Traumatic brain injury (TBI) is a leading cause of death and disability worldwide, affecting approximately 10 million people annually according to the World Health Organization. This burden disproportionately affects low and middle-income countries (LMIC), with annual TBI-related incidence rates of 150–170 per 100,000 people as compared to the global rate of 106 per 100,000 . Those in LMIC are twice as likely to die following severe TBI as compared to those in high-income countries .
Intracranial hemorrhage is a frequent and devastating sequelae of TBI, occurring between one-third to a half of cases [3, 4]. Intracranial hemorrhage is the leading cause of death in lethally injured trauma patients accounting for 40-50% of fatalities  and results in a significant amount of long-term disability .
It has been suggested that organized emergency response systems and prompt transfer to trauma centers improve TBI patient morbidity and mortality . An important adjunct to this is the availability of computed tomography (CT) scanners and neurosurgeons, with rapid surgical intervention resulting in a reduction in deaths . CT scanning is the imaging modality of choice in the identification of intracranial hemorrhage due to its speed and diagnostic capabilities, however, there is only one scanner per 3.5 million people in low-income countries versus one per 64,900 in high-income countries . There are also fewer neurosurgeons per patient, with one neurosurgeon per three million patients in Sub-Saharan Africa as compared to one per 20,000 in Europe . Scarce resources in LMIC compounded with the increased burden of TBI make this a pressing public health issue.
Prognostic modeling provides a unique opportunity to aid clinical judgment and diagnostic ability, as they combine readily available patient data to predict the possibility of an outcome of interest [11, 12]. The utility of these models in regards to TBI have shown to influence patient, next-of-kin and physician decision-making [13, 14]. Additionally, they have been demonstrated to be more accurate than a physician’s own predictive capabilities . This can have a particularly important role in LMIC as there is a lack of specialty training in trauma among the healthcare workforce and diagnostic capabilities are limited [12, 15]. The understanding and application of prognosis can be utilized in this setting to risk-stratify patients, and assist both care providers and family members with decisions to transfer patients to higher levels of care.
However, there is a paucity of prognostic models on TBI in LMIC, and no models currently exist that predict the risk of intracranial hemorrhage in this setting. The models that do exist suffer from multiple methodological flaws, including small sample sizes from a single center, inappropriate validation methods, and a lack of calibration or discrimination . This highlights the necessity of new research to create accurate TBI prognostic modeling to aid clinicians with outcome prediction, as single factors do not have sufficient predictive value .
The Medical Research Council CRASH-1 (corticosteroid randomization after significant head injury) trial is the largest randomized controlled trial to date conducted in patients with TBI from 2005 [18, 19]. The trial prospectively included patients within eight hours of injury, standardised their definitions of risk factors, and obtained CT scans of the head in over 75% of their patients. This allows for a large sample size to ensure high precision and valid prediction. Additionally, high recruitment of patients from LMIC allows for the identification of prognostic factors that are relevant to these settings. The results of this study demonstrated an association with corticosteroids and increased mortality of TBI patients. Prognostic models have been developed from this data to evaluate morbidity and mortality among TBI patients and have been externally validated in several settings; however, prediction of intracranial hemorrhage was not done [3, 20, 21].
The purpose of our study is to identify readily available risk factors for intracranial hemorrhage, and build a clinically useful prognostic model for intracranial hemorrhage among TBI patients in LMIC that can be used by those without specialty training in neurosurgery or trauma.