Observational studyBMJ quality & safety2020
Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data.
Observational study in BMJ quality & safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 61 citations in OpenAlex.
- A surveillance method to identify patients with sepsis from electronic health records in Hong Kong: a single centre retrospective study.BMC infectious diseases · 2020Trial
- Accuracy of Electronic Medical Records for Quantifying Rates of Sedative and Analgesic Infusions for Acute Disorders of Consciousness in Big Data Research.Neurocritical care · 2026Article
- Effect of restrictive fluid balance in critically ill patients with acute kidney injury: a target trial emulation.Critical care (London, England) · 2026Article
- Association between systemic immune-inflammation index and ICU-acquired infection in critically Ill patients without infection: a competing risks analysis from a retrospective cohort.BMC infectious diseases · 2026Article
- 72-hour SOFA changes and risk stratification for invasive mechanical ventilation in patients with community-acquired Pneumonia.Scientific reports · 2026Article
- Automated Calculation of Sequential Organ Failure Assessment (SOFA) Score in the Intensive Care Unit: Algorithm Development, Validation, and Association With 30-Day Mortality.Acta anaesthesiologica Scandinavica · 2026Article
- Accuracy of electronic medical records to quantify rates of sedative and analgesic infusions for acute disorders of consciousness big data research.medRxiv : the preprint server for health sciences · 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
- Validating adverse events in administrative healthcare data in ireland: a retrospective chart review study.BMC health services research · 2025Article
- The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence.Journal of internal medicine · 2025Review
- Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.BMC medical informatics and decision making · 2025Observational
- Development and prospective implementation of a large language model based system for early sepsis prediction.NPJ digital medicine · 2025Article
- Validation of a software application using electronic health records for automatic detection of community onset sepsis.Scientific reports · 2025Article
- Development and Prospective Implementation of a Large Language Model based System for Early Sepsis Prediction.medRxiv : the preprint server for health sciences · 2025Article
- Population Sepsis Incidence, Mortality, and Trends in Hong Kong Between 2009 and 2018 Using Clinical and Administrative Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025Article
- A dynamic customized electronic health record rule based clinical decision support tool for standardized adult intensive care metrics.JAMIA open · 2024Article
- Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.JMIR medical informatics · 2024Article
- Validity of Routinely Reported Rutherford Scores Reported by Clinicians as Part of Daily Clinical Practice.The International journal of angiology : official publication of the International College of Angiology, Inc · 2024Article
- A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024Article
- The accuracy of fully-automated algorithms for the surveillance of central venous catheter-related bloodstream infection in hospitalised patients.Antimicrobial resistance and infection control · 2024Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards.
methodsA rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by
resultsIn total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards.
conclusionsA fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.
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