ArticleInfection control and hospital epidemiology2018
Real-Time, Automated Detection of Ventilator-Associated Events: Avoiding Missed Detections, Misclassifications, and False Detections Due to Human Error.
Article in Infection control and hospital epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Ventilator-Associated Pneumonia (VAP) Prevention Bundle: A Multicenter Cross-Sectional Saudi Study to Assess Knowledge, Adherence, and Perceived Barriers Among ICU Practitioners in Hail Region.Pathogens (Basel, Switzerland) · 2026Article
- Precise identification of respiratory system air leakage via dynamic ventilator waveform monitoring: a controlled trial to improve mechanical ventilation care efficiency.Respiratory research · 2025Article
- Enhancing ICU Outcomes Through Intelligent Monitoring Systems: A Comparative Study on Ventilator-Associated Events.Journal of clinical medicine · 2024Article
- Ventilator-associated events: From surveillance to optimizing management.Journal of intensive medicine · 2023Review
- Automating surveillance for healthcare-associated infections: Rationale and current realities (Part I/III).Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Review
- A Method to Explore Variations of Ventilator-Associated Event Surveillance Definitions in Large Critical Care Databases in the United States.Critical care explorations · 2022Article
- Strategies to prevent ventilator-associated pneumonia, ventilator-associated events, and nonventilator hospital-acquired pneumonia in acute-care hospitals: 2022 Update.Infection control and hospital epidemiology · 2022Article
- An automated retrospective VAE-surveillance tool for future quality improvement studies.Scientific reports · 2021Article
- Article
- Ventilator-associated events versus ventilator-associated respiratory infections-moving into a new paradigm or merging both concepts, instead?Annals of translational medicine · 2018Review
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Authors and funding
11 authors.
Funding
Abstract
OBJECTIVETo validate a system to detect ventilator associated events (VAEs) autonomously and in real time.DESIGNRetrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.SETTINGThe Massachusetts General Hospital, a tertiary-care academic health center, during January-March 2015 (development cohort) and January-March 2016 (validation cohort).PATIENTSVentilated patients in 4 intensive care units.METHODSThe automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance-automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.RESULTSIn the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.CONCLUSIONSManual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.Infect Control Hosp Epidemiol 2018;826-833.
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