Sun Ziyi, Wang Zihan, Yun Zhangjun, Sun Xiaoning, Lin Jianguo, Zhang Xiaoxiao, Wang Qingqing, Duan Jinlong, Huang Li, Li Lin, Yao Kuiwu
Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Graduate School, Beijing University of Chinese Medicine, Beijing, China.
ESC Heart Fail. 2025 Feb;12(1):211-228. doi: 10.1002/ehf2.15066. Epub 2024 Sep 7.
This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clinical risk calculation tool was subsequently developed based on these findings.
This nested case-control study included 200 patients with chronic heart failure (CHF) from the China-Japan Friendship Hospital (September 2019 to December 2022). Sixty-five variables were collected, including basic information, physical and chemical examinations, and quality of life assessments. WHF occurrence within a 3-month follow-up was the outcome event. Variables were screened using LASSO regression, univariate analysis, and comparison of key variables in multiple ML models. Eighty per cent of the data was used for training and 20% for testing. The best models were identified by integrating nine ML algorithms and interpreted using SHAP, and to develop a final risk calculation tool. Among participants, 68 (34.0%) were female, with a mean age (standard deviation, SD) of 68.57 (12.80) years. During the follow-up, 60 participants (30%) developed WHF. N-terminal pro-brain natriuretic peptide (NT-proBNP), creatinine (Cr), uric acid (UA), haemoglobin (Hb), and emotional area score on the Minnesota Heart Failure Quality of Life Questionnaire were critical predictors of WHF occurrence. The random forest (RF) model was the best model to predict WHF with an area under the curve (AUC) (95% confidence interval, CI) of 0.842 (0.675-1.000), accuracy of 0.775, sensitivity of 0.900, specificity of 0.833, negative predictive value of 0.800, and positive predictive value of 0.600 for the test set. SHAP analysis highlighted NT-proBNP, UA, and Cr as significant predictors. An online risk predictor based on the RF model was developed for personalized WHF risk assessment.
This study identifies NT-proBNP, Cr, UA, Hb, and emotional area scores as crucial predictors of WHF in CHF patients. Among the nine ML algorithms assessed, the RF model showed the highest predictive accuracy. SHAP analysis further emphasized NT-proBNP, UA, and Cr as the most significant predictors. An online risk prediction tool based on the RF model was subsequently developed to enhance early and personalized WHF risk assessment in clinical settings.
本研究旨在开发并验证一个用于预测心力衰竭恶化（WHF）的最佳模型。比较了多种机器学习（ML）算法，并使用夏普利值附加解释（SHAP）对结果进行解读。随后基于这些发现开发了一种临床风险计算工具。
这项巢式病例对照研究纳入了来自中日友好医院（2019年9月至2022年12月）的200例慢性心力衰竭（CHF）患者。收集了65个变量，包括基本信息、理化检查和生活质量评估。3个月随访期内发生WHF为结局事件。使用套索回归、单因素分析以及多个ML模型中的关键变量比较对变量进行筛选。80%的数据用于训练，20%用于测试。通过整合9种ML算法确定最佳模型，并使用SHAP进行解读，以开发最终的风险计算工具。参与者中，68名（34.0%）为女性，平均年龄（标准差，SD）为68.57（12.80）岁。随访期间，60名参与者（30%）发生了WHF。N末端脑钠肽前体（NT-proBNP）、肌酐（Cr）、尿酸（UA）、血红蛋白（Hb）以及明尼苏达心力衰竭生活质量问卷中的情感领域得分是WHF发生的关键预测因素。随机森林（RF）模型是预测WHF的最佳模型，测试集的曲线下面积（AUC）（95%置信区间，CI）为0.842（0.675 - 1.000），准确率为0.775，敏感性为0.900，特异性为0.833，阴性预测值为0.800，阳性预测值为0.600。SHAP分析突出了NT-proBNP、UA和Cr作为显著预测因素。基于RF模型开发了一个在线风险预测器，用于个性化的WHF风险评估。
本研究确定NT-proBNP、Cr、UA、Hb和情感领域得分是CHF患者发生WHF的关键预测因素。在评估的9种ML算法中，RF模型显示出最高的预测准确性。SHAP分析进一步强调NT-proBNP、UA和Cr是最显著的预测因素。随后开发了一个基于RF模型的在线风险预测工具，以加强临床环境中WHF的早期和个性化风险评估。
