An automated retrospective VAE-surveillance tool for future quality improvement studies

Abstract Ventilator-associated pneumonia (VAP) is a frequent complication of mechanical ventilation and is associated with substantial morbidity and mortality. Accurate diagnosis of VAP relies in part on subjective diagnostic criteria. Surveillance according to ventilator-associated event (VAE) crit...

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Autores principales:	Oliver Wolffers, Martin Faltys, Janos Thomann, Stephan M. Jakob, Jonas Marschall, Tobias M. Merz, Rami Sommerstein
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/53f7e860157e4d5c8d89e56ac6334707
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spelling	oai:doaj.org-article:53f7e860157e4d5c8d89e56ac63347072021-11-21T12:17:50ZAn automated retrospective VAE-surveillance tool for future quality improvement studies10.1038/s41598-021-01402-32045-2322https://doaj.org/article/53f7e860157e4d5c8d89e56ac63347072021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01402-3https://doaj.org/toc/2045-2322Abstract Ventilator-associated pneumonia (VAP) is a frequent complication of mechanical ventilation and is associated with substantial morbidity and mortality. Accurate diagnosis of VAP relies in part on subjective diagnostic criteria. Surveillance according to ventilator-associated event (VAE) criteria may allow quick and objective benchmarking. Our objective was to create an automated surveillance tool for VAE tiers I and II on a large data collection, evaluate its diagnostic accuracy and retrospectively determine the yearly baseline VAE incidence. We included all consecutive intensive care unit admissions of patients with mechanical ventilation at Bern University Hospital, a tertiary referral center, from January 2008 to July 2016. Data was automatically extracted from the patient data management system and automatically processed. We created and implemented an application able to automatically analyze respiratory and relevant medication data according to the Centers for Disease Control protocol for VAE-surveillance. In a subset of patients, we compared the accuracy of automated VAE surveillance according to CDC criteria to a gold standard (a composite of automated and manual evaluation with mediation for discrepancies) and evaluated the evolution of the baseline incidence. The study included 22′442 ventilated admissions with a total of 37′221 ventilator days. 592 ventilator-associated events (tier I) occurred; of these 194 (34%) were of potentially infectious origin (tier II). In our validation sample, automated surveillance had a sensitivity of 98% and specificity of 100% in detecting VAE compared to the gold standard. The yearly VAE incidence rate ranged from 10.1–22.1 per 1000 device days and trend showed a decrease in the yearly incidence rate ratio of 0.96 (95% CI, 0.93–1.00, p = 0.03). This study demonstrated that automated VAE detection is feasible, accurate and reliable and may be applied on a large, retrospective sample and provided insight into long-term institutional VAE incidences. The surveillance tool can be extended to other centres and provides VAE incidences for performing quality control and intervention studies.Oliver WolffersMartin FaltysJanos ThomannStephan M. JakobJonas MarschallTobias M. MerzRami SommersteinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Oliver Wolffers Martin Faltys Janos Thomann Stephan M. Jakob Jonas Marschall Tobias M. Merz Rami Sommerstein An automated retrospective VAE-surveillance tool for future quality improvement studies
description	Abstract Ventilator-associated pneumonia (VAP) is a frequent complication of mechanical ventilation and is associated with substantial morbidity and mortality. Accurate diagnosis of VAP relies in part on subjective diagnostic criteria. Surveillance according to ventilator-associated event (VAE) criteria may allow quick and objective benchmarking. Our objective was to create an automated surveillance tool for VAE tiers I and II on a large data collection, evaluate its diagnostic accuracy and retrospectively determine the yearly baseline VAE incidence. We included all consecutive intensive care unit admissions of patients with mechanical ventilation at Bern University Hospital, a tertiary referral center, from January 2008 to July 2016. Data was automatically extracted from the patient data management system and automatically processed. We created and implemented an application able to automatically analyze respiratory and relevant medication data according to the Centers for Disease Control protocol for VAE-surveillance. In a subset of patients, we compared the accuracy of automated VAE surveillance according to CDC criteria to a gold standard (a composite of automated and manual evaluation with mediation for discrepancies) and evaluated the evolution of the baseline incidence. The study included 22′442 ventilated admissions with a total of 37′221 ventilator days. 592 ventilator-associated events (tier I) occurred; of these 194 (34%) were of potentially infectious origin (tier II). In our validation sample, automated surveillance had a sensitivity of 98% and specificity of 100% in detecting VAE compared to the gold standard. The yearly VAE incidence rate ranged from 10.1–22.1 per 1000 device days and trend showed a decrease in the yearly incidence rate ratio of 0.96 (95% CI, 0.93–1.00, p = 0.03). This study demonstrated that automated VAE detection is feasible, accurate and reliable and may be applied on a large, retrospective sample and provided insight into long-term institutional VAE incidences. The surveillance tool can be extended to other centres and provides VAE incidences for performing quality control and intervention studies.
format	article
author	Oliver Wolffers Martin Faltys Janos Thomann Stephan M. Jakob Jonas Marschall Tobias M. Merz Rami Sommerstein
author_facet	Oliver Wolffers Martin Faltys Janos Thomann Stephan M. Jakob Jonas Marschall Tobias M. Merz Rami Sommerstein
author_sort	Oliver Wolffers
title	An automated retrospective VAE-surveillance tool for future quality improvement studies
title_short	An automated retrospective VAE-surveillance tool for future quality improvement studies
title_full	An automated retrospective VAE-surveillance tool for future quality improvement studies
title_fullStr	An automated retrospective VAE-surveillance tool for future quality improvement studies
title_full_unstemmed	An automated retrospective VAE-surveillance tool for future quality improvement studies
title_sort	automated retrospective vae-surveillance tool for future quality improvement studies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/53f7e860157e4d5c8d89e56ac6334707
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An automated retrospective VAE-surveillance tool for future quality improvement studies

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