Search

BACKGROUND: Cerebral vasospasm (CV) is a feared complication which occurs after 20-40% of subarachnoid haemorrhage (SAH). It is standard practice to admit patients with SAH to intensive care for an extended period of resource-intensive monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date. METHODS: Patients with SAH admitted to UCLA from 2013 to 2022 and a validation cohort from VUMC from 2018 to 2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or no verapamil. At each institution, a light gradient boosting machine (LightGBM) was trained using five-fold cross validation to predict the primary endpoint at various hospitalization timepoints. FINDINGS: A total of 1750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 > 1 week in advance and ruled out 8% of non-verapamil patients with zero false negatives. 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. From VUMC, 1654 patients were included, 75 receiving verapamil. VUMC predictions averaged within 0.01 AUC points of UCLA predictions. INTERPRETATION: We present an accurate and early predictor of CVRV using machine learning with multi-center validation. This represents a significant step towards optimized clinical management and resource allocation in patients with SAH. FUNDING: Robert E. Freundlich is supported by National Center for Advancing Translational Sciences federal grant UL1TR002243 and National Heart, Lung, and Blood Institute federal grant K23HL148640; these funders did not play any role in this study. The National Institutes of Health supports Vanderbilt University Medical Center which indirectly supported these research efforts. Neither this study nor any other authors personally received financial support for the research presented in this manuscript. No support from pharmaceutical companies was received.

Patients who initially survive the rupture and repair of a brain aneurysm often take a devastating turn for the worse some days later and die or suffer permanent neurologic deficits. This catastrophic sequela is attributed to a delayed phase of global cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH), but we lack effective treatment. Here we present our view, based on 20 years of research, that the initial drop in blood flow at the time of rupture triggers genomic responses throughout the brain vasculature that manifest days later as increased vasoconstriction and decreased cerebral blood flow. We propose a novel treatment strategy to prevent DCI by early inhibition of the vascular mitogen-activated protein kinase (MAPK) pathway that triggers expression of vasoconstrictor and inflammatory mediators. We summarize evidence from experimental SAH models showing early treatment with MAPK inhibitors "switches off" these detrimental responses, maintains flow, and improves neurological outcome. This promising therapy is currently being evaluated in clinical trials.