冠状动脉慢性完全闭塞合并左室射血分数轻度降低的心力衰竭患者介入术后短期不良心血管事件的预测模型建立

Predictive Modeling of Short-Term Adverse Cardiovascular Events After Intervention in Patients with Coronary Artery Chronic Total Occlusion Combined with Heart Failure with Mildly Reduced Left Ventricular Ejection Fraction

目的预测冠状动脉慢性完全闭塞(CTO)合并左室射血分数轻度降低的心力衰竭(HFmrEF)患者行经皮冠状动脉介入治疗(PCI)后短期主要不良心血管事件(MACE)风险的发生,构建列线图预测模型并评价其性能。方法回顾性选取2020年9月1日—2023年3月1日就诊于新疆维吾尔自治区人民医院的CTO合并HFmrEF并行PCI患者共364例作为研究对象,术后随访1年,采用logistic回归分析预测PCI后发生MACE的独立危险因素,采用多因素分析构建列线图预测模型并采用Bootstrap法对模型进行内部验证;采用ROC曲线、曲线下面积、校准曲线和决策曲线评估模型的准确度、预测效能和临床净获益。结果多因素logistic分析显示,心律失常病史、病变开通、血红蛋白、N末端脑钠肽前体和中性粒细胞/淋巴细胞比值为预测PCI后MACE发生的独立危险因素,以此构建的列线图经内部验证,曲线下面积为0.919(95%CI 0.890~0.947),具有较好的一致性。决策曲线显示此预测模型可提供额外的临床净获益。结论本研究构建的预测CTO合并HFmrEF患者PCI后发生短期MACE的列线图模型具有较好的预测效能,有助于医护人员尽早制定对策,降低MACE的发生风险,提高患者生存概率。

Objective To predict the occurrence of short-term risk of major adverse cardiovascular event(MACE)after percutaneous coronary intervention(PCI)in patients with coronary artery chronic total occlusion(CTO)combined with heart failure with mildly reduced left ventricular ejection fraction(HFmrEF),to construct a nomogram prediction model and to evaluate its performance.Methods A total of 364 patients with CTO combined with HFmrEF who underwent PCI in People’s Hospital of Xinjiang Uygur Autonomous Region from September 1,2020 to March 1,2023 were retrospectively selected as the study subjects,and were followed up for 1 year after the procedure.Logistic regression analysis was used to predict the independent risk factors for the occurrence of MACE after PCI,multifactorial analysis was used to construct a nomogram prediction model and the model was internally validated using the Bootstrap method;the accuracy,predictive efficacy and net clinical benefit of the model were assessed using the ROC curve,area under the curve(AUC),calibration curve and decision curve.Results Multivariate logistic analysis showed that arrhythmia history,lesion opening,hemoglobin,N-terminal pro-brain natriuretic peptide and neutrophil to lymphocyte ratio were independent risk factors for predicting the occurrence of MACE after PCI,and the nomogram constructed in this way was internally validated with an AUC of 0.919(95%CI 0.890~0.947),which was in good agreement.The decision curve showed that this predictive model could provide additional net clinical benefit.Conclusion The nomogram prediction model constructed in this study to predict the occurrence of short-term MACE after PCI in patients with CTO combined with HFmrEF has good predictive efficacy,which can help healthcare professionals to formulate countermeasures as early as possible to reduce the risk of MACE and improve the chances of patient survival.