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Gillian D Sanders Ph.D. Lurdes Y Inoue Ph.D.

Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Bayesian Techniques, and Clinical Trials. Gillian D Sanders Ph.D. Lurdes Y Inoue Ph.D. Associate Professor of Medicine Associate Professor of Biostatistics Duke University University of Washington

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Gillian D Sanders Ph.D. Lurdes Y Inoue Ph.D.

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  1. Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Bayesian Techniques, and Clinical Trials Gillian D Sanders Ph.D. Lurdes Y Inoue Ph.D. Associate Professor of Medicine Associate Professor of Biostatistics Duke University University of Washington Funded by AHRQ R01-HS018505

  2. Specific Aims • To develop a generalizable decision modeling framework for the prevention of SCD • To use Bayesian statistical techniques to devise a model for predicting patient and population health and economic outcomes • To use the framework and Bayesian model from Specific Aims 1-2, and patient level data from existing clinical trials, to explore timely clinical and policy questions • To develop a web-based dissemination system to allow providers and policy makers to interact with the decision modeling framework and to explore clinical and policy questions as evidence evolves

  3. Timing of ICD Clinical Trials

  4. Timing of ICD Clinical Trials

  5. Clinical Characteristics of Trial Patients

  6. Clinical and Policy Questions Controlling for EF, ischemia, age, and NYHA class – are the patients within the available trials similar? Is there evidence that the devices used during the different trials differ in terms of their efficacies? Is there evidence that the ICD is effective in patients: Over 65 years of age? Over 75? With EF > 30% With non-ischemic disease With NYHA class I? II? III? IV? Are there specific patient subgroups for which The ICD is particularly ineffective or effective? Decision makers might benefit from additional trial data?

  7. Methods Considered patient-level data from 8 trials (MADIT-I, -II, MUSTT, DEFINITE, SCDHeFT, AVID, CASH, CABG) Primary outcome is overall survival Treatment: ICD versus control Prognostic variables Age (years) Ejection fraction (%) NYHA class (I, II, III, IV) Presence of ischemic disease (yes/no) Used Bayesian hierarchical model to explore data from trials and specific subgroups of interest

  8. Does ICD Efficacy Differ Among Trials?

  9. Is the ICD Effective?

  10. Is the ICD Effective in Patients over 65? Age [65,75)

  11. Is the ICD Effective in Patients with EF > 30%

  12. Is the ICD Effective in NYHA II Patients?

  13. Is the ICD Effective in NYHA III Patients?

  14. Is the ICD Effective in NYHA IV Patients?

  15. Is the ICD Effective in NYHA IV Patients?

  16. Is the ICD Effective in Ischemic Patients?

  17. Are Patients Within The Trials Similar?

  18. ICD Effect in Specific Patient Subgroups

  19. Clinical and Policy Research Priorities • Are there specific patient subgroups for which policy makers might benefit from additional clinical trial data and how best can such trials be designed given the available prior information from the existing clinical trials? • Are there clinical subgroups within the population at risk for SCD where the ICD appears to be particularly effective? Cost effective? Futile? • How best can we use current (and novel) risk stratification techniques to either rule in “low-risk” patients who are currently ineligible for ICD therapy, or rule out current “high-risk” patients who are currently recommended ICD implantation? • What are effects of comorbid diseases on the effectiveness and cost effectiveness of ICD use in the elderly population for primary or secondary prevention of SCD? • Are the outcomes observed in the community predicted by the available clinical trial evidence? • What is the cumulative survival benefit from SCD prevention therapies in the US? • How can the clinical trial data be best used to predict the prognosis of patients within the CMS ICD registry and as longitudinal data becomes available for the registry participants – how does our modeling framework predict the patients’ outcomes?

  20. Next Steps • Incorporate data from DINAMIT trial • Combine full patient level data sets • Further develop underlying Bayesian models • Explore main prognostic variables of: sex, age, NYHA class, LVEF, prior MI, and QRS duration • Look at additional endpoints of sudden cardiac death, rehospitalizations, quality of life, appropriate/ inappropriate shocks • Explore other potential prognostic variables including race, time from MI /CABG, renal disease, and ICD programming and types • Combine with decision analytic framework in a format that clinical providers and policymakers can use to explore the underlying evidence, our models, and the findings

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