Lin Fangbo, Zhou Meiyun
Rehabilitation Medicine Department, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University (The First Hospital of Changsha), Changsha, People's Republic of China.
Neurology Department, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.
BMC Psychiatry. 2025 Jul 1;25(1):668. doi: 10.1186/s12888-025-06885-2.
This study aimed to develop and validate a clinically applicable nomogram to predict depression risk in stroke patients by integrating multidimensional predictors from rehabilitation assessments, biochemical markers, and lifestyle metrics.
Using data from 767 stroke patients (training/testing: 363/242; external validation: 162) in the CHARLS database and the First Hospital of Changsha, the Least Absolute Shrinkage and Selection Operator (LASSO) regression identified five predictors: Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), sleep (optimal: 6-8 h), uric acid, and Triglyceride-Glucose-Body Mass Index (TyG-BMI). Multivariable logistic regression constructed the nomogram, validated through ROC analysis (AUC), calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP).
The nomogram demonstrated moderate to strong discrimination, with AUC values of 0.731 (training), 0.663 (testing), and 0.748 (external validation). Calibration plots confirmed high predictive accuracy, while DCA revealed substantial clinical utility. SHAP analysis ranked sleep (protective) and ADL (risk) as top contributors. Lower uric acid and TyG-BMI correlated with higher depression risk, contrasting prior studies on TyG-BMI.
This model enables rapid, cost-effective depression risk stratification using routine clinical data, prioritizing high-risk stroke patients for early intervention. Despite limitations (single-country data, unaddressed stroke subtypes), it bridges predictive analytics and clinical workflows, emphasizing sleep hygiene, metabolic monitoring, and functional rehabilitation.
本研究旨在通过整合康复评估、生化指标和生活方式指标中的多维预测因素，开发并验证一种临床适用的列线图，以预测中风患者的抑郁风险。
利用中国健康与养老追踪调查（CHARLS）数据库和长沙市第一医院的767例中风患者的数据（训练/测试：363/242；外部验证：162），最小绝对收缩和选择算子（LASSO）回归确定了五个预测因素：日常生活活动能力（ADL）、工具性日常生活活动能力（IADL）、睡眠（最佳：6 - 8小时）、尿酸和甘油三酯-葡萄糖-体重指数（TyG-BMI）。多变量逻辑回归构建了列线图，并通过ROC分析（AUC）、校准曲线、决策曲线分析（DCA）和SHapley加性解释（SHAP）进行验证。
列线图显示出中度到高度的区分能力，训练集、测试集和外部验证集的AUC值分别为0.731、0.663和0.748。校准图证实了高预测准确性，而DCA显示出显著的临床实用性。SHAP分析将睡眠（保护性）和ADL（风险性）列为主要贡献因素。较低的尿酸和TyG-BMI与较高的抑郁风险相关，这与之前关于TyG-BMI的研究结果相反。
该模型能够使用常规临床数据进行快速、经济有效的抑郁风险分层，将高危中风患者作为早期干预的重点。尽管存在局限性（单一国家数据、未涉及中风亚型），但它架起了预测分析与临床工作流程之间的桥梁，强调了睡眠卫生、代谢监测和功能康复。
