Establishment of nomogram model for risk of cardiogenic shock in patients with acute myocardial infarction
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摘要: 目的:分析急性心肌梗死(AMI)患者术后院内心源性休克(CS)的风险因素,依此建立预测AMI患者院内CS发生风险的列线图模型。方法:通过胸痛中心数据库及医院信息系统回顾性分析我院心内科住院且接受急诊冠状动脉(冠脉)介入治疗的327例AMI患者的临床资料,依据患者住院期间CS的发生情况将受试对象分为CS组(52例)和非CS组(275例),利用LASSO回归模型和多因素Logistic回归分析AMI患者发生院内CS的风险因素,并建立个性化的CS预测模型。结果:LASSO回归结果提示,白细胞计数、肌酐、尿素氮、尿酸、氨基末端脑钠肽前体、左室射血分数及合并新发心房颤动为AMI患者发生院内CS的重要风险因素(P<0.05)。利用上述7个预测指标构建了列线图模型。内部验证后,列线图预测AMI患者发生院内CS的AUC值为0.888(95%CI:0.840~0.922),灵敏度为0.832,特异度为0.782。校准曲线提示列线图模型的偏差校正曲线与理想曲线具有较好的一致性。临床决策曲线分析法提示列线图模型的预测概率阈值处于0~0.8时,患者的临床净收益水平最高。结论:本研究依据院内CS发生的重要风险因素构建了个性化的CS发生风险预测模型,经相关指标证实该预测模型具有较好的预测效率和临床适用性,能准确、有效地预测AMI患者院内CS的发生风险,从而协助临床医护人员筛选高CS风险患者,制定针对性的干预措施,降低AMI患者术后CS的发生率。Abstract: Objective: To analyze the risk factors of cardiogenic shock(CS) in patients with acute myocardial infarction(AMI) after operation and establish an nomogram model for predicting the risk of CS in patients with AMI.Methods: The clinical data of 327 AMI patients who were hospitalized in the Department of Cardiology of our hospital and received emergency coronary intervention were analyzed retrospectively through the chest pain center database and Hospital Information System(HIS). According to the incidence of CS during hospitalization, the subjects were divided into CS group(52 cases) and non-CS group(275 cases). The LASSO model and multivariate Logistic regression were used to analyze the risk factors of CS in AMI patients during hospitalization and to establish a personalized CS prediction model.Results: The LASSO regression results suggested that white blood cell level, creatinine, urea nitrogen, uric acid, NT-proBNP, LVEF and the combination of new onset atrial fibrillation were important risk factors for the occurrence of in-hospital CS in patients with AMI(P<0.05). The nomogram model was constructed using the above seven predictive indicators. After the internal validation, it was known that the AUC of the nomogram was 0.888(95%CI: 0.840-0.922), the sensitivity was 0.832, and the specificity was 0.782. The calibration curve indicated that the nomogram had good calibration. The Clinical Decision Curve Analysis(DCA) suggested that when the prediction probability threshold of the nomogram model was in the range of 0-0.8, the patient's clinical net benefit level was the highest.Conclusion: In this study, we constructed a personalized CS occurrence risk prediction model based on the important risk factors of CS occurrence. The relevant indicators have confirmed that the prediction model has good prediction efficiency and clinical applicability, and can accurately and effectively predict the occurrence risk of CS in patients with AMI in hospital, so as to assist clinical medical staff in screening patients with high CS risk and formulating targeted interventions to reduce the incidence of CS in patients with AMI after surgery.
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Key words:
- acute myocardial infarction /
- cardiogenic shock /
- nomograms /
- clinical decisions
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