Is the Kampala Trauma Score an Effective Predictor of Mortality in Low-Resource Settings? A Comparison of Multiple Trauma Severity Scores

Abstract

Background In the developed world, multiple injury severity scores have been used for trauma patient evaluation and study. However, few studies have supported the effectiveness of different trauma scoring methods in the developing world. The Kampala Trauma Score (KTS) was developed for use in resource-limited settings and has been shown to be a robust predictor of death. This study evaluates the ability of KTS to predict the mortality of trauma patients compared to other trauma scoring systems.Methods Data were collected on injured patients presenting to Central Hospital of Yaoundé, Cameroon from April 15 to October 15, 2009. The KTS, Injury Severity Score, Revised Trauma Score, Glasgow Coma Scale, and Trauma Injury Severity Score were calculated for each patient. Scores were evaluated as predictors of mortality using logistic regression models. Areas under receiver operating characteristic (ROC) curves were compared. Results Altogether, 2855 patients were evaluated with a mortality rate of 6 per 1000. Each score analyzed was a statistically significant predictor of mortality. The area under the ROC for KTS as a predictor of mortality was 0.7748 (95 % CI 0.6285-0.9212). There were no statistically significant pairwise differences between ROC areas of KTS and other scores. Similar results were found when the analysis was limited to severe injuries. Conclusions This comparison of KTS to other trauma scores supports the adoption of KTS for injury surveillance and triage in resource-limited settings. We show that the KTS is as effective as other scoring systems for predicting patient mortality.

Choice of injury scoring system in low- and middle-income countries: Lessons from Mumbai

“…When stratifying MGAP scores as low (23)(24)(25)(26)(27)(28)(29), intermediate (18)(19)(20)(21)(22) and high (3-17) risk for death, the in-hospital mortality rates were uniformly higher than in the original validation cohort of the MGAP score in France (Table 3). The area under the ROC curve for the MGAP score in the French validation cohort was 0.90, compared with 0.85 in the subset of patients with sufficient data to Fig.…”

“…MGAP values ranged from 10 to 29, with a median value of 23 . GAP values ranged from 6 to 24, with a median value of 19 (IQR [14][15][16][17][18][19][20][21][22].…”

“…One concern is that vital sign measurements occurred at the time of hospital triage, not at a fixed interval after the injury event, which may introduce bias as vital signs fluctuate over time and with resuscitative measures. However, vital signs collected at the time of hospital arrival are routinely used for injury scoring in low-and middle-income countries without adjusting for time since injury [19,22,[26][27][28]. In fact, because physiologic scoring systems do not rely on comprehensive anatomic evaluation, they potentially provide a more feasible means of estimating injury severity in low-and middle-income countries using readily available clinic or administrative data [29].…”

“…While ISS and RTS have been widely studied in high-income countries, none of these injury scoring systems have been rigorously validated in low-and middle-income countries [18,19]. There are substantial logistical demands associated with implementing the ISS, including detailed medical records, extensive radiographic studies and autopsy results, which are often unavailable in resource-poor settings [20].…”

Operative trauma in low-resource settings: The experience of Médecins Sans Frontières in environments of conflict, postconflict, and disaster

“…Although imperfect, the analysis of the initial urgent cohort provides more comparable data to the trauma literature from greater-resource settings and calls for future work on methods of assessment of injury severity that are implementable in LMICs, such as the Kampala Trauma Score, which has been used and validated in sub-Saharan Africa. 10,11 This study has several limitations. First, despite being a large dataset from a variety of countries, it is a retrospective analysis of the activities of a single organization and is limited to the scope of interventions being performed by MSF.…”