ArticleCritical care medicine2019
Variation in Identifying Sepsis and Organ Dysfunction Using Administrative Versus Electronic Clinical Data and Impact on Hospital Outcome Comparisons.
Article in Critical care medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 citations in OpenAlex.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- ICD-10-CM-Based Algorithms to Identify Bacteremia Incorporating Pathogen-Specific Codes: A Validation Study.Pharmacoepidemiology and drug safety · 2026Article
- Association Between Community-Level Social Determinants of Health and Sepsis Incidence and Mortality: A Scoping Review.Critical care explorations · 2026Article
- Direct Measurement of Organ Procurement Organization Donor Potential Using Comprehensive Referral Analysis: Feasibility and Implications for Assessing Organ Procurement Organization Performance.Transplantation · 2026Article
- Article
- Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model.JAMA network open · 2026Article
- Think sepsis, write sepsis, code sepsis - patient characteristics associated with sepsis (under-)coding in administrative health data.Infection · 2026Article
- Pre-admission prescription of selective serotonin reuptake inhibitors and mortality in critical illness: a national cohort study from 2020 to 2023.Annals of intensive care · 2026Article
- Association between isolation room admission in intensive care units and long-term psychiatric disorders: a nationwide cohort study.Critical care (London, England) · 2025Article
- New persistent opioid use among ICU survivors after discharge: incidence, predictors, and nationwide cohort analysis.Critical care (London, England) · 2025Article
- Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort study.EClinicalMedicine · 2025Article
- Accuracy of the modified Global Burden of Disease International Classification of Diseases coding methods for identifying sepsis: a prospective multicentre cohort study.Critical care (London, England) · 2025Observational
- Impacts of the COVID-19 pandemic on sepsis incidence, etiology and hospitalization costs in France: a retrospective observational study.BMC infectious diseases · 2025Observational
- SepsisJournal of medical microbiology · 2025Review
- Trends in Empiric Broad-Spectrum Antibiotic Use for Suspected Community-Onset Sepsis in US Hospitals.JAMA network open · 2024Article
- Understanding the biases to sepsis surveillance and quality assurance caused by inaccurate coding in administrative health data.Infection · 2024Article
- Improving Sepsis Outcomes in the Era of Pay-for-Performance and Electronic Quality Measures: A Joint IDSA/ACEP/PIDS/SHEA/SHM/SIDP Position Paper.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Care pathways of sepsis survivors: sequelae, mortality and use of healthcare services in France, 2015-2018.Critical care (London, England) · 2023Article
- Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data.Scientific reports · 2023Article
- Patterns, Predictors, and Intercenter Variability in Empiric Gram-Negative Antibiotic Use Across 928 United States Hospitals.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
Corrections and comments
- Commented on by
Authors and funding
22 authors at 7 institutions in 1 country.
Funding
Abstract
objectivesAdministrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. We evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons. DESIGN, SETTING, AND PATIENTS: Retrospective cohort study of 4.3 million adult encounters at 193 U.S. hospitals in 2013-2014.
interventionsNone. MEASUREMENTS AND MAIN
resultsSepsis was defined using electronic health record-derived clinical indicators of presumed infection (blood culture draws and antibiotic administrations) and concurrent organ dysfunction (vasopressors, mechanical ventilation, doubling in creatinine, doubling in bilirubin to ≥ 2.0 mg/dL, decrease in platelets to < 100 cells/µL, or lactate ≥ 2.0 mmol/L). We compared claims for sepsis prevalence and mortality rates between both methods. All estimates were reliability adjusted to account for random variation using hierarchical logistic regression modeling. The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-54%) for sepsis, 66% (range, 26-84%) for acute kidney injury, 39% (range, 16-60%) for thrombocytopenia, 36% (range, 29-44%) for hepatic injury, and 66% (range, 29-84%) for shock. Correlation between claims and clinical data was moderate for sepsis prevalence (Pearson coefficient, 0.64) and mortality (0.61). Among hospitals in the lowest sepsis mortality quartile by claims, 46% shifted to higher mortality quartiles using clinical data. Using implicit sepsis criteria based on infection and organ dysfunction codes also yielded major differences versus clinical data.
conclusionsVariation in the accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospitals' sepsis rates and outcomes. Using objective clinical data may facilitate more meaningful hospital comparisons.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.