Yang Jin, Xie Yan, Wang Tianyi, Pu You, Ye Ting, Huang Yunman, Song Baomei, Cheng Fengqin, Yang Zheng, Zhang Xianqin
Chengdu Medical College, China; Department of Cardiology, Sichuan Mianyang 404 Hospital, China.
Chengdu Medical College, China.
Geriatr Nurs. 2025 Mar-Apr;62(Pt A):145-156. doi: 10.1016/j.gerinurse.2025.01.022. Epub 2025 Feb 1.
Accurate identification of individuals at high risk for mild cognitive impairment (MCI) among chronic heart failure (CHF) patients is crucial for reducing rehospitalization and mortality rates. This study aimed to develop and validate a machine learning model to predict MCI risk in CHF patients. 602 CHF patients were included in this cross-sectional analysis. We constructed four machine learning models and assessed the models using the area under the receiver operating characteristic curve (AUC), calibration curve, and clinical decision curve. Results showed that scores of psychological and social adaptation management, age, free triiodothyronine, Self-rating Depression Scale scores, hemoglobin, sleep duration per night and gender were the best predictors and these factors were used to construct dynamic nomograms. Among all models, eXtreme Gradient Boosting (XGBoost) with an AUC of 0.940 performed the best in predicting the risk of MCI in CHF patients. Dynamic nomogram helps clinicians perform early screening in large populations.
准确识别慢性心力衰竭（CHF）患者中轻度认知障碍（MCI）的高危个体对于降低再住院率和死亡率至关重要。本研究旨在开发并验证一种机器学习模型，以预测CHF患者的MCI风险。本横断面分析纳入了602例CHF患者。我们构建了四种机器学习模型，并使用受试者操作特征曲线（AUC）下面积、校准曲线和临床决策曲线对模型进行评估。结果显示，心理和社会适应管理得分、年龄、游离三碘甲状腺原氨酸、自评抑郁量表得分、血红蛋白、每晚睡眠时间和性别是最佳预测因素，这些因素被用于构建动态列线图。在所有模型中，AUC为0.940的极端梯度提升（XGBoost）在预测CHF患者MCI风险方面表现最佳。动态列线图有助于临床医生在大量人群中进行早期筛查。
