背景:许多研究已经确定了长期护理机构中老年人使用身体约束(PR)的危险因素。然而,缺乏识别高风险个体的预测工具。
目的:我们旨在开发基于机器学习(ML)的模型来预测老年人的PR风险。
方法:本研究基于重庆市6家长期护理机构的1026名老年人进行了横断面二次数据分析,中国,从2019年7月到2019年11月。主要结果是使用PR(是或否),由2名收藏家直接观察确定。共有15个候选预测因子(老年人人口统计学和临床因素)可以从临床实践中常见和容易地收集,用于建立9个独立的ML模型:高斯朴素贝叶斯(GNB),k-最近邻(KNN),决策树(DT),逻辑回归(LR),支持向量机(SVM),随机森林(RF),多层感知器(MLP),极端梯度提升(XGBoost),和轻型梯度增压机(Lightgbm),以及堆叠合奏ML。使用准确性评估性能,精度,召回,F分,由上述指标权衡的综合评价指标(CEI),和受试者工作特征曲线下面积(AUC)。使用决策曲线分析(DCA)的净收益方法来评估最佳模型的临床实用性。通过10倍交叉验证测试模型。使用Shapley加法解释(SHAP)解释特征重要性。
结果:共有1026名老年人(平均83.5,SD7.6岁;n=586,57.1%的男性老年人)和265名受约束的老年人被纳入研究。所有ML模型都表现良好,AUC高于0.905,F评分高于0.900。2个最佳的独立模型是RF(AUC0.938,95%CI0.914-0.947)和SVM(AUC0.949,95%CI0.911-0.953)。DCA表明,RF模型比其他模型显示出更好的临床实用性。将堆叠模型与SVM相结合,射频,和MLP表现最好,AUC(0.950)和CEI(0.943)值,以及DCA曲线表明了最佳的临床效用。SHAP图表明,模型表现的重要贡献者与认知障碍有关,护理依赖,流动性下降,身体激动,还有一根留置管.
结论:RF和堆叠模型具有较高的性能和临床实用性。用于预测老年人PR概率的ML预测模型可以提供临床筛查和决策支持,这可以帮助医务人员对老年人的早期识别和PR管理。
Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals.
We aimed to develop machine learning (ML)-based models to predict the risk of PR in older adults.
This study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors\' direct observation. A total of 15 candidate predictors (older adults\' demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naïve Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP).
A total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube.
The RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.