Practical Examples of Patient Registry Comparison Studies: Understanding Registry Data

1. Practical Examples

2. Presenter disclosure information Bradley G Hammill Lesley H Curtis Soko Setoguchi Practical Examples FINANCIAL DISCLOSURE: None UNLABELED/UNAPPROVED USES DISCLOSURE: None

3. Example: Linked sample comparison Representativeness of a National Heart Failure Quality-of-Care Registry: Comparison of OPTIMIZE-HF and Non-OPTIMIZE-HF Medicare Patients Lesley H. Curtis, Melissa A. Greiner, Bradley G. Hammill, Lisa D. DiMartino, Alisa M. Shea, Adrian F. Hernandez and Gregg C. Fonarow Circ Cardiovasc Qual Outcomes 2009 2:377-384

4. Study objective Objective: Compare patient characteristics and health outcomes of Medicare beneficiaries enrolled in OPTIMIZE-HF with those not enrolled who were hospitalized for heart failure Also, compare OPTIMIZE-HF hospitals to other Medicare hospitals.

5. Analysis issues • Decisions to make • Which records to include • Comparisons of interest • Comparison group selection • Characteristics to compare

6. Which records to include • Patients potentially represented multiple times in each database • Hospitalizations • Take them all or one per patient? • If one per patient, take first or random? • Does it matter if patient has records in both groups?

7. Comparison of interest • Within Medicare: OPTIMIZE-HF v. non-OPTIMIZE-HF • Among all sites? • Among OPTIMIZE-HF sites? Define participation period? • Within OPTIMIZE-HF: Medicare v. non-Medicare • Among all sites? • Among linked sites? • Age restricted?

8. Possible comparisons Medicare Linked OPTIMIZE-HF OPTIMIZE sites Non-OPTIMIZE sites <65y Unlinked sites

9. Comparison group selection • OPTIMIZE-HF = “New or worsening HF” • Medicare = ? • HF diagnosis in any position on claim? • HF primary diagnosis only? What if OPTIMIZE-HF record is not primary?

10. Characteristics to compare • Within Medicare • Require prior claims eligibility (12m)? • Require follow-up period? • Claims-based comorbidities? Outcomes? • Can we use OPTIMIZE-HF variables at all?

11. Study setup • Within Medicare comparison (all sites) • OPTIMIZE-HF / CMS-linked records • Keep first per patient • Non-OPTIMIZE-HF records • Eliminate OPTIMIZE-HF pts • Take first hospitalization per patient in 2003-4 with primary diagnosis of HF • Compare claims-based comorbidities, mortality, and readmission

12. Findings • Registry hospitals differed from non-registry hospitals • Higher volume, more cardiac services available, more likely to be teaching hospitals • Patient demographic characteristics and comorbid conditions were similar

13. Findings • Observed outcomes, registry v. non-registry • In-hospital mortality was not significantly different (OPT=4.7% v Non-OPT=4.5%) • 1-year mortality was slightly different (OPT=37.2% v Non-OPT=35.7%) • 1-year readmission was slightly different (OPT=64.2% v Non-OPT=65.8%)

14. Example: Clinical effectiveness Clinical Effectiveness of Implantable Cardioverter-Defibrillators Among Medicare Beneficiaries With Heart Failure Adrian F. Hernandez, Gregg C. Fonarow, Bradley G. Hammill, Sana M. Al-Khatib, Clyde W. Yancy, Christopher M. O'Connor, Kevin A. Schulman, Eric D. Peterson and Lesley H. Curtis Circ Heart Fail 2010 3:7-13

15. Objective and analysis issues Objective: Evaluate the long-term clinical effectiveness of ICD therapy in older patients with heart failure • Analysis issues • Treatment and control group inclusion/exclusion criteria • Exposure definition

16. Inclusion/exclusion criteria • Indicated • Contraindicated • Include elective admits? • Age limit?

17. Exposure definition • Discharged with an ICD • New only? • Present at admission? • ICD planned after discharge

18. Study setup • Exclude contraindicated • Require EF  35%, exclude new onset HF • Exclude discharge to SNF, etc. • Exclude elective admits for lack of untreated comparison group • Exclude very old for lack of treated comparison group • New user design, exclude present at admission • Do not treat planned ICD as treated

19. Findings • Mortality was significantly lower among patients who received an ICD compared with those who did not (38.1% v 52.3% at 3 years) • Adjusted hazard ratio of mortality over 3 years for patients receiving an ICD was 0.71 (95% CI, 0.56 to 0.91)

20. Example: Clinical effectiveness Improvements in long-term mortality after myocardial infarction and increased use of cardiovascular drugs after discharge: a 10-year trend analysis Soko Setoguchi, Robert J Glynn, Jerry Avorn, Murray A Mittleman, Raisa Levin, Wolfgang C Winkelmayer J Am Coll Cariolol. 2008 51:1255-7

21. Objective and analysis issues Objective: Assess the relationship between increasing use of cardiovascular medications and trends in long-term prognosis after myocardial infarction (MI) in the elderly • Design/analytic issues • Defining ‘CV drug use’ • Start of follow-up • Avoid immortal person time bias

22. Potential explanations of improving survival over time

23. Defining CV drug use • Started recommend meds during hospitalization • Filled prescription after discharge • What timing? • Continued to take the medications for a certain period • What if some patients took it every day vs. others skipped them once in a while?

24. Defining CV drug use • Dictate hypothesis clearly would help • Increasing initiation of recommended CV meds during acute hospitalization improved prognosis in elderly patients after MI • Increasing initiation of recommended CV meds in outpatient setting …… • Increasing ‘continued use’ of recommended CV meds in outpatient setting ……..

25. Defining CV drug use • Things to consider in addition to choosing sound hypothesis • Availability of information • No inpatient drug use available • Aspirin use is not fully captured • Sample size • Lose more patients as you assess drug use over longer period

26. When to start the follow-up for an outcome? • Immortal person time bias • Increasing initiation of recommended CV meds during acute hospitalization improved prognosis in elderly patients after MI

27. Immortal person-time bias • Comparing survival of responders vs. non-responders to a chemotherapy • Usual method • Categorize patients into responders vs. non-responders based on tumor response • Compare survival from the start of the treatment • Length of survival affect the response Anderson J Clin Onc 1983

28. Immortal person-time bias example • 1st response evaluated at 2 months after chemotherapy • All patients who died before the 1st evaluation categorized as ‘non-responders’ • Survival was from the time of chemo to 1 year. • 2 month ‘guarantee’ time for all responders Anderson J Clin Onc 1983

29. Immortal person-time bias Suissa PDS 2007

30. Landmark method (analysis) • Landmark Method (Analysis) • ‘Select some fixed time after initiation of therapy as a landmark for conducting analysis’ • = starting follow-up after completion of exposure assessment • Limitations • Results may differ depending on which landmark is chosen • Loss of power • Cannot observe the entire hazard function Anderson J Clin Onc 1983

31. Study setup • All patients admitted to a hospital with MI (1995 -2004) using algorithm previously shown to have high accuracy (PPV of 94%) • All study patients survived at least 30 days after discharge from the index MI hospitalization • Long-term survival was observed from the 31st day after discharge to the date of death • Assessed • Trend in mortality • Trend in CV drug use (filled prescription within 30 days after discharge) • Trend in PCI during MI hospitalization • Assessed contribution of increasing CV drug use by sequentially including terms for the multivariate model

32. Time trends of treatment for MI Of 21,484 MI patients, 12,142 died during an average follow-up of 3.5 years. A trend towards increasing age and greater prevalence of comorbidities such as hypertension, peripheral vascular diseases, cerebrovascular diseases, diabetes, and chronic kidney disease was observed The use of percutaneous coronary interventions increased over time, whereas use of thrombolytic therapy decreased (Top) Use of all study drugs also increased over time. (Bottom)

33. Potential explanations of improving survival over time

34. Improving trend of long-term prognosis for MI

35. Improving trend of long-term prognosis for MI disappeared after adjusting for the recommended cardiovascular drug use

36. Use of CV procedures did not eliminate the calendar year effect completely

37. Lessons learned • The criteria for diagnosing MI have changed over the decade studied • likely resulting in an increasing fraction of patients having non-ST elevation MI (NSTEMI). • Unlikely to explain the findings completely. • No information on aspirin use and life style modification. • Studies suggest that use of aspirin is relatively stable after 1995 • unclear whether lifestyle has changed over time in the elderly population • Further investigation is necessary to elucidate the relative and individual contributions of these factors.