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Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study
BACKGROUND: In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis th...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822669/ https://www.ncbi.nlm.nih.gov/pubmed/35135570 http://dx.doi.org/10.1186/s13037-021-00316-3 |
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author | Pérez Zapata, Ana Isabel Rodríguez Cuéllar, Elías de la Fuente Bartolomé, Marta Martín-Arriscado Arroba, Cristina García Morales, María Teresa Loinaz Segurola, Carmelo Giner Nogueras, Manuel Tejido Sánchez, Ángel Ruiz López, Pedro Ferrero Herrero, Eduardo |
author_facet | Pérez Zapata, Ana Isabel Rodríguez Cuéllar, Elías de la Fuente Bartolomé, Marta Martín-Arriscado Arroba, Cristina García Morales, María Teresa Loinaz Segurola, Carmelo Giner Nogueras, Manuel Tejido Sánchez, Ángel Ruiz López, Pedro Ferrero Herrero, Eduardo |
author_sort | Pérez Zapata, Ana Isabel |
collection | PubMed |
description | BACKGROUND: In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis that a new “Trigger Tool” represents a sensitive predictor of adverse events in general surgery. METHODS: An observational multicenter validation study was performed among 31 hospitals in Spain. The previously described “Trigger Tool” based on 40 specific triggers was applied to validate the predictive power of predicting adverse events in the perioperative care of surgical patients. A prediction model was used by means of a binary logistic regression analysis. RESULTS: The prevalence of adverse events among a total of 1,132 surgical cases included in this study was 31.53%. The “Trigger Tool” had a sensitivity and specificity of 86.27% and 79.55% respectively for predicting these adverse events. A total of 12 selected triggers of overall 40 triggers were identified for optimizing the predictive power of the “Trigger Tool”. CONCLUSIONS: The “Trigger Tool” has a high predictive capacity for predicting adverse events in surgical procedures. We recommend a revision of the original 40 triggers to 12 selected triggers to optimize the predictive power of this tool, which will have to be validated in future studies. |
format | Online Article Text |
id | pubmed-8822669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88226692022-02-08 Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study Pérez Zapata, Ana Isabel Rodríguez Cuéllar, Elías de la Fuente Bartolomé, Marta Martín-Arriscado Arroba, Cristina García Morales, María Teresa Loinaz Segurola, Carmelo Giner Nogueras, Manuel Tejido Sánchez, Ángel Ruiz López, Pedro Ferrero Herrero, Eduardo Patient Saf Surg Research BACKGROUND: In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis that a new “Trigger Tool” represents a sensitive predictor of adverse events in general surgery. METHODS: An observational multicenter validation study was performed among 31 hospitals in Spain. The previously described “Trigger Tool” based on 40 specific triggers was applied to validate the predictive power of predicting adverse events in the perioperative care of surgical patients. A prediction model was used by means of a binary logistic regression analysis. RESULTS: The prevalence of adverse events among a total of 1,132 surgical cases included in this study was 31.53%. The “Trigger Tool” had a sensitivity and specificity of 86.27% and 79.55% respectively for predicting these adverse events. A total of 12 selected triggers of overall 40 triggers were identified for optimizing the predictive power of the “Trigger Tool”. CONCLUSIONS: The “Trigger Tool” has a high predictive capacity for predicting adverse events in surgical procedures. We recommend a revision of the original 40 triggers to 12 selected triggers to optimize the predictive power of this tool, which will have to be validated in future studies. BioMed Central 2022-02-08 /pmc/articles/PMC8822669/ /pubmed/35135570 http://dx.doi.org/10.1186/s13037-021-00316-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Research Pérez Zapata, Ana Isabel Rodríguez Cuéllar, Elías de la Fuente Bartolomé, Marta Martín-Arriscado Arroba, Cristina García Morales, María Teresa Loinaz Segurola, Carmelo Giner Nogueras, Manuel Tejido Sánchez, Ángel Ruiz López, Pedro Ferrero Herrero, Eduardo Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study |
title | Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study |
title_full | Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study |
title_fullStr | Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study |
title_full_unstemmed | Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study |
title_short | Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study |
title_sort | predictive power of the "trigger tool" for the detection of adverse events in general surgery: a multicenter observational validation study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822669/ https://www.ncbi.nlm.nih.gov/pubmed/35135570 http://dx.doi.org/10.1186/s13037-021-00316-3 |
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