Ji Yi, Wang Guodong, Hu Yue, Wang Xiaotong, Wu Wanling, Luo Yuanyuan, Pan Yanqing, Liu Jie, Li Lei, Zhu Hong, Pan Defeng
Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Cardiovascular Medicine Department, Beijing Bo'ai Hospital, China Rehabilitation Research Center, Capital Medical University, Beijing, China.
Front Cardiovasc Med. 2025 Jun 18;12:1492717. doi: 10.3389/fcvm.2025.1492717. eCollection 2025.
The study aims to evaluate the efficacy of a web-based dynamic nomogram predicting the risk of heart failure (HF)-related rehospitalization within 1 year in patients with HF with preserved ejection fraction (HFpEF).
The data of patients from two centers were categorized into training and test sets. Least absolute shrinkage and selection operator and multivariate logistic regression analysis were conducted on the training set data after selecting risk factors described in previous studies, and they were used to set up a nomogram. We then analyzed the area under the receiver operating characteristic curve (AUC-ROC) and calibration plot and conducted decision curve analysis (DCA) to confirm the efficacy of the nomogram.
The 1-year HF rehospitalization rates of patients with HFpEF were 23.7% and 22.8% in the two study centers, respectively. Age, body mass index, atrial fibrillation, triglyceride-glucose index, left ventricular ejection fraction, E/e, and angiotensin-converting enzyme inhibitors/angiotensin receptor blocker administration positively correlated with 1-year HF-related rehospitalization in patients with HFpEF. The dynamic nomogram was constructed based on the seven variables. The AUC-ROC of the training [0.801, 95% confidence interval (CI): 0.767-0.837] and the test datasets (0.773, 95% CI: 0.713-0.824) demonstrated that the model had good predictive ability for risk factors, the calibration plots demonstrated the excellent agreement. Additionally, the DCA curve showed that the model is highly effective with a threshold probability of 10%-80%.
The dynamic nomogram model effectively predicts HF-related rehospitalization risk within 1 year in patients with HFpEF and helps determine high-risk categories among them.
本研究旨在评估基于网络的动态列线图预测射血分数保留的心力衰竭（HFpEF）患者1年内发生心力衰竭（HF）相关再住院风险的有效性。
将来自两个中心的患者数据分为训练集和测试集。在选择先前研究中描述的风险因素后，对训练集数据进行最小绝对收缩和选择算子及多变量逻辑回归分析，并用于建立列线图。然后，我们分析了受试者操作特征曲线下面积（AUC-ROC）和校准图，并进行决策曲线分析（DCA）以确认列线图的有效性。
在两个研究中心，HFpEF患者的1年HF再住院率分别为23.7%和22.8%。年龄、体重指数、心房颤动、甘油三酯-葡萄糖指数、左心室射血分数、E/e以及血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂的使用与HFpEF患者1年HF相关再住院呈正相关。基于这七个变量构建了动态列线图。训练集（AUC-ROC为0.801，95%置信区间（CI）：0.767-0.837）和测试数据集（0.773，95%CI：0.713-0.824）表明该模型对风险因素具有良好的预测能力，校准图显示出极好的一致性。此外，DCA曲线表明该模型在阈值概率为10%-80%时非常有效。
动态列线图模型可有效预测HFpEF患者1年内发生HF相关再住院的风险，并有助于确定其中的高危类别。
