Evidence map›Paper›PMID 41283245›Full record

ArticleMedical sciences (Basel, Switzerland)2025

Derivation of a Simple Risk Scoring Scheme for Prediction of Severe Dengue Infection in Adult Patients in Thailand.

Surangrat Pongpan, Patcharin Khamnuan, Pantitcha Thanatrakolsri, Supa Vittaporn, Punnaphat Daraswang

Abstract read
In one paragraph

Article in Medical sciences (Basel, Switzerland), 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

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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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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Surangrat PongpanFaculty of Public Health, Thammasat University, Lampang 52190, Thailand.ORCID 0000-0001-7164-2412
Patcharin KhamnuanFaculty of Public Health, Thammasat University, Lampang 52190, Thailand.ORCID 0000-0002-7017-3041
Pantitcha ThanatrakolsriFaculty of Public Health, Thammasat University, Lampang 52190, Thailand.ORCID 0000-0002-5498-1698
Supa VittapornFaculty of Public Health, Thammasat University, Lampang 52190, Thailand.
Punnaphat DaraswangBuriram Hospital, Muang, Buriram 31000, Thailand.ORCID 0000-0001-8197-3530

Funding

Thammasat University Research Fund Contract No. TUFT-FF 44/2565the Thailand Science Research and Innovation Fundamental [Fund in 2022, Contract No. TUFF 56/2565]
6 · The paper itself

Abstract

BACKGROUND/

objectivesSevere dengue infection remains a major public health burden in Thailand, where timely identification of high-risk patients is essential for effective clinical management. Existing predictive models are often complex and less feasible in routine practice. This study aimed to develop a simple risk scoring system to predict dengue severity based on patient characteristics and routine clinical data.

methodsRetrospective data of adult dengue patients from nine general hospitals in Thailand from 2019 to 2022 were reviewed. Dengue infection was classified into two groups using the WHO 2009 modified criteria: non-severe (n = 577) and severe (n = 107). Demographic data, clinical characteristics, and laboratory findings were analyzed using logistic regression. Regression coefficients of significant predictors of severe dengue were converted into weighted item scores. Total scores were categorized into three risk levels based on probability distribution cut-off points.

resultsThe severity score stratified patients into three risk groups with significantly different prognoses: ≤2.0 points (low risk), 2.5-5.0 points (moderate risk), and ≥5.5 points (high risk). The positive likelihood ratios for low-, moderate-, and high-risk groups were 0.12, 1.05, and 28.76, respectively. The distribution of severity scores differed significantly between non-severe and severe cases. The scoring system discriminated between non-severe and severe dengue with an area under the receiver operating characteristic curve (AUROC) of 88.04% (95% CI, 83.99-92.08).

conclusionsThe derived dengue severity scoring system classified patients into low, moderate, and high risk with excellent discriminatory performance, effectively distinguishing non-severe from severe dengue infection.

Indexed as

DengueSevere DengueAdultAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk AssessmentRisk FactorsROC CurveSeverity of Illness IndexThailandYoung Adultadult patientsclinical prediction rulepredictive modelrisk scoresevere dengueThailand

Identifiers

PMID41283245
PMCPMC12641686

What Socratic holds

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LicenceCC BY
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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 Socratic graph.