Evidence map›Paper›PMID 33472837›Full record

SynthesisBMJ global health2021

Predictors of disease severity in children presenting from the community with febrile illnesses: a systematic review of prognostic studies.

Arjun Chandna, Rainer Tan, Michael Carter, Ann Van Den Bruel, Jan Verbakel, Constantinos Koshiaris, Nahya Salim, Yoel Lubell, Paul Turner, Kristina Keitel

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 6% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

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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

10 authors at 7 institutions in 6 countries.

Arjun Chandna *Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia arjun@tropmedres.ac arjunchandna@gmail.com.ORCID 0000-0003-1313-7922
Rainer Tan *Unisanté Centre for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
Michael CarterDepartment of Women and Children's Health, King's College London, London, UK.
Ann Van Den BruelAcademic Centre of General Practice, University of Leuven, Leuven, Flanders, Belgium.
Jan VerbakelAcademic Centre of General Practice, University of Leuven, Leuven, Flanders, Belgium.
Constantinos KoshiarisNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Nahya SalimIfakara Health Institute, Dar-es-Salaam, Tanzania.
Yoel LubellCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Paul Turner *Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia.ORCID 0000-0002-1013-7815
Kristina Keitel *Swiss Tropical and Public Health Institute, Basel, Basel-Stadt, Switzerland.
University of Oxford · GBAngkor Hospital for Children · KHIfakara Health Institute · TZKing's College London · GBKU Leuven · BESwiss Tropical and Public Health Institute · CHUniversity of Bern · CH

Funding

Wellcome Trust 106 698Z/14/Z
6 · The paper itself

Abstract

introductionEarly identification of children at risk of severe febrile illness can optimise referral, admission and treatment decisions, particularly in resource-limited settings. We aimed to identify prognostic clinical and laboratory factors that predict progression to severe disease in febrile children presenting from the community.

methodsWe systematically reviewed publications retrieved from MEDLINE, Web of Science and Embase between 31 May 1999 and 30 April 2020, supplemented by hand search of reference lists and consultation with an expert Technical Advisory Panel. Studies evaluating prognostic factors or clinical prediction models in children presenting from the community with febrile illnesses were eligible. The primary outcome was any objective measure of disease severity ascertained within 30 days of enrolment. We calculated unadjusted likelihood ratios (LRs) for comparison of prognostic factors, and compared clinical prediction models using the area under the receiver operating characteristic curves (AUROCs). Risk of bias and applicability of studies were assessed using the Prediction Model Risk of Bias Assessment Tool and the Quality In Prognosis Studies tool.

resultsOf 5949 articles identified, 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children were included. Heterogeneity between studies precluded formal meta-analysis. Malnutrition (positive LR range 1.56-11.13), hypoxia (2.10-8.11), altered consciousness (1.24-14.02), and markers of acidosis (1.36-7.71) and poor peripheral perfusion (1.78-17.38) were the most common predictors of severe disease. Clinical prediction model performance varied widely (AUROC range 0.49-0.97). Concerns regarding applicability were identified and most studies were at high risk of bias.

conclusionsFew studies address this important public health question. We identified prognostic factors from a wide range of geographic contexts that can help clinicians assess febrile children at risk of progressing to severe disease. Multicentre studies that include outpatients are required to explore generalisability and develop data-driven tools to support patient prioritisation and triage at the community level. PROSPERO REGISTRATION NUMBER: CRD42019140542.

Indexed as

HospitalizationModels, StatisticalBiasChildHumansPrognosisSeverity of Illness Indexchild healthdiseasesdisordersinfectionsinjuriespaediatricspublic healthsystematic review

Identifiers

PMID33472837
PMCPMC7818824
OpenAlexW3122478246

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.