Predictors of Early (<24-Hour) Mortality in Trauma Patients: A Multivariate Logistic Regression Analysis from a Nigerian Tertiary Hospital
Keywords:
Early Death, Logistic Regression, Mortality, Nigeria, Pre-hospital Care, Predictors, TraumaAbstract
Trauma is a leading cause of death in Nigeria, with a significant proportion of fatalities occurring within the first 24 hours of hospital admission. While the overall burden is known, there is a lack of analytical studies identifying the specific factors that predict these early deaths, which is critical for improving triage, optimizing resource allocation, and developing targeted interventions in resource-limited settings. This study aimed to identify the independent predictors of early (<24-hour) mortality among trauma patients at a major Nigerian referral center. A retrospective analysis was conducted on data from 118 trauma patients who died in the Accident and Emergency department of Alex Ekwueme Federal University Teaching Hospital, Abakaliki, between January 1 and December 31, 2022. The primary outcome was death within 24 hours of presentation. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of early mortality from data on patient demographics, pre-hospital transport, injury characteristics, and initial physiological parameters. Of the 118 trauma deaths, 93 (78.5%) occurred within the first 24 hours. In the multivariate analysis, a Glasgow Coma Scale (GCS) score of <9 on admission (OR: 12.5; 95% CI: 4.2- 37.1; P<0.001), the presence of a head and neck injury (OR: 8.3; 95% CI: 2.9-23.7; P<0.001), arrival at the hospital via informal transport (private car/bus or commercial tricycle) (OR: 4.1; 95% CI: 1.5-11.2; P=0.006), and systolic blood pressure <90 mmHg were significant independent predictors of early mortality. In conclusion, early trauma mortality in this Nigerian hospital is strongly predicted by the severity of brain injury and failures in the pre-hospital care system. These findings provide objective evidence for prioritizing interventions, including strengthening the chain of survival, enforcing safety laws to prevent severe head injuries, and urgently developing a formal emergency medical transport system. Clinically, these predictors can be used to rapidly identify high-risk patients for immediate, aggressive resuscitation and resource mobilization
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