1 / 53

Prediction Model Template from OHTS-EGPS Pooled Analyses Today’s version is November 14

Prediction Model Template from OHTS-EGPS Pooled Analyses Today’s version is November 14. A Prediction Model for Managing Ocular Hypertensive Patients. Presenter Name

charde-deleon
Download Presentation

Prediction Model Template from OHTS-EGPS Pooled Analyses Today’s version is November 14

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Prediction Model Template from OHTS-EGPS Pooled Analyses • Today’s version is November 14

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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.

  9. 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 .

  10. 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?

  11. 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

  12. 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

  13. Comparison of OHTS and EGPS: Study design*Similarities between OHTS and EGPS

  14. Collaborative analysis uses data only from participants not receiving medication: • OHTS Observation Group n=819 • EGPS Placebo Group n=500

  15. OHTS vs EGPS: Eligibility criteria*Similarities between OHTS and EGPS

  16. OHTS vs EGPS: Eligibility criteria *Similarities between OHTS and EGPS

  17. OHTS vs EGPS: Exclusion criteria*Similarities between OHTS and EGPS

  18. OHTS vs EGPS: Corneal thickness measurement*Similarities between OHTS and EGPS

  19. OHTS vs EGPS: POAG endpoint criteria *Similarities between OHTS and EGPS

  20. 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

  21. ResultsOHTS vs EGPS control groups: Baseline characteristics(Univariate analyses)

  22. ResultsOHTS vs EGPS control groups: Definition of baseline IOP (mmHg)

  23. OHTS vs EGPS control groups: Baseline characteristics

  24. OHTS vs EGPS control groups: 1st eye to develop POAG endpoint

  25. 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

  26. Steps in testing the validity of the OHTS prediction model • Perform separate analyses of OHTS Observation Group and EGPS Placebo Group (Multivariate Cox proportional hazards models) • Compare results of the two analyses

  27. 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: 180-187

  28. Multivariate Hazard Ratios for OHTS Observation group and EGPS Placebo group HR 95% CI Age Decade EGPS OHTS 1.37 (1.00, 1.88) 1.16 (0.94, 1.43) IOP (mm Hg) EGPS OHTS 1.11 (0.98,1.27) 1.21 (1.11, 1.31) CCT (40 µm decrease) EGPS OHTS 2.07 (1.49, 2.87) 2.00 (1.59, 2.50) 1.27 (1.04,1.54) 1.26 (1.12, 1.41) Vertical CD ratio EGPS by contour OHTS 1.05 (0.95, 1.16) 1.16 (0.95,1.41) PSD (per 0.2 dB increase) EGPS OHTS

  29. OHTS prediction model for POAG is confirmed in EGPS • Prediction model is validated... • In an independent European study population • In ocular hypertensive individualsnot on treatment • Thinner central corneal measurement is confirmed as a predictive factor for POAG

  30. 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

  31. Multivariate Hazard Ratios OHTS Observation Group, the EGPS Placebo Group Pooled OHTS and EGPS dataset Age Decade EGPS OHTS Pooled 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

  32. 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

  33. 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

  34. 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.

  35. 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

  36. 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)

  37. Accuracy of prediction models for POAG compared to Framingham Heart Study* D’Agostino et al. JAMA, 2001.

  38. Comparision of observed vs. predicted 5 year incidence of POAG for the OHTS-EGPS pooled sample Decile of Predicted Risk (112 participants per decile)

  39. Using the prediction model • Available on web free of charge •  https://ohts.wustl.edu/risk

  40. Home Page

  41. 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

  42. 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.

  43. Benefits of risk stratification • 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

  44. 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.

  45. 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

  46. 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

  47. 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

  48. 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

More Related