infection control & hospital epidemiology july 2018, vol. 39, no. 7 original article
Real-Time, Automated Detection of Ventilator-Associated Events: Avoiding Missed Detections, Misclassifications, and False Detections Due to Human Error
Erica S. Shenoy, MD, PhD;1,2,3,5,6 Eric S. Rosenthal, MD;3,4,5,6 Yu-Ping Shao, MS;4,5 Siddharth Biswal, MS;7 Manohar Ghanta, MS;4,5 Erin E. Ryan, MPH, CCRP;1 Dolores Suslak, MSN, CIC;1 Nancy Swanson, RN, CIC;1 Valdery Moura Junior, MS, MBA;4,5 David C. Hooper, MD;1,2,3,a M. Brandon Westover, MD, PhD3,4,5,6,a
objective. To validate a system to detect ventilator associated events (VAEs) autonomously and in real time.
design. Retrospective 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.
setting. The Massachusetts General Hospital, a tertiary-care academic health center, during January–March 2015 (development cohort) and January–March 2016 (validation cohort). patients. Ventilated patients in 4 intensive care units.
methods. The 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.
results. In 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.
conclusions. Manual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.
Infect Control Hosp Epidemiol 2018;39:826–833
In 2013, the Centers for Disease Control and Prevention (CDC) National Health Safety Network (NHSN), imple- mented the ventilator-associated event (VAE) surveillance algorithm definition.1 The VAE definition replaced prior sur- veillance for ventilator-associated pneumonia (VAP), with 3 tiers of conditions: ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC),
and possible VAP (PVAP). The definition was designed to rely on objective and potentially automatable criteria; its features were expected to improve reliability and efficiency of surveil- lance by utilizing data extractable from the electronic health record (EHR).2,3 Since that time, approaches to VAE surveillance using extracts from EHRs have been described,4–7 with varying levels
Affiliations: 1. Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts; 2. Division of Infectious Diseases, Massachusetts General
Hospital, Boston, Massachusetts; 3. Harvard Medical School, Boston, Massachusetts; 4. Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts; 5. Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts; 6. Health Sciences and Technology Program, Harvard Medical School, Boston, Massachusetts; 7. Department of Computer Science, Georgia Institute of Technology College of Computing, Atlanta, Georgia.
PREVIOUS PRESENTATION. This work was presented at ID Week 2017 (abstract no. 2151) on October 7, 2017, in San Diego, California. aSenior authors with equal contribution.
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2018/3907-0009. DOI: 10.1017/ice.2018.97 Received December 4, 2017; accepted April 5, 2018; electronically published May 17, 2018
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