ArticleInfection control and hospital epidemiology2021
Development of a fully automated surgical site infection detection algorithm for use in cardiac and orthopedic surgery research.
Article in Infection control and hospital epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02216227 (Checklist to Prevent MRSA Surgical Site Infections), which is not on this map. Cited by 4 papers.
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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.
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.
Checklist to Prevent MRSA Surgical Site Infections
Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Automated surveillance for surgical site infections (SSI) in hospitals and surveillance networks-expert perspectives for implementation.Antimicrobial resistance and infection control · 2024Article
- Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.JMIR medical informatics · 2024Article
- Implementation of a Prevention Bundle to Decrease Rates of Staphylococcus aureus Surgical Site Infection at 11 Veterans Affairs Hospitals.JAMA network open · 2023Article
- The development and validation of screening tools for semi-automated surveillance of surgical site infection following various surgeries.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a fully automated algorithm using data from the Veterans' Affairs (VA) electrical medical record (EMR) to identify deep-incisional surgical site infections (SSIs) after cardiac surgeries and total joint arthroplasties (TJAs) to be used for research studies.
designRetrospective cohort study.
settingThis study was conducted in 11 VA hospitals.
participantsPatients who underwent coronary artery bypass grafting or valve replacement between January 1, 2010, and March 31, 2018 (cardiac cohort) and patients who underwent total hip arthroplasty or total knee arthroplasty between January 1, 2007, and March 31, 2018 (TJA cohort).
methodsRelevant clinical information and administrative code data were extracted from the EMR. The outcomes of interest were mediastinitis, endocarditis, or deep-incisional or organ-space SSI within 30 days after surgery. Multiple logistic regression analysis with a repeated regular bootstrap procedure was used to select variables and to assign points in the models. Sensitivities, specificities, positive predictive values (PPVs) and negative predictive values were calculated with comparison to outcomes collected by the Veterans' Affairs Surgical Quality Improvement Program (VASQIP).
resultsOverall, 49 (0.5%) of the 13,341 cardiac surgeries were classified as mediastinitis or endocarditis, and 83 (0.6%) of the 12,992 TJAs were classified as deep-incisional or organ-space SSIs. With at least 60% sensitivity, the PPVs of the SSI detection algorithms after cardiac surgeries and TJAs were 52.5% and 62.0%, respectively.
conclusionsConsidering the low prevalence rate of SSIs, our algorithms were successful in identifying a majority of patients with a true SSI while simultaneously reducing false-positive cases. As a next step, validation of these algorithms in different hospital systems with EMR will be needed.
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