OBJECTIVEThe purpose of this study was to determine if patients with traumatic brain injury (TBI) in low- and middle-income countries who receive surgery have better outcomes than patients with TBI who do not receive surgery, and whether this differs with severity of injury.METHODSThe authors generated a series of Kaplan-Meier plots and performed multiple Cox proportional hazard models to assess the relationship between TBI surgery and TBI severity. The TBI severity was categorized using admission Glasgow Coma Scale scores: mild (14, 15), moderate (9–13), or severe (3–8). The authors investigated outcomes from admission to hospital day 14. The outcome considered was the Glasgow Outcome Scale–Extended, categorized as poor outcome (1–4) and good outcome (5–8). The authors used TBI registry data collected from 2013 to 2017 at a regional referral hospital in Tanzania.RESULTSOf the final 2502 patients, 609 (24%) received surgery and 1893 (76%) did not receive surgery. There were significantly fewer road traffic injuries and more violent causes of injury in those receiving surgery. Those receiving surgery were also more likely to receive care in the ICU, to have a poor outcome, to have a moderate or severe TBI, and to stay in the hospital longer. The hazard ratio for patients with TBI who underwent operation versus those who did not was 0.17 (95% CI 0.06–0.49; p < 0.001) in patients with moderate TBI; 0.2 (95% CI 0.06–0.64; p = 0.01) for those with mild TBI, and 0.47 (95% CI 0.24–0.89; p = 0.02) for those with severe TBI.CONCLUSIONSThose who received surgery for their TBI had a lower hazard for poor outcome than those who did not. Surgical intervention was associated with the greatest improvement in outcomes for moderate head injuries, followed by mild and severe injuries. The findings suggest a reprioritization of patients with moderate TBI—a drastic change to the traditional practice within low- and middle-income countries in which the most severely injured patients are prioritized for care.

OBJECTIVETraumatic brain injury (TBI) is a leading cause of death and disability worldwide, with a disproportionate burden of this injury on low- and middle-income countries (LMICs). Limited access to diagnostic technologies and highly skilled providers combined with high patient volumes contributes to poor outcomes in LMICs. Prognostic modeling as a clinical decision support tool, in theory, could optimize the use of existing resources and support timely treatment decisions in LMICs. The objective of this study was to develop a machine learning–based prognostic model using data from Kilimanjaro Christian Medical Centre in Moshi, Tanzania.METHODSThis study is a secondary analysis of a TBI data registry including 3138 patients. The authors tested nine different machine learning techniques to identify the prognostic model with the greatest area under the receiver operating characteristic curve (AUC). Input data included demographics, vital signs, injury type, and treatment received. The outcome variable was the discharge score on the Glasgow Outcome Scale–Extended.RESULTSThe AUC for the prognostic models varied from 66.2% (k-nearest neighbors) to 86.5% (Bayesian generalized linear model). An increasing Glasgow Coma Scale score, increasing pulse oximetry values, and undergoing TBI surgery were predictive of a good recovery, while injuries suffered from a motor vehicle crash and increasing age were predictive of a poor recovery.CONCLUSIONSThe authors developed a TBI prognostic model with a substantial level of accuracy in a low-resource setting. Further research is needed to externally validate the model and test the algorithm as a clinical decision support tool.