Observational studyPediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2020
Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data.
Observational study 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, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 49 citations in OpenAlex.
- Speed of intravenous fluid on glycocalyx integrity and central blood volume in a porcine septic shock model.Intensive care medicine experimental · 2026Article
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- Neuropathologic Autopsy Findings in Pediatric Sepsis: A Two-Center, Retrospective Study.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2026Article
- Derivation and Validation of Predictive Models for Early Pediatric Sepsis.JAMA pediatrics · 2025Article
- Machine learning prediction of mortality in pediatric fungemia using the Candida score.Scientific reports · 2025Article
- Phoenix Sepsis Criteria in Critically Ill Children: Retrospective Validation Using a United States Nine-Center Dataset, 2012-2018.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Article
- Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world.The Lancet. Digital health · 2024Review
- Predictive models for sepsis in children with Staphylococcus aureus bloodstream infections: a retrospective cohort study.BMC pediatrics · 2023Article
- Article
- Observational
- Hypothermia or hyperthermia, which is associated with patient outcomes in critically ill children with sepsis? -A retrospective study.BMJ open · 2023Article
- Integrating structured and unstructured data for timely prediction of bloodstream infection among children.Pediatric research · 2023Article
- Descriptive statistics and risk factor analysis of children with community-acquired septic shock.Journal of intensive care · 2023Article
- Investigating Racial and Socioeconomic Characteristics in Pediatric Sepsis Using Electronic Health Data.Hospital pediatrics · 2023Article
- Article
- Observational
- Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2022Article
- Relation of Procollagen Type III Amino Terminal Propeptide Level to Sepsis Severity in Pediatrics.Children (Basel, Switzerland) · 2021Article
- Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.Computers in biology and medicine · 2021Article
- Surviving Sepsis Screening: The Unintended Consequences of Continuous Surveillance.Hospital pediatrics · 2020Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesA method to identify pediatric sepsis episodes that is not affected by changing diagnosis and claims-based coding practices does not exist. We derived and validated a surveillance algorithm to identify pediatric sepsis using routine clinical data and applied the algorithm to study longitudinal trends in sepsis epidemiology.
designRetrospective observational study.
settingSingle academic children's hospital. PATIENTS: All emergency and hospital encounters from January 2011 to January 2019, excluding neonatal ICU and cardiac center. EXPOSURE: Sepsis episodes identified by a surveillance algorithm using clinical data to identify infection and concurrent organ dysfunction.
interventionsNone. MEASUREMENTS AND MAIN
resultsA surveillance algorithm was derived and validated in separate cohorts with suspected sepsis after clinician-adjudication of final sepsis diagnosis. We then applied the surveillance algorithm to determine longitudinal trends in incidence and mortality of pediatric sepsis over 8 years. Among 93,987 hospital encounters and 1,065 episodes of suspected sepsis in the derivation period, the surveillance algorithm yielded sensitivity 78% (95% CI, 72-84%), specificity 76% (95% CI, 74-79%), positive predictive value 41% (95% CI, 36-46%), and negative predictive value 94% (95% CI, 92-96%). In the validation period, the surveillance algorithm yielded sensitivity 84% (95% CI, 77-92%), specificity of 65% (95% CI, 59-70%), positive predictive value 43% (95% CI, 35-50%), and negative predictive value 93% (95% CI, 90-97%). Notably, most "false-positives" were deemed clinically relevant sepsis cases after manual review. The hospital-wide incidence of sepsis was 0.69% (95% CI, 0.67-0.71%), and the inpatient incidence was 2.8% (95% CI, 2.7-2.9%). Risk-adjusted sepsis incidence, without bias from changing diagnosis or coding practices, increased over time (adjusted incidence rate ratio per year 1.07; 95% CI, 1.06-1.08; p < 0.001). Mortality was 6.7% and did not change over time (adjusted odds ratio per year 0.98; 95% CI, 0.93-1.03; p = 0.38).
conclusionsAn algorithm using routine clinical data provided an objective, efficient, and reliable method for pediatric sepsis surveillance. An increased sepsis incidence and stable mortality, free from influence of changes in diagnosis or billing practices, were evident.
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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.