Zhang Xiaoang, Liao Yuping, Zhang Daying, Liu Weichen, Wang Zhijian, Jin Yaxin, Chen Shushu, Wei Jianmei
School of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, China; Department of Pain Medicine, the 1(st) affiliated hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
School of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, China; Department of Pain Medicine, the 1(st) affiliated hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Geriatr Nurs. 2025 Jan-Feb;61:699-708. doi: 10.1016/j.gerinurse.2024.10.025. Epub 2024 Nov 8.
Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and dementia. However, effective tools for identifying frailty in this population remain limited. This study aimed to explore frailty risk factors in older adults with chronic pain and to develop 9 machine learning models for frailty identification. The Shapley Additive Explanations (SHAP) method was used to explain the models. The Random Forest (RF) model performed best with 0.822 accuracy, 0.797 precision, and an AUC of 0.881. The variables in the RF model included: age, BMI, education level, pain duration, number of pain sites, pain level, depression, and Activity of Daily Living (ADL). Pain level, depression, and ADL were the 3 most important variables in the RF model. This model helps healthcare providers to identify frailty early, enabling timely interventions to improve patient outcomes and promote healthy aging.
衰弱在患有慢性疼痛的老年人中很常见，早期识别对于预防跌倒、残疾和痴呆等不良后果至关重要。然而，用于识别该人群衰弱的有效工具仍然有限。本研究旨在探索患有慢性疼痛的老年人的衰弱风险因素，并开发9种用于衰弱识别的机器学习模型。采用Shapley加法解释（SHAP）方法对模型进行解释。随机森林（RF）模型表现最佳，准确率为0.822，精确率为0.797，曲线下面积（AUC）为0.881。RF模型中的变量包括：年龄、体重指数（BMI）、教育水平、疼痛持续时间、疼痛部位数量、疼痛程度、抑郁以及日常生活活动能力（ADL）。疼痛程度、抑郁和ADL是RF模型中最重要的3个变量。该模型有助于医疗保健提供者早期识别衰弱，从而能够及时进行干预，改善患者预后并促进健康老龄化。
