The Second Clinical Medical College, Shaanxi University of Chinese Medicine, Xianyang, China; Department of Neurosurgery, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
Department of Neurosurgery, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
World Neurosurg. 2024 Oct;190:e891-e919. doi: 10.1016/j.wneu.2024.08.033. Epub 2024 Aug 14.
To explore mortality risk factors and to construct an online nomogram for predicting in-hospital mortality in traumatic brain injury (TBI) patients receiving invasive mechanical ventilation (IMV) in intensive care unit (ICU).
We retrospectively analyzed TBI patients on IMV in ICU from Medical Information Mart for Intensive Care IV database and 2 hospitals. Least absolute shrinkage and selection operation regression and multiple logistic regression were used to detect predictors of in-hospital mortality and to construct an online nomogram. The predictive performance of nomogram was evaluated using area under the receiver operating characteristic curves (AUC), calibration curves, decision curve analysis, and clinical impact curves.
Five hundred ten from Medical Information Mart for Intensive Care IV database were enrolled for nomogram construction (80%, n = 408) and internal validation (20%, n = 102). One hundred eighty-five from 2 hospitals were enrolled for external validation. Least absolute shrinkage and selection operation-logistic regression revealed predictors of in-hospital mortality among TBI patients on IMV in ICU included Glasgow Coma Scale (GCS) after ICU admission, Acute Physiology Score III (APS III) after ICU admission, neutrophil and lymphocyte ratio after IMV, blood urea nitrogen after IMV, arterial serum lactate after IMV, and in-hospital tracheotomy. The AUC, calibration curves, decision curve analysis, and clinical impact curves indicated the nomogram had good discrimination, calibration, clinical benefit, and applicability. The multimodel comparisons revealed the nomogram had higher AUC than GCS, APS III, and Simplified Acute Physiology Score II.
We constructed and validated an online nomogram based on routinely recorded factors at admission to ICU and at the beginning of IMV to target prediction of in-hospital mortality among TBI patients on IMV in ICU.
探讨创伤性脑损伤（TBI）患者接受重症监护病房（ICU）有创机械通气（IMV）后住院死亡率的危险因素，并构建在线列线图预测模型。
我们回顾性分析了 ICU 接受 IMV 的 TBI 患者的 Medical Information Mart for Intensive Care IV 数据库和 2 家医院的病历资料。采用最小绝对收缩和选择操作回归和多因素逻辑回归来检测住院死亡率的预测因素，并构建在线列线图。通过受试者工作特征曲线下面积（AUC）、校准曲线、决策曲线分析和临床影响曲线评估列线图的预测性能。
从 Medical Information Mart for Intensive Care IV 数据库中纳入 510 例患者用于构建列线图（80%，n=408）和内部验证（20%，n=102）。从 2 家医院纳入 185 例患者用于外部验证。最小绝对收缩和选择操作逻辑回归显示，ICU 接受 IMV 的 TBI 患者住院死亡率的预测因素包括 ICU 入院后格拉斯哥昏迷评分（GCS）、ICU 入院后急性生理学评分 III（APS III）、IMV 后中性粒细胞与淋巴细胞比值、IMV 后血尿素氮、IMV 后动脉血乳酸和院内气管切开术。AUC、校准曲线、决策曲线分析和临床影响曲线表明，该列线图具有良好的区分度、校准度、临床获益和适用性。多模型比较显示，该列线图的 AUC 高于 GCS、APS III 和简化急性生理学评分 II。
我们构建并验证了一个基于 ICU 入院时和 IMV 开始时常规记录的因素的在线列线图，用于预测 ICU 接受 IMV 的 TBI 患者的住院死亡率。
