Yang Zhikai, Ji Chen, Wang Ting, He Wei, Wan Yuhao, Zeng Min, Guo Di, Cui Lingling, Wang Hua
Department of Cardiology, Institute of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Eur J Med Res. 2025 Apr 28;30(1):336. doi: 10.1186/s40001-025-02450-5.
While the FRAIL scale has been used in primary care, cluster analysis on frail patients in a hospital setting has not been performed.
To identify potential subtypes of frail patients, and develop a simple, clinically applicable model for improved patient management.
The study included 214 frail patients aged 65 and above who were hospitalized in a hospital in Beijing from September 2018 to April 2019. This study applied the K-means clustering algorithm to analyze 27 variables, determining the optimal cluster number using the Elbow method and Silhouette coefficient. Key variables for predictive modeling were identified through LASSO (least absolute shrinkage and selection operator) regression, SVM-RFE (support vector machine-recursive feature elimination), and random forest techniques. A logistic regression model was then developed to predict patient subtypes, aimed at enhancing clinical identification and management of frailty subtypes.
Clustering analysis distinguished two unique subgroups among the frail patients, revealing significant disparities in clinical characteristics and survival outcomes. One-year survival rates for Class 1 and Class 2 were 62.51% and 47.51%, respectively. The logistic regression model exhibited robust predictive capability, with an AUC (Area under curve) of 0.88. Validation through 1000 bootstrap resamples confirmed the model's reliability, with an average AUC of 0.8707 and a 95% CI (Confidence intervals) of 0.8572 to 0.8792.
This study identifies two frailty subtypes in a hospital setting using unsupervised machine learning, demonstrating significant differences in survival outcomes. Clinical Trial registration ChiCTR1800017204; date of reqistration: 07/18/2018.
虽然衰弱量表已在初级保健中使用，但尚未对医院环境中的衰弱患者进行聚类分析。
识别衰弱患者的潜在亚型，并开发一种简单、临床适用的模型以改善患者管理。
该研究纳入了2018年9月至2019年4月在北京一家医院住院的214名65岁及以上的衰弱患者。本研究应用K均值聚类算法分析27个变量，使用肘部方法和轮廓系数确定最佳聚类数。通过LASSO（最小绝对收缩和选择算子）回归、支持向量机递归特征消除（SVM-RFE）和随机森林技术确定预测模型的关键变量。然后开发了一个逻辑回归模型来预测患者亚型，旨在加强对衰弱亚型的临床识别和管理。
聚类分析在衰弱患者中区分出两个独特的亚组，揭示了临床特征和生存结果的显著差异。1类和2类的一年生存率分别为62.51%和47.51%。逻辑回归模型表现出强大的预测能力，曲线下面积（AUC）为0.88。通过1000次自助重采样验证证实了模型的可靠性，平均AUC为0.8707，95%置信区间（CI）为0.8572至0.8792。
本研究使用无监督机器学习在医院环境中识别出两种衰弱亚型，表明生存结果存在显著差异。临床试验注册号：ChiCTR1800017204；注册日期：2018年7月18日。
