Predicting Vasospasm and Early Mortality in Severe Traumatic Brain Injury: A Model Using Serum Cytokines, Neuronal Proteins, and Clinical Data.

IF 3.9 2区 医学 Q1 CLINICAL NEUROLOGY Neurosurgery Pub Date : 2024-10-11 DOI:10.1227/neu.0000000000003224
Rima S Rindler, Henry Robertson, LaShondra De Yampert, Vivek Khatri, Pavlos Texakalidis, Sheila Eshraghi, Scott Grey, Seth Schobel, Eric A Elster, Nicholas Boulis, Jonathan A Grossberg
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Abstract

Background and objectives: Prediction of patient outcomes after severe traumatic brain injury (sTBI) is limited with current clinical tools. This study aimed to improve such prognostication by combining clinical data and serum inflammatory and neuronal proteins in patients with sTBI to develop predictive models for post-traumatic vasospasm (PTV) and mortality.

Methods: Fifty-three adult civilian patients were prospectively enrolled in the sTBI arm of the Surgical Critical Care Initiative (SC2i). Clinical, serum inflammatory, and neuronal protein data were combined using the parsimonious machine learning methods of least absolute shrinkage and selection operator (LASSO) and classification and regression trees (CART) to construct parsimonious models for predicting development of PTV and mortality.

Results: Thirty-six (67.9%) patients developed vasospasm and 10 (18.9%) died. The mean age was 39.2 years; 22.6% were women. CART identified lower IL9, lower presentation pulse rate, and higher eotaxin as predictors of vasospasm development (full data area under curve (AUC) = 0.89, mean cross-validated AUC = 0.47). LASSO identified higher Rotterdam computed tomography score and lower age as risk factors for vasospasm development (full data AUC 0.94, sensitivity 0.86, and specificity 0.94; cross-validation AUC 0.87, sensitivity 0.79, and specificity 0.93). CART identified high levels of eotaxin as most predictive of mortality (AUC 0.74, cross-validation AUC 0.57). LASSO identified higher serum IL6, lower IL12, and higher glucose as predictive of mortality (full data AUC 0.9, sensitivity 1.0, and specificity 0.72; cross-validation AUC 0.8, sensitivity 0.85, and specificity 0.79).

Conclusion: Inflammatory cytokine levels after sTBI may have predictive value that exceeds conventional clinical variables for certain outcomes. IL-9, pulse rate, and eotaxin as well as Rotterdam score and age predict development of PTV. Eotaxin, IL-6, IL-12, and glucose were predictive of mortality. These results warrant validation in a prospective cohort.

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预测严重创伤性脑损伤的血管痉挛和早期死亡率:使用血清细胞因子、神经元蛋白和临床数据的模型。
背景和目标:目前的临床工具对严重创伤性脑损伤(sTBI)后患者预后的预测有限。本研究旨在通过结合 sTBI 患者的临床数据、血清炎症蛋白和神经元蛋白来开发创伤后血管痉挛(PTV)和死亡率的预测模型,从而改善这种预后:53名成年平民患者前瞻性地加入了外科重症监护计划(SC2i)的创伤性脑损伤组。使用最小绝对收缩和选择算子(LASSO)以及分类和回归树(CART)等简易机器学习方法将临床、血清炎症和神经元蛋白数据结合起来,构建预测PTV发展和死亡率的简易模型:36例(67.9%)患者出现血管痉挛,10例(18.9%)患者死亡。平均年龄为 39.2 岁,22.6% 为女性。CART 确定了较低的 IL9、较低的脉搏率和较高的 eotaxin 是血管痉挛发生的预测因子(全数据曲线下面积 (AUC) = 0.89,平均交叉验证 AUC = 0.47)。LASSO 将较高的鹿特丹计算机断层扫描评分和较低的年龄确定为血管痉挛发生的风险因素(全数据 AUC 0.94,灵敏度 0.86,特异性 0.94;交叉验证 AUC 0.87,灵敏度 0.79,特异性 0.93)。CART 确定高水平的 eotaxin 最能预测死亡率(AUC 0.74,交叉验证 AUC 0.57)。LASSO 发现血清 IL6 较高、IL12 较低和葡萄糖较高可预测死亡率(全数据 AUC 0.9,灵敏度 1.0,特异性 0.72;交叉验证 AUC 0.8,灵敏度 0.85,特异性 0.79):结论:创伤性脑损伤后炎症细胞因子水平对某些结果的预测价值可能超过传统的临床变量。IL-9、脉搏率、Eotaxin以及鹿特丹评分和年龄可预测PTV的发生。Eotaxin、IL-6、IL-12 和葡萄糖可预测死亡率。这些结果需要在前瞻性队列中进行验证。
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来源期刊
Neurosurgery
Neurosurgery 医学-临床神经学
CiteScore
8.20
自引率
6.20%
发文量
898
审稿时长
2-4 weeks
期刊介绍: Neurosurgery, the official journal of the Congress of Neurological Surgeons, publishes research on clinical and experimental neurosurgery covering the very latest developments in science, technology, and medicine. For professionals aware of the rapid pace of developments in the field, this journal is nothing short of indispensable as the most complete window on the contemporary field of neurosurgery. Neurosurgery is the fastest-growing journal in the field, with a worldwide reputation for reliable coverage delivered with a fresh and dynamic outlook.
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