Abstract:
Weight‐for‐age (WFA) growth faltering often precedes severe acute malnutrition
(SAM) in children, yet it is often missed during routine growth
monitoring. Automated interpretation of WFA growth within electronic health
records could expedite the identification of children at risk of SAM. This study
aimed to develop an automated screening tool to predict SAM risk from WFA
growth, and to determine its predictive ability compared with simple changes in
weight or WFA z‐score. To develop the screening tool, South African child
growth experts (n = 30) rated SAM risk on 100 WFA growth curves, which were
then used to train an artificial neural network (ANN) to assess SAM risk from
consecutive WFA z‐scores. The ANN was validated in 185 children under five
(63 SAM cases; 122 controls) using diagnostic accuracy methodology. The
ANN's performance was compared with that of changes in weight or WFA
z‐score. Even though experts' SAM risk ratings of the WFA growth curves
differed considerably, the ANN achieved a sensitivity of 73.0% (95% confidence
interval [CI]: 60.3; 83.4), specificity of 86.1% (95% CI: 78.6; 91.7) and receiveroperating
characteristic curve area of 0.795 (95% CI: 0.732; 0.859) during
validation with real cases, outperforming changes in weight or WFA z‐scores.
The ANN, as an automated screening tool, could markedly improve the identification of children at risk of SAM using routinely collected WFA growth
information.
