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Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters

Background: Peripheral artery disease (PAD) is a significant burden, particularly among patients with severe disease requiring invasive treatment. We applied a general Machine Learning (ML) workflow and investigated if a multi-dimensional marker set of standard clinical parameters can identify patie...

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Autores principales: Sonnenschein, Kristina, Stojanović, Stevan D., Dickel, Nicholas, Fiedler, Jan, Bauersachs, Johann, Thum, Thomas, Kunz, Meik, Tongers, Jörn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8533252/
https://www.ncbi.nlm.nih.gov/pubmed/34680572
http://dx.doi.org/10.3390/biomedicines9101456
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author Sonnenschein, Kristina
Stojanović, Stevan D.
Dickel, Nicholas
Fiedler, Jan
Bauersachs, Johann
Thum, Thomas
Kunz, Meik
Tongers, Jörn
author_facet Sonnenschein, Kristina
Stojanović, Stevan D.
Dickel, Nicholas
Fiedler, Jan
Bauersachs, Johann
Thum, Thomas
Kunz, Meik
Tongers, Jörn
author_sort Sonnenschein, Kristina
collection PubMed
description Background: Peripheral artery disease (PAD) is a significant burden, particularly among patients with severe disease requiring invasive treatment. We applied a general Machine Learning (ML) workflow and investigated if a multi-dimensional marker set of standard clinical parameters can identify patients in need of vascular intervention without specialized intra–hospital diagnostics. Methods: This is a retrospective study involving patients with stable PAD (sPAD, Fontaine Class I and II, n = 38) and unstable PAD (unPAD, Fontaine Class III and IV, n = 18) in need of invasive therapeutic measures. ML algorithms such as Random Forest were utilized to evaluate a matrix consisting of multiple routinely clinically available parameters (age, complete blood count, inflammation, lipid, iron metabolism). Results: ML has enabled a generation of an Artificial Intelligence (AI) PAD score (AI-PAD) that successfully divided sPAD from unPAD patients (high AI-PAD in sPAD, low AI-PAD in unPAD, cutoff at 50 AI-PAD units). Furthermore, the probability score positively coincided with gold-standard intra-hospital mean ankle-brachial index (ABI). Conclusion: AI-based tools may be promising to enable the correct identification of patients with unstable PAD by using existing clinical information, thus supplementing clinical decision making. Additional studies in larger prospective cohorts are necessary to determine the usefulness of this approach in comparison to standard diagnostic measures.
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spelling pubmed-85332522021-10-23 Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters Sonnenschein, Kristina Stojanović, Stevan D. Dickel, Nicholas Fiedler, Jan Bauersachs, Johann Thum, Thomas Kunz, Meik Tongers, Jörn Biomedicines Article Background: Peripheral artery disease (PAD) is a significant burden, particularly among patients with severe disease requiring invasive treatment. We applied a general Machine Learning (ML) workflow and investigated if a multi-dimensional marker set of standard clinical parameters can identify patients in need of vascular intervention without specialized intra–hospital diagnostics. Methods: This is a retrospective study involving patients with stable PAD (sPAD, Fontaine Class I and II, n = 38) and unstable PAD (unPAD, Fontaine Class III and IV, n = 18) in need of invasive therapeutic measures. ML algorithms such as Random Forest were utilized to evaluate a matrix consisting of multiple routinely clinically available parameters (age, complete blood count, inflammation, lipid, iron metabolism). Results: ML has enabled a generation of an Artificial Intelligence (AI) PAD score (AI-PAD) that successfully divided sPAD from unPAD patients (high AI-PAD in sPAD, low AI-PAD in unPAD, cutoff at 50 AI-PAD units). Furthermore, the probability score positively coincided with gold-standard intra-hospital mean ankle-brachial index (ABI). Conclusion: AI-based tools may be promising to enable the correct identification of patients with unstable PAD by using existing clinical information, thus supplementing clinical decision making. Additional studies in larger prospective cohorts are necessary to determine the usefulness of this approach in comparison to standard diagnostic measures. MDPI 2021-10-13 /pmc/articles/PMC8533252/ /pubmed/34680572 http://dx.doi.org/10.3390/biomedicines9101456 Text en © 2021 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
Sonnenschein, Kristina
Stojanović, Stevan D.
Dickel, Nicholas
Fiedler, Jan
Bauersachs, Johann
Thum, Thomas
Kunz, Meik
Tongers, Jörn
Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters
title Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters
title_full Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters
title_fullStr Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters
title_full_unstemmed Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters
title_short Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters
title_sort artificial intelligence identifies an urgent need for peripheral vascular intervention by multiplexing standard clinical parameters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8533252/
https://www.ncbi.nlm.nih.gov/pubmed/34680572
http://dx.doi.org/10.3390/biomedicines9101456
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