Evidence map›Paper›PMID 29344875›Full record

ArticleIndian journal of pediatrics2018

Development of a Prognostic Prediction Model to Determine Severe Dengue in Children.

Priya Sreenivasan, Geetha S, Sasikala K

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Article in Indian journal of pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 4 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

11 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Prognostic Models in Patients with Dengue: A Systematic Review.The American journal of tropical medicine and hygiene · 2025
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  8. Frontiers in public health · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Priya SreenivasanDepartment of Pediatrics, Government Medical College, Thiruvananthapuram, Thiruvananthapuram District, Kerala, 695011, India. priyavineed16@gmail.com.
Geetha SDepartment of Pediatrics, Government Medical College, Thiruvananthapuram, Thiruvananthapuram District, Kerala, 695011, India.
Sasikala KClinical Epidemiology Research & Training Centre (CERTC), Government Medical College, Thiruvananthapuram, Kerala, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a prognostic prediction model using the seven warning signs highlighted by WHO revised Dengue fever classification 2009 to determine severe dengue in children.

methodsIn this prospective analytical study conducted in a tertiary care centre, consecutive sampling of all children aged 1mo to 12y admitted with serologically confirmed Dengue was done from May 2015 through August 2016. After excluding 27 patients with co-infections and co-morbidities, 359 patients were followed up daily to assess clinical and laboratory progression till discharge/ death. Independent predictors were abdominal pain or tenderness, persistent vomiting, lethargy, mucosal bleed, clinical fluid accumulation, hepatomegaly >2 cm and rising hematocrit concurrent with platelet count <100 × 10

resultsAmong 359 children, 93 progressed to severe dengue. In univariate analysis, significant predictors were clinical fluid accumulation (OR 4.773, p = 0.000, 95%CI 2.511-9.075), persistent vomiting (OR 1.944, p = 0.010, 95%CI 1.170-3.225), mucosal bleed (OR 2.045, p = 0.019, 95%CI 1.127-3.711) and hematocrit ≥0.40 concurrent with platelet count <100 × 10

conclusionsAmong seven WHO warning signs, predictors of severe dengue as suggested by the present multivariable prediction model include clinical fluid accumulation, persistent vomiting and hematocrit ≥0.40 concurrent with platelet count <100 × 10

Indexed as

ChildChild, PreschoolFemaleHematocritHumansIndiaInfantMalePrognosisProspective StudiesSevere DengueVomitingDengue feverPediatricPrediction modelSevere dengueWarning signsWHO 2009 classification

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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.