Validation of a semiautomated system for surveillance of surgical site infection after cesarean section

Surveillance of surgical site infection after cesarean section is challenging due to the high volume of these surgeries. A manual chart review of women undergoing cesarean section between January and June 2017 (675 charts, 40 infections) was compared to charts identified via an algorithm (141 charts...

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Autores principales: Shitrit, Pnina, Mudrik, Ravid, Gottesman, Bat-Sheva, Chowers, Michal Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2022
Materias:
Concise Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9588442/
https://www.ncbi.nlm.nih.gov/pubmed/34180384
http://dx.doi.org/10.1017/ice.2021.264
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author Shitrit, Pnina
Mudrik, Ravid
Gottesman, Bat-Sheva
Chowers, Michal Y.
author_facet Shitrit, Pnina
Mudrik, Ravid
Gottesman, Bat-Sheva
Chowers, Michal Y.
author_sort Shitrit, Pnina
collection PubMed
description Surveillance of surgical site infection after cesarean section is challenging due to the high volume of these surgeries. A manual chart review of women undergoing cesarean section between January and June 2017 (675 charts, 40 infections) was compared to charts identified via an algorithm (141 charts, 39 infections). The algorithm achieved 97.5% sensitivity and 83.9% specificity and reduced the workload of infection control personnel.
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spelling pubmed-95884422022-10-26 Validation of a semiautomated system for surveillance of surgical site infection after cesarean section Shitrit, Pnina Mudrik, Ravid Gottesman, Bat-Sheva Chowers, Michal Y. Infect Control Hosp Epidemiol Concise Communication Surveillance of surgical site infection after cesarean section is challenging due to the high volume of these surgeries. A manual chart review of women undergoing cesarean section between January and June 2017 (675 charts, 40 infections) was compared to charts identified via an algorithm (141 charts, 39 infections). The algorithm achieved 97.5% sensitivity and 83.9% specificity and reduced the workload of infection control personnel. Cambridge University Press 2022-10 2021-06-25 /pmc/articles/PMC9588442/ /pubmed/34180384 http://dx.doi.org/10.1017/ice.2021.264 Text en © The Society for Healthcare Epidemiology of America 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Concise Communication
Shitrit, Pnina
Mudrik, Ravid
Gottesman, Bat-Sheva
Chowers, Michal Y.
Validation of a semiautomated system for surveillance of surgical site infection after cesarean section
title Validation of a semiautomated system for surveillance of surgical site infection after cesarean section
title_full Validation of a semiautomated system for surveillance of surgical site infection after cesarean section
title_fullStr Validation of a semiautomated system for surveillance of surgical site infection after cesarean section
title_full_unstemmed Validation of a semiautomated system for surveillance of surgical site infection after cesarean section
title_short Validation of a semiautomated system for surveillance of surgical site infection after cesarean section
title_sort validation of a semiautomated system for surveillance of surgical site infection after cesarean section
topic Concise Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9588442/
https://www.ncbi.nlm.nih.gov/pubmed/34180384
http://dx.doi.org/10.1017/ice.2021.264
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