Characterising paediatric mortality during and after acute illness in Sub-Saharan Africa and South Asia: a secondary analysis of the CHAIN cohort using a machine learning approach

Abstract

Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC).

Methods: A cohort of 3101 children aged 2-24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering.

Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0-2), and children without signs of severe illness (3% died, 95% CI: 2-4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62-82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92-100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0-1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0-1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25-37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34-62%).

Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations.

Funding: Bill & Melinda Gates FoundationOPP1131320.

Keywords: Explainable machine learning; Malnutrition; Paediatric mortality; Post-discharge mortality; Wasting.

Fig. 1

Modelling pipeline applied to data from the Childhood Acute Illness & Nutrition (CHAIN) Cohort. AUC—area under the curve.

Fig. 2

Top 25 predictors of 30-day and 180-day mortality. The 25 variables most influential variables in each model are display in descending order. Each participant has one dot on each variable line, this dot is colored by the value of that variable–pink for a high value, blue for a low value, grey for a missing value. The dots are positioned along the x-axis according to contribution of that variable to the child's predicted risk, left-side indicating the variable lower the predicted risk and the right-side increased the risk. For example, the pink dots on the left side of the MUAC variable in the 30-day model indicate that high MUAC was associated with lower risk of death. adm—admission, Alk. phosphatase—Alkaline Phosphatase, ALT—Alanine transaminase, disch.—discharge, HAZ—height-for-age z-score, hosp. —hospital, ICU—intensive care unit admission, Inorg. phosphate—inorganic phosphate, MUAC—mid upper arm circumference, WAZ—weight-for-age z-score, WLZ—weight-for-height z-score.

Fig. 3

Kaplan Meier survival curves for the clusters derived from the 30-day and post-discharge models. Children at risk and number events in each cluster are given for major timepoints in Appendix 4, Supplementary Table S3.

Fig. 4

Phenotypes of 30-dayand post-dischargemortality. The color and value of each cell represents the standardised difference between the mean of the cluster and the total sample mean for the relevant variable, i.e. (Mean_cluster–Mean_sample)/Standard Deviation_sample. Alk. phosphatase—Alkaline Phosphatase, ALT—Alanine transaminase, disch—discharge, HAZ—height-for-age z-score, HIV- human immunodeficiency virus, hosp.—hospital, Inorg. phosphate—inorganic phosphate, LAMA—leaving against medical advice, MUAC—mid upper arm circumference, RDT—rapid diagnostic test, WAZ—weight-for-age z-score, WLZ—weight-for-height z-score.