EPI-820 Evidence-Based Medicine (EBM)

1. EPI-820 Evidence-Based Medicine (EBM) LECTURE 1: INTRODUCTION Mat Reeves BVSc, MS, PhD, Dip ACVS Department of Epidemiology Michigan State University

2. Objectives: • 1. Understand principles of EBM, what it is and what it is’nt. • 2. Define HSR, and understand components of clinical epidemiology • 3. Understand distinction between disease and illness and their consequences • 4. Understand the different definitions of normal and abnormal • 5. Understand clinical application of RR, RD and NNT

3. I. What is Evidence Based Medicine? • Evidence-based medicine (EBM) is the conscientious, explicit and judicious use of the current best evidence in making decisions about the care of individual patients (Sackett 1996).

4. IMPORTANT CONCEPTS: • synthesis of individual clinical expertise and external evidence from systematic research. • stresses expertise in information gathering, synthesis and incorporation • de-emphasizes memorization. • rebellious disregard for authoritarian “expert opinion”. • relies heavily on medical informatics technology (e.g., WEB based searches of MEDLINE)

5. EBM aims to improve patient care by: • more effective and efficient use of diagnostic tests • better use of individual patient preferences • better use of prognostic markers • better use of effective but safe therapeutic/ rehabilitative/preventive treatments.

6. 5 steps for using EBM in clinical practice • 1. Convert need for clinical information into answerable questions. • 2. Track down the information using “library- research” techniques . • 3. Critically appraise evidence - validity and clinical applicability. • 4. Make a decision! • 5. Evaluate and continually monitor clinical performance.

7. What are Health Service Research and Outcome Research? • Health Service Research (HSR) is the integration of epidemiologic, sociologic, economic and other analytical sciences to the study of health services. Focus is on the needs, demands, supply, use, and outcomes of health care delivery.

8. HSR involves evaluationof health services: • structure - resources, facilities and manpower. • process - where, how and by whom is health care provided. • output - amount, nature and cost of health services • outcome - what are the measurable benefits of health care? • Outcomes Research = the study of the outcomes of interventions (= Clinical epidemiology).

9. II. What is clinical epidemiology? • Clinical epidemiology is the methodological foundation for EBM: • quantitative approach (or "science") applied to the "art" of clinical practice. • provides foundation for thorough, efficient clinical practice • addresses questions at issue during clinician-patient interactions

10. IssueQuestion? • Normal/abnormal? Is the patient sick? What abnormalities are associated with disease? • Diagnosis How accurate are diagnostic tests? • Risk factors What factors are associated with disease risk (or poor outcome)? • Prognosis What is the likely outcome? • Treatment How does treatment change course? • Prevention How does early detection improve outcome? Can we prevent disease? • Cause What factors result in the disease? What is the underlying pathogenesis?

11. Clinical epidemiology helps physicians to: • be thorough yet efficient • characterize uncertainty • select and interpret diagnostic test information • choose optimal therapeutic intervention (= clinical decision analysis).

12. Pneumonia Yellow Fever Hypertension Sleeping sickness Diabetes Femur fracture Obesity Depression Chronic Fatigue syndrome Near sightedness Hypothyroidism Osteoporosis Chronic back pain Atrial fibrillation Strep pneumonia infection Multiple sclerosis Chronic Hepatitis B infection Disease versus illness • Disease / Illness / Sickness?

13. Components and implications of health - disease, illness and sickness • What is disease? • No accepted definition • Susser (1979): • a physiological or psychological (homeostatic) disturbance

14. What is disease? • Determinants: • Access to medical care - since it is usually diagnosed (determined) by medical professionals • Case definition (medical) • Understanding: • Variable - excellent to very poor • Focus: • Doctors (centered on patho-physiology) • Medical Profession Approach: • Cure through intervention • Measurement: • Disease-free survival

15. What is illness? • Susser (1979): • The subjective state of the person who feels aware of not being well • Patients’ expression of ill-health = symptoms such as pain, nausea, depression, tiredness etc (individual subjective expression of symptoms) • Interaction of underlying disease/problem and the patients’ reaction to it. (N.B. disease may or may not be present!)

16. What is illness? • Determinants: • Learned behaviour (Hayes, 1978) • Part socially determined (influenced by culture, ethnicity, age, gender) • Understanding: • Often poor (e.g., backache, alcoholism, complaining well) • Or good but no cure (e.g., diabetes, arthritis) • Focus: • Patient • Medical Profession Approach: • Tertiary prevention: palliation, improve functional capacity, decr. complications • Measurement: • QALY, DALY

17. What is sickness? • Susser (1979): • The state of social dysfunction i.e., the role that the individual assumes when ill • Social role that person assumes when ill

18. What is sickness? • Determinants: • Heavily influenced by the setting or predicament (socially determined) • Influenced by environment (e.g., peace vs war), culture, time period, ethnicity, age, gender • Understanding: • Usually poor • Focus: • Patient/society • Medical Profession Approach: • Tertiary prevention? • Measurement: • QALY, DALY

19. IV. Normality and abnormality • Last (1995): • a) within the usual range of variation in a given population or group • b) in good health or indicative or predictive of good health (“normal” indicates a low probability of disease) • c) pertaining to the normal, Gaussian distribution (a range of values i.e., ±2 SD)

20. Clinical use of normal • Normal often means “typical” of the general population • e.g., Chol 200mg/dl in 50 yr US male • Normal also means not requiring further follow-up or intervention. • requires judgement and experience

21. Laboratory use of normal • Normal is usually defined in terms of a reference range [±2 SD] • Limitations: • 1. Assumes data are normally (Gaussian) distributed but most clinical data aren’t! • 2. Choice of ±2 SD is arbitrary - why not choose 90% or 99%? • 3. No general relationship between the degree of statistical unusualness and clinical disease e.g., anemia and clinical signs, and serum cholesterol and AMI

22. Laboratory use of normal • Limitations Cont’d: • 4. The reference range depends on the reference population used. • a) may include diseased persons. • b) not the population that undergoes tests (they are healthy!). • c) age/sex/race composition of population and subject selection process needs to be considered. • 5. The reference range may or may not “predict” abnormality accurately. What is the Se and Sp of the reference range? • 6. The exact reference limits are unstable ( based on a small number of individuals in the “tails” of the distribution)

23. V. Risks and rates (practical applications) • A. Cumulative Incidence rate • CIR= Num. of newly disease indv. for a specific time period Total number of population-at-risk for same time period • ranges from 0 to 1 (it’s a proportion!) • must be accompanied by a specified time period • average risk

24. Risks and rates • B. Incidence Density Rate • IDR = Number of newly disease individuals Sum of time periods for all disease-free indv.-at-risk • denominator is "person-time" or "population time" • ranges from 0 to infinity • measure of the instantaneous force or speed • dimension = reciprocal of time i.e., time-1

25. Clinical Interpretation of Risks, Rates and Relative Risks (RR) • Clinical uses of risk and rates are often confused • Both provide meaningful statements regarding “commonness” • BUT in most clinical environments: • numerator is easy to obtain • denominator is very difficult to estimate!!

26. Relative Risk (RR) • Measure of the magnitude of association • Typical clinical interpretation (RCT): • “what is the relative probability of the event in the treatment group compared to the control group” • not a very useful measure of the impact of treatment or risk factor intervention (need RD and/or PAF) • Note: • RR is not reciprocal in nature (unlike the OR) • most cases of disease do not have the risk factor - unless the RR is large (>5) and the prevalence high (>25%), and • the majority of people with the risk factor will not have disease (Rose, 1982)

27. Relative Risk Reduction (RRR) • RRR = 1 – RR • If RR = 0.75, then RRR = 25% • = the proportionate risk difference or “by how much in relative terms is the event rate decreased”

28. The Risk Difference (RD) (or attributable risk) • More clinically useful measure of the relationship between a risk factor and disease • RD = Risk (exposed) - Risk (unexposed) • Table 1.1. Ten Year Risk of Coronary Death, Men 45-65, Whitehall Study • Serum Chol. Risk RR RD • Quintile I 2.9% 1.9 5.4 - 2.9 = 2.5% • Quintile V 5.4%

29. Risk Difference (RD) • RD = the excess risk that a patient faces, or • “what is the absolute difference in event rates between the treatment and control groups” • Men in Quintile 5 have a 2.5% increased risk of death from CHD cf men in Quintile 1 over 10-years. • RD can be negative (implying protective effect). • RD is much more meaningful/useful when facing important clinical decisions

30. Table 1.2. Comparison of RR and RD: a hypothetical example of Hormone Replacement Therapy, Women 60-69 Baseline 10-yr 10-yr RD Mortality Risk RR (HRT) (HRT - None) • CHD 10% 0.65 6.5 - 10 = -3.5% • Breast CA 2% 1.25 2.5 - 2 = +0.5% • Net Benefit = - 3.0%

31. Table 1.3. Two Year Risk of Sudden Death Associated with QT Prolongation Relative to History of AMI (Algra et al, 1991) Hx AMINo Hx AMI No QT prolongation 3.5% 1.1% QT prolongation 7.0% 2.5% • RR (QT vs No) 2.0 2.4 • RD (QT vs No) 3.5% 1.4%

32. Risk Difference (RD) • Clinical impact of QT prolongation is much greater in men with a prior MI - a fact only appreciated by the RD. • A RD may be large enough to be of major clinical importance even when RR is only modest. • RR is often misinterpreted by clinicians and patients when comparing benefits and risks (see Naylor, 1993; Forrow 1992; Malenka 1993; Bucher 1994). • In contrast to RR, the RD depends on background disease incidence, and can vary markedly from one population to another. So, you cannot translate RD calculated in one population to another! • RD (like the CIR) is time dependent - the value will increase over time

33. The Number Needed To Treat (NNT) • Very useful clinical measure that conveys similar information as the RD (Laupacis et al., 1988). • NNT = 1 / RD • Example: • Among patients with prior MI, the RD between patients with and without QT prolongation is 3.5%. So, • Approximately 29 patients with QT prolongation would have to undergo treatment to prevent one sudden death • (i.e., NNT = 1/0.035 = 28.6).

34. The Number Needed To Treat (NNT) • The NNT is usually calculated for interventions that are known to do good i.e., where the RR is < 1.0. • The NNT directly summarizes the effort needed to gain potential clinical benefit – “How many patients do I need to treat to prevent one event?” • High NNT = bad, Low NNT = good • NNT depends on the efficacy of the intervention (= RR) and the underlying baseline risk • Number needed to harm and number needed to screen.

35. Table 1.4. Effect of Base-line Risk and Relative Risk of intervention of NNT

36. Clinical Use of the OR (vs RR)(See Sinclair 1994) • OR are commonly reported in RCT’s and meta-analyses, despite the fact that their clinical use and interpretation is difficult: • OR lacks intuitive clinical appeal • “Mistaken identify” problem (…its not a RR!) • OR deviates substantially from RR when • treatment effects are large and/or • when baseline risks are high (Fig 2) • Influence of changing baseline risks influences: • Interpretation of sub-group findings • Predicted effect in other clinical populations