The MOST (Mortality Score for TBI): A novel prediction model beyond CRASH-Basic and IMPACT-Core for isolated traumatic brain injury.

Mert Karabacak, Pemla Jagtiani, Kristen Dams-O'Connor, Eric Legome, Zachary L Hickman, Konstantinos Margetis
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Abstract

Background: Due to significant injury heterogeneity, outcome prediction following traumatic brain injury (TBI) is challenging. This study aimed to develop a simple model for high-accuracy mortality risk prediction after TBI.

Study design: Data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) from 2019 to 2021 was used to develop a summary score based on age, the Glasgow Coma Scale (GCS) component subscores, and pupillary reactivity data. We then compared the predictive accuracy to that of the Corticosteroid Randomisation After Significant Head Injury Trial (CRASH)-Basic and International Mission for Prognosis and Analysis of Clinical Trial in TBI (IMPACT)-Core models. Two separate series of sensitivity analyses were conducted to further assess our model's generalizability. We evaluated predictive performance of the models with discrimination [the area under the receiver-operating characteristic curves (AUC), sensitivity, specificity] and calibration (Brier score). Discriminative ability was compared with DeLong tests.

Results: 259,404 patients were included in the present study (mean age, 60 years; 93,495 (36 %) female). The mortality score after TBI (MOST) model (AUC = 0.875) had better discrimination (DeLong test p values < 0.00001) than CRASH-Basic (AUC = 0.837) and IMPACT-Core (AUC = 0.821) models, and superior calibration (MOST = 0.02729, CRASH-Basic = 0.02962, IMPACT-Core = 0.02962) in predicting in-hospital mortality. The MOST model similarly outperformed in predicting 3-, 7-, 14-, and 30-day mortality.

Conclusion: The MOST model can be rapidly calculated and outperforms two widely used models for predicting mortality in TBI patients. It utilizes a larger, contemporaneous dataset that reflects modern trauma care.

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MOST(创伤性脑损伤死亡率评分):超越 CRASH-Basic 和 IMPACT-Core 的新型孤立性创伤性脑损伤预测模型。
背景:由于损伤具有明显的异质性,因此创伤性脑损伤(TBI)后的结果预测具有挑战性。本研究旨在开发一个简单的模型,用于高精度预测创伤性脑损伤后的死亡风险:研究设计:我们利用美国外科学院(ACS)创伤质量计划(TQP)2019 年至 2021 年的数据,根据年龄、格拉斯哥昏迷量表(GCS)分量子评分和瞳孔反应性数据开发了一个汇总评分。然后,我们将其预测准确性与重大头部损伤后皮质类固醇随机化试验(CRASH)-基本模型和国际创伤性脑损伤临床试验预后与分析任务(IMPACT)-核心模型进行了比较。为了进一步评估模型的通用性,我们分别进行了两个系列的敏感性分析。我们通过辨别力[接收者工作特征曲线下面积(AUC)、灵敏度、特异性]和校准(布赖尔评分)评估了模型的预测性能。结果:本研究共纳入 259,404 名患者(平均年龄 60 岁;93,495 人(36%)为女性)。与 CRASH-Basic 模型(AUC = 0.837)和 IMPACT-Core 模型(AUC = 0.821)相比,创伤性脑损伤后死亡率评分(MOST)模型(AUC = 0.875)具有更好的分辨能力(DeLong 检验 p 值 < 0.00001),在预测院内死亡率方面具有更好的校准能力(MOST = 0.02729,CRASH-Basic = 0.02962,IMPACT-Core = 0.02962)。MOST 模型在预测 3 天、7 天、14 天和 30 天死亡率方面同样表现出色:结论:在预测创伤性脑损伤患者的死亡率方面,MOST 模型可以快速计算,并且优于两种广泛使用的模型。它利用了一个更大的、反映现代创伤护理的同期数据集。
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