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Enhancing Healthcare: Integrating Evidence-Based Medicine and Local Guidelines

Understand the importance of evidence-based medicine and local guidelines in clinical decision-making, focusing on the concepts of burden, barriers, behaviors, and balance. Learn how to personalize treatment decisions by integrating clinical expertise and patient values effectively.

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Enhancing Healthcare: Integrating Evidence-Based Medicine and Local Guidelines

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  1. EBM and E-B Guidelines • EBM integrates evidence, expertise, and the unique biology and values of individual patients. • Local EB Provision ought to integrate evidence, expertise, and the unique biology and values of the local scene.

  2. EBM and E-B Guidelines • The best evidence comes from systematic reviews (such as Cochrane) and/or E-B journals of 2º publication: • Much more likely (than personal search and critical appraisal) to be true • Saves the clinician’s precious (scarce!) time • Avoids error and duplication of effort

  3. EBM and E-B Guidelines • But NO systematic review can (or should try to) identify the “4 B’s: • Burden • Barriers • Behaviours • Balance • They can ONLY be determined at the local (or even patient) level

  4. 1. Burden • The burden of illness, disability, and untimely death that would occur if the evidence were NOT applied • the consequences of doing nothing

  5. 2. Barriers • Patient-values & preferences • Geography • Economics • Administration/Organisation • Tradition • “Expert” opinion

  6. 3. Behaviours • The behaviours required from providers and patients if the evidence is applied. • All that guidelines can do is specify the former!

  7. 4. Balance • The opportunity cost of applying this guideline rather than some other one.

  8. Killer B’s • Burden: too small to warrant action. • Barriers: ultimately down to patients’ values. • Behaviours: may not be achievable. • Balance: may favour another guideline over this one.

  9. Two monumental wastes of time and energy • First, national/international evidence-summarising groups prescribing how patients everywhere should be treated. • Their expertise: predicting the health consequences if you do treat. • Their ignorance: the local B’s, and whether killer B’s are operating.

  10. Two monumental wastes of time and energy • Second, local groups attempting to systematically review the evidence. • Their expertise: identifying the local B’s and eliminating the killer B’s • Their ignorance: searching for all relevant evidence; Chinese; performing tests for heterogeneity.

  11. Applying a study result to my patient • Never interested in “generalising” • Am interested in a special form of extrapolation: particularising

  12. Extrapolating (particularising) to my individual patient: • First and foremost: Is my patient so different from those in the trial that its results can make no contribution to my treatment decision? • if no contribution, I restart my search • if it could help, I need to integrate the evidence with my clinical expertise and my patient’s unique biology and values...

  13. To add Clinical Expertise and Patient’s Biology & Values: • What is my patient’s RISK ? • of the event the treatment strives to prevent? • of the side-effect of treatment? • What is my pt’s RESPONSIVENESS? • What is the treatment’s FEASIBILITY in my practice/setting? • What are my patient’s VALUES ?

  14. To add Clinical Expertise and Patient’s Biology & Values: • I begin by considering Risk and Responsiveness for the event I hope to prevent with the treatment: • The report gives me (or I can calculate) an Absolute Risk Reduction [ARR] for the average patient in the trial. • ARR = probability that Rx will help the average patient.

  15. For example, Warfarin in nonvalvular atrial fibrillation: After 1.8 years of follow-up in an RCT: • Control Event Rate (placebo) = 4.3% • Exper. Event Rate (warfarin) = 0.9% • so, for the average patient in the trial, the probability of being helped, or Absolute Risk Reduction = (CER - EER) = 3.4% ACPJC 1993;118:42

  16. How can I adjust that ARR for my pt’s Risk and Responsiveness? • Could try to do this in absolute terms: • my Patient’s Expected Event Rate: PEER • and multiply that by the RRR • and factor in my Patient’s expected responsiveness • Clinicians are not very accurate at estimating absolute Risk and Responsiveness

  17. How can I adjust that ARR for my pt’s Risk and Responsiveness? • Clinicians are pretty good at estimating their patient’s relative Risk and Responsiveness • So, I express them as decimal fractions: • f~risk (if at three times the risk, f~risk = 3) • f~resp (if only half as responsive [e.g., low compliance], f~resp = 0.5)

  18. How can I adjust that ARR for my pt’s Risk and Responsiveness? • probability that Rx will help my patient = ARR x f~risk x f~resp • If ARR is 3.4% • and I judge that their f~risk is 3 • and that their f~resp is 0.5 • then the probability that warfarin will help my patient = 3.4% x 3 x 0.5 = 5.1%

  19. Must also consider the probability that I will do harm: • In the case of warfarin: serious bleeding (requiring transfusion) from the g-i tract, or into the urine, soft tissues or oropharynx. • Absolute Risk Increase = 3% at 1 yr, so ARI estimated to be 5% in 1.8 years ACPJC 1994;120:52

  20. …and adjust the probability of harm for my patient • Again, can express my clinical judgement in relative terms: f~harm • Given my patient’s age, I judge their f~harm to be doubled: 2 • then the probability that Rx will harm my patient = ARI x f~harm = 5% x 2 = 10%

  21. Can now begin to estimate the Likelihood of Help vs. Harm • Probability of help: ARR (embolus) x f~risk x f~resp = 5.1% • Probability of harm: ARI (haemorrhage) x f~harm = 10% • My patient’s Likelihood of Being Helped vs. Harmed [LHH] is: (5.1% to 10%) or 2 to 1 against warfarin! • …or is it ?

  22. The LHH has to include my patient’s values • I need to take into account my patient’s views (“preferences,” “utilities”) about the relative severity: • of the bleed I might cause • to the embolus I hope to prevent • Expressed in relative terms = s~ • if the bleed is half as bad as the embolus, then s~ = 0.5

  23. On in-patient services in Oxford and Toronto: • When Dr. Sharon Straus has described a typical embolic stroke (with its residual disability) and typical moderate bleed (brief hospitalisation and transfusion but no permanent disability): • for most of her patients, a bleed is only 1/5th as bad as a stroke • so the s~ is 0.2

  24. So the LHH becomes: • {ARR for embolus} x {f~risk} x {f~resp} vs. {ARI for bleed} x {f-harm} x {s~} • 3.4% x 3 x 0.5 = 5.1% vs. 5% x 2 x 0.2 = 2% • LHH = 5.1 to 2 or 2.5 to1 • (I am more than twice as likely to help than harm my patient if they accept my offer of Rx)

  25. We can work out the LHH for most patients <6 minutes • To be feasible on our service: has to be “do-able” in 3 minutes.

  26. Reactions from our patients • All are grateful that their values/opinions are being sought • »1/3 want to see the calculations, perhaps change their value for s~, and make up their own minds. • »1/3 adopt the LHH as presented. • »1/3 say “Whatever you tell me, doctor!”

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