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 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:

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 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 1 st 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 OHTSEGPS Study DesignUnmasked randomized clinical trial Double masked randomized clinical trial Large Sample1,636 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

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 OHTSEGPS 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 OHTSEGPS 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 OHTSEGPS 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 OHTSEGPS 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 OHTSEGPS 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 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 OHTS Observation Group n=819 EGPS Placebo Group n=500 Female58%52% Mean Age (Years) Race African origin Caucasian/other 25.2% 74.8% 0% 100% Median follow-up6.6 yrs4.8 yrs Results OHTS vs EGPS control groups: Baseline characteristics (Univariate analyses)

November 2006 Results OHTS vs EGPS control groups: Definition of baseline IOP (mmHg) OHTS Observation GroupEGPS Placebo Group Original definition of baseline IOP (mm Hg) 2-3 IOPs at Randomization Visit SD 2-3 IOPs at 1 Eligibility Visit 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 SD 2-3 IOPs at 1 Eligibility Visit plus 1 IOP at 6 month visit Mean of 1 or 2 eyes SD New definition of baseline IOP used data from 2-3 visits and improved estimate of baseline IOP.

November 2006 Baseline Factors OHTS Observation Mean + S.D. Average of 2 eyes EGPS Placebo Mean + S.D. Average of 2 eyes or value of one eye New baseline IOP mmHg Vertical C/D ratio by contour CCT (µm) PSD (dB) OHTS vs EGPS control groups: Baseline characteristics

November 2006 Outcome OHTS Observation Group N=819 EGPS Placebo Group N=500 Total POAG (Incidence per year) 104 POAG of % per year 61 POAG of % per year Detection Method Visual field only 33 32% % Disc only 56 54% % Visual field & disc at same visit 15 14%0 0.0% OHTS vs EGPS control groups: 1 st 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 Age IOP CCT PSD Vertical 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 > 0.05 D’Agostino et al., JAMA;2001:

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)

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 Ratios OHTS 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

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 ModelsC-statistic *Framingham Heart Study prediction model applied to different studies OHTS observation group0.76 EGPS placebo group0.73 Pooled OHTS EGPS sample0.74 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)

November 2006 Using the prediction model Available on web free of charge

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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:

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 Centers Belgium  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