–11. doi:10.2147/COPD.S129714
166 Feng Z, Wang T, Liu P, et al. Efficacy of Various Scoring Systems for Predicting the 28-Day Survival Rate among Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease Requiring Emergency Intensive Care. Can Respir J 2017;2017:3063510. doi:10.1155/2017/3063510
167 Almagro P, Yun S, Sangil A, et al. Palliative care and prognosis in COPD: a systematic review with a validation cohort. Int J Chron Obstruct Pulmon Dis 2017;12:1721–9. doi:10.2147/COPD.S135657
168 Lau CS, Siracuse BL, Chamberlain RS. Readmission After COPD Exacerbation Scale: determining 30-day readmission risk for COPD patients. Int J Chron Obstruct Pulmon Dis 2017;12:1891–902. doi:10.2147/COPD.S136768
169 Vanasse A, Courteau J, Couillard S, et al. Predicting One-year Mortality After a ‘First’ Hospitalization for Chronic Obstructive Pulmonary Disease: An Eight-Variable Assessment Score Tool. COPD 2017;14:490–7. doi:10.1080/15412555.2017.1343814
170 Duenk RG, Verhagen C, Bronkhorst EM, et al. Development of the ProPal-COPD tool to identify patients with COPD for proactive palliative care. Int J Chron Obstruct Pulmon Dis 2017;12:2121–8. doi:10.2147/COPD.S140037
171 Yao C, Liu X, Tang Z. Prognostic role of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio for hospital mortality in patients with AECOPD. Int J Chron Obstruct Pulmon Dis 2017;12:2285–90. doi:10.2147/COPD.S141760
172 Bernabeu-Mora R, García-Guillamón G, Valera-Novella E, et al. Frailty is a predictive factor of readmission within 90 days of hospitalization for acute exacerbations of chronic obstructive pulmonary disease: a longitudinal study. Ther Adv Respir Dis 2017;11:383–92. doi:10.1177/1753465817726314
173 Winther JA, Brynildsen J, Høiseth AD, et al. Prognostic and diagnostic significance of copeptin in acute exacerbation of chronic obstructive pulmonary disease and acute heart failure: data from the ACE 2 study. Respir Res 2017;18:184. doi:10.1186/s12931-017-0665-z
174 Zhong X, Lee S, Zhao C, et al. Reducing COPD readmissions through predictive modeling and incentive-based interventions. Health Care Manag Sci 2019;22:121–39. doi:10.1007/s10729-017-9426-2
175 Pavliša G, Labor M, Puretić H, et al. Anemia, hypoalbuminemia, and elevated troponin levels as risk factors for respiratory failure in patients with severe exacerbations of chronic obstructive pulmonary disease requiring invasive mechanical ventilation. Croat Med J 2017;58:395–405.
176 Rezaee ME, Ward CE, Nuanez B, et al. Examining 30-day COPD readmissions through the emergency department. Int J Chron Obstruct Pulmon Dis 2018;13:109–20. doi:10.2147/COPD.S147796
177 Hakim MA, Garden FL, Jennings MD, et al. Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease. Clin Epidemiol 2018;10:51–9. doi:10.2147/CLEP.S149574
178 Esteban C, Castro-Acosta A, Alvarez-Martínez CJ, et al. Predictors of one-year mortality after hospitalization for an exacerbation of COPD. BMC Pulm Med 2018;18:18. doi:10.1186/s12890-018-0574-z
179 Serra-Picamal X, Roman R, Escarrabill J, et al. Hospitalizations due to exacerbations of COPD: A big data perspective. Respir Med 2018;145:219–25. doi:10.1016/j.rmed.2018.01.008
180 Cerezo Lajas A, Gutiérrez González E, Llorente Parrado C, et al. Readmission Due to Exacerbation of COPD: Associated Factors. Lung 2018;196:185–93. doi:10.1007/s00408-018-0093-y
181 Epstein D, Nasser R, Mashiach T, et al. Increased red cell distribution width: A novel predictor of adverse outcome in patients hospitalized due to acute exacerbation of chronic obstructive pulmonary disease. Respir Med 2018;136:1–7. doi:10.1016/j.rmed.2018.01.011
182 Botle A, Honeyford K, Chowdhury F, et al. Factors associated with hospital emergency readmission and mortality rates in patients with heart failure or chronic obstructive pulmonary disease: a national
Page 77 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
observational study. Southampton: : Health Services and Delivery Research 2018.
183 Schuler M, Wittmann M, Faller H, et al. Including changes in dyspnea after inpatient rehabilitation improves prediction models of exacerbations in COPD. Respir Med 2018;141:87–93. doi:10.1016/j.rmed.2018.06.027
184 Madkour AM, Adly NN. Predictors of in-hospital mortality and need for invasive mechanical ventilation in elderly COPD patients presenting with acute hypercapnic respiratory failure. Egypt J Chest Dis Tuberc 2013;62:393–400. doi:10.1016/j.ejcdt.2013.07.003
185 Murata GH, Gorby MS, Kapsner CO, et al. A multivariate model for the prediction of relapse after outpatient treatment of decompensated chronic obstructive pulmonary disease. Arch Intern Med 1992;152:73–7.
186 Kim S, Clark S, Camargo CA. Mortality after an emergency department visit for exacerbation of chronic obstructive pulmonary disease. COPD 2006;3:75–81.
187 Roche N, Zureik M, Soussan D, et al. Predictors of outcomes in COPD exacerbation cases presenting to the emergency department. Eur Respir J 2008;32:953–61. doi:10.1183/09031936.00129507
188 Stiell IG, Clement CM, Aaron SD, et al. Clinical characteristics associated with adverse events in patients with exacerbation of chronic obstructive pulmonary disease: a prospective cohort study. CMAJ 2014;186:E193-204. doi:10.1503/cmaj.130968
189 Quintana JM, Esteban C, Unzurrunzaga A, et al. Predictive score for mortality in patients with COPD exacerbations attending hospital emergency departments. BMC Med 2014;12:66. doi:10.1186/1741-7015-12-66
190 Esteban C, Arostegui I, Garcia-Gutierrez S, et al. A decision tree to assess short-term mortality after an emergency department visit for an exacerbation of COPD: a cohort study. Respir Res 2015;16:151. doi:10.1186/s12931-015-0313-4
191 Esteban C, Garcia-Gutierrez S, Legarreta MJ, et al. One-year Mortality in COPD After an Exacerbation: The Effect of Physical Activity Changes During the Event. COPD 2016;13:718–25. doi:10.1080/15412555.2016.1188903
192 Liu H, Zhang T, Ye J. Analysis of risk factors for hospital mortality in patients with chronic obstructive pulmonary diseases requiring invasive mechanical ventilation. Chin Med J (Engl) 2007;120:287–93.
193 Afessa B, Morales IJ, Scanlon PD, et al. Prognostic factors, clinical course, and hospital outcome of patients with chronic obstructive pulmonary disease admitted to an intensive care unit for acute respiratory failure. Crit Care Med 2002;30:1610–5.
194 Xiao K, Guo C, Su L, et al. Prognostic value of different scoring models in patients with multiple organ dysfunction syndrome associated with acute COPD exacerbation. J Thorac Dis 2015;7:329–36. doi:10.3978/j.issn.2072-1439.2014.11.27
195 Conti V, Paone G, Mollica C, et al. Predictors of outcome for patients with severe respiratory failure requiring non invasive mechanical ventilation. Eur Rev Med Pharmacol Sci 2015;19:3855–60.
196 Echevarria C, Steer J, Heslop-Marshall K, et al. Validation of the DECAF score to predict hospital mortality in acute exacerbations of COPD. Thorax 2016;71:133–40. doi:10.1136/thoraxjnl-2015-207775
197 Ferrer M, Ioanas M, Arancibia F, et al. Microbial airway colonization is associated with noninvasive ventilation failure in exacerbation of chronic obstructive pulmonary disease. Crit Care Med 2005;33:2003–9.
198 Hu W-S, Lin C-L. Use of CHA2DS2-VASc Score to Predict New-Onset Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients - Large-Scale Longitudinal Study. Circ J 2017;81:1792–7. doi:10.1253/circj.CJ-17-0130
199 Ooi H, Chen L-H, Ni Y-L, et al. CHA2DS2-VASc scores predict major adverse cardiovascular events in patients with chronic obstructive pulmonary disease. Clin Respir J 2018;12:1038–45. doi:10.1111/crj.12624
Page 78 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
200 Austin PC, Stanbrook MB, Anderson GM, et al. Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease. Ann Epidemiol 2012;22:881–7. doi:10.1016/j.annepidem.2012.09.011
201 Golpe R, Mengual-Macenlle N, Sanjuán-López P, et al. Prognostic Indices and Mortality Prediction in COPD Caused by Biomass Smoke Exposure. Lung 2015;193:497–503. doi:10.1007/s00408-015-9731-9
202 Edwards L, Perrin K, Wijesinghe M, et al. The value of the CRB65 score to predict mortality in exacerbations of COPD requiring hospital admission. Respirology 2011;16:625–9. doi:10.1111/j.1440-1843.2011.01926.x
203 Hodgson LE, Dimitrov BD, Congleton J, et al. A validation of the National Early Warning Score to predict outcome in patients with COPD exacerbation. Thorax 2017;72:23–30. doi:10.1136/thoraxjnl-2016-208436
204 Chang CL, Sullivan GD, Karalus NC, et al. Predicting early mortality in acute exacerbation of chronic obstructive pulmonary disease using the CURB65 score. Respirology 2011;16:146–51. doi:10.1111/j.1440-1843.2010.01866.x
205 Hu G, Zhou Y, Wu Y, et al. The Pneumonia Severity Index as a Predictor of In-Hospital Mortality in Acute Exacerbation of Chronic Obstructive Pulmonary Disease. PLoS One 2015;10:e0133160. doi:10.1371/journal.pone.0133160
206 Steer J, Norman EM, Afolabi OA, et al. Dyspnoea severity and pneumonia as predictors of in-hospital mortality and early readmission in acute exacerbations of COPD. Thorax 2012;67:117–21. doi:10.1136/thoraxjnl-2011-200332
207 Shorr AF, Sun X, Johannes RS, et al. Predicting the need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: comparing the CURB-65 and BAP-65 scores. J Crit Care 2012;27:564–70. doi:10.1016/j.jcrc.2012.02.015
208 Rothnie KJ, Smeeth L, Pearce N, et al. Predicting mortality after acute coronary syndromes in people with chronic obstructive pulmonary disease. Heart 2016;102:1442–8. doi:10.1136/heartjnl-2016-309359
209 Burke RE, Schnipper JL, Williams M V, et al. The HOSPITAL Score Predicts Potentially Preventable 30-Day Readmissions in Conditions Targeted by the Hospital Readmissions Reduction Program. Med Care 2017;55:285–90. doi:10.1097/MLR.0000000000000665
210 Pedone C, Scarlata S, Forastiere F, et al. BODE index or geriatric multidimensional assessment for the prediction of very-long-term mortality in elderly patients with chronic obstructive pulmonary disease? a prospective cohort study. Age Ageing 2014;43:553–8. doi:10.1093/ageing/aft197
211 Chen R, Xing L, You C, et al. Prediction of prognosis in chronic obstructive pulmonary disease patients with respiratory failure: A comparison of three nutritional assessment methods. Eur J Intern Med 2018;57:70–5. doi:10.1016/j.ejim.2018.06.006
Page 79 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
Confidential: For Review Only
Page 80 of 80
https://mc.manuscriptcentral.com/bmj
BMJ
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960