Estimating mortality risk in burn patients admitted at Rwanda's largest referral hospital.

BACKGROUND: Burns is a disease of poverty, disproportionately affecting populations in low- and middle-income countries, where most of the injuries and the deaths caused by burns occurs. In Sub-Saharan Africa, it is estimated that one fifth of burn victims die from their injuries. Mortality prediction indexes are used to estimate outcomes after provided burn care, which has been used in burn services of high-income countries over the last 60 years. It remains to be seen whether these are reliable in low-income settings. This study aimed to analyze in-hospital mortality and to apply mortality estimation indexes in burn patients admitted to the only specialized burn unit in Rwanda.

METHODS: This retrospective study included all patients with burns admitted at the burn unit (BU) of the University Teaching Hospital in Kigali (CHUK) between 2005 and 2019. Patient data were collected from the BU logbook. Descriptive statistics were calculated with frequency (%) and median (interquartile range, IQR). Association between burns characteristics and in-hospital mortality was calculated with Fisher's exact test, and Wilcoxon rank, as appropriate. Mortality estimation analysis, including Baux score, Lethal Area 50 (LA50), and point of futility, was calculated in those patients with complete data on age and TBSA. LA50 and point-of-futility were calculated using logistic regression.

RESULTS: Among the 1093 burn patients admitted at the CHUK burn unit during the study period, 49% (n=532) had complete data on age and TBSA. Their median age, TBSA, and Baux score were 3.4 years (IQR 1.9-17.1), 15% (IQR 11-25), and 24 (IQR 16-38), respectively. Overall, reported in-hospital mortality was 13% (n=121/931), LA50 for Baux score was 89.9 (95% CI 76.2-103.7), and the point-of-futility was at a Baux score of 104.

CONCLUSION: Mortality estimation indexes based on age and TBSA are feasible to use in low-income settings. However, implementation of systematic data collection would contribute to a more accurate calculation of the mortality risk.