The new SUMPOT to predict postoperative complications using an Artificial Neural Network

Abstract An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at ri...

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Autores principales:	Cosimo Chelazzi, Gianluca Villa, Andrea Manno, Viola Ranfagni, Eleonora Gemmi, Stefano Romagnoli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bccb74f3749a456b9508bcb8ebff282a
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spelling	oai:doaj.org-article:bccb74f3749a456b9508bcb8ebff282a2021-11-28T12:15:42ZThe new SUMPOT to predict postoperative complications using an Artificial Neural Network10.1038/s41598-021-01913-z2045-2322https://doaj.org/article/bccb74f3749a456b9508bcb8ebff282a2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01913-zhttps://doaj.org/toc/2045-2322Abstract An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.Cosimo ChelazziGianluca VillaAndrea MannoViola RanfagniEleonora GemmiStefano RomagnoliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cosimo Chelazzi Gianluca Villa Andrea Manno Viola Ranfagni Eleonora Gemmi Stefano Romagnoli The new SUMPOT to predict postoperative complications using an Artificial Neural Network
description	Abstract An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.
format	article
author	Cosimo Chelazzi Gianluca Villa Andrea Manno Viola Ranfagni Eleonora Gemmi Stefano Romagnoli
author_facet	Cosimo Chelazzi Gianluca Villa Andrea Manno Viola Ranfagni Eleonora Gemmi Stefano Romagnoli
author_sort	Cosimo Chelazzi
title	The new SUMPOT to predict postoperative complications using an Artificial Neural Network
title_short	The new SUMPOT to predict postoperative complications using an Artificial Neural Network
title_full	The new SUMPOT to predict postoperative complications using an Artificial Neural Network
title_fullStr	The new SUMPOT to predict postoperative complications using an Artificial Neural Network
title_full_unstemmed	The new SUMPOT to predict postoperative complications using an Artificial Neural Network
title_sort	new sumpot to predict postoperative complications using an artificial neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bccb74f3749a456b9508bcb8ebff282a
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The new SUMPOT to predict postoperative complications using an Artificial Neural Network

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