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Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review

PURPOSE: Extracorporeal membrane oxygenation (ECMO) has been increasingly used in the last years to provide hemodynamic and respiratory support in critically ill patients. In this scenario, prognostic scores remain essential to choose which patients should initiate ECMO. This systematic review aims...

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Autores principales: Giordano, Luca, Francavilla, Andrea, Bottio, Tomaso, Dell’Amore, Andrea, Gregori, Dario, Navalesi, Paolo, Lorenzoni, Giulia, Baldi, Ileana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015918/
https://www.ncbi.nlm.nih.gov/pubmed/36918967
http://dx.doi.org/10.1186/s13643-023-02211-7
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author Giordano, Luca
Francavilla, Andrea
Bottio, Tomaso
Dell’Amore, Andrea
Gregori, Dario
Navalesi, Paolo
Lorenzoni, Giulia
Baldi, Ileana
author_facet Giordano, Luca
Francavilla, Andrea
Bottio, Tomaso
Dell’Amore, Andrea
Gregori, Dario
Navalesi, Paolo
Lorenzoni, Giulia
Baldi, Ileana
author_sort Giordano, Luca
collection PubMed
description PURPOSE: Extracorporeal membrane oxygenation (ECMO) has been increasingly used in the last years to provide hemodynamic and respiratory support in critically ill patients. In this scenario, prognostic scores remain essential to choose which patients should initiate ECMO. This systematic review aims to assess the current landscape and inform subsequent efforts in the development of risk prediction tools for ECMO. METHODS: PubMed, CINAHL, Embase, MEDLINE and Scopus were consulted. Articles between Jan 2011 and Feb 2022, including adults undergoing ECMO reporting a newly developed and validated predictive model for mortality, were included. Studies based on animal models, systematic reviews, case reports and conference abstracts were excluded. Data extraction aimed to capture study characteristics, risk model characteristics and model performance. The risk of bias was evaluated through the prediction model risk-of-bias assessment tool (PROBAST). The protocol has been registered in Open Science Framework (https://osf.io/fevw5). RESULTS: Twenty-six prognostic scores for in-hospital mortality were identified, with a study size ranging from 60 to 4557 patients. The most common candidate variables were age, lactate concentration, creatinine concentration, bilirubin concentration and days in mechanical ventilation prior to ECMO. Five out of 16 venous-arterial (VA)-ECMO scores and 3 out of 9 veno-venous (VV)-ECMO scores had been validated externally. Additionally, one score was developed for both VA and VV populations. No score was judged at low risk of bias. CONCLUSION: Most models have not been validated externally and apply after ECMO initiation; thus, some uncertainty whether ECMO should be initiated still remains. It has yet to be determined whether and to what extent a new methodological perspective may enhance the performance of predictive models for ECMO, with the ultimate goal to implement a model that positively influences patient outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-023-02211-7.
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spelling pubmed-100159182023-03-16 Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review Giordano, Luca Francavilla, Andrea Bottio, Tomaso Dell’Amore, Andrea Gregori, Dario Navalesi, Paolo Lorenzoni, Giulia Baldi, Ileana Syst Rev Research PURPOSE: Extracorporeal membrane oxygenation (ECMO) has been increasingly used in the last years to provide hemodynamic and respiratory support in critically ill patients. In this scenario, prognostic scores remain essential to choose which patients should initiate ECMO. This systematic review aims to assess the current landscape and inform subsequent efforts in the development of risk prediction tools for ECMO. METHODS: PubMed, CINAHL, Embase, MEDLINE and Scopus were consulted. Articles between Jan 2011 and Feb 2022, including adults undergoing ECMO reporting a newly developed and validated predictive model for mortality, were included. Studies based on animal models, systematic reviews, case reports and conference abstracts were excluded. Data extraction aimed to capture study characteristics, risk model characteristics and model performance. The risk of bias was evaluated through the prediction model risk-of-bias assessment tool (PROBAST). The protocol has been registered in Open Science Framework (https://osf.io/fevw5). RESULTS: Twenty-six prognostic scores for in-hospital mortality were identified, with a study size ranging from 60 to 4557 patients. The most common candidate variables were age, lactate concentration, creatinine concentration, bilirubin concentration and days in mechanical ventilation prior to ECMO. Five out of 16 venous-arterial (VA)-ECMO scores and 3 out of 9 veno-venous (VV)-ECMO scores had been validated externally. Additionally, one score was developed for both VA and VV populations. No score was judged at low risk of bias. CONCLUSION: Most models have not been validated externally and apply after ECMO initiation; thus, some uncertainty whether ECMO should be initiated still remains. It has yet to be determined whether and to what extent a new methodological perspective may enhance the performance of predictive models for ECMO, with the ultimate goal to implement a model that positively influences patient outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-023-02211-7. BioMed Central 2023-03-15 /pmc/articles/PMC10015918/ /pubmed/36918967 http://dx.doi.org/10.1186/s13643-023-02211-7 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
Giordano, Luca
Francavilla, Andrea
Bottio, Tomaso
Dell’Amore, Andrea
Gregori, Dario
Navalesi, Paolo
Lorenzoni, Giulia
Baldi, Ileana
Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review
title Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review
title_full Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review
title_fullStr Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review
title_full_unstemmed Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review
title_short Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review
title_sort predictive models in extracorporeal membrane oxygenation (ecmo): a systematic review
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015918/
https://www.ncbi.nlm.nih.gov/pubmed/36918967
http://dx.doi.org/10.1186/s13643-023-02211-7
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