严重创伤性脑损伤成人死亡率的潜在预测因素。

IF 2.8 3区 医学 Q3 NEUROSCIENCES
Rachel Marta, Yaroslavska Svitlana, Kreniov Konstiantyn, Mamonowa Maryna, Dobrorodniy Andriy, Oliynyk Oleksandr
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引用次数: 0

摘要

背景:成人严重创伤性脑损伤(sTBI)仍然是世界范围内导致死亡和残疾的主要原因。早期识别可靠的预后预测因子对于风险分层和ICU管理至关重要。止血障碍和代谢因素如身体质量指数(BMI)被认为是潜在的预后指标,但证据仍然有限。方法:我们进行了一项回顾性多中心研究,纳入了2023年9月至2024年7月在乌克兰三家三级重症监护病房住院的307例sTBI(格拉斯哥昏迷评分≤8)成年患者。所有患者均行血肿清除术和开颅减压术。入院后12 h内采集实验室参数(APTT、INR、纤维蛋白原、血小板、d -二聚体)。BMI由测量的身高和体重计算得出。采用l1正则化逻辑回归和随机森林算法进行预测建模。职业不平衡在SMOTE中得到了解决。通过AUC、精度、校准和特征重要性来评估模型性能。结果:28天全因死亡率为32.9%。与幸存者相比,非幸存者的GCS评分明显较低,INR、d -二聚体和APTT值较高。非常高的VIF值表明预测因子之间存在严重的多重共线性。由于完全分离,经典逻辑回归无法估计;因此,应用正则化逻辑回归和随机森林。随机森林表现出更高的性能(AUC 0.95,准确率≈90%),而逻辑回归(AUC 0.77,准确率70.1%),尽管考虑到小样本量和潜在的过拟合,结果必须谨慎解释。特征重要性分析确定BMI升高、APTT延长和d -二聚体升高是死亡率的主要预测因素。排除BMI的敏感性分析仍有较强的表现(AUC 0.91),证实了凝血标志物和GCS的预后价值。结论:成年sTBI患者的死亡率与入院时止血受损、肥胖和神经功能低下密切相关。基于机器学习的建模展示了有希望的预测准确性,但本质上是探索性的。由于回顾性设计、严重的多重共线性、潜在的过拟合和缺乏外部验证,研究结果应谨慎解释。需要更大的、前瞻性的、多中心的研究来证实这些结果,并改善严重创伤性脑损伤的早期风险分层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Potential Predictors of Mortality in Adults with Severe Traumatic Brain Injury.

Background: Severe traumatic brain injury (sTBI) in adults remains a leading cause of mortality and disability worldwide. Early identification of reliable predictors of outcome is crucial for risk stratification and ICU management. Disturbances of hemostasis and metabolic factors such as body mass index (BMI) have been proposed as potential prognostic markers, but evidence remains limited.

Methods: We conducted a retrospective, multicenter study including 307 adult patients with sTBI (Glasgow Coma Scale ≤ 8) admitted to three tertiary intensive care units in Ukraine between September 2023 and July 2024. All patients underwent surgical evacuation of hematomas and decompressive craniotomy. Laboratory parameters (APTT, INR, fibrinogen, platelets, D-dimer) were collected within 12 h of admission. BMI was calculated from measured height and weight. Predictive modeling was performed using L1-regularized logistic regression and Random Forest algorithms. Class imbalance was addressed with SMOTE. Model performance was assessed by AUC, accuracy, calibration, and feature importance.

Results: The 28-day all-cause mortality was 32.9%. Compared with survivors, non-survivors had significantly lower GCS scores and higher INR, D-dimer, and APTT values. Very high VIF values indicated severe multicollinearity between predictors. Classical logistic regression was not estimable due to perfect separation; therefore, regularized logistic regression and Random Forest were applied. Random Forest demonstrated higher performance (AUC 0.95, accuracy ≈ 90%) than logistic regression (AUC 0.77, accuracy 70.1%), although results must be interpreted cautiously given the small sample size and potential overfitting. Feature importance analysis identified increased BMI, prolonged APTT, and elevated D-dimer as leading predictors of mortality. Sensitivity analysis excluding BMI still yielded strong performance (AUC 0.91), confirming the prognostic value of coagulation markers and GCS.

Conclusions: Mortality in adult sTBI patients was strongly associated with impaired hemostasis, obesity, and low neurological status at admission. Machine learning-based modeling demonstrated promising predictive accuracy but is exploratory in nature. Findings should be interpreted with caution due to retrospective design, severe multicollinearity, potential overfitting, and absence of external validation. Larger, prospective, multicenter studies are needed to confirm these results and improve early risk stratification in severe TBI.

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来源期刊
Brain Sciences
Brain Sciences Neuroscience-General Neuroscience
CiteScore
4.80
自引率
9.10%
发文量
1472
审稿时长
18.71 days
期刊介绍: Brain Sciences (ISSN 2076-3425) is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes and short communications in the areas of cognitive neuroscience, developmental neuroscience, molecular and cellular neuroscience, neural engineering, neuroimaging, neurolinguistics, neuropathy, systems neuroscience, and theoretical and computational neuroscience. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
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