Evidence map›Paper›PMID 34469615›Full record

ArticleTropical medicine & international health : TM & IH2021

Assessing the performance of clinical diagnostic models for dehydration among patients with cholera and undernutrition in Bangladesh.

Monique Gainey, Kexin Qu, Stephanie C Garbern, Meagan A Barry, John Austin Lee, Sabiha Nasrin, Mahmuda Monjory, Eric J Nelson, Rochelle Rosen, Nur H Alam and 2 more

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in Tropical medicine & international health : TM & IH, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 87% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Monique GaineyRhode Island Hospital, Providence, RI, USA.
Kexin QuDepartment of Biostatistics, Brown University School of Public Health, Providence, RI, USA.
Stephanie C GarbernDepartment of Emergency Medicine, Alpert Medical School, Brown University, Providence, RI, USA.
Meagan A BarryDepartment of Emergency Medicine, Alpert Medical School, Brown University, Providence, RI, USA.
John Austin LeeDepartment of Emergency Medicine, Alpert Medical School, Brown University, Providence, RI, USA.
Sabiha NasrinInternational Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Mahmuda MonjoryInternational Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Eric J NelsonDepartments of Pediatrics and Environmental and Global Health, Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
Rochelle RosenDepartment of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, RI, USA.
Nur H AlamInternational Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Christopher H SchmidDepartment of Biostatistics, Brown University School of Public Health, Providence, RI, USA.
Adam C LevineDepartment of Emergency Medicine, Alpert Medical School, Brown University, Providence, RI, USA.
Brown University · USInternational Centre for Diarrhoeal Disease Research · BDProvidence College · USUniversity of Florida Health · US

Funding

External Validation and Implementation of a Novel Mobile Health Application for Acute Diarrhea Management across the LifespanR01DK116163 · NIDDK · RHODE ISLAND HOSPITAL · PI Adam Carl Levine · 2018 to 2026
$4.7M
NIDDK NIH HHS R01 DK116163
6 · The paper itself

Abstract

objectiveAccurately 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.

methodsIn 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.

resultsA 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.

conclusionsBoth 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.

Indexed as

AdolescentAdultAlgorithmsArea Under CurveBangladeshChildChild, PreschoolCholeraDehydrationFemaleFluid TherapyHumansMaleMalnutritionMiddle AgedPredictive Value of Testscholeradehydrationdiarrhoeaglobal healthundernutrition

Identifiers

PMID34469615
PMCPMC9118139
OpenAlexW3196992626

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.