The 2nd Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Heart Center, Department of Cardiovascular Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China.
Sci Rep. 2024 Jun 19;14(1):14114. doi: 10.1038/s41598-024-64965-x.
The aim of this study was to develop a simple but effective nomogram to predict risk of septic cardiomyopathy (SCM) in the intensive care unit (ICU). We analyzed data from patients who were first admitted to the ICU for sepsis between 2008 and 2019 in the MIMIC-IV database, with no history of heart disease, and divided them into a training cohort and an internal validation cohort at a 7:3 ratio. SCM is defined as sepsis diagnosed in the absence of other cardiac diseases, with echocardiographic evidence of left (or right) ventricular systolic or diastolic dysfunction and a left ventricular ejection fraction (LVEF) of less than 50%. Variables were selected from the training cohort using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to develop an early predictive model for septic cardiomyopathy. A nomogram was constructed using logistic regression analysis and its receiver operating characteristic (ROC) and calibration were evaluated in two cohorts. A total of 1562 patients participated in this study, with 1094 in the training cohort and 468 in the internal validation cohort. SCM occurred in 13.4% (147 individuals) in the training cohort, 16.0% (75 individuals) in the internal validation cohort. After adjusting for various confounding factors, we constructed a nomogram that includes SAPS II, Troponin T, CK-MB index, white blood cell count, and presence of atrial fibrillation. The area under the curve (AUC) for the training cohort was 0.804 (95% CI 0.764-0.844), and the Hosmer-Lemeshow test showed good calibration of the nomogram (P = 0.288). Our nomogram also exhibited good discriminative ability and calibration in the internal validation cohort. Our nomogram demonstrated good potential in identifying patients at increased risk of SCM in the ICU.
本研究旨在开发一种简单有效的列线图来预测重症监护病房（ICU）中脓毒症性心肌病（SCM）的风险。我们分析了 2008 年至 2019 年期间在 MIMIC-IV 数据库中首次因败血症入住 ICU 的患者的数据，这些患者没有心脏病史，按照 7:3 的比例将其分为训练队列和内部验证队列。SCM 的定义为在无其他心脏疾病的情况下诊断为败血症，伴有超声心动图左（或右）心室收缩或舒张功能障碍和左心室射血分数（LVEF）<50%的证据。使用最小绝对收缩和选择算子（LASSO）回归从训练队列中选择变量，以开发脓毒症性心肌病的早期预测模型。使用逻辑回归分析构建列线图，并在两个队列中评估其接收者操作特征（ROC）和校准。共有 1562 名患者参与了本研究，其中 1094 名在训练队列中，468 名在内部验证队列中。在训练队列中，SCM 的发生率为 13.4%（147 例），在内部验证队列中为 16.0%（75 例）。在调整了各种混杂因素后，我们构建了一个包含 SAPS II、肌钙蛋白 T、CK-MB 指数、白细胞计数和心房颤动的列线图。训练队列的曲线下面积（AUC）为 0.804（95%CI 0.764-0.844），Hosmer-Lemeshow 检验表明该列线图具有良好的校准度（P=0.288）。我们的列线图在内部验证队列中也表现出良好的区分能力和校准度。我们的列线图在识别 ICU 中发生 SCM 风险增加的患者方面具有良好的潜力。
