ArticlePediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2025
Pediatric Sepsis Phenotype in a Single-Center Cohort Covering 2010-2020: Evolution in Day 1-Day 3 Trajectory and Potential Prognostic Value.
Article in Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in pediatric intensive care units: current applications in sepsis management.World journal of pediatrics : WJP · 2026Pooled it
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3 authors.
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Abstract
objectivesTo examine the utility of day 3 sepsis phenotype classifications compared with day 1 and whether these could be reliably identified using routine clinical data on day 1.
designRetrospective cohort study of pediatric patients managed 2010-2014 and 2018-2020.
settingAcademic children's hospital. PATIENTS: One thousand eight hundred twenty-eight children (1 mo to 18 yr old) admitted to the PICU with suspected infection who received a minimum of 7 days of systemic antibiotics.
interventionsNone. MEASUREMENTS AND MAIN
resultsSubjects showed significant evolution of phenotype from day 1 to day 3, with 31.7-60.9% remaining the same type. Outcomes were worst in those classifying as type D on day 3, with mortality as high as 16.6% in those that were classified as type D on both days 1 and 3, as well as 11.3% in those initially classified as type C (a lower mortality type) on day 1 but type D on day 3. Accurate statistical prediction of day 3 types using multinomial logistic regression and random forest and day 1 data was poor, despite attempts to improve performance.
conclusionsIn our retrospective cohort of patients with sepsis, we identified significant evolution in phenotype over the first 3 days of illness. Day 3 phenotypes may provide more accurate statistical prediction of outcomes, but identification of day 3 phenotypes using data available early in the course of illness is challenging. New methods will likely be required to improve performance in this area.
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