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The Value of Observational Research A Case Study Approach Hal V. Barron, MD 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 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 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??? 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. NRMI : IABP Use and Outcome IABP - IABP + 69.2 70 49.5 Mortality (%) 60 44.8 50 40 30 20 10 0 TT PPTCA Type of Reperfusion Therapy 47.5 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 Data from the TIMI 2 Study BB+ BB- 6 5.2 5 4 3 2.2 2 0.8 1 0 0 100mg tPA 150 mg tPA 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? NRMI: BB Use and ICH Unadjusted ICH Rate (%) Immediate BB No Immediate BB 2.5 2 1.5 1 0.5 0 <65 65-74 AGE 75 NRMI: BB Use and ICH Immediate BB No Immediate BB 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Male Female 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. 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” – Safety surveillance : Identification of rare events or subgroup analysis – Drug utilization patterns – Natural history of disease – Efficacy vs Effectiveness 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 • 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). Death or CHF Death 10.4 10 20.9 20 8 15 6 4.9 10 3.8 4 7.8 5-10 10-15 5 2 0 7.9 0 0-5 5-10 10-15 >15 0 0 0-5 >15 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” – Safety surveillance : Identification of rare events or subgroup analysis – Drug utilization patterns – Natural history of disease – Efficacy vs Effectiveness ICH Risk following t-PA:NRMI 2 12 Adjusted OR 10 8 6 4 2 0 M<65 F<65 M 65-74 . Gurwitz et al. 1998 Annals Int Med. 129; 597-604 F 65-74 M>75 F>75 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 All Patients in NRMI 2 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. 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. Methods - Study Population SymptomsHosp <6 hrs ST Segment or LBBB Contraindications to thrombolytic Rx No IRT N=20,319 IRT N=64,344 Reperfusion Therapy Less Likely More Likely 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 Underutilizing of IRT In High Risk Patients OR: In-Hospital Death OR: Receiving IRT Female Gender 1.47 0.88 History of CHF 1.23 0.35 History of Stroke 1.76 0.66 History of Diabetes 1.12 0.78 Current Smoker 0.68 1.34 Anterior Wall MI 1.76 1.59 Rales On Initial PE 2.06 0.69 Pulmonary Edema on Initial PE 2.19 0.34 Variable 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. 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 Sex Differences in Early Mortality After Myocardial Infarction Vaccarino et al. N Engl J Med 1999;341:217-25 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 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 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 Study Sample EXCLUSIONS: • • • • Age <30 and > 90 Patients transferred from other hospitals Patients transferred to other hospitals N for analysis: 384,878 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 RESULTS Selected Patient Characteristics by Sex Mean age History of MI (%) History of CHF (%) History of HTN (%) History of diabetes (%) Chest pain (%) ST elevation (%) CHF or cardiog. shock (%) Hospital mortality (%) Women 72 Men 66 24 21 59 33 63 38 27 17 28 13 47 25 72 42 19 11 Factors Disproportionately more Common in Women at Younger Ages • Demographic factors – Non-White race – Medicaid insurance • Admission data – – – – Delay to presentation >6 hrs No ST elevation CHF, pulmonary edema Hypotension or cardiogenic shock • Medical history – Hx of CHF – Hx of diabetes – Hx of stroke • Treatments – No coronary reperfusion therapy – No use of IV beta-blockers History of Diabetes 40 35 30 25 Men Women % 20 15 10 5 0 30-59 60-69 70-79 Age 80-89 Presentation After 6 hrs from Symptom Onset 45 40 35 30 25 % 20 15 10 5 0 Men Women 30-59 60-69 70-79 Age 80-89 Hypotension on Admission 6 5 4 Men Women %3 2 1 0 30-59 60-69 70-79 Age 80-89 Overall Effect of Female Sex on Mortality (traditional approach) Unadjusted Age adjusted OR of Mortality Women Vs. Men (95% CI) 1.54 1.14 (1.51-1.57) (1.12-1.17) Hospital Mortality Rates by Sex and Age (Unadjusted) Sex-Age Interaction: P<0.001 Hospital Mortality (%) 25 20 15 Men Women 10 5 0 <50 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 Age Effect of Female Sex on Mortality by Age (Unadjusted) OR (Women Vs. Men) 3.0 2.5 2.0 Predicted Actual 1.5 1.0 0.5 30 35 40 45 50 55 60 Age 65 70 75 80 85 90 Impact of Overall Adjustment 3.0 OR (Women Vs. Men) 2.5 Unadjusted 2.0 1.5 Adjusted 1.0 0.5 30 35 40 45 50 55 60 Age 65 70 75 80 85 90 Summary / Conclusions • 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 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 Trials Comparing Primary PTCA With Fibrinolytic Therapy: PAMI Cohort 12.0 12 10 8 6.5 6 5.1 4 2.6 2 0 In-hospital death P=0.06 Grines CL, et al. N Engl J Med. 1993;328:673-679. In-hospital death or nonfatal reinfarction P=0.02 PTCA t-PA Composite Outcome (%) Trials Comparing Primary PTCA With Fibrinolytic Therapy: GUSTO-IIb Cohort 18 16 14 12 10 8 6 4 2 0 16.1 13.7 14.1 PTCA t-PA 9.6 30 days 6 months P=0.033 P=NS GUSTO-IIb Angioplasty Substudy Investigators. N Engl J Med. 1997;336:1621-1628. Meta-analysis of Mortality Benefit With Primary PTCA Versus Fibrinolytic Therapy Rate % Study Group PTCA Lytic Therapy Odds Ratio (95% CI) Absolute Risk Reduction, % (95% CI) 4.0 5.9 0.66 (0.29 to1.50) 1.9 (-2.7 to 4.1) 3- to 4-hour t-PA 3.5 5.7 0.60 (0.24 to1.41) 2.2 (-2.2 to 4.3) Accelerated t-PA 5.0 7.2 0.68 (0.42 to 1.08) 2.2 (-0.5 to 4.0) Total 4.4 6.5 0.66 (0.46 to 0.94) Streptokinase Weaver WD, et al. JAMA. 1997;678:2093-2098. 2.1 (0.4 to 3.4) Trials Comparing Primary PTCA With Fibrinolytic Therapy: MITI Cohort 100 Primary Angioplasty Fibrinolytic Therapy Survival (%) 95 90 85 80 75 P=NS 70 0 0.5 1 1.5 2 2.5 3 Time After Discharge (years) Every NR, et al. N Engl J Med. 1996;335:1253-1260. 3.5 4 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 Randomized Trial Results Versus Community-Setting Results: NRMI-2 Cohort n=2958, lytic eligible, no shock at presentation Percent 5.2 PTCA 5.4 t-PA 5.6 6.2 8 6 4 2 0 In-hospital mortality P=NS Tiefenbrunn AJ, et al. J Am Coll Cardiol. 1998;31:1240-1245. In-hospital mortality or nonfatal stroke P=NS Mortality (%) rt-PA PTCA Overall STE or LBBB 1st ECG 5.4 5.3 5.2 5.5 3.4 16.5 3.5 14.4 Male Female 4.5 9.6 5.2 8.9 Inferior MI Anterior MI 3.9 7.6 3.9 7.1 Low Risk Not Low Risk 2.9 7.5 2.8 7.4 Age < 75 yr. Age > 75 yr. Odds Ratio and 95% CI 0.5 rt-PA better 1.0 1.5 PTCA better PPTCA versus tPA (Death and Nonfatal Stroke) t-PA 20 PPTCA 18.4 18 16 14.6 14 12 10 8 6 6.2 5.6 6.1 5.9 4.1 4 3.9 2 0 All STE or LBBB Age >75 Age <75 Efficacy vs Effectiveness • Why might they differ? Importance of Door-to-Balloon Time: 30-Day Mortality in the GUSTO-IIb Cohort 25 14.1 P=0.001 Mortality (%) 20 15 6.4 10 5 0 3.7 4 1 < 60 61-75 76-90 >91 Door-to-Balloon Time (minutes) Berger PB, et al. Circulation. 1999;100:14-20. PTCA not performed Treatment effect modifiers Death during Hospitalization (%) Thrombolytic Therapy 8 7 6 5 4 3 2 1 0 5.85 6.21 Primary Angioplasty 5.87 5.37 4.46 3.37 Low Volume Medium Volume High Volume Hospital-specific primary angioplasty volume category 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). 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 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