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Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables

INTRODUCTION: Recent evidence confirms that the treatment of acute appendicitis is not necessarily surgical, and selected patients with uncomplicated appendicitis can benefit from a non-operative management. Unfortunately, no cost-effective test has been proven to be able to effectively predict the...

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Autores principales: Afzal, Barza, Cirocchi, Roberto, Dawani, Aruna, Desiderio, Jacopo, Di Cintio, Antonio, Di Nardo, Domenico, Farinacci, Federico, Fung, James, Gemini, Alessandro, Guerci, Lorenzo, Kam, Sen Yin Melina, Lakunina, Svetlana, Madi, Lee, Mazzetti, Stefano, Nadyrshine, Bakhtiar, Shams, Ola, Ranucci, Maria Chiara, Ricci, Francesco, Sharmin, Afroza, Trastulli, Stefano, Yasin, Tanzela, Bond-Smith, Giles, Tebala, Giovanni D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882741/
https://www.ncbi.nlm.nih.gov/pubmed/36707812
http://dx.doi.org/10.1186/s13017-023-00478-8
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author Afzal, Barza
Cirocchi, Roberto
Dawani, Aruna
Desiderio, Jacopo
Di Cintio, Antonio
Di Nardo, Domenico
Farinacci, Federico
Fung, James
Gemini, Alessandro
Guerci, Lorenzo
Kam, Sen Yin Melina
Lakunina, Svetlana
Madi, Lee
Mazzetti, Stefano
Nadyrshine, Bakhtiar
Shams, Ola
Ranucci, Maria Chiara
Ricci, Francesco
Sharmin, Afroza
Trastulli, Stefano
Yasin, Tanzela
Bond-Smith, Giles
Tebala, Giovanni D.
author_facet Afzal, Barza
Cirocchi, Roberto
Dawani, Aruna
Desiderio, Jacopo
Di Cintio, Antonio
Di Nardo, Domenico
Farinacci, Federico
Fung, James
Gemini, Alessandro
Guerci, Lorenzo
Kam, Sen Yin Melina
Lakunina, Svetlana
Madi, Lee
Mazzetti, Stefano
Nadyrshine, Bakhtiar
Shams, Ola
Ranucci, Maria Chiara
Ricci, Francesco
Sharmin, Afroza
Trastulli, Stefano
Yasin, Tanzela
Bond-Smith, Giles
Tebala, Giovanni D.
author_sort Afzal, Barza
collection PubMed
description INTRODUCTION: Recent evidence confirms that the treatment of acute appendicitis is not necessarily surgical, and selected patients with uncomplicated appendicitis can benefit from a non-operative management. Unfortunately, no cost-effective test has been proven to be able to effectively predict the degree of appendicular inflammation as yet, therefore, patient selection is too often left to the personal choice of the emergency surgeon. Our paper aims to clarify if basic and readily available blood tests can give reliable prognostic information to build up predictive models to help the decision-making process. METHODS: Clinical notes of 2275 patients who underwent an appendicectomy with a presumptive diagnosis of acute appendicitis were reviewed, taking into consideration basic preoperative blood tests and histology reports on the surgical specimens. Variables were compared with univariate and multivariate analysis, and predictive models were created. RESULTS: 18.2% of patients had a negative appendicectomy, 9.6% had mucosal only inflammation, 53% had transmural inflammation and 19.2% had gangrenous appendicitis. A strong correlation was found between degree of inflammation and lymphocytes count and CRP/Albumin ratio, both at univariate and multivariate analysis. A predictive model to identify cases of gangrenous appendicitis was developed. CONCLUSION: Low lymphocyte count and high CRP/Albumin ratio combined into a predictive model may have a role in the selection of patients who deserve appendicectomy instead of non-operative management of acute appendicitis.
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spelling pubmed-98827412023-01-29 Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables Afzal, Barza Cirocchi, Roberto Dawani, Aruna Desiderio, Jacopo Di Cintio, Antonio Di Nardo, Domenico Farinacci, Federico Fung, James Gemini, Alessandro Guerci, Lorenzo Kam, Sen Yin Melina Lakunina, Svetlana Madi, Lee Mazzetti, Stefano Nadyrshine, Bakhtiar Shams, Ola Ranucci, Maria Chiara Ricci, Francesco Sharmin, Afroza Trastulli, Stefano Yasin, Tanzela Bond-Smith, Giles Tebala, Giovanni D. World J Emerg Surg Research INTRODUCTION: Recent evidence confirms that the treatment of acute appendicitis is not necessarily surgical, and selected patients with uncomplicated appendicitis can benefit from a non-operative management. Unfortunately, no cost-effective test has been proven to be able to effectively predict the degree of appendicular inflammation as yet, therefore, patient selection is too often left to the personal choice of the emergency surgeon. Our paper aims to clarify if basic and readily available blood tests can give reliable prognostic information to build up predictive models to help the decision-making process. METHODS: Clinical notes of 2275 patients who underwent an appendicectomy with a presumptive diagnosis of acute appendicitis were reviewed, taking into consideration basic preoperative blood tests and histology reports on the surgical specimens. Variables were compared with univariate and multivariate analysis, and predictive models were created. RESULTS: 18.2% of patients had a negative appendicectomy, 9.6% had mucosal only inflammation, 53% had transmural inflammation and 19.2% had gangrenous appendicitis. A strong correlation was found between degree of inflammation and lymphocytes count and CRP/Albumin ratio, both at univariate and multivariate analysis. A predictive model to identify cases of gangrenous appendicitis was developed. CONCLUSION: Low lymphocyte count and high CRP/Albumin ratio combined into a predictive model may have a role in the selection of patients who deserve appendicectomy instead of non-operative management of acute appendicitis. BioMed Central 2023-01-27 /pmc/articles/PMC9882741/ /pubmed/36707812 http://dx.doi.org/10.1186/s13017-023-00478-8 Text en © The Author(s) 2023 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
Afzal, Barza
Cirocchi, Roberto
Dawani, Aruna
Desiderio, Jacopo
Di Cintio, Antonio
Di Nardo, Domenico
Farinacci, Federico
Fung, James
Gemini, Alessandro
Guerci, Lorenzo
Kam, Sen Yin Melina
Lakunina, Svetlana
Madi, Lee
Mazzetti, Stefano
Nadyrshine, Bakhtiar
Shams, Ola
Ranucci, Maria Chiara
Ricci, Francesco
Sharmin, Afroza
Trastulli, Stefano
Yasin, Tanzela
Bond-Smith, Giles
Tebala, Giovanni D.
Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables
title Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables
title_full Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables
title_fullStr Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables
title_full_unstemmed Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables
title_short Is it possible to predict the severity of acute appendicitis? Reliability of predictive models based on easily available blood variables
title_sort is it possible to predict the severity of acute appendicitis? reliability of predictive models based on easily available blood variables
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882741/
https://www.ncbi.nlm.nih.gov/pubmed/36707812
http://dx.doi.org/10.1186/s13017-023-00478-8
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