Zarrin David, Suri Abhinav, McCarthy Karen, Gaonkar Bilwaj, Wilson Bayard, Colby Geoffrey, Freundlich Robert, Macyszyn Luke, Gabel Eilon
David Geffen School of Medicine.
Department of Anesthesiology, Vanderbilt University Medical Center.
Res Sq. 2024 Feb 5:rs.3.rs-3617246. doi: 10.21203/rs.3.rs-3617246/v1.
Cerebral vasospasm (CV) is a feared complication occurring in 20-40% of patients following subarachnoid hemorrhage (SAH) and is known to contribute to delayed cerebral ischemia. It is standard practice to admit SAH patients to intensive care for an extended period of vigilant, resource-intensive, clinical monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date.
SAH patients admitted to UCLA from 2013-2022 and a validation cohort from VUMC from 2018-2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or ICU downgrade. At each institution, a light gradient boosting machine (LightGBM) was trained using five- fold cross validation to predict the primary endpoint at various timepoints during hospital admission. Receiver-operator curves (ROC) and precision-recall (PR) curves were generated.
A total of 1,750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 an average of over one week in advance, and successfully ruled out 8% of non-verapamil patients with zero false negatives. Minimum leukocyte count, maximum platelet count, and maximum intracranial pressure were the variables with highest predictive accuracy. Our models predicted "no CVRV" vs "CVRV within three days" vs "CVRV after three days" with AUCs=0.88, 0.83, and 0.88, respectively. For external validation at VUMC, 1,654 patients were included, 75 receiving verapamil. Predictive models at VUMC performed very similarly to those at UCLA, averaging 0.01 AUC points lower.
We present an accurate (AUC=0.88) and early (>1 week prior) predictor of CVRV using machine learning over two large cohorts of subarachnoid hemorrhage patients at separate institutions. This represents a significant step towards optimized clinical management and improved resource allocation in the intensive care setting of subarachnoid hemorrhage patients.
脑血管痉挛（CV）是蛛网膜下腔出血（SAH）后20%-40%患者会出现的一种可怕并发症，已知会导致迟发性脑缺血。将SAH患者长时间收治在重症监护室进行警惕性高、资源密集型的临床监测是标准做法。在迄今为止规模最大且唯一的多中心研究中，我们使用机器学习来预测需要维拉帕米治疗的脑血管痉挛（CVRV）。
纳入2013年至2022年入住加州大学洛杉矶分校（UCLA）的SAH患者以及2018年至2023年来自范德堡大学医学中心（VUMC）的验证队列患者。对于每位患者，通过主要终点（即首次使用维拉帕米或重症监护病房降级）提取172个独特的重症监护病房（ICU）变量。在每个机构，使用五折交叉验证训练轻梯度提升机（LightGBM），以预测住院期间不同时间点的主要终点。生成受试者操作特征曲线（ROC）和精确率-召回率（PR）曲线。
UCLA共纳入1750例患者，其中125例接受维拉帕米治疗。LightGBM在平均提前一周多的时间内实现了ROC曲线下面积（AUC）为0.88，并成功排除了8%的非维拉帕米治疗患者，假阴性为零。最低白细胞计数、最高血小板计数和最高颅内压是预测准确性最高的变量。我们的模型预测“无CVRV”与“三天内出现CVRV”与“三天后出现CVRV”的AUC分别为0.88、0.83和0.88。在VUMC进行外部验证时，纳入1654例患者，其中75例接受维拉帕米治疗。VUMC的预测模型表现与UCLA的非常相似，平均AUC值低0.01个点。
我们在两个独立机构的两个大型蛛网膜下腔出血患者队列中，使用机器学习提出了一种准确的（AUC=0.88）且早期（提前>1周）的CVRV预测指标。这代表着在优化蛛网膜下腔出血患者重症监护环境下的临床管理和改善资源分配方面迈出了重要一步。
