2025 年第 4 期 第 20 卷

慢性心力衰竭急性发作患者心源性休克的危险因素以及Nomogram预测模型构建和验证

The risk factors of cardiogenic shock in patients with acute exacerbation of chronic heart failure and to construct and verify a Nomogram prediction model

作者：李菲菲彭思芹陈红俞荷花

英文作者：Li Feifei Peng Siqin Chen Hong Yu Hehua

单位：海军军医大学第二附属医院急诊重症医学科，上海200003

英文单位：Department of Emergency Intensive Care Medicine Second Affiliated Hospital of Naval Medical University Shanghai 200003 China

关键词：慢性心力衰竭急性发作；心源性休克；肾功能恶化；心房颤动；三尖瓣反流；N末端B型脑钠肽前体；Nomogram预测模型

英文关键词：Acuteexacerbationofchronicheartfailure;Cardiogenicshock;Deteriorationofrenalfunction; Atrialfibrillation;Tricuspidregurgitation;N-terminalpro-brainnatriureticpeptide;Nomogrampredictionmodel

• 摘要：

• 目的 探讨慢性心力衰竭急性发作（ACHF）患者并发心源性休克（CS）的危险因素，构建Nomogram预测模型并进行验证。方法 回顾性选取2022年6月至2024年6月海军军医大学第二附属医院收治的216例ACHF患者。根据是否并发CS分为CS组（38例）和非CS组（178例）。多因素Logistic回归方法分析ACHF患者并发CS的危险因素，基于危险因素构建Nomogram预测模型，使用一致性指数、校准曲线和决策曲线分析评估Nomogram预测模型的预测准确性、拟合度和临床效用。结果 多因素Logistic回归分析结果显示心房颤动、三尖瓣反流、肾功能恶化、高水平N末端B型脑钠肽前体（NT-proBNP）均为ACHF患者并发CS的危险因素（均P＜0.05）。基于Logistic回归危险因素构建Nomogram预测模型，经验证该预测模型显示良好的鉴别力（曲线下面积为0.893，95%置信区间：0.844～0.931，P<0.05）、校准度（一致性指数为0.902，平均绝对误差为0.003）。决策曲线分析表明该预测模型净收益较高，提示临床适用性高。结论 肾功能恶化、心房颤动、三尖瓣反流、高水平NT-proBNP是ACHF患者并发CS的危险因素，基于上述危险因素构建预测模型具有较高的预测准确性和临床适用性。

• Objective To explore the risk factors of cardiogenic shock (CS) in patients with acute exacerbation of chronic heart failure (ACHF), and to construct and verify a Nomogram prediction model. Methods A total of 216 ACHF patients admitted to Second Affiliated Hospital of Naval Medical University from June 2022 to June 2024 were retrospectively selected. According to the presence or absence of CS, the patients were divided into CS group (38 cases) and non-CS group (178 cases). Multivariate Logistic regression was used to analyze the risk factors of ACHF patients with CS, and a Nomogram prediction model was constructed based on the risk factors. Consistency index, calibration curve and decision curve analysis were used to evaluate the prediction accuracy, fit degree and clinical utility of the Nomogram prediction model. Results Multivariate Logistic regression analysis showed that atrial fibrillation, tricuspid regurgitation, deteriorating renal function and high level of N-terminal pro-brain natriuretic peptide (NT-proBNP) were risk factors for CS in ACHF patients (all P<0.05). The Nomogram prediction model was constructed based on Logistic regression risk factors, and it was verified that the prediction model showed good discrimination (the area under the curve was 0.893, with 95% confidence interval of 0.844 to 0.931, P<0.05) and calibration (C-index was 0.902, mean absolute error was 0.003). Decision curve analysis showed that the net benefit of the prediction model was high, suggesting high clinical applicability. Conclusion Deterioration of renal function, atrial fibrillation, tricuspid regurgitation and high level of NT-proBNP are risk factors for CS in ACHF patients. The prediction model based on the above risk factors has high prediction accuracy and clinical applicability.