Mapping of disease-specific Oxford Knee Score onto EQ-5D-5L utility index in knee osteoarthritis

Hadeer Fawaz; Omaima Yassine; Abdullah Hammad; Ramez Bedwani; Ghada Abu-Sheasha

doi:10.1186/s13018-023-03522-0

Mapping of disease-specific Oxford Knee Score onto EQ-5D-5L utility index in knee osteoarthritis

J Orthop Surg Res. 2023 Feb 2;18(1):84. doi: 10.1186/s13018-023-03522-0.

Authors

Hadeer Fawaz¹, Omaima Yassine², Abdullah Hammad³, Ramez Bedwani², Ghada Abu-Sheasha²

Affiliations

¹ Department of Biomedical Informatics and Medical Statistics, Medical Research Institute, University of Alexandria, 165, Horreya Avenue, Hadara, Alexandria, Egypt. mri.hadeer.m.informatics17@alexu.edu.eg.
² Department of Biomedical Informatics and Medical Statistics, Medical Research Institute, University of Alexandria, 165, Horreya Avenue, Hadara, Alexandria, Egypt.
³ Department of Orthopaedic Surgery and Traumatology, El‑Hadra Hospital, University of Alexandria, Alexandria, Egypt.

Abstract

Background: EQ5D is a generic measure of health. It provides a single index value for health status that can be used in the clinical and economic evaluation of healthcare. Oxford Knee Score (OKS) is a joint-specific outcome measure tool designed to assess symptoms and function in osteoarthritis patients after joint replacement surgery. Though widely used, it has the disadvantage of lacking health index value. To fill the gap between functional and generic questionnaires with economic value, we linked generic EQ-5D-5L to the specific OKS to give a single index value for health status in KOA patients.

Questions/purposes: Developing and evaluating an algorithm to estimate EuroQoL generic health utility scores (EQ-5D-5L) from the disease-specific OKS using data from patients with knee osteoarthritis (KO).

Patients and methods: This is a cross-sectional study of 571 patients with KO. We used four distinct mapping algorithms: Cumulative Probability for Ordinal Data, Penalized Ordinal Regression, CART (Classification and Regression Trees), and Ordinal random forest. We compared the resultant models' degrees of accuracy.

Results: Mobility was best predicted by penalized regression with pre-processed predictors, usual activities by random forest, pain/discomfort by cumulative probability with pre-processed predictors, self-care by random forest with RFE (recursive feature elimination) predictors, and anxiety/depression by CART with RFE predictors. Model accuracy was lowest with anxiety/depression and highest with mobility and usual activities. Using available country value sets, the average MAE was 0.098 ± 0.022, ranging from 0.063 to 0.142; and the average MSE was 0.020 ± 0.008 ranging from 0.008 to 0.042.

Conclusions: The current study derived accurate mapping techniques from OKS to the domains of EQ-5D-5L, allowing for the computation of QALYs in economic evaluations. A machine learning-based strategy offers a viable mapping alternative that merits further exploration.

Keywords: EQ-5D-5L; Model mapping; Oxford Knee Score (OKS) questionnaire; Quality of life utility index.

MeSH terms

Cross-Sectional Studies
Health Status
Humans
Osteoarthritis, Knee* / diagnosis
Osteoarthritis, Knee* / surgery
Pain
Quality of Life
Surveys and Questionnaires