Clinical Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, 60 West Wuning Road, Dongyang, Zhejiang, 322100, China.
Ann Clin Transl Neurol. 2024 Sep;11(9):2350-2359. doi: 10.1002/acn3.52148. Epub 2024 Jul 7.
Patients with traumatic brain injury (TBI) who are admitted to the intensive care unit often exhibit critical conditions; thus, early prediction of in-hospital mortality is crucial. In this study, we aimed to develop a reliable and easily promotable model for predicting the in-hospital mortality of critically ill patients with TBI using easily accessible indicators and validate the model using external data.
Patient data from the Medical Information Mart for Intensive Care-IV 2.2 database were used as training and internal validation sets to establish and internally validate the prognostic model. Data from the Affiliated Dongyang Hospital of Wenzhou Medical University were used for external validation. The Boruta algorithm was used for the initial feature selection, followed by univariate and multivariate logistic regression analyses to identify the final independent predictors. The predictive performance was evaluated using a receiver operating characteristic curve, calibration curve, clinical practicality decision curve analysis, and clinical impact curve.
This study included 3225 patients (training set: 2042; internal validation set: 874; and external validation set: 309). Ten variables were selected for inclusion in the nomogram model: age, mechanical ventilation usage, vasoactive agent usage, intracerebral hemorrhage, temperature, respiration rate, white blood cell count, platelet count, red blood cell distribution width, and glucose. The nomogram demonstrated good predictive performance in both the internal and external validation sets.
We developed an externally validated nomogram that exhibited good discrimination, calibration, and clinical utility for predicting in-hospital mortality in critically ill patients with TBI.
入住重症监护病房的创伤性脑损伤（TBI）患者常处于危急状态，因此，早期预测院内死亡率至关重要。本研究旨在使用易于获取的指标，建立并验证一种预测 TBI 重症患者院内死亡率的可靠且易于推广的模型。
使用医疗信息集市-重症监护 IV 2.2 数据库中的患者数据作为训练集和内部验证集，建立和内部验证预测模型。温州医科大学附属东阳医院的数据用于外部验证。使用 Boruta 算法进行初始特征选择，然后进行单变量和多变量逻辑回归分析，以确定最终的独立预测因子。使用接收者操作特征曲线、校准曲线、临床实用性决策曲线分析和临床影响曲线评估预测性能。
本研究共纳入 3225 例患者（训练集：2042 例；内部验证集：874 例；外部验证集：309 例）。纳入列线图模型的 10 个变量为：年龄、机械通气使用、血管活性药物使用、脑出血、体温、呼吸频率、白细胞计数、血小板计数、红细胞分布宽度和血糖。该列线图在内部和外部验证集中均表现出良好的预测性能。
我们开发了一种经外部验证的列线图，可对 TBI 重症患者的院内死亡率进行良好的预测，具有良好的区分度、校准度和临床实用性。
