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CAT 2: Therapy

CAT 2: Therapy. Where Do We Get Evidence?. Your patients Colleagues Published anecdotal cases Case series. Cohort studies Randomized control trials Systematic review Meta-analyses. Scenario. 4 month old infant w/ RSV bronchiolitis Hospitalized for 2.5 days for O2 and IVF

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CAT 2: Therapy

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  1. CAT 2: Therapy

  2. Where Do We Get Evidence? • Your patients • Colleagues • Published anecdotal cases • Case series • Cohort studies • Randomized control trials • Systematic review • Meta-analyses

  3. Scenario • 4 month old infant w/ RSV bronchiolitis • Hospitalized for 2.5 days for O2 and IVF • Family h/o asthma • Parents wonder if there’s “anything out there” to prevent the infant from developing asthma

  4. Answerable Clinical QuestionPICO • Patient – Infant w/ RSV bronchiolitis • Intervention – Given a course of montelukast • Comparison – Compared with infants without montelukast • Outcome – Decrease the subsequent asthma illnesses?

  5. The Search • Pubmed search MeSH database • Terms “Respiratory Syncytial Virus” and “montelukast” • You find a double-blind, placebo-controlled study of infants hospitalized with RSV bronchiolitis evaluating efficacy of montelukast in reducing subsequent asthma exacerbations Bisgaard J for the Study Group on Montelukast and RSV: A Randomized Trial of Montelukast in RSV Postbronchiolitis. Am J of Respir Crit Care Med 2003; 167:379-383.

  6. Is the study VALID? • What are the RESULTS? • Can I APPLY the results to my patient?

  7. Validity: Primary Guides • Randomized trial? • Patients accounted for at end of trial? • Follow up long enough? • Intention to treat?

  8. Intention to Treat • The principle of attributing all patients to the group to which they were randomized • Preserves the value of randomization • Prognostic factors that we do and don’t know about will be, on average, equally distributed between the two groups, and the effect we see will be just that due to the treatment assigned

  9. Intention to Treat • Excluding non-compliant patients from the analysis leaves behind those who may be destined to have a better outcome, and destroys the unbiased comparison provided by randomization • In many randomized trials, non-compliant patients from both tx and plac groups have fared worse than compliant patients • Patients too sick to receive a tx shouldn’t only count as control cases

  10. Validity: Secondary Guides • Were patients, study personnel, health care workers blinded to treatment? • Groups similar at start of trial? • Aside from the treatment itself, were both groups treated equally?

  11. Therapy - Results

  12. CER = 10/55= 0.18 • EER = 4/61 = 0.07 • ARR = 0.18-0.07 = 0.11 • RR = 0.07/0.18 = 0.38 • RRR = [1-0.38] x 100=62% • NNT = 1/ ARR = 1/0.11= 9

  13. Number Needed to Treat (NNT) • Your personal treatment threshold • I would be willing to treat… • 3 patients to see benefit in one • 5 patients to see benefit in one • 10 patients to see benefit in one • 100 patients to see benefit in one

  14. Results • How precise was the estimate of treatment effect? • Calculated RR is a point estimate • True RR is somewhere within the 95% CONFIDENCE INTERVAL • Tighter the CI, the more likely the calculated RR is near the true number • Larger sample sizelarger the number of outcome eventsgreater confidence that the true risk is close to what we have observed

  15. 95% Confidence Intervals • If the value 0 lies in your 95% CI for your ARR, your result is NOT significant…there may be no difference between treatment and placebo • If your 95% CI includes a negative number, your patient may get worse with treatment…STOP

  16. Applicability • Are the results applicable to my patient? • Is our patient so different from those in the study that its results cannot apply? • Is the treatment feasible in our setting? • What are our patient’s potential benefits and harms from the therapy? • What are our patient’s values and expectations for both the outcome we are trying to prevent, and the treatment we are offering?

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