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Child and Family Outcomes After PICU Admission: Creation of an Open Access Literature Database Using a Global Team of
Rebecca E Hay1,2, David J Zorko3, Katie O'Hearn4
1Division of Pediatric Critical Care, Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada.
Insights
A global team and machine learning efficiently synthesized research on child health outcomes after pediatric intensive care unit (PICU) admission. This created an open-access database for future studies on post-PICU patient recovery.
Area of Science:
- Pediatric critical care medicine
- Health outcomes research
- Systematic review methodology
Background:
- Growing body of literature on child health outcomes post-PICU admission necessitates efficient synthesis.
- Need for an open-access repository to consolidate research on longer-term health outcomes after pediatric intensive care unit (PICU) discharge.
Purpose of the Study:
- To create an open-access scoping repository of literature on longer-term health outcomes after PICU admission.
- To utilize a large multinational team (crowdsourcing) and a machine learning (ML) algorithm for literature synthesis.
Main Methods:
- Registered scoping review conducted across MEDLINE, Embase, CINAHL, and CENTRAL databases (2000-2022).
- Hybrid approach combining human crowdsourcing (Evidence Hackathon participants) and ML for screening over 16,000 citations.
- Studies included observational or interventional research on children (0-17 years) and families, with outcomes measured >2 weeks post-PICU discharge.
Main Results:
- 1,301 studies were included after full-text review from 16,055 eligible citations.
- Literature screening was completed in under 2 months, adhering to systematic review standards.
- The hybrid human crowdsourcing and ML approach achieved 98% sensitivity.
Conclusions:
- A collaborative, global PICU team integrated with ML successfully synthesized large datasets efficiently and accurately.
- The developed open-access database facilitates future research, networking, and collaborative engagement in post-PICU outcomes.
- Future work should focus on database maintenance, utilization, and dissemination of research findings.
Objectives:
As research examining child health outcomes after PICU admission grows, so does the need for the identification and synthesis of a large body of literature. We aimed to create an open-access scoping repository of literature describing longer-term health outcomes after PICU admission, using a large multinational team (crowdsourcing) and a machine learning (ML) algorithm.
Data Sources:
We performed a registered scoping review (OSF DOI10.17605/OSF.IO/HE5VB; Registered November 21, 2022) using MEDLINE, Embase, CINAHL, and CENTRAL databases, 2000-2022, with no language restrictions.
Study Selection:
Observational or interventional studies describing outcomes of children (0-17 yr old) and their families or caregivers measured greater than 2 weeks post-PICU discharge. Titles and abstracts and full texts were initially screened by a large team of PICU healthcare workers and researchers who were recruited as part of an Evidence Hackathon event at the 2022 World Federation of Pediatric Intensive and Critical Care Societies conference. Initial screening results from 5000 citations were used to develop and validate an ML algorithm, after which a hybrid human crowdsourcing and ML approach was used to screen the remaining 11,055 studies.
Data Extraction:
Not applicable.
Data Synthesis:
Of 16,055 eligible citations, 1,301 met the criteria at full text for inclusion in the database. The screening was completed in just under 2 months while adhering to the gold standard systematic review methodology. Sensitivity for the hybrid human crowdsourcing and ML was 98%.
Conclusions:
A collaborative, global PICU team integrated with ML was successful in efficient and accurate large data synthesis, producing a scoping open-access database of studies reporting on post-PICU outcomes. The development of this repository has implications for future reviews, providing opportunities for networking and collaborative engagement in research. The next steps should examine database maintenance, utilization, and dissemination of research findings.
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