Mortality following hospital discharge remains a significant threat to child health, particularly in resource-limited settings. In Uganda, the Smart Discharges risk-prediction models have successfully predicted children at the highest risk of death after hospital admission for sepsis to guide a risk-based approach to post-discharge care. We externally validated five models derived from Smart Discharges Uganda in a new cohort of children ages 0 days to 60 months admitted for suspected sepsis at two hospitals in Rwanda. Of 1218 total children (n=413, Kigali; n=805, Ruhengeri), 1161 lived to discharge (95.3%) and 1123 of those completed 6-month follow-up (96.7%). The overall rate of post-discharge mortality was 4.8% (n=58). All five prediction models tested achieved an area under the receiver-operating curve (AUROC) greater than 0.7 (range 0.706 - 0.738). Low outcome rates resulted in moderately wide confidence intervals. Model degradation ranged from 1.1% to 7.7%, as determined by the percent reduction in AUROC between the internal validation of the original Ugandan cohort and the external Rwandan cohort. Calibration plots showed good calibration for all models at predicted probabilities below 10%. There were too few outcomes to assess calibration among those at the highest predicted risk levels. Discrimination was good with minimal degradation of the model despite low outcome rates. Future work to assess model calibration among the highest risk groups is required to ensure models are broadly generalizable to all children with suspected sepsis in Rwanda and in similar, resource-limited settings.

The authors have declared no competing interest.

This study was funded by: A Thrasher Foundation Early Career Award (Hooft) University of British Columbia University of California San Francisco Emergency Medicine Global Health Section

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