Evidence map›Paper›PMID 32032262›Full record

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.

Scott L Weiss, Fran Balamuth, Marianne Chilutti, Mark Jason Ramos, Peter McBride, Nancy-Ann Kelly, K Joy Payton, Julie C Fitzgerald, Jeffrey W Pennington

Open access · greenAbstract readObservational Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.5field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 49 citations in OpenAlex.

  1. Article
  2. Article
  3. 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 · 2026
    Article
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  6. 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 · 2025
    Article
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  17. 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 · 2022
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Scott L Weiss
Fran BalamuthPediatric Sepsis Program at the Children's Hospital of Philadelphia, Philadelphia, PA.
Marianne ChiluttiDepartment of Biomedical and Health Informatics, Children's Hospital of Philadelphia, PA.
Mark Jason RamosDepartment of Biomedical and Health Informatics, Children's Hospital of Philadelphia, PA.
Peter McBrideDepartment of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Nancy-Ann KellyDepartment of Anesthesiology and Critical Care, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
K Joy PaytonDepartment of Biomedical and Health Informatics, Children's Hospital of Philadelphia, PA.
Julie C FitzgeraldDepartment of Anesthesiology and Critical Care, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Jeffrey W PenningtonDepartment of Biomedical and Health Informatics, Children's Hospital of Philadelphia, PA.
Children's Hospital of Philadelphia · USUniversity of Pennsylvania · US

Funding

Pediatric septic acute kidney injury: personalizing antibiotic dosing through understanding acute kidney injury risk factors and biomarker profilesK23DK119463 · NIDDK · CHILDREN'S HOSP OF PHILADELPHIA · PI FITZGERALD, JULIE C. HOLLOWS · 2020 to 2024
$810k
Mitochondrial Dysfunction in Pediatric SepsisK23GM110496 · NIGMS · CHILDREN'S HOSP OF PHILADELPHIA · PI WEISS, SCOTT L · 2015 to 2018
$723k
NIDDK NIH HHS K23 DK119463NIGMS NIH HHS K23 GM110496
6 · The paper itself

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.

Indexed as

AlgorithmsElectronic Health RecordsEpidemiological MonitoringAdolescentChildChild, PreschoolFemaleHospital MortalityHospitals, PediatricHumansIncidenceInfantIntensive Care Units, PediatricMaleRetrospective StudiesSepsis

Identifiers

PMID32032262
PMCPMC7008717
OpenAlexW3005470438

What OpenQuestion holds

Textmetadata
LicenceTDM
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.