2. Therapeutics: Finding a “Cure”

3. Why Assess Therapy Articles? Evidence-based medicine Starting point for treatment decisions Apply evidence to your patients Quality matters

4. Quality 08 0 27 RR Relative Risk vs. Quality (Trials of TCA’s in HA prevention) Courtesy of Jeff Jackson, MD MPH (unpublished data) Why Does Quality Matter…?

5. Objectives Use the medical literature to find relevant trials to answer a patient-based clinical question Describe the steps in critically appraising a clinical trial of a therapeutic intervention Demonstrate how a focused clinical question can efficiently generate an evidence-based decision in patient care Guyatt, et al. User’s Guide to the Medical Literature Series, No. II: How to Use an Article About Therapy or Prevention (A. ‘Are the Results of the Study Valid’ and B. ‘What were the Results and Will They Help Me in Caring for My Patients?’) Originally published in JAMA, 1993 Vol. 270(21) and 1994 Vol. 271(1)

6. Clinical Case* 58yo male  ER w/ rest anginal chest discomfort progressing x 6 hours h/o CABG two years ago (LIMA to LAD, SVG to OM, SVG to PDA) PMH: HTN, HLD, impaired glucose tolerance Meds: Metoprolol 100mg bid, Lisinopril 40mg qd, Lipitor 80mg qhs, Glucophage 500mg bid, ASA Current non-smoker (30 pack-year history, quit 2 years ago). *Nayak Special

7. Clinical Case (continued) Exam: BP 142/80 HR 90 Chest: CTA Bilat RRR, S1 S2, II/VI early sys murmur at RUSB no JVD or edema, normal distal pulses Abd: no bruits/masses ECG: NSR, 1mm ST depression and TW flattening in anterolateral leads Patient stabilized in CCU on NTG drip and IV beta blocker. ACE-I/Statin/ASA continued.

8. Clinical Case (continued) Normal CBC/chem panel CKMBTrop I Initial enzymes:190 5.5 0.2 Second set:400 9.0 3.3 Echo (HD #1): normal LVSF, no focal WMA, aortic sclerosis without stenosis Left heart catheterization (HD #2): patent grafts with diffuse, severe native vessel coronary disease, but no focal lesions for PCI

9. How might you articulate a clinical question about therapeutic options for this gentleman…?

10. Asking the Right Question Background questions – general knowledge Foreground questions – more specific (‘PICO’)  Patient (or problem) of interest  Intervention of interest– “an exposure”  Comparison intervention (if relevant)  Outcome of clinical interest How do you manage ACS? In NSTEMI patients with preserved LV…?

11. Foreground Question: Our Case In patients with Acute Coronary Syndrome (ACS) w/o ST elevation… …does adding clopidogrel to ASA… …reduce cardiovascular mortality

14. MeSH is your friend

15. The search for a ‘CURE’….

16. McMaster’s Method Validity Results Generalizability

17. Validity Randomization (4 questions) Blinding (1 question) What’s so important about randomization?….

18. Randomization Why do it? Eliminates selection bias Both known and unknown confounders are randomized How can it be maintained? Complete follow up Intention-to-treat analysis Once randomized, always analyzed

19. Validity Primary Guides Randomized? All patients accounted for … Follow up complete? Intention to treat analysis used? Skim abstract, methods, results OR Is it worth Your time … ?

20. Validity Secondary Guides Were patients, physicians, and outcome assessors blinded? Were the groups similar (was there an adequate Table 1)? If not, were adjustments made? Were the groups treated equally?

21. Results How large was the treatment effect? How precise was the estimate? Were confidence intervals given?

22. Measures of the Effect Absolute Risk Reduction (ARR) Proportion control – Proportion experimental Number Needed to Treat (NNT) = 1/ARR Number Needed to Harm (NNH) = 1/ARR Proportion experimental – Proportion control 11.4% - 9.3% = 2.1% (composite CV mortality) 18.8% - 16.5% = 2.3% (above or refractory ischemia) 1 / 0.021 = 48 (or 1/0.23 = 43) 3.7% - 2.7% = 1% (major bleeding) 1 / 0.01 = 100 (life-threatening bleed, CVA not significant)

23. Measures of Effects Relative Risk (RR) Proportion experimental/Proportion control Relative Risk Reduction (RRR) 1-RR x 100% 1 - 0.8 x 100% = 20% 0.093 / 0.114 = 0.80 Compared with Absolute Risk Reduction of 2.1%

24. Precision of Effect Estimate? p values Are Confidence Intervals Given? If 95% CI doesn’t contain null value (ie 1 for relative risk and 0 for risk difference), then p value always < 0.05 CI adds better estimate of precision to p value

25. Generalizable? Can the results be applied to my patients? Inclusion/exclusion criteria Subgroup (age, race, gender) All clinically important outcomes considered? Likely benefits worth the potential harms and costs?

26. Summary Focused question (PICO) Targeted search/study selection Validity Randomization preserved Blinded Results Absolute Risk Reduction, NNT Generalizable Study population similar to your patients Potential benefits worth the risks

27. Questions?