Evidence map›Paper›PMID 39078272›Full record

ArticleThe Journal of infectious diseases2025

Comparison of Predictive Models for Severe Dengue: Logistic Regression, Classification Tree, and the Structural Equation Model.

Hyelan Lee, Anon Srikiatkhachorn, Siripen Kalayanarooj, Aaron R Farmer, Sangshin Park

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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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. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hyelan LeeGraduate School of Urban Public Health, University of Seoul, Republic of Korea.
Anon SrikiatkhachornDepartment of Cell and Molecular Biology, Institute for Immunology and Informatics, University of Rhode Island, Providence.
Siripen KalayanaroojDepartment of Pediatrics, Queen Sirikit National Institute of Child Health, Bangkok, Thailand.
Aaron R FarmerDepartment of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Sangshin ParkGraduate School of Urban Public Health, University of Seoul, Republic of Korea.ORCID 0000-0003-2407-0962

Funding

VIRAL DETERMINANTS OF SEVERE DENGUE DISEASEP01AI034533 · NIAID · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI ROTHMAN, ALAN L · 1994 to 2022
$42.0M
Korea government 2020R1G1A1100914National Research Foundation of KoreaNIAID NIH HHS P01 AI034533NIH HHS P01 AI034533-27
6 · The paper itself

Abstract

backgroundThis study aimed to compare the predictive performance of 3 statistical models-logistic regression, classification tree, and structural equation model (SEM)-in predicting severe dengue illness.

methodsWe adopted a modified classification of dengue illness severity based on the World Health Organization's 1997 guideline. We constructed predictive models using demographic factors and laboratory indicators on the day of fever occurrence, with data from 2 hospital cohorts in Thailand (257 Thai children). Different predictive models for each category of severe dengue illness were developed employing logistic regression, classification tree, and SEM. The model's discrimination abilties were analyzed with external validation data sets from 55 and 700 patients not used in model development.

resultsFrom external validation based on predictors on the day of presentation to the hospital, the area under the receiver operating characteristic curve was from 0.65 to 0.84 for the regression models from 0.73 to 0.85 for SEMs. Classification tree models showed good results of sensitivity (0.95 to 0.99) but poor specificity (0.10 to 0.44).

conclusionsOur study showed that SEM is comparable to logistic regression or classification tree, which was widely used for predicting severe forms of dengue.

Indexed as

DengueModels, StatisticalSevere DengueAdolescentChildChild, PreschoolFemaleHumansInfantLogistic ModelsMaleROC CurveSeverity of Illness IndexThailandclassification treelogistic regressionpredictive modelpredictive validitysevere denguestructural equation model

Identifiers

PMID39078272
PMCPMC11793037

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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.