Assessing the performance of clinical diagnostic models for dehydration among patients with cholera and undernutrition in Bangladesh. Issue 11 (16th September 2021) - Gainey, Monique; Qu, Kexin; Garbern, Stephanie C.; Barry, Meagan A.; Lee, John Austin; Nasrin, Sabiha; Monjory, Mahmuda; Nelson, Eric J.; Rosen, Rochelle; Alam, Nur H.; Schmid, Christopher H.; Levine, Adam C.
Abstract: Objective: Accurately assessing dehydration severity is a critical step in reducing mortality from diarrhoea, but is complicated by cholera and undernutrition. This study seeks to assess the accuracy of two clinical diagnostic models for dehydration among patients over five years with cholera and undernutrition and compare their respective performance to the World Health Organization (WHO) algorithm. Methods: In this secondary analysis of data collected from the NIRUDAK study, accuracy of the full and simplified NIRUDAK models for predicting severe and any dehydration was measured using the area under the Receiver Operator Characteristic curve (AUC) among patients over five with/without cholera and with/without wasting. Bootstrap with 1000 iterations was used to compare the m‐index for each NIRUDAK model to that of the WHO algorithm. Results: A total of 2, 139 and 2, 108 patients were included in the nutrition and cholera subgroups respectively with an overall median age of 35 years (IQR = 42) and 49.6% female. All subgroups had acceptable discrimination in diagnosing severe or any dehydration (AUC > 0.60); though the full NIRUDAK model performed best among patients without cholera, with an AUC of 0.82 (95%CI:0.79, 0.85) and among patients without wasting, with an AUC of 0.79 (95%CI:0.76, 0.81). Compared with the WHO's algorithm, both the full and simplified NIRUDAK models performed significantly better in terms of their m‐index ( p  < 0.001) for all comparisons, except for the simplified NIRUDAK model in the wasting group. Conclusions: Both the full and simplified NIRUDAK models performed less well in patients over five years with cholera and/or wasting; however, both performed better than the WHO algorithm.