Machine learning-based identification of non-responders to a 12-month digital self-management in knee osteoarthritis

Abstract

Knee osteoarthritis (KOA) places a significant burden on individuals and healthcare systems worldwide. Although self-management programs (SMPs) offer accessible support for KOA management, individual responses vary. Therefore, early identification of those unlikely to show substantial improvement with self-management is important to adapt treatment plans promptly, introduce more effective interventions when needed, and ultimately improve patient outcomes by avoiding prolonged use of strategies that do not produce responses. This study aims to develop a machine learning-based approach to identify potential non-responders to a digital SMP using characteristics at program entry. Data were obtained from a previously conducted 12-month app-based SMP for KOA. Responders were defined as individuals whose arthritis self-efficacy (ASE) and health-related quality of life (HRQoL) outcomes both improved. Non-responders included those who showed improvement in only one outcome, no improvement in either, or deterioration in one or both outcomes. After excluding participants who did not complete the SMP or who underwent knee-related surgery and/or hospitalization during the study period, 57 participants were included in the analysis. Body mass index, presence of non-musculoskeletal comorbidities, ASE score, HRQoL index, and hip range of motion were the input features for model development. Gradient boosting decision tree achieved the best performance, with an AUC of 0.822 and balanced sensitivity and specificity in identifying non-responders. These findings present the feasibility of using machine learning to early identify individuals with limited expected benefit from digital self-management. Such identification may facilitate more efficient, tailored, and proactive strategies for managing KOA. Future research should prioritize external validation in larger and more diverse cohorts.

Keywords: Knee Osteoarthritis, Digital Self-management, Machine Learning, Early Identification

DOI: 10.54941/ahfe1007492