Cardiologist at Cardiology and Vascular Medicine Department of Medical, Public Health, and Nursing Faculty Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.
Resident of Cardiology and Vascular Medicine Department of Medical, Public Health, and Nursing Faculty Universitas Gadjah Mada, Indonesia.
Indian Heart J. 2022 Nov-Dec;74(6):513-518. doi: 10.1016/j.ihj.2022.11.002. Epub 2022 Nov 9.
The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences for clinical decision-making. Previous score of parameters collected after 24 h was considered too late to predict mortality. As a result, we attempted to develop a CICU admission risk score to predict hospital mortality using indicators collected within 24 h.
Data were obtained from SCIENCE registry from January 1, 2021 to December 21, 2021. Outcomes of 657 patients (mean age 58.91 ± 12.8 years) were recorded retrospectively. Demography, risk factors, comorbidities, vital signs, laboratory and echocardiography data at 24-h of patient admitted to CICU were analysed by multivariate logistic regression to create two models of scoring system (probability and cut-off model) to predict in-hospital mortality of any cause.
From a total of 657 patients, the hospital mortality was 15%. The significant predictors of mortality were male, acute heart failure, hemodynamic instability, pneumonia, baseline creatinine ≥1.5 mg/dL, TAPSE <17 mm, and the use of mechanical ventilator within first 24-h of CICU admission. Based on Receiver Operating Characteristic (ROC) curve analysis a cut off of ≥3 is considered to be a high risk of in-hospital mortality (sensitivity 75% and specificity 65%).
The initial 24-h SCIENCE admission risk rating system can be used to predict in-hospital mortality in patients admitted to the CICU with a high degree of sensitivity and specificity.
应用预后评分系统来识别重症监护病房（CICU）入院后 24 小时内死亡风险，对临床决策具有重要意义。之前，在 24 小时后收集的参数评分被认为太晚而无法预测死亡率。因此，我们试图开发一种 CICU 入院风险评分系统，以使用 24 小时内收集的指标来预测医院死亡率。
数据来自 SCIENCE 注册中心，时间为 2021 年 1 月 1 日至 2021 年 12 月 21 日。回顾性记录了 657 例患者（平均年龄 58.91±12.8 岁）的结局。对入院后 24 小时内 CICU 患者的人口统计学、危险因素、合并症、生命体征、实验室和超声心动图数据进行多变量逻辑回归分析，创建两种评分系统（概率和截断模型）的模型，以预测任何原因的院内死亡率。
在总共 657 例患者中，医院死亡率为 15%。死亡的显著预测因素是男性、急性心力衰竭、血流动力学不稳定、肺炎、基线肌酐≥1.5mg/dL、TAPSE<17mm 和在 CICU 入院后 24 小时内使用机械通气。基于接受者操作特征（ROC）曲线分析，≥3 被认为是院内死亡率高的风险（敏感性为 75%，特异性为 65%）。
初始 24 小时 SCIENCE 入院风险评分系统可用于预测 CICU 入院患者的院内死亡率，具有高度的敏感性和特异性。
