Predictors of Six-Month Functional Outcome After Chronic Subdural Hematoma Surgery: Logistic Regression Versus Machine Learning.

Overview

1. Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; (S.S.); (J.G.); (S.P.)
2. Centre for Radiology, University Clinical Centre of Vojvodina, Hajduk Veljkova 1, 21000 Novi Sad, Serbia
3. Department of Neurosurgery, University Clinical Centre of Vojvodina, Hajduk Veljkova 1, 21000 Novi Sad, Serbia
4. Clinic of Physical Medicine and Rehabilitation, University Clinical Centre of Vojvodina, Hajduk Veljkova 1, 21000 Novi Sad, Serbia
5. Institute of Lowland Forestry and Environment, University of Novi Sad, 21102 Novi Sad, Serbia

Abstract

Background/Objectives: Six-month functional outcome after chronic subdural hematoma (CSDH) surgery has not been reliably predicted in Southeastern European cohorts. We asked whether machine learning (ML) classifiers outperform logistic regression. Methods: This study included a single-center retrospective cohort of 78 surgically treated CSDH patients (Novi Sad, Serbia). The primary outcome was favorable six-month outcome (Glasgow Outcome Scale 4–5). Missing covariates were multiply imputed (multiple imputation by chained equations, MICE, m = 20), and estimates were pooled using Rubin’s rules. A three-predictor model was pre-specified; six ML classifiers were trained on five preoperative predictors and evaluated using stratified 10-fold repeated cross-validation, with metrics derived from held-out predictions. Results: Favorable outcome occurred in 50/78 (64.1%). Preoperative Karnofsky Performance Status (KPS) was the only independent predictor (odds ratio, OR = 1.14 per point, 95% confidence interval, CI 1.07–1.20, p < 0.001). Cortical atrophy grade was associated univariately (OR = 0.57, 95% CI 0.34–0.96, p = 0.033) but not after adjustment for KPS (OR = 1.08, 95% CI 0.53–2.22, p = 0.833). Discrimination was indistinguishable across the six classifiers (AUC 0.870–0.885; all pairwise DeLong p ≥ 0.123); the three-predictor model reached an AUC of 0.903 (0.833–0.972). Conclusions: Preoperative KPS was the only independent predictor. No ML classifier outperformed logistic regression; a three-predictor model performed at least as well. External validation is required.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability Statement

The data presented in this study are available on reasonable request from the corresponding author. The data are not publicly available because they are derived from identifiable hospital records and their release is restricted by the institutional data protection policy of the University Clinical Centre of Vojvodina. The full reproducible analysis pipeline is available from the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 9 keywords, 1 funder, 34 references.

Cite

This paper

Juković, M., Stošić, S., Golubović, J., Pantelinac, S., & Stojanović, D. B. (2026). Predictors of Six-Month Functional Outcome After Chronic Subdural Hematoma Surgery: Logistic Regression Versus Machine Learning. Journal of clinical medicine, 15(17), 6822. https://doi.org/10.3390/jcm15176822

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