The use of patient-specific equipoise to support shared decision-making for clinical care and enrollment into clinical trials. (February 2019)

• Record Type: Journal Article
• Authors: Selker, Harry P.; Daudelin, Denise H.; Ruthazer, Robin; Kwong, Manlik; Lorenzana, Rebecca C.; Hannon, Daniel J.; Wong, John B.; Kent, David M.; Terrin, Norma; Moreno-Koehler, Alejandro D.; McAlindon, Timothy E.
• Is Part Of: Journal of clinical and translational science. Volume 3(2019)Supplement 1
• Page Start: 27
• Page End: 36
• Publication Date: 2019-02
• Subjects: Stakeholder engagement, -- shared decision-making, -- decision support, -- mathematical equipoise, -- clinical equipoise, -- predictive models; Clinical medicine -- Research -- Periodicals; Medicine, Experimental -- Periodicals; Human experimentation in medicine -- Periodicals; 616.027
• DOI: 10.1017/cts.2019.380
• Languages: English
• ISSNs: 2059-8661

Abstract

Abstract: Background: To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient–clinician shared decision-making about care and RCT enrollment, based on "mathematical equipoise." Objectives: As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis. Methods: With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making. Results: The KOMET predictive regression model for knee pain had four patient-specific variables, and an r 2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r 2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received. Conclusions: Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.