Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage

Abstract

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.

Keywords: Cerebral vasospasm; Machine learning; Prediction; Verapamil.

Fig. 1

Prospective model ROC 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 h, 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.

Fig. 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. Panel a shows AUCs at each prediction timepoint for each model. Panel b shows precisions at the threshold where Recall = 1.00.

Fig. 3

Retrospective model ROC 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 h, 1 day, 3 days, 5 days, 7 days, and 10 days of ICU prior to the primary endpoint (CVRV or no CVRV).

Fig. 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. Panel a displays AUCs at each prediction timepoint for each model. Panels b and c show importance score analysis: b shows averaged ranked variable importance scores over all prediction timepoints and c 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). Importance score rank of top three strongest predictor variables for the conservative model at each prediction interval.

Fig. 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.