Evidence map›Paper›PMID 36921001›Full record

ArticlePLoS neglected tropical diseases2023

Demographic characteristics, clinical symptoms, biochemical markers and probability of occurrence of severe dengue: A multicenter hospital-based study in Bangladesh.

Jingli Yang, Abdullah Al Mosabbir, Enayetur Raheem, Wenbiao Hu, Mohammad Sorowar Hossain

Full text readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS neglected tropical diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
–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

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

21 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Epidemiological Characteristics of Dengue Infection in Bangladesh: A Systematic Review.International journal of environmental research and public health · 2026
    Pooled it
  2. Prognostic Models in Patients with Dengue: A Systematic Review.The American journal of tropical medicine and hygiene · 2025
    Pooled it
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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

5 authors.

Jingli YangEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Abdullah Al MosabbirDepartment of Emerging and Neglected Diseases, Biomedical Research Foundation, Dhaka, Bangladesh.
Enayetur RaheemDepartment of Emerging and Neglected Diseases, Biomedical Research Foundation, Dhaka, Bangladesh.
Wenbiao HuEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.ORCID 0000-0001-6422-9240
Mohammad Sorowar HossainDepartment of Emerging and Neglected Diseases, Biomedical Research Foundation, Dhaka, Bangladesh.ORCID 0000-0001-7143-2909

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Establishing reliable early warning models for severe dengue cases is a high priority to facilitate triage in dengue-endemic areas and optimal use of limited resources. However, few studies have identified the complex interactive relationship between potential risk factors and severe dengue. This research aimed to assess the potential risk factors and detect their high-order combinative effects on severe dengue. A structured questionnaire was used to collect detailed dengue outbreak data from eight representative hospitals in Dhaka, Bangladesh, in 2019. Logistic regression and machine learning models were used to examine the complex effects of demographic characteristics, clinical symptoms, and biochemical markers on severe dengue. A total of 1,090 dengue cases (158 severe and 932 non-severe) were included in this study. Dyspnoea (Odds Ratio [OR] = 2.87, 95% Confidence Interval [CI]: 1.72 to 4.77), plasma leakage (OR = 3.61, 95% CI: 2.12 to 6.15), and hemorrhage (OR = 2.33, 95% CI: 1.46 to 3.73) were positively and significantly associated with the occurrence of severe dengue. Classification and regression tree models showed that the probability of occurrence of severe dengue cases ranged from 7% (age >12.5 years without plasma leakage) to 92.9% (age ≤12.5 years with dyspnoea and plasma leakage). The random forest model indicated that age was the most important factor in predicting severe dengue, followed by education, plasma leakage, platelet, and dyspnoea. The research provides new evidence to identify key risk factors contributing to severe dengue cases, which could be beneficial to clinical doctors to identify and predict the severity of dengue early.

Indexed as

DengueSevere DengueBangladeshBiomarkersChildDemographyHospitalsHumansLogistic ModelsBiomarkers

Identifiers

PMID36921001
PMCPMC10042364

What OpenQuestion holds

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LicenceCC BY
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identifiers read4
Read underepoch 390

Registered trials

None linked

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.