Construction of a nomogram for the short-term poor prognosis of patients with acute ST-segment elevation myocardial infarction
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摘要: 目的:明确急性ST段抬高型心肌梗死(STEMI)患者发病30 d内出现主要心血管不良事件(MACE)的危险因素并建立列线图预测模型。方法:选取2018年1月—12月期间就诊于苏州大学附属第一医院的STEMI患者作为建模组,通过逐步回归法找出危险因素并建立列线图预测模型,前瞻性收集2019年1月—2020年1月就诊于该医院的STEMI患者作为验证组。通过绘制受试者工作特征曲线(ROC)与校准曲线对该模型的准确性进行验证。结果:列线图预测模型共纳入Killip分级、肌酐、休克指数、白细胞/平均血小板体积以及中性粒细胞/淋巴细胞、NT-proBNP,共计6个临床指标。模型的ROC曲线下面积为0.905(95%CI:0.868~0.942,P<0.001),当概率为0.267时模型的准确性最好(敏感度0.807,特异度0.902,约登指数0.709)。验证人群的ROC曲线下面积为0.832(95%CI:0.796~0.892,P<0.001)。校准曲线显示出该模型的预测概率与患者实际发生MACE的概率具有较高的一致性。结论:本研究建立的列线图预测模型能够较好地预测STEMI患者住院期间是否发生MACE。
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关键词:
- 急性ST段抬高型心肌梗死 /
- 主要心血管不良事件 /
- 预后 /
- 列线图
Abstract: Objective: To identify risk factors for major adverse cardiovascular events(MACE) in patients with acute ST-segment elevation myocardial infarction(STEMI) within 30 days of onset and develop a nomogram prediction model in patients with acute STEMI.Methods: STEMI patients who visited the First Affiliated Hospital of Soochow University from January 2018 to December 2018 were selected as the modeling group to identify the risk factors and establish a nomogram prediction model by stepwise regression, and STEMI patients who visited the hospital from January 2019 to January 2020 were prospectively collected as the validation group. The accuracy of this model was validated by plotting a receiver operating curve versus a calibration curve.Results: The nomogram prediction model incorporated Killip class, creatinine, shock index, leukocyte/mean platelet volume as well as neutrophil/lymphocyte, NT-proBNP for a total of six clinical parameters. The area under the receiver operating curve of the model was 0.905(95%CI: 0.868-0.942, P<0.001), and the accuracy of the model was best when the probability was 0.267(sensitivity 0.807, specificity 0.902, Youden index 0.709). Area under the ROC curve was 0.832(95%CI: 0.796-0.892, P<0.001) for the validation population. The calibration curve showed high agreement between the predicted probabilities of the model and the patients' actual probability of experiencing a MACE.Conclusion: The nomogram prediction model developed in this study was better able to predict whether MACE occurred during hospitalization in patients with STEMI. -
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