ArticleHealth and quality of life outcomes2026
Predictive analysis of health-related quality of life trajectories in older patients with chronic pain based on explainable machine learning models.
Article in Health and quality of life outcomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHealth-related quality of life (HRQoL) is a vital indicator of evaluating care outcomes and prognosis, yet little is understood about its developmental trajectories in older patients with chronic pain. This study aimed to identify latent HRQoL trajectories and their predictors, and to develop explainable machine learning models for predicting HRQoL deterioration.
methodsThis prospective cohort study assessed 608 older patients with chronic pain at admission and at 1, 3, and 6 months post-admission, collecting data on HRQoL, general characteristics, pain level, activities of daily living (ADL), depression, and perceived social support. Growth mixture modeling was applied to identify trajectories of physical and mental HRQoL. Predictors were selected using LASSO regression and SVM-RFE. Nine explainable machine learning models were developed for both components, and SHAP interpreted the outputs. An HRQoL decision-support dashboard was developed to facilitate potential clinical application.
resultsThree physical HRQoL trajectories were identified: Stable High, Decline and Low Stability, alongside two mental HRQoL trajectories: Improvement and Decline. Key predictors included education level, pain duration, pain level, ADL, depression, and perceived social support, with ADL and pain level being the most influential for physical and mental HRQoL, respectively.
conclusionsThis dual-trajectory study identified five distinct HRQoL patterns in older patients with chronic pain, elucidating key predictors via explainable machine learning. The proposed HRQoL decision-support dashboard may provide an interpretable tool to support understanding of predictive relationships and assist healthcare professionals in HRQoL assessment. CLINICAL TRIAL NUMBER: Not applicable.
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