The Malawi trauma score: A model for predicting trauma-associated mortality in a resource-poor setting
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Updated: Jan 22, 2026

Pseudofracture: An Acute Peripheral Tissue Trauma Model
Published on: April 18, 2011
Jared Gallaher1, Malcolm Jefferson1, Carlos Varela2
1Department of Surgery, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
Background:
Globally, traumatic injury is a leading cause of morbidity and mortality in low-income countries. Current tools for predicting trauma-associated mortality are often not applicable in low-resource environments due to a lack of diagnostic adjuncts. This study sought to derive and validate a model for predicting mortality that requires only a history and physical exam.
Methods:
We conducted a retrospective analysis of all patients recorded in the Kamuzu Central Hospital trauma surveillance registry in Lilongwe, Malawi from 2011 through 2014. Using statistical randomization, 80% of patients were used for derivation and 20% were used for validation. Logistic regression modeling was used to derive factors associated with mortality and the Malawi Trauma Score (MTS) was constructed. The model fitness was tested.
Results:
62,354 patients are included. Patients are young (mean age 23.0, SD 15.9 years) with a male preponderance (72%). Overall mortality is 1.8%. The MTS is tabulated based on initial mental status (alert, responds to voice, responds only to pain or worse), anatomical location of the most severe injury, the presence or absence of a radial pulse on examination, age, and sex. The score range is 2-32. A mental status exam of only responding to pain or worse, head injury, the absence of a radial pulse, extremes of age, and male sex all conferred a higher probability of mortality. The ROC area under the curve for the derivation cohort and validation cohort were 0.83 (95% CI 0.78, 0.87) and 0.83 (95% CI 0.75, 0.92), respectively. A MTS of 25 confers a 50% probability of death.
Conclusions:
The MTS provides a reliable tool for trauma triage in sub-Saharan Africa and helps risk stratify patient populations. Unlike other models previously developed, its strength is its utility in virtually any environment, while reliably predicting injury- associated mortality.
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