Developing software to predict patient responses to knee osteoarthritis treatments and to identify patients for possible enrollment in randomized controlled trials / Harry P. Selker.

Format:

Book

Author/Creator:

Selker, Harry P., author.

Language:

English

Subjects (All):

Osteoarthritis.

Decision making--Mathematical models.

Decision making.

Physical Description:

1 online resource (1 PDF file (126 pages)) : illustrations

Place of Publication:

Washington, DC : Patient-Centered Outcomes Research Institute (PCORI), 2019.

Summary:

BACKGROUND: Although they represent a standard of evidence, randomized controlled trials (RCTs) often fall short because of insufficient or unrepresentative enrollment, and many needed trials are never conducted. This leaves gaps in evidence to inform patient care decisions and creates a need for a method to facilitate RCTs in usual care settings. As medical therapies become increasingly less satisfactory for patients with osteoarthritis, an average of 680 886 patients receive surgical knee replacement per year in the United States. Yet, there have been no substantial comparative effectiveness RCTs of medical vs surgical total knee replacement (TKR). The question about TKR for knee osteoarthritis is suitable for exploring a method that would facilitate the conduct of comparative effectiveness RCTs by assisting discernment of patient-specific equipoise between treatments. Clinical equipoise is a prerequisite for enrollment into an RCT; likewise, mathematical equipoise is the use of mathematical models to predict and compare patient-specific outcomes of alternative treatment options that should be considered when enrolling patients into an RCT. When the predictions are similar, suggesting equipoise, then random treatment assignment may be justified, and the patient may feel more comfortable enrolling in the RCT. When the predictions suggest one treatment is better than another, trial enrollment may be inappropriate, but the predictions still can inform clinical decision-making. OBJECTIVES: This project aimed to use mathematical equipoise for making patient-specific comparisons of alternative treatment outcomes of TKR vs nonsurgical treatment of knee osteoarthritis as a way to consider enrollment into a comparative effectiveness RCT. METHODS: We first obtained the views of patient stakeholders with knee osteoarthritis to identify key pain and physical function outcomes. After creating a consolidated database from non-RCT sources of knee osteoarthritis outcomes, and adjusting for the inherent differences between the databases, we developed multivariable mathematical models that predict patient-specific pain and physical function outcomes for TKR or nonsurgical treatment. We then developed the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) user interface based on these models to discern patient-specific equipoise. We pilot tested the interface to assess usability and responsiveness to the needs of patients and physicians and its adequacy for supporting shared decision-making, both for RCT enrollment and for treatment. RESULTS: We incorporated KOMET regression models into prototype KOMET decision support software, which we successfully pilot tested in a range of clinics. Patients found it very helpful in making treatment decisions, but only 7 of the 12 understood the concept of equipoise. CONCLUSIONS: This project demonstrated the use of mathematical equipoise as a method for providing patient-specific decision support for shared patient-physician decision-making for selecting between alternative treatments and considering enrollment into a comparative effectiveness RCT. LIMITATIONS AND SUBPOPULATION CONSIDERATIONS: Although largely accomplishing its intended objectives, as an early stage in the development of mathematical equipoise decision support, this project has limitations related to the available clinical data, the modeling methods and variables, and the prototype software. The next step will be to conduct a larger-scale test, and then to implement it for its intended use--the conduct of a comparative effectiveness trial in usual care settings.

Contents:

Background

Patient and Stakeholder Participation

Methods

Results

Discussion

Conclusions

References

Acknowledgments

Appendices.

Notes:

Description based on publisher supplied metadata and other sources.