Evidence map›Paper›PMID 34782637›Full record

ArticleScientific reports2021

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

Oliver Wolffers, Martin Faltys, Janos Thomann, Stephan M Jakob, Jonas Marschall, Tobias M Merz, Rami Sommerstein

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Oliver WolffersDepartment of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland. oliver.wolffers@insel.ch.
Martin FaltysDepartment of Intensive Care Medicine, Bern University Hospital, University of Bern, Bern, Switzerland.
Janos ThomannDepartment of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland.
Stephan M JakobDepartment of Intensive Care Medicine, Bern University Hospital, University of Bern, Bern, Switzerland.
Jonas MarschallDepartment of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland.
Tobias M MerzDepartment of Intensive Care Medicine, Bern University Hospital, University of Bern, Bern, Switzerland.
Rami SommersteinDepartment of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland. rami.sommerstein@hirslanden.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Quality ImprovementQuality of Health CareDisease ManagementDisease SusceptibilityHospitals, UniversityHumansIntensive Care UnitsPneumonia, Ventilator-AssociatedPublic Health SurveillanceRetrospective StudiesSensitivity and SpecificitySwitzerlandTertiary Care Centers

Identifiers

PMID34782637
PMCPMC8593155

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.