Prediction Model Template from OHTS-EGPS Pooled Analyses Today’s version is November 14
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Transcript of Prediction Model Template from OHTS-EGPS Pooled Analyses Today’s version is November 14
November 2006
Prediction Model Template from OHTS-EGPS Pooled Analyses
Today’s version is November 14
November 2006
A Prediction Model for Managing Ocular Hypertensive Patients
Presenter Name
The Ocular Hypertension Treatment Study Group (OHTS)National Eye Institute, National Center for Minority Healtlh and Health
Disparities, NIH grants EY 09307, EY09341, EY015498, Unrestricted Grant from Research to Prevent Blindness, Merck Research Laboratories
and Pfizer, Inc.
The European Glaucoma Prevention Study (EGPS)
European Commission BMH4-CT-96-1598 and Merck Research Laboratories
November 2006
Ocular hypertension
Ocular hypertension occurs in 4%-8% of people in the United States over age 40 (3-6 million people)
The number of affected people will increase with the aging of the population
Associated with large costs for patient examinations, tests and treatment
November 2006
Ocular hypertension
Elevated IOP is a leading risk factor for development of POAG
Only modifiable risk factor for POAG
Patients can lose a substantial proportion of their nerve fiber layer before POAG is detected by standard clinical tests
Quigley HA, et al. Arch Ophthal 1981;99:635
November 2006
Why do we need a prediction model?
2002 OHTS publication showed that early treatment reduces the incidence of POAG by more than 50%
However, only 1% of ocular hypertensive individuals develop POAG per year
Clear that treating all ocular hypertensive patients is neither medically nor economically justified
November 2006
Why do we need a prediction model?
Common in the past to base management decisions on a single predictive factor – usually IOP
What level of IOP do you treat?– IOP 24 mmHg?– IOP 26 mmHg?– IOP 28 mmHg?– IOP 30 mmHg?
This approach ingores other important predictive factors
November 2006
Why do we need a prediction model?
A prediction model stratifies ocular hypertensive individuals by level of risk
– To guide the frequency of visits and tests– To ascertain the benefit of early treatment
November 2006
In 2002, the Ocular Hypertension Treatment Study (OHTS) published a prediction model for POAG based on...
– Data from 1,636 ocular hypertensive participants randomized to either observation or topical hypotensive medication
– Median follow-up 6.6 years
Gordon et al, Arch Ophthalmol. 2002; 120: 714-720.
November 2006
Factors predictive for the development of POAG
in 2002 OHTS model 5 baseline factors increased the risk of developing
POAG
– Older age– Higher Intraocular pressure– Thinner central cornea– Larger vertical cup/disc ratio by contour– Higher pattern standard deviation
Diabetes decreased the risk of POAG
.
November 2006
2002 OHTS model needed to be confirmed
in a large, independent sample
2002 prediction model based on data from treated and untreated ocular hypertensive individuals – A prediction model should be based solely on untreated
individuals
OHTS sample included 25% African American participants– Is the prediction model valid in other groups?
OHTS was 1st study to report central cornea thickness as a powerful predictor of POAG– Can this finding be confirmed?
November 2006
A large indepent sample available through the European Glaucoma Prevention Study (EGPS)
– EGPS is a randomized clinical trial of 1,077 ocular hypertensive individuals randomized to either placebo or dorzolamide
– Median follow-up 4.8 years
November 2006
Purpose of collaboration with EGPS
To test the 2002 OHTS prediction model for the development of glaucoma in a large, independent sample
Before undertaking a collaboration with EGPS, the two study protocols were compared
November 2006
Comparison of OHTS and EGPS: Study design
*Similarities between OHTS and EGPS
OHTS EGPSStudy Design Unmasked
randomized clinical trial
Double masked randomized clinical trial
Large Sample 1,636 participants
22 clinics in United States
1,077 participants
18 clinics in 4 countries
Randomization
Groups
Observation
Any commercially available medication
Placebo
Dorzolamide
POAG Endpoint
Masked endpoint ascertainment
Masked endpoint ascertainment
November 2006
Collaborative analysis uses data only from participants not receiving medication:
– OHTS Observation Group n=819
– EGPS Placebo Group n=500
November 2006
OHTS vs EGPS: Eligibility criteria *Similarities between OHTS and EGPS
OHTS EGPS
Age (years) 40-80 inclusive > 30
Ocular eligibility criteria
Both eyes needed to meet all criteria
Both eyes required to meet all criteria except only one eye needed to meet IOP criterion
21% of EGPS participants had one eye ineligible because of IOP below entry criterion.
Collaborative analysis was repeated including and excluding participants enrolled with one eye eligible
November 2006
OHTS vs EGPS: Eligibility criteria *Similarities between OHTS and EGPS
OHTS EGPS
Normal optic discs
Clinical exam
Review of stereophotos by masked readers
Similar
Normal and reliable visual fields
Humphrey 30-2 Visual Fields
Masked readers
Humphrey 30-2 Visual Fields
Octopus 32-2 Visual Fields
Masked readers
20% of EGPS participants were tested using Octopus 32-2 visual fields. Octopus loss variance and mean defect were converted to Humphrey pattern standard deviation and mean deviation (Anderson et. al., 1999).
November 2006
OHTS vs EGPS: Exclusion criteria *Similarities between OHTS and EGPS
OHTS EGPS
Ocular exclusions
Excluded pigmentary dispersion syndrome and pseudoexfoliation
Included pigmentary dispersion syndrome and pseudoexfoliation
Collaborative analysis excluded EGPS participants (19 placebo participants) with pigmentary dispersion syndrome or pseudoexfoliation.
November 2006
OHTS vs EGPS: Corneal thickness measurement
*Similarities between OHTS and EGPS
OHTS EGPS
Central corneal thickness measurements
DGH 500 Ultrasound mean of 5 measurements
Identical
November 2006
OHTS vs EGPS: POAG endpoint criteria
*Similarities between OHTS and EGPS
OHTS EGPS
Definition of abnormality
3 consecutive VFs with PSD < 0.05 or GHT < 0.01
Or
2 consecutive stereophotographs showing deterioration
3 consecutive VFs with visual field defects
Or
1 stereophotograph showing deterioration
Confirmation of abnormality
Masked readers Masked readers
Attribution of abnormality to POAG
Masked
Endpoint Committee
Masked
Endpoint Committee
November 2006
Collaborative analysis is feasible
OHTS and EGPS protocols are similar enough to test the validity of the prediction model after resolution of study differences
Different enough in measures, geographic distribution and patient characteristics to test the generalizability of the OHTS prediction model
November 2006
Baseline Factors
OHTSObservation
Group
n=819
EGPS
PlaceboGroup
n=500
Female 58% 52%
Mean Age (Years) 55.7 + 9.7 57.7+10.2
RaceAfrican originCaucasian/other
25.2%
74.8%
0%100%
Median follow-up 6.6 yrs 4.8 yrs
ResultsOHTS vs EGPS control groups:
Baseline characteristics(Univariate analyses)
November 2006
ResultsOHTS vs EGPS control groups:
Definition of baseline IOP (mmHg)
OHTS Observation Group EGPS Placebo Group
Original definition of baseline IOP (mm Hg)
2-3 IOPs at Randomization Visit
24.9 + 2.7 SD
2-3 IOPs at 1 Eligibility Visit
23.5 + 1.7 SD
New definition of baseline IOP(mm Hg)
4-6 IOPs at 2 Qualifying Visits
plus
2-3 IOPs at Randomization Visit
Mean of 2 eyes
25.1 + 2.0 SD
2-3 IOPs at 1 Eligibility Visit
plus
1 IOP at 6 month visit
Mean of 1 or 2 eyes
22.4 + 2.0 SD
New definition of baseline IOP used data from 2-3 visits and improved estimate of baseline IOP.
November 2006
Baseline Factors
OHTSObservation
Mean + S.D.
Average of 2 eyes
EGPSPlacebo
Mean + S.D.
Average of 2 eyes or value of one eye
New baseline IOP mmHg 25.1 + 2.0 22.4 + 2.0
Vertical C/D ratio by contour 0.39 + 0.19 0.32 + 0.14
CCT (µm) 574.3 + 37.8 571.6 + 35.9
PSD (dB) 1.90 + 0.21 2.02 + 0.55
OHTS vs EGPS control groups: Baseline characteristics
November 2006
Outcome
OHTSObservation Group
N=819
EGPSPlacebo Group
N=500
Total POAG(Incidence per year)
104 POAG of 819
1.9% per year
61 POAG of 500
2.5% per year
Detection Method
Visual field only 33 32% 37 60.7%
Disc only 56 54% 24 39.3%
Visual field & disc
at same visit
15 14% 0 0.0%
OHTS vs EGPS control groups: 1st eye to develop POAG endpoint
November 2006
Why was the incidence of POAG higher in EGPS than in OHTS?
Differences in entry criteria
Differences in POAG endpoint criteria
Differences in risk characteristics of participants
November 2006
Steps in testing the validity of the OHTS prediction model
1. Perform separate analyses of OHTS Observation Group and EGPS Placebo Group
(Multivariate Cox proportional hazards models)
2. Compare results of the two analyses
November 2006
Results of independent multivariate analyses OHTS vs EGPS:
Separate predictive models in OHTS and in EGPS identified the same 5 predictors for POAG
AgeIOPCCTPSDVertical cup/disc ratio by contour
The predictive factors in the OHTS model and the EGPS model have similar hazard ratios
All comparisons of hazard ratios by t-test, p values > 0.05D’Agostino et al., JAMA;2001: 180-187
November 2006
Age Decade EGPS
OHTS
Multivariate Hazard Ratios for
OHTS Observation group and EGPS Placebo group
IOP (mm Hg) EGPS
OHTS
CCT (40 µm decrease) EGPS
OHTS
Vertical CD ratio EGPS
by contour OHTS
PSD (per 0.2 dB increase) EGPS
OHTS
1.37 (1.00, 1.88)
1.16 (0.94, 1.43)
HR 95% CI
1.11 (0.98,1.27)
1.21 (1.11, 1.31)
2.07 (1.49, 2.87)
2.00 (1.59, 2.50)
1.27 (1.04,1.54)
1.26 (1.12, 1.41)
1.05 (0.95, 1.16)
1.16 (0.95,1.41)
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
November 2006
OHTS prediction model for POAG is confirmed in EGPS
Prediction model is validated...– In an independent European study population – In ocular hypertensive individuals
not on treatment
Thinner central corneal measurement is confirmed as a predictive factor for POAG
November 2006
Next step was to pool OHTS and EGPS data in the same prediction model
To increase the sample size to 1,319 participants (165 POAG endpoints)
To tighten 95% confidence intervals for estimates of hazard ratios for POAG
November 2006
Age Decade EGPS
OHTS
Pooled
Multivariate Hazard RatiosOHTS Observation Group, the EGPS Placebo Group
Pooled OHTS and EGPS dataset
IOP (mm Hg) EGPS
OHTS Pooled
CCT (40 µm decrease) EGPS
OHTS
Pooled
Vertical CD Ratio (per 0.1 increase) EGPS
OHTS
Pooled
PSD (per 0.2 dB increase) EGPS
OHTS
Pooled
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
November 2006
Factors not in the prediction model: Heart disease
In univariate analyses, history of heart disease was a significant predictive factor in OHTS but not in EGPS
In multivariate analyses, heart disease was not a significant predictive factor in OHTS, EGPS or the pooled sample
November 2006
Factors not in the prediction model: Diabetes
History of diabetes reduced the risk of developing POAG in the 2002 OHTS prediction model
The effect of diabetes was difficult to estimate in current OHTS models – data based solely on self-report
Diabetes was not significant in univariate or multivariate EGPS prediction models
Because of poor statistical estimation, diabetes was not included in the final prediction models
November 2006
Which model performs best?
A model averaging data from both eyes?
A model using data from the worst eye?
A model using data from both eyes including asymmetry between the eyes?
These models all perform similarly and correlation coefficients ranging from 0.94 – 0.98.
November 2006
The OHTS and EGPS pooled data were reanalyzed using tree analyses to look for predictive factors that might be missed in
Cox model
Results from tree analyses– Identified the same 5 predictive factors
for POAG (Age, IOP, CCT, Vertical C/D, PSD)
– Confirmed that heart disease, diabetes, hypertension, myopia and self-identified race had no detectable effect on risk of developing POAG
November 2006
How accurate is the OHTS-EGPS prediction model for POAG?
The accuracy of prediction models in discriminating between patients who do and do not develop a disease is measured using the C statistic
C statistic ranges from 0.50 (random agreement) to 1.00 (perfect agreement)
November 2006
Accuracy of prediction models for POAG compared to Framingham Heart Study*
Prediction Models C-statistic
*Framingham Heart Study prediction model applied to different studies
0.63 - 0.83
OHTS observation group 0.76
EGPS placebo group 0.73
Pooled OHTS EGPS sample 0.74
D’Agostino et al. JAMA, 2001.
November 2006
Comparision of observed vs. predicted 5 year incidence of POAG for the OHTS-EGPS pooled sample
Decile of Predicted Risk (112 participants per decile)
Observed PredictedP
roba
bilit
y
0.00
0.04
0.08
0.12
0.16
0.20
0.24
0.28
0.32
0.36
1 2 3 4 5 6 7 8 9 10
November 2006
Using the prediction model
Available on web free of charge https://ohts.wustl.edu/risk
Home Page
November 2006
Benefits of risk stratification to clinicians and patients
Decide on frequency of visits and tests
Ascertain the benefit of early treatment
Potentially reduce medical costs
November 2006
Cost Utility Analysis
Kymes et. al.*, reported that it was cost effective to treat ocular hypertensive individuals with > 2% per year risk of developing POAG
*Kymes et al., AJO, 2006;141: 997-1008.
November 2006
Approximately 30%-40% of the participants in the pooled sample have <1% per year risk of developing POAG
Many of these individuals could be seen and tested once a year
Most of these individuals do not require treatment
Potential cost savings
Benefits of risk stratification
November 2006
LIMITATIONS AND CAUTIONS
There is no guarantee that the predicted risk is accurate for a specific patient.
The predictions are more likely to be accurate for patients who are similar to the patients studied in the OHTS and the EGPS, and if your testing protocols for your patients resemble those used in the studies.
The model predicts the development of early POAG. It is not clear whether the model also predicts progression of established disease or the development of visual disability.
The model is based on baseline parameters. Changes during follow-up will alter the risk of developing POAG.
November 2006
Limitations and Cautions: Application of prediction models to individual patients
must include information outside the model
THE PREDICTIONS ARE DESIGNED TO AID BUT NOT TO REPLACE CLINICAL JUDGMENT.
Need to consider factors such as health status, life expectancy and patient preferences
– An 18 year old ocular hypertensive with a low 5-year risk of developing POAG might be a candidate for treatment
– A seriously ill 63 year old ocular hypertensive with a high 5-year risk of developing POAG might not be a candidate for treatment
November 2006
Summary
5 baseline factors accurately stratify ocular hypertensive individuals by their risk for developing POAG: – Age– IOP– Central corneal thickness– PSD– Vertical cup/disc ratio by contour
November 2006
Summary OHTS prediction model for POAG has
demonstrated high external validity
– OHTS model validated in EGPS sample and Diagnostic Innovations in Glaucoma Study sample (Medeiros FA, et al., Archives of Ophthalmology, 2005.)
– Model accurately predicts development of POAG in ocular hypertensive individuals not on treatment.
– Predictive model is accurate in self-identified whites and African Americans
November 2006
Next Steps Clarify the effects of diabetes, cardiovascular disease, ethnic
origin, myopia and family history of glaucoma on the risk of developing POAG
Test the generalizability of the predictive model in other populations
Add new diagnostic technology– Quantitative assessments of disc and nerve fiber layer parameters– Psychophysical tests
Identify new predictive factors– Diet– Environmental exposures– Genetic factors
Predictive models will evolve with new information
November 2006
Collaborative Group
Ocular Hypertension Treatment Study
Mae Gordon Michael Kass Phil Miller Julie Beiser Feng Gao Ralph D’Agostino
– Consulting Statistician, Boston University
European Glaucoma Prevention Study
Valter Torri Stefano Miglior Irene Floriani Davide Poli Ingrid Adamsons
OHTS Clinical Centers Bascom Palmer Eye Institute Eye Consultants of Atlanta Eye Physicians and Surgeons Cullen Eye Institute Devers Eye Institute Emory Eye Institute Henry Ford Hospitals Johns Hopkins University Krieger Eye Institute Howard University University of Maryland University of California, Los
Angeles Charles Drew University Kellogg Eye Center Kresge Eye Institute Great Lakes Eye Institute University of Louisville
Mayo Clinic New York Eye & Ear Infirmary Ohio State University Ophthalmic Surgeons & Consultants Pennsylvania College of Optometry MCP/Hahnemann University Scheie Eye Institute Keystone Eye Associates University of California, Davis University of California, San Diego University of California, San
Francisco University Suburban Health Center University of Ophthalmic Consultants Washington Eye Physicians &
Surgeons Eye Associates of Washington, DC Washington University, St. Louis
November 2006
EGPS Clinical CentersBelgium University of Antwerpen University of Buxelles University of Gent
Germany University of Leuven University of Mainz University of Freiburg University of Heidelberg University of Wuerzburg
Portugal Coimbra, AIBILI Viseu, S. Teotonio Hospital Lisbon, S. Jose’ Hospital
Italy University of Milan, S. Paolo
Hospital University of Milan, L. Sacco
Hospital University of Verona University of Parma Oftalmico Hospital, Rome S. Giovanni Hospital, Rome Fatebenefratelli Hospital, Rome