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Background <p>Sleep disorders exhibit a high prevalence among chronic pain (CP) patients, yet knowledge gaps regarding sleep disorder trajectories in older CP patients may hinder healthy aging. This study aimed to identify sleep disorder trajectories and their predictive factors, and to develop tools for predicting these trajectories using an explainable machine learning (XML) approach.</p> Methods <p>This prospective cohort study was conducted in hospitalized older adults (aged ≥ 60 years) with chronic pain. Data on general characteristics, pain level, anxiety, depression, and perceived social support were collected at admission. Sleep disorder was assessed from admission to six months during routine chronic pain management, including pharmacologic treatment, non-pharmacologic interventions, psychosocial support, nursing care, and discharge education. Predictors were selected using Boruta algorithm and LASSO regression, and ten XML models were developed.</p> Results <p>Among 596 patients, the prevalence of sleep disorders in the overall cohort ranged from 42.8% to 52.2% across the four assessment time points. Four heterogeneous sleep disorder trajectories (stable-low, high-declining, moderate-increasing, and high-persistent) and seven predictors were identified. The XML model determined pain level, depression, anxiety, and perceived social support were the most important predictors.</p> Conclusion <p>Sleep disorders in older CP patients exhibited a high prevalence from admission to six months, manifesting in four distinct trajectories of deterioration or improvement. These findings may enhance the understanding of the heterogeneous progression of sleep disorders. Prioritizing key factors such as pain level, depression, anxiety, and perceived social support in preventive strategies and risk stratification may inform more targeted interventions to improve sleep health outcomes.</p>

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Predicting sleep disorder trajectories in older patients with chronic pain: an explainable machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Xiaoang Zhang,
  • Yaqing Hu,
  • Weichen Liu,
  • Yuping Liao,
  • Andi Zhou,
  • Shushu Chen,
  • Daying Zhang,
  • Jianmei Wei

摘要

Background

Sleep disorders exhibit a high prevalence among chronic pain (CP) patients, yet knowledge gaps regarding sleep disorder trajectories in older CP patients may hinder healthy aging. This study aimed to identify sleep disorder trajectories and their predictive factors, and to develop tools for predicting these trajectories using an explainable machine learning (XML) approach.

Methods

This prospective cohort study was conducted in hospitalized older adults (aged ≥ 60 years) with chronic pain. Data on general characteristics, pain level, anxiety, depression, and perceived social support were collected at admission. Sleep disorder was assessed from admission to six months during routine chronic pain management, including pharmacologic treatment, non-pharmacologic interventions, psychosocial support, nursing care, and discharge education. Predictors were selected using Boruta algorithm and LASSO regression, and ten XML models were developed.

Results

Among 596 patients, the prevalence of sleep disorders in the overall cohort ranged from 42.8% to 52.2% across the four assessment time points. Four heterogeneous sleep disorder trajectories (stable-low, high-declining, moderate-increasing, and high-persistent) and seven predictors were identified. The XML model determined pain level, depression, anxiety, and perceived social support were the most important predictors.

Conclusion

Sleep disorders in older CP patients exhibited a high prevalence from admission to six months, manifesting in four distinct trajectories of deterioration or improvement. These findings may enhance the understanding of the heterogeneous progression of sleep disorders. Prioritizing key factors such as pain level, depression, anxiety, and perceived social support in preventive strategies and risk stratification may inform more targeted interventions to improve sleep health outcomes.