Adding discharge characteristics to improve six-month post-discharge mortality prediction in under-five children with suspected sepsis in Ugandan hospitals

Abstract Background Many children under five die post hospital discharge in low-and middle-income countries (LMICs), particularly after treatment for severe infections. While some models exist, evidence on risk prediction for post-discharge mortality remains limited, with most relying solely on admission characteristics, overlooking in-hospital disease progression and discharge features. Methods We used secondary data from prospective cohort studies in six Ugandan hospitals (2012-2021) to update models at discharge. Of 8,810 children included, 3,665 were aged <6 months and 5,145 were aged 6-60 months. Models were developed utilizing an elastic net regression approach, with admission variables selected a priori and discharge variables selected based on variable importance ranking. Performance was evaluated by applying 10-fold cross-validation, area under the receiver operating characteristic curve (AUROC), Brier score, and Net Reclassification Index (NRI). Results Models augmented with discharge characteristics outperformed admission-only models. For children aged <6 months, the model AUROC improved by 5.1% (95% CI 3.0 – 7.3, P<0.001 ), achieving an AUROC of 0.81 and a Brier score of 0.06. In the 6–60m cohort, the model AUROC increased by 4.4% (95% CI 2.0 – 6.9, P<0.001 ), with an AUROC of 0.79 and a Brier score of 0.04. The NRI was 10.41% for children <6 months and 14.51% for those 6-60m and was achieved primarily through a reduction of false positive rates. Conclusion Adding only three discharge characteristics to the post-discharge mortality model based on admission characteristics enhanced prediction accuracy, including model calibration, discrimination and risk stratification compared to admission-only models. Key Messages Post-discharge mortality risk prediction models that incorporated discharge characteristics performed better than admission-only models for children under five in Uganda. The augmented model achieved stronger discrimination, improved calibration and substantial reclassification gains, primarily by reducing false positive rates. Most of the benefits of these improved models stem from accurately identifying low-risk survivors, which reduces unwarranted follow-up and enables the more effective utilization of the health system’s limited resources.