EVIDENCE BASED MEDICINE

1. EVIDENCE BASED MEDICINE Prognosis Ross Lawrenson

2. Critically appraising a prognosis paper

3. I. Are the results valid? • In other words was this a well designed study in a relevant population. The best design of study to answer a prognosis question is a prospective cohort study. • Go through the worksheet questions 1-6 to help you decide whether you are likely to believe the results of the paper you are considering.

4. 1. Did the study address a clearly focused question? • Can you define • The population they studied • The intervention • The comparison group • The outcomes

5. Differences between risk and prognosis • A distinction should be made between factors associated with an increased risk of developing a disease (risk factor) and those that predict a worse prognosis once the disease is present (prognostic factors).

6. Myocardial infarction • Risk factors • Age • Male • Cigarette smoking • Hypertension • LDL/HDL

7. Myocardial infarction • Prognostic factors • Age • Male • Anterior infarction • Hypotension • Ventricular arrhythmia

8. Rates used to describe prognosis

9. 2. Was an inception cohort assembled? a) Were patients identified at an early and uniform point in the course of their disease? b) Were the diagnostic criteria, disease severity, co-morbidity and demographic details for inclusion clearly specified?

10. a)Were patients identified at an early and uniform point in the course of their disease? • If observation is begun at different points in the course of disease for the various patients in the cohort, description of their subsequent course will lack precision and the relative timing of such events as recurrence or death would be difficult to interpret or would be misleading.

11. Inception cohort • Cohorts should be observed starting from a point in time called zero time. This point should be described clearly and be at a well-defined point in the course of disease - for example the onset of symptoms, time of diagnosis, beginning of treatment - for each patient.

12. Inception cohort • For studies of prognosis the term inception cohort is used to describe a group of people who are assembled near the onset (“inception”) of disease.

13. Survival cohort bias. • In a survival cohort some of the patients who present at the beginning are not included in the follow up.

14. True cohort Observed improvement Measure outcomes Improved 75 Not improved 75 Assemble cohort (n=150 50% Assemble patients (n=50) Survival cohort Begin follow up (n=50) Measure outcomes Improved 40 Not improved 10 80% Not observed (n=100) Drop outs Improved 35 Not improved 65

15. b) Were the diagnostic criteria, disease severity, co-morbidity and demographic details for inclusion clearly specified? • These should be clearly listed and should be capable of being reproduced by other researchers. This will also allow an appraisal of the precision of the diagnosis and later will help decide on the applicability of the findings to patients in your own practice.

16. 3. Was the referral pattern described? a) Was there likely to be referral bias? b) Diagnostic bias?

17. 3. Was the referral pattern described? • Many reports on prognosis in the medical literature come from hospitals and academic centres and patients seen in these centres are not a representative sample of patients who are seen in the community. For example they may be referred if they are not responding to treatment or they have a troublesome complication of their diseases.

18. Selection or sampling bias • Selection bias relates to the systematic error introduced into a study due to the method of selection of subjects from the study population.

19. Important sources of selection bias include: • Non random sample selected e.g.. volunteers, workers, hospital patients • Hard to trace people are omitted • Large number of refusals in selected population • Large number of people dropping out of the study

20. Diagnostic (measurement or information) bias can be introduced because of: • Subject variation - physiological, psychological or induced • Observer variation - intra and inter observer variation, lack of independence in the observer (blinding) or error in the administration of the test • Limitations of the method - I.e. the test may be inappropriate, the method may be intrinsically unreliable or there may be an error in the administration of the test.

21. 4. Was complete follow up achieved?a) Were all patients entered in the study accounted for in the results?b) Was patients clinical status known at the end of the follow up period?

22. a) Were all patients entered in the study accounted for in the results?

23. b) Was patients clinical status known at the end of the follow up period? • A major potential source of bias can be introduced if there are a large number of patients lost to follow up. If there is a differential here - for instance if high risk patients are those that are also most mobile and most easily lost then the remaining patients will have a overly optimistic outcome.

24. 5. Were objective outcome criteria developed and used?

25. Some end points in the course of disease • Cure • Death • Response (Percent of patients showing some evidence of improvement following an intervention). • Remission (Percent of patients entering a phase in which disease is no longer detectable) • Recurrence (Percent of patients who have return of disease after a disease free interval)

26. Approaches used to describe the prognosis of disease • Case Fatality - Percent of patients with a disease who die of it.

27. Approaches used to describe the prognosis of disease • Five-year survival - Percent of patients surviving 5 years from some point in the course of the disease.

28. Approaches used to describe the prognosis of disease • Median survival time - Time at which 50% of patients still alive.

29. Approaches used to describe the prognosis of disease • Person years - The sum of the number of years of observation in each individual in the study

30. Approaches used to describe the prognosis of disease • Observed survival - Life tables or Kaplan Meier plots.

31. Limitation of commonly used rates to describe prognosis

32. Survival analysis: understanding survival curves

33. Survival curves

34. 6. Was outcome assessment blind?

35. 7. Was adjustment for extraneous prognostic factors carried out?e.g. stratification or multivariate regression.

36. Methods of controlling bias • Randomization • Restriction • Matching • Stratification • Simple adjustment • Multiple regression

37. Randomization • Assign patients to groups in a way that gives each patient an equal chance of falling into one or the other group.

38. Restriction • Limit the range of characteristics of patients in the study

39. Matching • For each patient in one group select one or more patients with the same characteristics (except for the one under study) for a comparison group.

40. Stratification • Compares rates within subgroups (strata) with otherwise similar probability of the outcome.

41. Simple adjustment • Mathematically adjust crude rates for one or a few characteristics so that equal weight is given to strata of similar risk.

42. Multiple regression • Adjust for differences in a large number of factors related to outcome using mathematical modelling techniques.