ArticleFrontiers in surgery2026
Predictive models for the occurrence of expansive intracranial hematomas and outcomes after surgical evacuation in patients with traumatic brain injury in Uganda: a prospective cohort study.
Article in Frontiers in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Hematoma expansion is a frequent and clinically important complication of traumatic intracranial hemorrhage and is associated with poor functional outcomes. Early identification of patients at risk of expansive intracranial hematomas (EIH) remains challenging, particularly in low-resource settings. Objective: To determine the prevalence of EIH and develop exploratory predictive models for EIH occurrence and neurosurgical outcomes among patients with traumatic brain injury (TBI) in Uganda. Methods: We conducted a prospective cohort study among adult patients with TBI and radiologically confirmed intracranial hematomas at Mulago National Referral Hospital. EIH was defined using serial non-contrast computed tomography as hematoma growth greater than 33% or an absolute increase greater than 6 mL within 72 h of injury. Firth bias-reduced logistic regression was used for sparse binary outcomes. EIH prediction was evaluated using patient-level fitted probabilities, five-fold cross-validation, bootstrap optimism correction, calibration, and the Brier score; LASSO, elastic-net logistic regression, and random forest were compared as exploratory prediction approaches. Statistical significance was set at Results: Among 324 patients, EIH occurred in 192 patients, representing 59.3% of the cohort. Patients with EIH had significantly poorer quality of life at 3 and 6 months postoperatively ( Conclusion: EIH was highly prevalent among patients with TBI in Uganda and was associated with poorer postoperative recovery. A parsimonious Firth model based on routinely available clinical and radiological variables showed strong internally validated discrimination, while penalized logistic models performed at least as well as random forest in cross-validation. These prediction models remain exploratory and require external multicenter validation before clinical implementation.
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