How to collect and report outcomes of heart valve surgery Hanneke Takkenberg Dept. of...
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Transcript of How to collect and report outcomes of heart valve surgery Hanneke Takkenberg Dept. of...
How to collect and report outcomes of heart valve surgery
Hanneke Takkenberg
Dept. of Cardio-Thoracic Surgery
Erasmus University Medical Center, Rotterdam
The Netherlands
East European Heart Valve Postgraduate Course Sep 2007
Survival after mechanical AVR relative to general population
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0,1
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0,3
0,4
0,5
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0 5 10 15 20 25 30 35 40 45
Time (years since AVR
Su
rviv
al (
%/1
00)
survival 40-year old patient after AVR
40-year old male general population
Components of mortality after valve procedures
Operative mortality Valve-related events (Edmunds 1996 Guidelines/new Guidelines coming
soon!!!):
Structural valve deterioration
Non-structural valve deterioration
Endocarditis
Thrombo-embolism
Bleeding
Valve thrombosis
And their consequences: death, reop, invalidation
Excess mortality yet unexplained
Study designs
Randomized trial Highest level of evidence
Usually difficult to accomplish
Selection bias
Cohort study Prospective
Retrospective
Case-control study Useful when the outcome is rare
Study designs
Randomized trialRandomized trial Highest level of evidenceHighest level of evidence
Usually difficult to accomplishUsually difficult to accomplish
Selection biasSelection bias
Cohort study Prospective
Retrospective
Case-control studyCase-control study Useful when the outcome is rareUseful when the outcome is rare
How to collect heart valve surgery data?
Make a plan (and create a budget) first!
How to collect heart valve surgery data?
Make a plan (and create a budget) first!
Define your variables carefully and document this
Follow the Reporting guidelines (new ones coming up!!!)
Build a database (for example MS Access)
Obtain approval from your Institutional Review Board
Prospective rather than retrospective (recall bias, missing information)
Set up your annual prospective follow-up (using queries in MS Access)
Check your database periodically for quality and completeness of data (10% of entered data contains errors)
How to report outcome after heart valve operations
Descriptives: Describe the patient and procedure characteristics
Number of deaths/events (early and late)
Incidence rates of late events (number/year, Weibull,…)
Describe modes of failure, clinical status, last echo
Logistic regression: To assess factors that may influence early outcome, OR is calculated
Univariate versus multivariate
Kaplan- Meier analysis (time-to event model in the presence of censored cases): To describe freedom from death/events
Comparison of KM-estimates: log rank test
Cox regression: To assess factors that may influence outcome over time, HR is calculated
Univariate versus multivariate
Type of Data
Goal Measurement (from Gaussian Population)
Rank, Score, or Measurement (from
Non- Gaussian Population)
Binomial(Two Possible
Outcomes)
Survival Time
Describe one group Mean, SD Median, interquartile range Proportion Kaplan Meier survival curve
Compare one group to a hypothetical value
One-sample t test Wilcoxon test Chi-squareor
Binomial test **
Compare two unpaired groups
Unpaired t test Mann-Whitney test Fisher's test(chi-square for large
samples)
Log-rank test or Mantel-Haenszel*
Compare two paired groups
Paired t test Wilcoxon test McNemar's test Conditional proportional hazards regression*
Compare three or more unmatched groups
One-way ANOVA Kruskal-Wallis test Chi-square test Cox proportional hazard regression**
Compare three or more matched groups
Repeated-measures ANOVA
Friedman test Cochrane Q** Conditional proportional hazards regression**
Quantify association between two variables
Pearson correlation Spearman correlation Contingency coefficients**
Predict value from another measured variable
Simple linear regressionor
Nonlinear regression
Nonparametric regression** Simple logistic regression* Cox proportional hazard regression*
Predict value from several measured or binomial variables
Multiple linear regression*or
Multiple nonlinear regression**
Multiple logistic regression* Cox proportional hazard regression*
Standard methods of outcome assessment after heart valve operations
Descriptives: Describe the patient and procedure characteristics
Number of deaths/events (early and late)
Incidence rates of late events (number/year, Weibull,…)
Describe modes of failure, clinical status, last echo
Logistic regression:Logistic regression: To assess factors that may influence early outcome, OR is calculatedTo assess factors that may influence early outcome, OR is calculated
Univariate versus multivariateUnivariate versus multivariate
Kaplan- Meier analysis (time-to event model in the presence of censored cases):Kaplan- Meier analysis (time-to event model in the presence of censored cases): To describe freedom from death/eventsTo describe freedom from death/events
Comparison of KM-estimates: log rank testComparison of KM-estimates: log rank test
Cox regression:Cox regression: To assess factors that may influence outcome over time, HR is calculatedTo assess factors that may influence outcome over time, HR is calculated
Univariate versus multivariateUnivariate versus multivariate
Continuous variables:
Mean
(Median)
Standard deviation
Range
Discrete variables:
Proportion
Number
In your methods section
provide clear definitions
of your parameters
Do provide counts
but also explain!
Description of the 6 CABG complications:
Description of the causes of operative mortality:
Early versus late complications
Early complications (<30 days postop or during hospitalization): Describe using proportions (%) and numbers
Late complications (>30 days postop): Describe using numbers, incidence rates, or other functions (Weibull,
Gompertz, 2-period risk)
Incidence rate = number of complications / number of patient years
Example:
Incidence rates are also used by the FDA for measuring the OPCs of valves
OPC = Objective performance criteria
Example of a Weibull function for SVD
Age-dependent freedom from SVD after allograft aortic valve replacement
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25Time since valve replacement (yrs)
Fre
edom
from
SV
D
65 yr old patient55 yr old patient45 year old patient35 year old patient25 year old patient
Standard methods of outcome assessment after heart valve operations
Descriptives:Descriptives: Describe the patient and procedure characteristicsDescribe the patient and procedure characteristics
Number of deaths/events (early and late)Number of deaths/events (early and late)
Incidence rates of late events (number/year, Weibull,…)Incidence rates of late events (number/year, Weibull,…)
Describe modes of failure, clinical status, last echoDescribe modes of failure, clinical status, last echo
Logistic regression: To assess factors that may influence early outcome, OR is calculated
Univariate versus multivariate
Kaplan- Meier analysis (time-to event model in the presence of censored cases):Kaplan- Meier analysis (time-to event model in the presence of censored cases): To describe freedom from death/eventsTo describe freedom from death/events
Comparison of KM-estimates: log rank testComparison of KM-estimates: log rank test
Cox regression:Cox regression: To assess factors that may influence outcome over time, HR is calculatedTo assess factors that may influence outcome over time, HR is calculated
Univariate versus multivariateUnivariate versus multivariate
Logistic regression
Is used to study factors that may influence a bivariate outcome that is not time-dependent
Outcome measure is Odds Ratio: OR
First perform univariate logistic regression analysis: One factor at once into the model
Repeat this for all factors that you think may affect outcome
Preferably use continuous measures of factors instead of categories
Then perform multivariate logistic regression analysis: All factors that were significant in the univariate model (p<0.05 or 0.10)
Note: do not put too many factors in (1 per 7-10 outcomes)
Avoid factors that represent approximately the same (example ECC time and aortic cross clamp time)
Example of logistic regression analysis Univariate model
OR (95% CI)
P-valueMultivariate model
OR (95% CI)
P-value
Patient age (yrs) 1.07 (1.03-1.12) 0.001 1.07 (1.01-1.12) 0.016
Creatinin (mol/L) 1.005 (1.00-1.01) 0.004 1.005 (1.00-1.01) 0.002
Perfusion time (min) 1.007 (1.00-1.01) 0.004 1.004 (1.00-1.01) NS
Procedure-related CABG 9.85 (1.65-38.77) 0.01 13.14 (1.27-136.21) 0.03
NYHA class 1.67 (1.06-2.60) 0.03 1.46 (0.89-2.40) NS
Gender 2.52 (0.88-7.22) 0.09 -- --
Active endocarditis 2.46 (0.80-7.53) 0.12 -- --
Urgency (within 24 hrs) 2.16 (0.57-8.13) NS -- --
Concomitant procedures 2.25 (0.75-6.75) NS -- --
Circulatory arrest 0.53 (0.07-4.15) NS -- --
Prior operations 1.41 (0.47-4.28) NS -- --
SC vs ARR technique 1.06 (0.35-3.19) NS -- --
Ventilatory support 2.70 (0.56-13.17) NS -- --
Left ventricular function 0.79 (0.34-1.84) NS -- --
Independent risk factorsIndependent risk factors
Standard methods of outcome assessment after heart valve operations
Descriptives:Descriptives: Describe the patient and procedure characteristicsDescribe the patient and procedure characteristics
Number of deaths/events (early and late)Number of deaths/events (early and late)
Incidence rates of late events (number/year, Weibull,…)Incidence rates of late events (number/year, Weibull,…)
Describe modes of failure, clinical status, last echoDescribe modes of failure, clinical status, last echo
Logistic regression:Logistic regression: To assess factors that may influence early outcome, OR is calculatedTo assess factors that may influence early outcome, OR is calculated
Univariate versus multivariateUnivariate versus multivariate
Kaplan- Meier analysis (time-to event model in the presence of censored cases): To describe freedom from death/events
Comparison of KM-estimates: log rank test
Cox regression:Cox regression: To assess factors that may influence outcome over time, HR is calculatedTo assess factors that may influence outcome over time, HR is calculated
Univariate versus multivariateUnivariate versus multivariate
Example of a KM cumulative survival graph
Cumulative survival after subcoronary implantation versus root replacement
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0 1 2 3 4 5 6 7 8 9
Time (years since operation)
Cum
ulat
ive
surv
ival
subcoronary implantation
root replacement
At Risk (N): SC ARR
1 year 89 1263 years 87 865 years 81 547 years 67 259 years 44 4
Always mention number of patients at
risk over time!
If <10% is still at risk estimates are no
longer valid
Log-rank test: p<0.01
Standard methods of outcome assessment after heart valve operations
Descriptives:Descriptives: Describe the patient and procedure characteristicsDescribe the patient and procedure characteristics
Number of deaths/events (early and late)Number of deaths/events (early and late)
Incidence rates of late events (number/year, Weibull,…)Incidence rates of late events (number/year, Weibull,…)
Describe modes of failure, clinical status, last echoDescribe modes of failure, clinical status, last echo
Logistic regression:Logistic regression: To assess factors that may influence early outcome, OR is calculatedTo assess factors that may influence early outcome, OR is calculated
Univariate versus multivariateUnivariate versus multivariate
Kaplan- Meier analysis (time-to event model in the presence of censored cases):Kaplan- Meier analysis (time-to event model in the presence of censored cases): To describe freedom from death/eventsTo describe freedom from death/events
Comparison of KM-estimates: log rank testComparison of KM-estimates: log rank test
Cox regression: To assess factors that may influence outcome over time, HR is calculated
Univariate versus multivariate
Cox regression analysis
Is simply logistic regression that has time as a covariable
Therefore it allows study of factors that may influence the
occurrence of complications (death/valve-related events) over time
Outcome measure = hazard ratio (HR)
Newer statistical methods
Actual versus actuarial (KM) method: The Kaplan Meier method is in general very useful for describing outcome over time
However, when a non-fatal event is described by means of the KM method there is
an overestimate of the risk that a patient may experience this event.
Why? Because the patient may die before the event occurs
The actual method corrects for the competing risk of death
Important:
The actual risk can be misused to make valve performance look betterThe actual risk can be misused to make valve performance look better
Actuarial method: describes valve outcomeActuarial method: describes valve outcome
Actual method : describes patient outcome (and not valve performance!)Actual method : describes patient outcome (and not valve performance!)
Simulation methodsSimulation methods
Longitudinal data analysis (for example echo data)Longitudinal data analysis (for example echo data)
Beware: Too much information!
More information or a copy of this presentation:
E-mail:
[email protected] download this presentation at:
www.cardiothoracicresearch.nl(first register)