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Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study

Background: The association between gastrointestinal intolerance during early enteral nutrition (EN) and adverse clinical outcomes in critically ill patients is controversial. We aimed to assess the prognostic value of enteral feeding intolerance (EFI) markers during early ICU stays and to predict e...

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Autores principales: Raphaeli, Orit, Statlender, Liran, Hajaj, Chen, Bendavid, Itai, Goldstein, Anat, Robinson, Eyal, Singer, Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305247/
https://www.ncbi.nlm.nih.gov/pubmed/37375609
http://dx.doi.org/10.3390/nu15122705
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author Raphaeli, Orit
Statlender, Liran
Hajaj, Chen
Bendavid, Itai
Goldstein, Anat
Robinson, Eyal
Singer, Pierre
author_facet Raphaeli, Orit
Statlender, Liran
Hajaj, Chen
Bendavid, Itai
Goldstein, Anat
Robinson, Eyal
Singer, Pierre
author_sort Raphaeli, Orit
collection PubMed
description Background: The association between gastrointestinal intolerance during early enteral nutrition (EN) and adverse clinical outcomes in critically ill patients is controversial. We aimed to assess the prognostic value of enteral feeding intolerance (EFI) markers during early ICU stays and to predict early EN failure using a machine learning (ML) approach. Methods: We performed a retrospective analysis of data from adult patients admitted to Beilinson Hospital ICU between January 2011 and December 2018 for more than 48 h and received EN. Clinical data, including demographics, severity scores, EFI markers, and medications, along with 72 h after admission, were analyzed by ML algorithms. Prediction performance was assessed by the area under the receiver operating characteristics (AUCROC) of a ten-fold cross-validation set. Results: The datasets comprised 1584 patients. The means of the cross-validation AUCROCs for 90-day mortality and early EN failure were 0.73 (95% CI 0.71–0.75) and 0.71 (95% CI 0.67–0.74), respectively. Gastric residual volume above 250 mL on the second day was an important component of both prediction models. Conclusions: ML underlined the EFI markers that predict poor 90-day outcomes and early EN failure and supports early recognition of at-risk patients. Results have to be confirmed in further prospective and external validation studies.
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spelling pubmed-103052472023-06-29 Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study Raphaeli, Orit Statlender, Liran Hajaj, Chen Bendavid, Itai Goldstein, Anat Robinson, Eyal Singer, Pierre Nutrients Article Background: The association between gastrointestinal intolerance during early enteral nutrition (EN) and adverse clinical outcomes in critically ill patients is controversial. We aimed to assess the prognostic value of enteral feeding intolerance (EFI) markers during early ICU stays and to predict early EN failure using a machine learning (ML) approach. Methods: We performed a retrospective analysis of data from adult patients admitted to Beilinson Hospital ICU between January 2011 and December 2018 for more than 48 h and received EN. Clinical data, including demographics, severity scores, EFI markers, and medications, along with 72 h after admission, were analyzed by ML algorithms. Prediction performance was assessed by the area under the receiver operating characteristics (AUCROC) of a ten-fold cross-validation set. Results: The datasets comprised 1584 patients. The means of the cross-validation AUCROCs for 90-day mortality and early EN failure were 0.73 (95% CI 0.71–0.75) and 0.71 (95% CI 0.67–0.74), respectively. Gastric residual volume above 250 mL on the second day was an important component of both prediction models. Conclusions: ML underlined the EFI markers that predict poor 90-day outcomes and early EN failure and supports early recognition of at-risk patients. Results have to be confirmed in further prospective and external validation studies. MDPI 2023-06-10 /pmc/articles/PMC10305247/ /pubmed/37375609 http://dx.doi.org/10.3390/nu15122705 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Raphaeli, Orit
Statlender, Liran
Hajaj, Chen
Bendavid, Itai
Goldstein, Anat
Robinson, Eyal
Singer, Pierre
Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study
title Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study
title_full Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study
title_fullStr Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study
title_full_unstemmed Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study
title_short Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study
title_sort using machine-learning to assess the prognostic value of early enteral feeding intolerance in critically ill patients: a retrospective study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305247/
https://www.ncbi.nlm.nih.gov/pubmed/37375609
http://dx.doi.org/10.3390/nu15122705
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