Evidence-Based Medicine

1. EBM - Harm: Applying NNT & NNH A. Bornstein, MD, FACC Department of Public Health Weill Cornell Medical College New York, NY ABB MD FACC

2. 5 Step Model of Evidence-Based Medicine 1) 2) 3) 4) 5) 2) Acquire 1) Ask 5) Evaluate 3) Appraise 4) Apply • Ask answerable questions developed from information needs regarding patient under your care • Acquirebest evidence to answer the clinical questions with optimum efficiency • Appraise critically, that best evidence for its validity, applicability, & usefulness • Apply the results of this appraisal in caring for the patients in your practice • Evaluate your decision-making performance ABB MD FACC

3. Which Type of Evidence Would You Like to Appraise? ABB MD FACC

4. Search Strategy by Question Type: Etiology/Harm • Evaluation of a therapeutic, preventive, screening, or diagnostic intervention for risk or harm, or non-therapeutic exposure or behavior • MeSH terms • Epidemiologic factors • Causality • Precipitating factors • Risk factors • Epidemiologic methods • Risk • MeSH subheadings • Pathophysiology • Physiology • Etiology • Words & phrases • Relative risk ABB MD FACC

5. Search Strategies: Acquire Systematic reviews Guidelines; HTA (Health Technology Assessment) reports; Decision analysis; or Economic evaluation Type of studies Single studies Systematic reviews Single RCTs Meta-Analyses RCTs Cohort studies Case-Control studies Single studies 1) Ask Non-randomized studies Case reports 2) Acquire Acquire 4) Apply Systems 3) Appraise Computerized decision support Summaries Evidence-based text books as Guidelines or HTA reports 5-S approach to sources for search strategy Sources Synopses Evidence-based journal abstracts 5) Evaluate Syntheses Systematic reviews, Decision analysis, or Economic evaluation Studies Original journal articles

6. Answering Clinical Questions: Search Strategy DARE: Database of Abstracts of Reviews of Effectiveness CATS: Critically Appraised Topics CENTRAL: Cochrane Central Register of Controlled Trials POEM: Patient-Oriented Evidence that Matters FPIN: Family Physicians Inquiry Network TRIP: Turning Research Into Practice (meta-search engine) Bandolier: Journal of EBM healthcare, written by Oxford scientists (pre-appraised evidence source) SUMSearch: Selects best resources for your question, formats question, & searches based on results (meta-search engine)

7. EBM-Harm: Relationship Between Treatment and Outcome Exposure to treatment (offending agent) Cancer Ca++ antagonists Cohorts Case - Control Randomization helps make the 2 treatment groups (a+b) identical for all other causes of cancer, so any statistically significant  in cancer in Ca++ antagonist group EER = [a/(a+b)] is valid RCTs are ill-suited, however, (size, duration, & ethics), for evaluating rare possible harmful exposures (validity of study designs to detect harm is inversely proportional to their feasibility) Sackett DL, Rosenberg WMC, et al. Evidence based Medicine; How to Practice & Teach EBM. 2000. Churchill Livingstone. P 155-167 ABB MD FACC

8. Confounders • Because the decision to treat & accept exposure risk has a purpose, and is not randomized, in a cohort study ‘exposed’ patients may differ from ‘non-exposed’ patients for important determinants of outcome (confounders) • Confounders have 3 properties: 1) major determinants of outcome; 2) extraneous to questions posed; 3) unequally distributed between exposed and non-exposed study patients • Both cohorts must be comparable or adjustment must be made for confounders (can adjust only for confounders already known & measured; randomization attempts to balance groups for unknown confounders not yet discovered) • Hypothetical example: if people with  BP were more likely to develop cancer than normotensive individuals,  BP would be a confounder of risk of cancer in people using &/or not using Ca++ antagonists

9. Case Series and Case Reports Exposure to treatment (offending agent) Cancer Ca++ antagonists Case reports Finally, we may find only a few case reports of 1 or a few patients who developed an adverse effect while receiving the suspected offending treatment (a) If the outcome is unusual & serious enough, such case reports or case series may be sufficient to answer the question, but because these studies lack a comparison group, they are usually sufficient only for hypothesis generation, thus pointing to the need for further study Sackett DL, Rosenberg WMC, et al. Evidence based Medicine; How to Practice & Teach EBM. 2000. Churchill Livingstone. P 155-167 ABB MD FACC

10. Appraisal of the Evidence Susceptibility to Bias (Confounders) Progress of study Beginning (Selection Bias) 1) Ask 2) Acquire Completion of study End (Reporting Bias) 4) Apply 3) Appraise Initial phase of study Middle (Information Bias) Appraise Results: 1) Validity 2) Importance 3) Relevance RR, OR, RRR, ARR, RRI, ARI Effect: Association vs. Causation NNT, NNH Impact 5) Evaluate CI (Confidence Intervals) Precision ABB MD FACC

11. Number Needed to Harm (NNH) Sackett DL, Rosenberg WMC, et al. Evidence based Medicine; How to Practice & Teach EBM. 2000. Churchill Livingstone. P 155-167 • (NNH) the number needed to harm is an epidemiological measure that indicates how many patients need to be exposed to a risk factor to cause harm in 1 patient that would not otherwise have been harmed • NNH is defined as inverse of attributable risk or 1/ARI (the lower NNH, the worse the risk-factor) • NNH is similar to NNT, where NNT usually refers to a therapeutic intervention and NNH to a detrimental effect or risk factor; NNH is computed with respect to ‘exposure’ & ‘non-exposure’ • If probabilities pexposure & pnon-exposure are known, then NNH = 1/(pexposure- pnon-exposure); NNH helps decide if it is prudent to proceed with a particular treatment which may expose patient to harm while providing therapeutic benefit; if clinical endpoint is devastating enough without drug (e.g., death, heart attack), drug with low NNH may still be indicated in particular situations if NNT is < NNH (converse for side-effects, or drug benefit) ABB MD FACC

12. Evidence Based Medicine – Harm: Validity • Evaluate evidence about causationfor: • Validity • Importance • Relevance to our patient • Assessing validity of evidence is crucial to avoid a false (+) conclusion or type 1 error (agent does cause an adverse event when, in fact, it does not), or a false (-) conclusion or type 2 error (agent does not cause an adverse event when, in truth, it does) Sackett DL, Rosenberg WMC, et al. Evidence based Medicine; How to Practice & Teach EBM. 2000. Churchill Livingstone. P 155-167 • Questions about whether a medical intervention is harming a patient are often posed by both physicians & patients; i.e., “Is the risk of death or cancer increased by Ca++ antagonists?”; or a patient may come to clinic with headlines like: “HYPERTENSION MED KILLS!” • To answer questions about harm, a physician 1st needs to evaluate evidence for causation (as opposed to just association) ABB MD FACC

13. Assessing Validity of Evidence in Studies About Harm Sackett DL, Rosenberg WMC, et al. Evidence based Medicine; How to Practice & Teach EBM. 2000. Churchill Livingstone. P 155-167 • Were groups clearly defined & similar in all important ways other than exposure to offending treatment or cause? (baseline risk) • Were treatments & outcomes measured same way in both groups? (objective outcome assessment &/or blinded to exposure) • Was the follow-up of sufficient duration for the outcome to occur? (‘5 and 20’ rule): if <5% lost to follow-up, probably leads to little bias; if >20% lost to follow-up, seriously threatens validity • Do results of the harm study fulfill diagnostic criteria for causation? • Is it clear that exposure preceded the onset of the outcome? • Is there a dose-response gradient or relationship? • Is there (+) evidence from a ‘dechallenge-rechallenge’ study? • Is the association consistent from study to study? • Does the association make biological sense? ABB MD FACC

14. Formulas Used in Calculations of Patient Risk Adverse outcome Yes Adverse outcome No a b d c Exposed to treatment Yes Exposed to treatment No Case-Control studies are Retrospective, therefore use: OR (odds ratio): ratio of odds of event occurring in 1st group (experimental group), to odds of it occurring in 2nd group (control group) OR = a/(a + b)/b/(a + b)/c/(c + d)/d/(c + d) = a/b/c/d = (ad)/(bc) RCTs or Cohort studies are Prospective, therefore use: RR (relative risk): ratio of risk of an event (developing disease) relative to exposure; always tells us whether the observed outcome (effect) occurs more or less often in exposed group than in unexposed group; RR calculations are identical whether question about therapy or a question about harm. RR = EER/CER;EER (experimental event rate) = a/(a + b); CER (control event rate) = c/(c + d)

15. Formulas Used in Calculations of Patient Risk Adverse outcome Yes Adverse outcome No a b d c Exposed to treatment Yes Exposed to treatment No • RRR (relative risk reduction): difference between EER and CER divided by CER; RR = (EER-CER)/CER= 1- (EER/CER) x 100%; usually expressed as % • ARI (absolute risk increase): ARI = CER-EER = [c/(c + d) - a/(a + b)] ARR = EER-CER = [a/(a + b)] - [c/(c + d)]; reflects baseline risk & more accurately indicate magnitude of treatment effect • NNH (number needed to harm) is 1 divided by the ARI NNH = 1/ARI = 1/[a/(a + b)]-[c/(c + d)] (worst NNH = 1: treatment  everyone worse)

16. Placebo-Controlled Trials of New Drug for Acute MI A) B) Bars = 30-day mortality rate in different groups of patients with acute MI & CHF B) Trials of group of patients at high risk for adverse outcome & another group of patients at low risk for adverse outcome

17. Balancing Benefit & Harm in Individual Patients 1A) Benefit Table: 1B) Benefit & Harm Table: 1A) Benefit Table: potential benefit of a new treatment (drug X) 1B) Benefit & Harm Table: we must now factor potential harm (adverse effect associated with using drug X) into the clinical decision; in clinical trials of drug X, risk of severe GI bleed  3-fold over 3 years in pts who received drug X (RR = 3); population-based study reported risk of severe GI bleed for women in your patients' age group  0.1%/year (regardless of stroke risk);

18. Strength of Association Between Exposure and Outcome Adverse Outcome yes no Treatment yes Treatment no • Different study architectures require different methods for estimating strength of association (for randomized trials & cohort studies, results are described by calculating incidence (risk) of adverse event in treated patients relative to untreated patients: RR = [a/(a + b)]/[c/(c + d)]) • i.e., if 1000 patients receive treatment & 20 have an adverse outcome, a = 20; (a + b) = (20 + 980) then a/(a + b) = 2%. If just 2 of 1000 patients who do not receive treatment for same target disorder experienced adverse event, c = 2; d = 998, then c/(c + d) = (2 + 998) = 2/1000 = 0.2%; relative risk RR = 2%/0.2% = 10 meaning that patients receiving suspect treatment are 10 X as likely to suffer the adverse event as patients treated the other way. ABB MD FACC

19. Magnitude & Precision of Association Between Exposure and Outcome Exposed to treatment Yes Exposed to treatment No Adverse outcome Yes Adverse outcome No a = 90 b = 45 d = 55 c = 10 Because case-control studies sample outcomes & retrospectively look at exposures, we cannot calculate ‘incidence’ such as RR (relative risk) from them; strength of association in a case-control study can only be indirectly estimated and is presented as odds ratio (or relative odds): OR = a/b/c/d = (ad)/(bc) i.e., if 100 cases of adverse outcome are identified and 90 of them received the putative causal agent, a = 90; c = 10; if 100 control patients (free of adverse outcome) are assembled & 45 of them got the suspect treatment, b = 45; d = 55 (OR) = (ad)/(bc) = (90×55)/(45×10) = 11 meaning that relative odds of experiencing the adverse event for patients exposed to putative causal agent is 11 X that of those patients not receiving it. ABB MD FACC

20. How Big Should Relative Risks & Odds Ratios Be Before We Should Be Impressed by Them? Odds ratio (OR) or relative risk (RR) >1 indicate that  risk of adverse outcome associated with exposure When (RR) or (OR) = 1, adverse outcome is no more likely to occur with than without exposure to the suspected agent) Because cohort studies and case-control studies are susceptible to many biases, we must ensure that OR is greater than that which could result from bias alone; (20 X is sufficient to indicate causality) ABB MD FACC

21. Harm: Using Magnitude of OR & RR to Assess Statistical Significance • For well-conducted epidemiological study, OR of 20 X increase is sufficient to indicate causality! • Odds ratio from case-control study is not impressive unless OR >4 X for minor adverse events (value set at progressively lower levels as severity of event increases • Less potential bias in cohort study might regard RR >3 X as convincing for more severe adverse events • In the cohort study of Ca++ antagonists & cancer, RR was an unimpressive 1.4 ABB MD FACC

22. How Big Should Relative Risks & Odds Ratios Be Before We Should Be Impressed by Them? Although OR and RR indicate the strength of association, it must be translated into measure that is useful & intelligible; for this, NNH (number needed to harm is used; number of patients who need to be exposed to the putative causal agent to produce 1 additional harmful event); NNH is calculated from RCTs & cohort studies similar to NNT, but as the reciprocal of the difference in adverse event rates NNH = 1/ARI = 1/[a/(a + b)] – [c/(c + d)] For OR derived from a case-control study, the calculation is more complex (can’t determine incidence in a case-control study)1 – [PEER × (1 – OR)]/(1 – PEER) × PEER × (1 – OR) where PEER is patient expected event rate (adverse event rate in individuals not exposed to the putative cause) Patient expected event rate (PEER) refers to rate of events expected in a patient who received no treatment or conventional treatment ABB MD FACC

23. How Big Should Relative Risks & Odds Ratios Be Before We Should Be Impressed by Them? Sensitivity analysis is used to determine whether odds ratio or relative risk (OR) or (RR) is impressive (performed on a report that includes ‘correction’ for potential confounders, comparing unadjusted measure of association with one in which at least one known confounder has been adjusted out; if this produces further decrease in (RR) or (OR), both are suspect If adjusted (RR) or (OR) is stable or rises rather than falls, confidence in validity of association is greater; in the study (cancer risk & Ca++ antagonist use), RR rose from 1.4 to 1.7 when adjusted for differences in patients’ baseline characteristics (suggests confounding didn’t explain results) ABB MD FACC

24. Confidence Intervals Precision: in addition to looking at the magnitude of (RR) or (OR), we need to look at its precision by CI (confidence intervals) around point estimates of individualized NNT based on odds ratios (OR) & risk ratios (RR) Credibility is highest when entire confidence interval is narrow & remains within a clinically importantly increased risk (in this study, 95% CI = 1.27-2.34 which is statistically significant)

25. Application of Evidence Similarity of characteristics and health states of the population to our patient 1) Ask Apply 2) Acquire 4) Apply 3) Appraise Feasibility within patient and practice circumstances 5) Evaluate Integration with relevant scientific knowledge from other sources

26. NNT & NNH: Case Scenario Mentes BB, Irkorucu O, Akin M, et al. Comparison of botulinum toxin injection and internal lateral sphincterotomy for the treatment of chronic anal fissure. Dis Colon Rectum. 2003;46:232–7. A 32-yr-old woman presents to your office complaining of severe anal pain, which increases when defecating and is associated with bleeding and anal itching. This is the third time she has consulted for the same symptoms in the last 8 months. An anal fissure was previously observed during anoscopy with thick edges and grade II internal hemorrhoids. The previous episodes had been treated with general measures and NTG ointment for 10 days with only mild improvement of symptoms. ABB MD FACC

27. NNT & NNH: EBM Approach Mentes BB, Irkorucu O, Akin M, et al. Comparison of botulinum toxin injection and internal lateral sphincterotomy for the treatment of chronic anal fissure. Dis Colon Rectum. 2003;46:232–7. Double-blind randomized controlled trial of 111 patients was found comparing botulinum toxin vs. internal lateral sphincterotomy as a definitive management for chronic anal fissure. Study demonstrated that the success rate in the botulinum group was 75% (46/61) 12 months later, & sphincterotomy group had 94% (47/50) success rate (p = 0.008). Same study demonstrated that sphincterotomy group was associated with a significantly higher complication rate (16% or 8/50 cases of anal incontinence!), compared to complication rate (0%) no complications occurring in the botulinum group (p < 0.001). ABB MD FACC

28. NNT: Statistical Analysis Mentes BB, Irkorucu O, Akin M, et al. Comparison of botulinum toxin injection and internal lateral sphincterotomy for the treatment of chronic anal fissure. Dis Colon Rectum. 2003;46:232–7. Absolute risk reduction (ARR) defines the absolute arithmetical difference in rate, percentage, or proportion of patients having an outcome between experimental & control participants in a trial, calculated as Experimental Event Rate - Control Event Rate (ARR) =[a/(a+b)] - [c/(c+d)] = (EER) - (CER) = 19% In the case, the experimental group (sphincterotomy group) and the control group (botulinum group) can be defined in a foregoing study; ARR = [EER (94%) - CER (75%)] = 19% Number needed to treat (NNT) defines # of patients who need to be treated in order to achieve 1 additional favorable outcome, calculated as NNT = 1/ARR = 1/0.19 = 5 ABB MD FACC

29. Formulas Used to Calculate Patient Risk Reduction Calculations used to analyze results of clinical trials & cohort studies for benefit to patient (patient benefit) *Absolute Risk Reduction = experimental event rate - control event rate †Number Needed to Treat is 1 divided by the absolute risk reduction Adverse outcome Beneficial outcome Totals Present (Case) Absent (Control) Present (Case) Absent (Control) Exposed to treatment sphincterotomy Yes (RCT or Cohort) a = 47 b = 3 a + b = 50 d = 15 c + d = 61 No (RCT or Cohort) c = 46 EER (Experimental Event Rate) = a/(a+b) = 47/(47+3) = 94% CER (Control Event Rate) = c/(c+d) = 46/(46+15)= 75% OR (Odds Ratio) = a/b/c/d = (ad)/(bc) = (47x15)/(3x46) = 705/138 = 5 RR (Relative Risk) = [a/(a+b)]/[c/(c+d)] = 1.25 ARR (Absolute Risk Reduction)* = [a/(a+b)] - [c/(c+d)] = 19% NNT (Number Needed to Treat)† = 1/ARR = 1/[a/(a+b)] - [c/(c+d)] = 5 Totals a + c b + d a + b + c + d Dis Colon Rectum. 2003;46:232–7.

30. NNH: Statistical Analysis Mentes BB, Irkorucu O, Akin M, et al. Comparison of botulinum toxin injection and internal lateral sphincterotomy for the treatment of chronic anal fissure. Dis Colon Rectum. 2003;46:232–7. Absolute risk increase (ARI) defines the absolute arithmetical difference in rates of bad outcome between experimental and control patients in a trial, calculated as (EER - CER) =(16% - 0%) = 16% Number needed to harm (NNH) defines the number of patients who, if receiving the experimental treatment, would lead to 1 additional patient being harmed, compared with patients who received the control treatment, calculated as (1/ARI) = 1/0.16 = 6

31. Formulas Used to Calculate Patient Risk Increase Calculations used to analyze results of clinical trials & cohort studies for benefit to patient (patient benefit) *Absolute Risk Reduction = experimental event rate - control event rate †Number Needed to Treat is 1 divided by the absolute risk reduction Adverse outcome Adverse outcome Totals Present (Case) Absent (Control) Present (Case) Absent (Control) Exposed to treatment sphincterotomy Yes (RCT or Cohort) a = 8 b = 42 a + b = 50 d = 61 c + d = 61 No (RCT or Cohort) c = 0 EER (Experimental Event Rate) = a/(a+b) = 8/(42+8) = 16% CER (Control Event Rate) = c/(c+d) = 0/(0+61)= 0% ARI (Absolute Risk Increase)* = [a/(a+b)] - [c/(c+d)] = .16 NNH (Number Needed to Harm)† = 1/ARI = 1/[a/(a+b)] - [c/(c+d)] = 6 Totals a + c b + d a + b + c + d

32. NNH: Statistical Analysis • Absolute risk increase (ARI) defines absolute arithmetical difference in rates of bad outcome between experimental & control patients in a trial, calculated as (EER - CER), in this case (16% - 0%) = 16% • Number needed to harm (NNH) defines the number of patients who, if receiving the experimental treatment, would lead to 1 additional patient harmed, compared with patients who received the control treatment, calculated as NNH = (1/ARI); in this case; 1/0.16 = 6 • Likelihood of being helped versus harmed (LHH) defines the number of patients who stand to benefit from treatment for every patient harmed, calculated as (1/NNT) versus (1/NNH) or ARR versus ARI; in this case, (1/5):(1/6) = 0.2 versus 0.16 = 1.25 ABB MD FACC

33. NNT & NNH: Statistical Analysis • EER: proportion of patients in experimental group in which event is observed • CER: proportion of patients in control group in which event is observed • Patient expected event rate (PEER): rate of events expected in a patient who has not received experimental or conventional treatment • Relative risk reduction (RRR): proportional reduction in rates of bad outcomes between experimental and control participants in a trial, calculated as RRR = 1-(EER − CER) x 100% = (EER − CER)/CER • Note: because all statistics are determined in a sample population, they are estimates of truth;  to know accuracy, must be accompanied by 95% CI or limits where true point estimates rest 95% of the time ABB MD FACC

34. NNT (Number Needed To Treat) For every 20 patients treated with experimental treatment, there are 4 additional healed; NNT is 20/4 = 5; Therefore, 5 patients must be treated with experimental treatment in order to get 1 additional cure. ARR: absolute risk reduction; NNT: number needed to treat; EER: experimental event rate; CER: control event rate ABB MD FACC

35. Defining NNT (Number Needed to Treat) Versus NNH (Number Needed to Harm) EER: experimental event rate; CER: control event rate ABB MD FACC

36. NNT (NNH) Nomogram NNT (NNH) RRR (%) RRI (%) AR (%) Chatellier G, Zapletal E, Lemaitre D, Menard J, Degoulet P. The number needed to treat: a clinically useful nomogram in its proper context. BMJ. 1996;312:426-429.

37. NNT & NNH: Applying Statistics to Clinical Questions Relying on the data from the above-mentioned study, it can be concluded that internal lateral sphincterotomy is more efficacious than botulinum toxin injection into the internal anal sphincter to heal chronic anal fissure; 94% versus 75% (p = 0.008) NNT thus becomes 5 or [1/ARR] = [1/(.94 − .75)], meaning that 5 patients must undergo internal lateral sphincterotomy to cure 1 additional patient than treating them with botulinum toxin. However, internal lateral sphincterotomy is also associated with a higher rate of anal incontinence; ARI = 16%. NNH (number needed to harm) thus becomes 6 ([1/ARI] or [1/0.16%]), meaning that 1 additional patient may suffer anal incontinence for each of the 6 patients undergoing lateral sphincterotomy compared with patients who received botulinum toxin. ABB MD FACC

38. NNT & NNH: Applying Results to Your Patient 1) Ask 2) Acquire 4) Apply 3) Appraise 5) Evaluate • Since translating evidence obtained through medical literature can be very challenging; clinicians need to clearly judge and understand how to apply evidence to individual patients; need to know population baseline risk • In order to accomplish this objective, clinicians ought to answer at least these 4 questions: • Are there important differences between my patient and the average patient who participated in the study? • Is the suggested treatment achievable in my setting? • What are the possible benefits vs. harms for my patient? • What are my patient’s preferences ABB MD FACC

39. NNT & NNH: Applying Results to Your Patient • LHH (likelihood of being helped vs. harmed): (1/NNT vs. 1/NNH) = (1/5:1/6) = (0.2:0.16) = 1.2 • If data are combined in an aggregate ratio (assuming our patient has same baseline risk as average patient in the study), LHH turns out to be = (1/NNT) versus (1/NNH); (1/5):(1/6) = 0.2 versus 0.16 = 1.2. • In this case scenario, after reviewing the data, it seems that although lateral sphincterotomy is associated with a significantly higher rate of healing chronic anal fissure, it is also associated with an increased risk of anal incontinence. • In other words, for each patient that a lateral sphincterotomy may help, it can harm another one! A patient can be told that despite lateral sphincterotomy being more efficacious it is also more dangerous than botulinum toxin in healing chronic anal fissure. ABB MD FACC

40. NNT & NNH: Applying Results to Your Patient • This factor must be taken into account since the pros and cons of an intervention vary among patients; to work out the baseline risk of a patient there are 3 strategies: • If a study reports baseline risk of various subgroups; baseline risk for similar subgroup to our patient can be used. • Baseline risk can be estimated from scientific literature based on papers that describe prognosis of similar patients. • Use clinical and sensible judgment. ABB MD FACC

41. NNT & NNH: Applying Results to Your Patient From the above example it can be demonstrated that LHH relies on NNT and NNH figures and these also depend on the baseline risk. However, clinicians must take into account that the average NNT and NNH obtained in a specific trial may not be applicable to an individual patient, thus it is very important to consider what a patient’s baseline risk of an event is in order to determine a specific NNT & NNH; with relative differences in baseline risk to calculate the patient specific NNT, we need to divide the average NNT by (ft) For example, if we felt that our patient was at ½ the risk of global clinical deterioration compared with the average patient in the study we identified (ft = 0.5), we would calculate our patient’s specific NNT as NNT / ft = 6/0.5 = 12 Another issue relating to NNH & NNT is time-frame of complications after an intervention; the development of incontinence may not occur immediately after the proposed benefit of sphincterotomy. ABB MD FACC

42. NNT & NNH: Applying Results to Your Patient • In the 1st two strategies NNT is calculated as 1/(PEER × RRR) assuming that the RRR is stable across range of baseline risks, but if there is information indicating that the RRR is unstable for various subgroups, the most suitable RRR must be used in order to calculate the NNT; baseline risk (BR) of an individual patient is factor f • If using clinical judgment, the patient risk of the outcome event can be estimated and compared to the average control patient in the study by converting it to a fraction (ft), where ft can be =1, >1, or <1. • If it is considered that the patient has equal, greater or lesser risk than those in the trials, the same method to calculate a specific NNH can be used, using (fh). • NNT foryour patient = NNT/(ft) or NNT = 1/PEER x RRR NNH foryour patient = NNH/(fh) or NNH = 1/PEER x RRI ABB MD FACC

43. NNT & NNH: Applying Results to Your Patient For instance, in the above scenario, after examining risk of side effects, the average patient had reported in the trial, it can be calculated that above mentioned patient has 1/3 the risk (fh = 0.3) of suffering the main side effect (anal incontinence), therefore, his NNH is 20 (NHH/fh= 6/0.3) = 20 rather than 6. Severity factor (S): risk-adjusted LHHs still ignore each patient's values. To incorporate a patients’ preference into LHH, he/she should be informed about a target event to be prevented & potential side effects from treatment; patient has to score in a rating scale values & preferences of the eventual outcomes, that is, 0 = death & 1 = health. In a hypothetical case scenario if the patient assigns a value of 0.9 to repetitive application of botulinum toxin & 0.1 to incontinence from the sphincterotomy, 2 ratings can be used to understand that he/she believes that the adverse event of a sphincterotomy is 9 times worse than botulinum toxin. ABB MD FACC

44. NNT & NNH: Applying Results to Your Patient This number is the severity factor or S; incorporating patients’ preferences in the LHH, would yield: [(1/NNT) × ft] : [(1/NNH) × fh × S] = [(1/5) × 1] : [(1/6) × 0.3 × 9] = 0.2:0.4 or 2 to 1 in favor of botulinum toxin treatment. Bottom line: we must consider many aspects when choosing the most suitable & feasible treatment for our specific patient taking into account (-) as well as (+) aspects induced by any medical or surgical intervention; 1 excellent tool that physicians may rely on for this goal is LHH by any prescribed treatment, a construct integrating in a ratio, number of patients who need to be treated to achieve 1 additional favorable outcome, (NNT) versus number of patients who, if they received experimental treatment, would lead to 1 additional patient being harmed, compared to patients receiving control treatment (NNH). ABB MD FACC