Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients

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.

Figure 1. Prospective Model AUC and PR Curves

Prospective model ROC and PR curves using five-fold cross validation. Binary prospective model results predicting CVRV or not during admission are shown in a-l. Panels a-f and g-l are AUCs and PR curves, respectively, using 4 hours, 1 day, 3 days, 5 days, 7 days, and 10 days of ICU data since ICU admission. Trinary prospective model results predicting CVRV in <=3 days, CVRV in >3 days, or no CVRV during admission, are shown in panels m-o, which show AUC curves using 1 day, 5 days, and 10 days of ICU data since ICU admission.

Figure 2. Prospective Model and Logistic Regression AUC and Theoretical CVRV Rule-Out

Prospective institutional and conservative models and control model (logistic regression) AUCs and CVRV rule-out performance over time. Panels a and b display AUCs at each prediction timepoint for each model (a graphically, b in table format). Panels c and d show precisions at the threshold where Recall=1.00 (c graphically, d in table format).

Figure 3. Retrospective Model AUC and PR Curves

Retrospective model ROC and PR curves using five-fold cross validation. Binary retrospective model results predicting CVRV or not during admission are shown in panels a-l. Panels a-f and g-l are AUCs and PR curves, respectively, using 4 hours, 1 day, 3 days, 5 days, 7 days, and 10 days of ICU prior to the primary endpoint (CVRV or ICU downgrade).

Figure 4. Retrospective Model and Logistic Regression AUCs and Importance Scores

Retrospective institutional, conservative, and control (logistic regression) models AUCs over time alongside retrospective conservative model importance score analysis. Panels a and b display AUCs at each prediction timepoint for each model (a graphically, b in table format). Panels c and d show importance score analysis: c shows averaged ranked variable importance scores over all prediction timepoints and d shows temporal fluctuation in importance of the three predictor variables with the overall highest variable importance scores (10 = #1 overall predictor, 9 = #2 overall predictor, etc).

Figure 5. External Validation of Vasospasm Prediction

Prospective, trinary model trained and tested on the conservative set of clinical predictor variables from the VUMC dataset. Panel a displays ROCs and AUCs of five-fold cross validations for each group with the model trained on 1 day of ICU data, panel b with 5 days of ICU data, and panel c with 10 days of ICU data.