1. The Value of Observational Research A Case Study Approach Hal V. Barron, MD

2. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasible –when RCTs are unethical (Does smoking really cause cancer?) –when the sample size needed for a RCT is prohibitive Examine associations for hypothesis generation Describe what is happening in the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

3. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasibleExamine associations and attempt to speculate on causality when RCTs are not feasible –when RCTs are unethical (Does smoking really cause cancer?) –when the sample size needed for a RCT is prohibitive Examine associations for hypothesis generation Describe what is happening in the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

4. Examine associations and attempt to speculate on causality when RCTs are not feasible Studies have demonstrated the importance of establishing and maintaining a patent infarct related artery in the setting of acute myocardial infarction (AMI) complicated by cardiogenic shock. The purpose of the present study was to determine whether the use of Intra-aortic baloon pumping (IABP) is associated with a survival advantage in patients with AMI complicated by cardiogenic shock. Why not do a RCT???

5. National Registry of Myocardial Infarction (NRMI) : IABP Use and Outcome Using data from the National Registry of Myocardial Infarction 2 (NRMI 2), we evaluated 23,180 patients who presented with or developed cardiogenic shock during the hospitalization.

6. NRMI : IABP Use and Outcome

7. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasibleExamine associations and attempt to speculate on causality when RCTs are not feasible –when RCTs are unethical (Does smoking really cause cancer?) –when the sample size needed for a RCT is prohibitive Examine associations for hypothesis generation Describe what is happening in the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

8. Data from the TIMI 2 Study

9. Examine associations and attempt to speculate on causality when RCTs are not feasible Do beta-blockers reduce intra-cranial hemorrhage ICH rates when given immediately following tPA for AMI –Does this meet the FINER criteria? –What is the rate of ICH following tPA? –Is a 30% reduction meaningful? –What size trial would need to be conducted?

10. NRMI: BB Use and ICH Unadjusted ICH Rate (%) AGE

11. NRMI: BB Use and ICH Unadjusted ICH Rate

12. NRMI and Beta-blocker Use Multivariate Analysis: Effect of Drug Therapy Administered Within 24 Hours on Intracranial Hemorrhage Rate Medication Adjusted OR (95% Cl)  blocker 0.69 (0.57-0.84)* ACE inhibitor 0.75 (0.55-1.03) Calcium channel antagonist 1.27 (0.98-1.64) Lidocaine 0.93 (0.76-1.13) Intravenous magnesium 1.05 (0.76-1.45) Intravenous nitroglycerin 0.86 (0.69-1.09) *p<0.001. Cl = confidence intervals; OR = odds ratio; other abbreviation as in Table 1.

13. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasible –unethical studies –sample size is prohibitive Examine associations for hypothesis generationExamine associations for hypothesis generation Describe what is happening oin the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

14. The Association Between White Blood Cell Count, Epicardial Blood Flow, Myocardial Perfusion, and Clinical Outcomes in the Setting of Acute Myocardial Infarction Hal V. Barron, M.D.; Christopher P. Cannon, M.D.; Sabina A. Murphy, M.P.H.; Susan J. Marble, M.S., R.N.; Eugene Braunwald, M.D.; and C. Michael Gibson, M.S., M.D.; for the TIMI 10 Study Group Background: Patients with elevated white blood cell (WBC) counts during acute myocardial infarction (AMI) have a higher risk of adverse outcomes. Objectives: The goal of this study was to determine the relationship between the WBC count and angiographic characteristics to gain insight into this pathophysiology of this clinical observation. Methods: Angiographic and clinical data from 936 patients in the TIMI 10A and TIMI 10B trials was used to evaluate these relationships

15. Results : The development of new congestive heart failure was associated with significantly higher WBC counts (13.3  8.9, n=64 vs 10.8  3.5, p<0.0001), an observation which remained significant in a multivariable model adjusting for all potential confounding variables (O.R. 1.2 per 1 unit increase in WBC count, p<0.001).

16. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasible –unethical studies –sample size is prohibitive Examine associations for hypothesis generation Describe what is happening oin the “real world”Describe what is happening oin the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

17. ICH Risk following t-PA:NRMI 2 Gurwitz et al. 1998 Annals Int Med. 129; 597-604.

18. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasible –unethical studies –sample size is prohibitive Examine associations for hypothesis generation Examine associations to identify treatment modifiers Describe what is happening oin the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

19. All Patients in NRMI 2

20. Background Initial reperfusion therapy (IRT) is beneficial for patients with acute myocardial infarction (AMI) A minority of patients with AMI receive IRT. Underutilization could be related to: –the absence of clear indications, – perceived contraindications and –physicians’ reluctance to prescribe IRT.

21. Hypothesis To determine what percent of patients identified as having clear indications for initial reperfusion therapy (IRT) do not receive this life-saving therapy and To identify patient subgroups who are at greatest risk for not receiving IRT.

22. Methods - Study Population Symptoms  Hosp <6 hrs ST Segment  or LBBB Contraindications to thrombolytic Rx No IRT N=20,319 IRTN=64,344

23. LBBB No CP Age > 75 Prior CHF Prior MI Prior Stroke Killip 3# Killip 2# Prior Angina Diabetes Female Prior Revasc. Anterior MI* Prior HTN Caucasian Current Smoker Prehospital ECG Sx < 3 hrs. 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 Less LikelyMore Likely Reperfusion Therapy

24. Underutilizing of IRT In High Risk Patients

25. Conclusions At least 31% of patients presenting with AMI are appropriate for IRT 1 in 4 patients appropriate for IRT do not receive this life-saving therapy. The underutilization is particularly evident in the elderly, women and other patients at increased risk for in-hospital mortality.

26. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasible –when RCTs are unethical (Does smoking really cause cancer?) –when the sample size needed for a RCT is prohibitive Examine associations for hypothesis generation Describe what is happening in the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

27. Sex Differences in Early Mortality After Myocardial Infarction Vaccarino et al. N Engl J Med 1999;341:217-25

28. Background Literature is conflicting about whether short-term mortality after MI is higher in women than in men after adjusting for age and other prognostic factors Traditional approach: compare all the men and all the women, adjusting for age and other factors

29. Specific Aims To test the following hypotheses: 1. the mortality of women relative to men is not constant across ages 2. the younger the age of the patients, the higher the risk of death in women relative to men To identify factors that may account for the higher mortality rates of women compared with men

30. Data Source Second National Registry of Myocardial Infarction (NRMI-2) 1,658 participating U.S. hospitals N=691,995 MI patients enrolled up to 1/31/98

31. Study Sample EXCLUSIONS: Age 90 Patients transferred from other hospitals Patients transferred to other hospitals N for analysis: 384,878

32. Methods of Analysis Multiple logistic regression with hospital death as outcome 1. Traditional analysis approach: main effect of female sex after adjusting for age 2. Test for sex-age interaction 3. Sequential adjustment for other covariables

33. RESULTS Selected Patient Characteristics by Sex WomenMen Mean age72 66 History of MI (%) 2428 History of CHF (%) 2113 History of HTN (%) 5947 History of diabetes (%) 3325 Chest pain (%) 6372 ST elevation (%) 3842 CHF or cardiog. shock (%) 2719 Hospital mortality (%) 1711

34. Factors Disproportionately more Common in Women at Younger Ages Demographic factors –Non-White race –Medicaid insurance Medical history –Hx of CHF –Hx of diabetes –Hx of stroke Admission data –Delay to presentation >6 hrs –No ST elevation –CHF, pulmonary edema –Hypotension or cardiogenic shock Treatments –No coronary reperfusion therapy –No use of IV beta-blockers

35. History of Diabetes

36. Presentation After 6 hrs from Symptom Onset

37. Hypotension on Admission

38. Overall Effect of Female Sex on Mortality (traditional approach) OR of Mortality Women Vs. Men(95% CI) Unadjusted 1.54(1.51-1.57) Age adjusted 1.14(1.12-1.17)

39. Hospital Mortality Rates by Sex and Age (Unadjusted) Sex-Age Interaction: P<0.001

40. Effect of Female Sex on Mortality by Age (Unadjusted) Age 30 35 40 455055 60 65 70 75 80 85 90

41. Unadjusted Impact of Overall Adjustment Adjusted Age OR (Women Vs. Men) 30 35 40 45 50 55 60 65 70 75 80 85 90

42. A higher risk of death in women relative to men is seen in the younger age groups only There is a linear increase of risk for women relative to men going from older to younger age The younger the patients’ age, the higher the risk of death of women relative to men Adjustment for covariables explains only 1/3 of the higher mortality risk for women at younger ages Summary / Conclusions

43. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasible –when RCTs are unethical (Does smoking really cause cancer?) –when the sample size needed for a RCT is prohibitive Examine associations for hypothesis generation Examine associations to identify treatment modifiers Describe what is happening in the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

44. P=0.06 P=0.02 Grines CL, et al. N Engl J Med. 1993;328:673-679. Trials Comparing Primary PTCA With Fibrinolytic Therapy: PAMI Cohort 12.0

45. P=0.033P=NS GUSTO-IIb Angioplasty Substudy Investigators. N Engl J Med. 1997;336:1621-1628. Trials Comparing Primary PTCA With Fibrinolytic Therapy: GUSTO-IIb Cohort Composite Outcome (%)

46. Rate % Study Group PTCA Lytic Therapy Odds Ratio (95% CI) Absolute Risk Reduction, % (95% CI) Streptokinase 4.05.90.66 (0.29 to1.50)1.9 (-2.7 to 4.1) 3- to 4-hour t-PA 3.55.70.60 (0.24 to1.41)2.2 (-2.2 to 4.3) Accelerated t-PA 5.07.20.68 (0.42 to 1.08)2.2 (-0.5 to 4.0) Total 4.46.50.66 (0.46 to 0.94)2.1 (0.4 to 3.4) Meta-analysis of Mortality Benefit With Primary PTCA Versus Fibrinolytic Therapy Weaver WD, et al. JAMA. 1997;678:2093-2098.

47. Time After Discharge (years) Every NR, et al. N Engl J Med. 1996;335:1253-1260. P=NS Trials Comparing Primary PTCA With Fibrinolytic Therapy: MITI Cohort 00.511.522.533.54

48. PPTCA versus tPA :NRMI 2 4,939 nontransfer pts underwent PPTCA within 12 hrs from Sx onset 24,705 pts received tPA Lytic ineligable and shock pts were excluded

49. Randomized Trial Results Versus Community-Setting Results: NRMI-2 Cohort Tiefenbrunn AJ, et al. J Am Coll Cardiol. 1998;31:1240-1245. P=NS Percent n=2958, lytic eligible, no shock at presentation

50. Overall 5.4 5.2 STE or LBBB 1st ECG 5.3 5.5 Age < 75 yr. 3.4 3.5 Age > 75 yr.16.514.4 Male 4.5 5.2 Female 9.6 8.9 Inferior MI 3.9 3.9 Anterior MI 7.6 7.1 Low Risk 2.9 2.8 Not Low Risk 7.5 7.4 rt-PAPTCA Mortality (%) 0.5 1.0 1.5 Odds Ratio and 95% CI rt-PA betterPTCA better

51. PPTCA versus tPA (Death and Nonfatal Stroke)

52. Efficacy vs Effectiveness Why might they differ?

53. Berger PB, et al. Circulation. 1999;100:14-20. P=0.001 Door-to-Balloon Time (minutes) Importance of Door-to-Balloon Time: 30-Day Mortality in the GUSTO-IIb Cohort Mortality (%) <

54. Treatment effect modifiers Rates of Death during Hospitalization for Myocardial Infarction among patients treated with thrombolytic therapy versus primary angioplasty. The interaction between reperfusion strategy and primary angioplasty volume was significant (p<.01). Death during Hospitalization (%) Hospital-specific primary angioplasty volume category

55. Overview Review what we can learn from observational data Examine associations and attempt to speculate on causality when RCTs are not feasible –when RCTs are unethical (Does smoking really cause cancer?) –when the sample size needed for a RCT is prohibitive Examine associations for hypothesis generation Describe what is happening in the “real world” –Safety surveillance : Identification of rare events or subgroup analysis –Drug utilization patterns –Natural history of disease –Efficacy vs Effectiveness

56. Conclusions Observational research studies can be very valuable –They provide information not obtainable from RCTs –They provide important information when RCTs are not feasible Observational research studies can be very misleading as well –They can never really clarify causality (only associations) –Measured and especially unmeasured confounders can be a VERY BIG problem!-more to come on this