背景:治疗和预防颅内高压(IH)以最大程度地减少继发性脑损伤是创伤性脑损伤(TBI)的神经重症监护管理的核心。提前预测IH的发作允许更积极的预防性治疗。本研究旨在开发用于预测TBI患者IH事件的随机森林(RF)模型。
方法:我们分析了接受有创颅内压(ICP)监测的重症监护病房患者的前瞻性收集数据。术后早期(前6小时)持续ICP>22mmHg的患者被排除在关注尚未发生的IH事件。最初6小时的ICP相关数据用于提取线性(ICP,脑灌注压,压力反应性指数,和脑脊液代偿储备指数)和非线性特征(ICP和脑灌注压的复杂性)。IH定义为ICP>22mmHg持续>5分钟,在随后的ICP监测期间,重度IH(SIH)为ICP>22mmHg,持续>1小时。然后使用基线特征(年龄,性别,和初始格拉斯哥昏迷评分)以及线性和非线性特征。进行五倍交叉验证以避免过度拟合。
结果:该研究包括69名患者。43例患者(62.3%)发生IH事件,其中30人(43%)进入SIH。IH事件的中位时间为9.83h,对于SIH事件,时间为11.22h。RF模型在预测IH方面表现出可接受的性能,曲线下面积(AUC)为0.76,在预测SIH方面表现优异(AUC=0.84)。交叉验证分析证实了结果的稳定性。
结论:提出的RF模型可以预测随后的IH事件,特别严重的,TBI患者使用术后早期ICP数据。它为研究人员和临床医生提供了一个潜在的预测途径和框架,可以帮助在早期阶段需要更深入的神经治疗的患者进行分类。
BACKGROUND: Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients.
METHODS: We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting.
RESULTS: The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results.
CONCLUSIONS: The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.