The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery.

Autor: Verberk JDM; Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands.; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands.; Department of Epidemiology and Surveillance, Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands., van der Werff SD; Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden. suzanne.ruhe.van.der.werff@ki.se.; Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden. suzanne.ruhe.van.der.werff@ki.se., Weegar R; Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden., Henriksson A; Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden., Richir MC; Department of Surgery, Cancer Centre, University Medical Centre Utrecht, Utrecht, the Netherlands., Buchli C; Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.; Department of Pelvic Cancer, GI Oncology and Colorectal Surgery Unit, Karolinska University Hospital, Stockholm, Sweden., van Mourik MSM; Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands., Nauclér P; Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden.; Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
Jazyk: angličtina
Zdroj: Antimicrobial resistance and infection control [Antimicrob Resist Infect Control] 2023 Oct 26; Vol. 12 (1), pp. 117. Date of Electronic Publication: 2023 Oct 26.
DOI: 10.1186/s13756-023-01316-x
Abstrakt: Background: In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).
Methods: Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.
Results: From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.
Conclusions: The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.
(© 2023. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje