Linking evidence-based medicine therapeutic summary measures to clinical decision analysis

Evidence-based medicine (EBM) seeks to improve clinical practice by evaluating the quality of clinical evidence and ensuring that only the "best" evidence from clinical research is used in the management of individual patients. EBM has contributed to our understanding of the meaning of the benefit and harm of treatment as reported in the literature, and it is often promoted as an aid to clinical decision making. However, EBM therapeutic summary measures reflect only a single dimension of clinical decision making. The purpose of this work is to show how EBM therapeutic summary measures can be effectively incorporated into medical decision making.

The effective application of the therapeutic summary measures advocated by EBM requires their integration into the framework of clinical decision analysis. Clinical decision analysis involves not only the identification and specification of the probabilities of clinical events but also the assessment of their relative values or utilities. We present here several analytic models for the integration of EBM therapeutic summary measures within the framework of clinical decision analysis.

As expected, our analysis demonstrated that treatment should never be administered if its harm is greater than its efficacy, which is generally expressed as relative risk reduction. Likewise, a diagnostic test should never be ordered if the therapeutic harm is greater than the therapeutic efficacy. Intervention is always favored if the number needed to treat to avoid one adverse outcome (NNT) is smaller than the number needed to treat to harm one individual (NNH). When faced with a choice between two therapeutic options, the action threshold above which an intervention is favored can be expressed in terms of the harm inflicted (H) as H x NNT or NNT/NNH. If a patient's preferences are taken into account as relative value judgments (RV) of adverse events relative to that of therapeutic events, the action threshold is defined as NNT x (RV/NNH).

In the setting of clinical decision making, EBM summary measures derived from population studies can be effectively used to define diagnostic and therapeutic action thresholds that may help in the management of individual patients.