Wang Wei Yun, Holland Ian C, Fong Christine T, Blacker Samuel N, Lele Abhijit V
Department of Anesthesiology and Pain Medicine, Harborview Medical Center, University of Washington, Seattle, WA 98104, USA.
School of Medicine, University of Washington, Seattle, WA 98104, USA.
J Clin Med. 2024 Nov 22;13(23):7055. doi: 10.3390/jcm13237055.
This study aimed to develop and validate a stratified risk model for predicting high opioid use in patients with acute brain injury due to stroke or traumatic brain injury (TBI) admitted to a neurocritical care intensive care unit. : We examined the factors associated with the use of high-opioids (≥75th quartile, ≥17.5 oral morphine equivalent/ICU day) in a retrospective cohort study including patients with acute ischemic stroke, spontaneous intracerebral hemorrhage, spontaneous subarachnoid hemorrhage, and TBI. We then developed, trained, and validated a risk model to predict high-dose opioids. : Among 2490 patients aged 45-64 years ( = -0.25), aged 65-80 years ( = -0.97), and aged ≥80 years ( = -1.17), a history of anxiety/depression ( = 0.57), a history of illicit drug use ( = 0.79), admission diagnosis ( = 1.21), lowest Glasgow Coma Scale Score (GCSL) [GCSL 3-8 ( = -0.90], {GCS L 9-12 (( = -0.34)], mechanical ventilation ( = 1.21), intracranial pressure monitoring ( = 0.69), craniotomy/craniectomy ( = 0.6), and paroxysmal sympathetic hyperactivity ( = 1.12) were found to be significant predictors of high-dose opioid use. When validated, the model demonstrated an area under the curve ranging from 0.72 to 0.82, accuracy ranging from 0.68 to 0.91, precision ranging from 0.71 to 0.94, recall ranging from 0.75 to 1, and F1 ranging from 0.74 to 0.95. : A personalized stratified risk model may allow clinicians to predict the risk of high opioid use in patients with acute brain injury due to stroke or TBI. Findings need validation in multi-center cohorts.
本研究旨在开发并验证一种分层风险模型，用于预测入住神经重症监护病房的因中风或创伤性脑损伤（TBI）导致急性脑损伤患者的高阿片类药物使用情况。：在一项回顾性队列研究中，我们检查了与高剂量阿片类药物使用（≥第75四分位数，≥17.5口服吗啡当量/重症监护病房日）相关的因素，该研究纳入了急性缺血性中风、自发性脑出血、自发性蛛网膜下腔出血和TBI患者。然后，我们开发、训练并验证了一个预测高剂量阿片类药物的风险模型。：在2490名年龄在45 - 64岁（=-0.25）、65 - 80岁（=-0.97）和≥80岁（=-1.17）的患者中，焦虑/抑郁病史（=0.57）、非法药物使用史（=0.79）、入院诊断（=1.21）、最低格拉斯哥昏迷量表评分（GCSL）[GCSL 3 - 8（=-0.90）]、{GCS L 9 - 12（（=-0.34）]、机械通气（=1.21）、颅内压监测（=0.69）、开颅手术/颅骨切除术（=0.6）和阵发性交感神经过度兴奋（=1.12）被发现是高剂量阿片类药物使用的显著预测因素。经验证，该模型的曲线下面积范围为0.72至0.82，准确率范围为0.68至0.91，精确率范围为0.71至0.94，召回率范围为0.75至1，F1范围为0.74至0.95。：个性化分层风险模型可能使临床医生能够预测因中风或TBI导致急性脑损伤患者高阿片类药物使用的风险。研究结果需要在多中心队列中进行验证。
