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Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes
Stress echocardiography (SE) is based on regional wall motion abnormalities and coronary flow velocity reserve (CFVR). Their independent prognostic capabilities could be better studied with a machine learning (ML) approach. The study aims to assess the SE outcome data by conducting an analysis with...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504503/ https://www.ncbi.nlm.nih.gov/pubmed/36143307 http://dx.doi.org/10.3390/jpm12091523 |
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author | Cortigiani, Lauro Azzolina, Danila Ciampi, Quirino Lorenzoni, Giulia Gaibazzi, Nicola Rigo, Fausto Gherardi, Sonia Bovenzi, Francesco Gregori, Dario Picano, Eugenio |
author_facet | Cortigiani, Lauro Azzolina, Danila Ciampi, Quirino Lorenzoni, Giulia Gaibazzi, Nicola Rigo, Fausto Gherardi, Sonia Bovenzi, Francesco Gregori, Dario Picano, Eugenio |
author_sort | Cortigiani, Lauro |
collection | PubMed |
description | Stress echocardiography (SE) is based on regional wall motion abnormalities and coronary flow velocity reserve (CFVR). Their independent prognostic capabilities could be better studied with a machine learning (ML) approach. The study aims to assess the SE outcome data by conducting an analysis with an ML approach. We included 6881 prospectively recruited and retrospectively analyzed patients with suspected (n = 4279) or known (n = 2602) coronary artery disease submitted to clinically driven dipyridamole SE. The outcome measure was all-cause death. A random forest survival model was implemented to model the survival function according to the patient’s characteristics; 1002 patients recruited by a single, independent center formed the external validation cohort. During a median follow-up of 3.4 years (IQR 1.6–7.5), 814 (12%) patients died. The mortality risk was higher for patients aged >60 years, with a resting ejection fraction < 60%, resting WMSI, positive stress-rest WMSI scores, and CFVR < 3.The C-index performance was 0.79 in the internal and 0.81 in the external validation data set. Survival functions for individual patients were easily obtained with an open access web app. An ML approach can be fruitfully applied to outcome data obtained with SE. Survival showed a constantly increasing relationship with a CFVR < 3.0 and stress-rest wall motion score index > Since processing is largely automated, this approach can be easily scaled to larger and more comprehensive data sets to further refine stratification, guide therapy and be ultimately adopted as an open-source online decision tool. |
format | Online Article Text |
id | pubmed-9504503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95045032022-09-24 Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes Cortigiani, Lauro Azzolina, Danila Ciampi, Quirino Lorenzoni, Giulia Gaibazzi, Nicola Rigo, Fausto Gherardi, Sonia Bovenzi, Francesco Gregori, Dario Picano, Eugenio J Pers Med Article Stress echocardiography (SE) is based on regional wall motion abnormalities and coronary flow velocity reserve (CFVR). Their independent prognostic capabilities could be better studied with a machine learning (ML) approach. The study aims to assess the SE outcome data by conducting an analysis with an ML approach. We included 6881 prospectively recruited and retrospectively analyzed patients with suspected (n = 4279) or known (n = 2602) coronary artery disease submitted to clinically driven dipyridamole SE. The outcome measure was all-cause death. A random forest survival model was implemented to model the survival function according to the patient’s characteristics; 1002 patients recruited by a single, independent center formed the external validation cohort. During a median follow-up of 3.4 years (IQR 1.6–7.5), 814 (12%) patients died. The mortality risk was higher for patients aged >60 years, with a resting ejection fraction < 60%, resting WMSI, positive stress-rest WMSI scores, and CFVR < 3.The C-index performance was 0.79 in the internal and 0.81 in the external validation data set. Survival functions for individual patients were easily obtained with an open access web app. An ML approach can be fruitfully applied to outcome data obtained with SE. Survival showed a constantly increasing relationship with a CFVR < 3.0 and stress-rest wall motion score index > Since processing is largely automated, this approach can be easily scaled to larger and more comprehensive data sets to further refine stratification, guide therapy and be ultimately adopted as an open-source online decision tool. MDPI 2022-09-16 /pmc/articles/PMC9504503/ /pubmed/36143307 http://dx.doi.org/10.3390/jpm12091523 Text en © 2022 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 Cortigiani, Lauro Azzolina, Danila Ciampi, Quirino Lorenzoni, Giulia Gaibazzi, Nicola Rigo, Fausto Gherardi, Sonia Bovenzi, Francesco Gregori, Dario Picano, Eugenio Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes |
title | Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes |
title_full | Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes |
title_fullStr | Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes |
title_full_unstemmed | Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes |
title_short | Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes |
title_sort | machine learning algorithms for prediction of survival by stress echocardiography in chronic coronary syndromes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504503/ https://www.ncbi.nlm.nih.gov/pubmed/36143307 http://dx.doi.org/10.3390/jpm12091523 |
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