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.
Authors
Affiliations (9)
Affiliations (9)
- Department of Surgery, Neurosurgery, College of Medicine, Makerere University, Kampala, Uganda.
- Faculty of Medicine, Université Catholique du Graben, Butembo, Democratic Republic of the Congo.
- Neurological Surgery, New Deal Sarl Hospital, International Clinic for Advanced Medicine in Kivu (CIMAK), Goma, Democratic Republic of the Congo.
- Faculty of Medicine, Université Catholique de Louvain, Brussels, Belgium.
- Department of Pharmacology & Therapeutics, Makerere University College of Health Sciences, Kampala, Uganda.
- Department of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.
- Duke Global Neurosurgery and Neurology, Duke University, Durham, NC, United States.
- Directorate of Surgical Services, Neurosurgical Unit, Mulago National Referral Hospital, Kampala, Uganda.
- Neurological and Behavioral Outcomes Center, University Hospitals Cleveland Medical Center & Case Western Reserve University School of Medicine, Cleveland, OH, United States.
Abstract
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. 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. 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 <i>p</i> < 0.05. 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 (<i>p</i> < 0.010). For EIH prediction, the Firth bias-reduced model showed an apparent AUC of 0.854 and a bootstrap optimism-corrected AUC of 0.845. In five-fold cross-validation, pooled out-of-fold AUCs were 0.835 for LASSO, 0.837 for elastic net, and 0.804 for random forest, indicating no predictive advantage of random forest over penalized logistic regression. Key predictors included subdural hematoma, diffuse axonal injury, systolic and diastolic blood pressure, and the interaction between skull fracture and subdural hematoma. Models for postoperative outcomes were exploratory and internally validated only. 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.