Measuring clinical uncertainty and equipoise by applying the agreement study methodology to patient management decisions

Abstract

Abstract

Background Clinical uncertainty and equipoise are vague notions that play important roles in contemporary problems of medical care and research, including the design and conduct of pragmatic trials. Our goal was to show how the reliability study methods normally used to assess diagnostic tests can be applied to particular management decisions to measure the degree of uncertainty and equipoise regarding the use of rival management options.

Methods We first use thrombectomy in acute stroke as an illustrative example of the method we propose. We then review, item by item, how the various design elements of diagnostic reliability studies can be modified in order to measure clinical uncertainty.

Results The thrombectomy example shows sufficient disagreement and uncertainty to warrant the conduct of additional randomized trials. The general method we propose is that a sufficient number of diverse individual cases sharing a similar clinical problem and covering a wide spectrum of clinical presentations be assembled into a portfolio that is submitted to a variety of clinicians who routinely manage patients with the clinical problem.

Discussion Clinicians are asked to independently choose one of the predefined management options, which are selected from those that would be compared within a randomized trial that would address the clinical dilemma. Intra-rater agreement can be assessed at a later time with a second evaluation. Various professional judgments concerning individual patients can then be compared and analyzed using kappa statistics or similar methods. Interpretation of results can be facilitated by providing examples or by translating the results into clinically meaningful summary sentences.

Conclusions Measuring the uncertainty regarding management options for clinical problems may reveal substantial disagreement, provide an empirical foundation for the notion of equipoise, and inform or facilitate the design/conduct of clinical trials to address the clinical dilemma.