Affiliation:
1. David Geffen School of Medicine
2. Department of Anesthesiology, Vanderbilt University Medical Center
3. UCLA Department of Neurosurgery
4. Vanderbilt University Medical Center
5. UCLA
Abstract
Abstract
Background
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.
Methods
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.
Results
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.
Conclusions
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.
Publisher
Research Square Platform LLC
Reference30 articles.
1. A review of cerebral vasospasm in aneurysmal subarachnoid haemorrhage Part I: Incidence and effects;Dorsch NWC;J Clin Neurosci,1994
2. EEG Monitoring to Detect Vasospasm after Subarachnoid Hemorrhage |… https://www.reliasmedia.com/articles/34004-eeg-monitoring-to-detect-vasospasm-after-subarachnoid-hemorrhage.
3. Frontera, J. A. et al. Defining Vasospasm After Subarachnoid Hemorrhage. Stroke 40, 1963–1968 (2009).
4. Current Options for the Management of Aneurysmal Subarachnoid Hemorrhage-Induced Cerebral Vasospasm: A Comprehensive Review of the Literature;Dabus G;Interv Neurol,2013
5. Diringer, M. N. et al. Critical care management of patients following aneurysmal subarachnoid hemorrhage: recommendations from the Neurocritical Care Society’s Multidisciplinary Consensus Conference. Neurocrit Care 15, 211–240 (2011).