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External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country
BACKGROUND: Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surve...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485951/ https://www.ncbi.nlm.nih.gov/pubmed/37679824 http://dx.doi.org/10.1186/s13756-023-01288-y |
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author | van der Werff, Suzanne D. Verberk, Janneke D.M. Buchli, Christian van Mourik, Maaike S.M. Nauclér, Pontus |
author_facet | van der Werff, Suzanne D. Verberk, Janneke D.M. Buchli, Christian van Mourik, Maaike S.M. Nauclér, Pontus |
author_sort | van der Werff, Suzanne D. |
collection | PubMed |
description | BACKGROUND: Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. METHODS: The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. RESULTS: Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4–99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. CONCLUSIONS: The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery. |
format | Online Article Text |
id | pubmed-10485951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104859512023-09-09 External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country van der Werff, Suzanne D. Verberk, Janneke D.M. Buchli, Christian van Mourik, Maaike S.M. Nauclér, Pontus Antimicrob Resist Infect Control Brief Report BACKGROUND: Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. METHODS: The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. RESULTS: Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4–99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. CONCLUSIONS: The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery. BioMed Central 2023-09-08 /pmc/articles/PMC10485951/ /pubmed/37679824 http://dx.doi.org/10.1186/s13756-023-01288-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Brief Report van der Werff, Suzanne D. Verberk, Janneke D.M. Buchli, Christian van Mourik, Maaike S.M. Nauclér, Pontus External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country |
title | External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country |
title_full | External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country |
title_fullStr | External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country |
title_full_unstemmed | External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country |
title_short | External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country |
title_sort | external validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485951/ https://www.ncbi.nlm.nih.gov/pubmed/37679824 http://dx.doi.org/10.1186/s13756-023-01288-y |
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