Background: This study aims to construct and validate an early-stage nomogram for predicting hospital mortality of ICU patients with acute myocardial infarction (AMI), to help clinicians determine the appropriate intervention.
Methods: The primary cohort of 2704 patients diagnosed with acute myocardial infarction in admission records from eICU-Collaborative Research Database (eICU-CRD) v2.0. Univariate logistic regression analysis and multivariate logistic regression analysis were enrolled for the construction of the predictive nomogram. Demographic factors, history of clinical cardiovascular disease, vital signs, the use of vasopressors, urine output, and serum variables in the first 24 hours were included in this analysis. The nomogram was evaluated by performance traits including Harrell’s concordance index (C-index) and area under the receiver operating characteristic (AUC) analysis, calibration curve, and decision curve analysis (DCA). The nomogram was validated in a different cohort containing 1026 subjects collected from MIMIC-III Database v1.4. Finally, in order to compare the performance with other classic prediction models, AUC analysis, calibration curve, DCA and accuracy analysis (net reclassification improvement (NRI)) were conducted for three ICU scores in validated cohort.
Results: The nomogram revealed 14 predictors of the first 24 hours derived from univariate and multivariable analyses, including age, history of peripheral vascular disease, atrial fibrillation, cardiogenic shock and cardiac arrest, the use of norepinephrine, urine output, white blood cell (WBC), hemoglobin (Hb), red blood cell (RBC), red cell distribution width (RDW), glucose, bicarbonate and magnesium. The C-index of this nomogram was 0.834 (95% CI 0.812 to 0.856). Then, the result of AUC analysis, the DCA and calibration curve indicated that our nomogram was feasible for clinical prediction. The predictive ability and clinical use of the nomogram were verified in the validated cohort. The AUC analysis of ICU scores showed that the AUC of these score systems was ranged from 0.811 to 0.860 (the AUC of nomogram: 0.885). Moreover, our nomogram also showed a better performance in calibration curve and DCA NRI.
Conclusion: The study presents a prediction nomogram incorporating 14 variables that could help identify AMI patients admitted in ICU who might have a high risk of hospital mortality in the first hospitalized 24 hours. This nomogram showed a better performance than normal ICU score systems.



中文翻译：

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预测重症监护病房急性心肌梗死患者住院死亡率的列线图

背景：本研究旨在构建和验证用于预测 ICU 急性心肌梗死 (AMI) 患者住院死亡率的早期列线图，以帮助临床医生确定适当的干预措施。
方法：来自 eICU-协作研究数据库 (eICU-CRD) v2.0 的入院记录中诊断为急性心肌梗死的 2704 名患者的主要队列。单变量逻辑回归分析和多变量逻辑回归分析被纳入预测列线图的构建。该分析包括人口统计学因素、临床心血管疾病史、生命体征、血管加压药的使用、尿量和前 24 小时内的血清变量。列线图通过性能特征进行评估，包括 Harrell 一致性指数 (C-index) 和接受者操作特征 (AUC) 分析下的面积、校准曲线和决策曲线分析 (DCA)。列线图在包含从 MIMIC-III 数据库 v1.4 收集的 1026 名受试者的不同队列中得到验证。最后，
结果：列线图显示前 24 小时的 14 个预测因子来自单变量和多变量分析，包括年龄、外周血管疾病史、心房颤动、心源性休克和心脏骤停、去甲肾上腺素的使用、尿量、白细胞 (WBC)、血红蛋白 (Hb)、红细胞 (RBC)、红细胞分布宽度 (RDW)、葡萄糖、碳酸氢盐和镁。该列线图的 C 指数为 0.834（95% CI 0.812 至 0.856）。然后，AUC 分析结果、DCA 和校准曲线表明我们的列线图可用于临床预测。在经过验证的队列中验证了列线图的预测能力和临床应用。ICU评分的AUC分析表明，这些评分系统的AUC范围为0.811至0.860（列线图的AUC：0.885）。而且，
结论：该研究提出了一个包含 14 个变量的预测列线图，可以帮助识别入住 ICU 的 AMI 患者，这些患者在首次住院 24 小时内可能具有高住院死亡率。该列线图显示出比正常 ICU 评分系统更好的性能。