Ning Zhenlian, Li Bing, Ning Ziming, Zhu Beili, Zhao Mengfan, Huang Bin
The Second Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou, China.
The First People's Hospital of Pingdingshan, Pingdingshan, China.
PLoS One. 2025 Jun 24;20(6):e0325036. doi: 10.1371/journal.pone.0325036. eCollection 2025.
Patients with acute coronary syndrome (ACS) are at increased risk of in-hospital heart failure (HF) following percutaneous coronary intervention (PCI), yet understanding of the associated risk factors is limited. This study aims to identify predictors of in-hospital HF after PCI and to develop and validate a clinical prediction model for the early identification of high-risk patients.
We retrospectively analyzed data from the patients hospitalized for ACS who underwent PCI at Henan Provincial Hospital of Traditional Chinese Medicine from 01/01/2019-01/10/2023. Patients were classified into non-HF and HF groups based on the occurrence of heart failure after PCI. LASSO regression and logistic regression were employed to identify potential predictors. The model's diagnostic efficacy was assessed using receiver operating characteristic curves and calibration curves, while decision curve analysis and clinical impact curve were utilized to evaluate clinical benefits.
A total of 309 patients were included in this study, of whom 79.93% were male, with a mean age of 57.84. Key predictors included New York Heart Association (NYHA) classification, smoking status, right coronary artery occlusion after PCI, left ejection fraction (LVEF), and N-terminal fragment of brain natriuretic peptides. The area under the curve (AUC) was 0.910 (95% CI: 0.868-0.953), indicating strong predictive ability. Decision curve analysis and clinical impact curve demonstrated good clinical applicability of the nomogram.
The identified predictors and the prediction model can be used in identifying high-risk individuals who develop HF hospital admission after PCI, or as a basis for further guiding personalized prevention and treatment.
急性冠状动脉综合征（ACS）患者在经皮冠状动脉介入治疗（PCI）后发生院内心力衰竭（HF）的风险增加，但对相关危险因素的了解有限。本研究旨在确定PCI后院内HF的预测因素，并开发和验证用于早期识别高危患者的临床预测模型。
我们回顾性分析了2019年1月1日至2023年1月10日在河南省中医院因ACS住院并接受PCI的患者的数据。根据PCI后是否发生心力衰竭，将患者分为非HF组和HF组。采用LASSO回归和逻辑回归来识别潜在的预测因素。使用受试者工作特征曲线和校准曲线评估模型的诊断效能，同时利用决策曲线分析和临床影响曲线评估临床益处。
本研究共纳入309例患者，其中79.93%为男性，平均年龄为57.84岁。关键预测因素包括纽约心脏协会（NYHA）分级、吸烟状况、PCI后右冠状动脉闭塞、左心室射血分数（LVEF）和脑钠肽N末端片段。曲线下面积（AUC）为0.910（95%CI：0.868-0.953），表明具有较强的预测能力。决策曲线分析和临床影响曲线表明列线图具有良好的临床适用性。
所确定的预测因素和预测模型可用于识别PCI后住院时发生HF的高危个体，或作为进一步指导个性化预防和治疗的依据。
