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

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. Keywords: Post-discharge mortality, Risk prediction model, Elastic Net regression, Low-and middle-income countries, Child mortality, Critical illness.