Evidence map›Paper›PMID 34111209›Full record

ArticlePloS one2021

Common data elements for predictors of pediatric sepsis: A framework to standardize data collection.

Alishah Mawji, Edmond Li, Arjun Chandna, Teresa Kortz, Samuel Akech, Matthew O Wiens, Niranjan Kissoon, Mark Ansermino

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 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

16 citing papers in PubMed, 1 synthesis or guideline 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

8 authors.

Alishah MawjiDepartment of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0000-0002-1591-947X
Edmond LiSchool of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.
Arjun ChandnaCambodia Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia.ORCID 0000-0003-1313-7922
Teresa KortzDepartment of Pediatrics, Institute of Global Health Sciences, University of California, San Francisco, California, United States of America.
Samuel AkechKenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.ORCID 0000-0001-5126-1225
Matthew O WiensCentre for International Child Health, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada.
Niranjan KissoonDepartment of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada.
Mark AnserminoDepartment of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada.

Funding

Fever Etiology and Prognostic Point-of-Care Biomarkers in African Children with Severe Febrile IllnessK23AI144029 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KORTZ, TERESA · 2020 to 2024
$984k
NIAID NIH HHS K23 AI144029Wellcome Trust
6 · The paper itself

Abstract

backgroundStandardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings.

methodsA preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings.

resultsWe generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information.

conclusionRoutine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.

Indexed as

AlgorithmsChildCommon Data ElementsDelphi TechniqueEarly DiagnosisHumansMobile ApplicationsSepsisTriage

Identifiers

PMID34111209
PMCPMC8192005

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Registered trials

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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.