• LimMervyn Jun RuiMJRDepartment of Neurosurgery, University Surgical Centre, National University Hospital, Singapore, Singapore. Electronic address: mervynlim@u.nus.edu., QuekRaphael Hao ChongRHCDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore., Kai Jie Ng, Benjamin Yong-Qiang Tan, Leonard Leong Litt Yeo, Ying Liang Low, SoonBetsy Kar HoonBKHDepartment of Diagnostic Imaging, National University Hospital, Singapore, Singapore., Will Ne-Hooi Loh, Kejia Teo, NgaVincent Diong WengVDWDepartment of Neurosurgery, University Surgical Centre, National University Hospital, Singapore, Singapore., Tseng Tsai Yeo, and Mehul Motani.
• Department of Neurosurgery, University Surgical Centre, National University Hospital, Singapore, Singapore. Electronic address: mervynlim@u.nus.edu.
• World Neurosurg. 2024 Feb 1; 182: e262e269e262-e269.

ObjectiveThe role of surgery in spontaneous intracerebral hemorrhage (SICH) remains controversial. We aimed to use explainable machine learning (ML) combined with propensity-score matching to investigate the effects of surgery and identify subgroups of patients with SICH who may benefit from surgery in an interpretable fashion.MethodsWe conducted a retrospective study of a cohort of 282 patients aged ≥21 years with SICH. ML models were developed to separately predict for surgery and surgical evacuation. SHapley Additive exPlanations (SHAP) values were calculated to interpret the predictions made by ML models. Propensity-score matching was performed to estimate the effect of surgery and surgical evacuation on 90-day poor functional outcomes (PFO).ResultsNinety-two patients (32.6%) underwent surgery, and 57 patients (20.2%) underwent surgical evacuation. A total of 177 patients (62.8%) had 90-day PFO. The support vector machine achieved a c-statistic of 0.915 when predicting 90-day PFO for patients who underwent surgery and a c-statistic of 0.981 for patients who underwent surgical evacuation. The SHAP scores for the top 5 features were Glasgow Coma Scale score (0.367), age (0.214), volume of hematoma (0.258), location of hematoma (0.195), and ventricular extension (0.164). Surgery, but not surgical evacuation of the hematoma, was significantly associated with improved mortality at 90-day follow-up (odds ratio, 0.26; 95% confidence interval, 0.10-0.67; P = 0.006).ConclusionsExplainable ML approaches could elucidate how ML models predict outcomes in SICH and identify subgroups of patients who respond to surgery. Future research in SICH should focus on an explainable ML-based approach that can identify subgroups of patients who may benefit functionally from surgical intervention.Copyright © 2023 Elsevier Inc. All rights reserved.

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