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.
Highlights
Note that as per the Centers for Disease Control and Prevention (CDC) protocol, we included all ventilated patients in the denominator, even those not ventilated for 4 calendar days which per definition could not qualify for ventilator-associated event (VAE)
An implementation of a retrospective VAE surveillance tool is feasible, even when the data management system at the local ICU was initially not set up for this purpose.We believe that this approach can be taken in hospitals using similar patient data management systems and only at modest cost and effort
We created a VAE surveillance which is ready to be implemented in an ICU with a patient data management system using high sampling frequency
Summary
Clinical criteria to diagnose VAP lack sensitivity and specificity when compared to autopsy reports[5] and were shown to be associated with substantial interobserver variability[6], making the diagnosis of VAP difficult and not uniformly defined. In 2013 the United States’ Centers for Disease Control and Prevention (CDC) released its new surveillance protocol for ventilator-associated events (VAE) in order to address the above-mentioned problems[7]. Screening for VAC has shown non-inferior sensitivity and specificity for diagnosing V AP8 when compared to classical criteria like national healthcare safety network PNEU criteria[9] requiring clinical signs of pulmonary infection, Scientific Reports | (2021) 11:22264. Our aim was to create a fully automated VAE surveillance tool for the first two tiers of the CDC VAE surveillance protocol and assess its diagnostic accuracy. We aimed to determine whether the retrospectively identified cases indicated a dynamic in the VAE incidences from 2008 to 2016 and to establish a baseline incidence
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