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Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance

BACKGROUND: Global longitudinal shortening (GL‐Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated...

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Autores principales: Xue, Hui, Artico, Jessica, Davies, Rhodri H., Adam, Robert, Shetye, Abhishek, Augusto, João B., Bhuva, Anish, Fröjdh, Fredrika, Wong, Timothy C., Fukui, Miho, Cavalcante, João L., Treibel, Thomas A., Manisty, Charlotte, Fontana, Marianna, Ugander, Martin, Moon, James C., Schelbert, Erik B., Kellman, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245823/
https://www.ncbi.nlm.nih.gov/pubmed/35132872
http://dx.doi.org/10.1161/JAHA.121.023849
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author Xue, Hui
Artico, Jessica
Davies, Rhodri H.
Adam, Robert
Shetye, Abhishek
Augusto, João B.
Bhuva, Anish
Fröjdh, Fredrika
Wong, Timothy C.
Fukui, Miho
Cavalcante, João L.
Treibel, Thomas A.
Manisty, Charlotte
Fontana, Marianna
Ugander, Martin
Moon, James C.
Schelbert, Erik B.
Kellman, Peter
author_facet Xue, Hui
Artico, Jessica
Davies, Rhodri H.
Adam, Robert
Shetye, Abhishek
Augusto, João B.
Bhuva, Anish
Fröjdh, Fredrika
Wong, Timothy C.
Fukui, Miho
Cavalcante, João L.
Treibel, Thomas A.
Manisty, Charlotte
Fontana, Marianna
Ugander, Martin
Moon, James C.
Schelbert, Erik B.
Kellman, Peter
author_sort Xue, Hui
collection PubMed
description BACKGROUND: Global longitudinal shortening (GL‐Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated artificial intelligence (AI) solution can be of strong clinical interest. METHODS AND RESULTS: The model was implemented on cardiac magnetic resonance scanners with automated in‐line processing. Reproducibility was evaluated in a scan–rescan data set (n=160 patients). The prognostic association with adverse events (death or hospitalization for heart failure) was evaluated in a large patient cohort (n=1572) and compared with feature tracking global longitudinal strain measured manually by experts. Automated processing took ≈1.1 seconds for a typical case. On the scan–rescan data set, the model exceeded the precision of human expert (coefficient of variation 7.2% versus 11.1% for GL‐Shortening, P=0.0024; 6.5% versus 9.1% for MAPSE, P=0.0124). The minimal detectable change at 90% power was 2.53 percentage points for GL‐Shortening and 1.84 mm for MAPSE. AI GL‐Shortening correlated well with manual global longitudinal strain (R (2)=0.85). AI MAPSE had the strongest association with outcomes (χ(2), 255; hazard ratio [HR], 2.5 [95% CI, 2.2–2.8]), compared with AI GL‐Shortening (χ(2), 197; HR, 2.1 [95% CI,1.9–2.4]), manual global longitudinal strain (χ(2), 192; HR, 2.1 [95% CI, 1.9–2.3]), and left ventricular ejection fraction (χ(2), 147; HR, 1.8 [95% CI, 1.6–1.9]), with P<0.001 for all. CONCLUSIONS: Automated in‐line AI‐measured MAPSE and GL‐Shortening can deliver immediate and highly reproducible results during cardiac magnetic resonance scanning. These results have strong associations with adverse outcomes that exceed those of global longitudinal strain and left ventricular ejection fraction.
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spelling pubmed-92458232022-07-01 Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance Xue, Hui Artico, Jessica Davies, Rhodri H. Adam, Robert Shetye, Abhishek Augusto, João B. Bhuva, Anish Fröjdh, Fredrika Wong, Timothy C. Fukui, Miho Cavalcante, João L. Treibel, Thomas A. Manisty, Charlotte Fontana, Marianna Ugander, Martin Moon, James C. Schelbert, Erik B. Kellman, Peter J Am Heart Assoc Original Research BACKGROUND: Global longitudinal shortening (GL‐Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated artificial intelligence (AI) solution can be of strong clinical interest. METHODS AND RESULTS: The model was implemented on cardiac magnetic resonance scanners with automated in‐line processing. Reproducibility was evaluated in a scan–rescan data set (n=160 patients). The prognostic association with adverse events (death or hospitalization for heart failure) was evaluated in a large patient cohort (n=1572) and compared with feature tracking global longitudinal strain measured manually by experts. Automated processing took ≈1.1 seconds for a typical case. On the scan–rescan data set, the model exceeded the precision of human expert (coefficient of variation 7.2% versus 11.1% for GL‐Shortening, P=0.0024; 6.5% versus 9.1% for MAPSE, P=0.0124). The minimal detectable change at 90% power was 2.53 percentage points for GL‐Shortening and 1.84 mm for MAPSE. AI GL‐Shortening correlated well with manual global longitudinal strain (R (2)=0.85). AI MAPSE had the strongest association with outcomes (χ(2), 255; hazard ratio [HR], 2.5 [95% CI, 2.2–2.8]), compared with AI GL‐Shortening (χ(2), 197; HR, 2.1 [95% CI,1.9–2.4]), manual global longitudinal strain (χ(2), 192; HR, 2.1 [95% CI, 1.9–2.3]), and left ventricular ejection fraction (χ(2), 147; HR, 1.8 [95% CI, 1.6–1.9]), with P<0.001 for all. CONCLUSIONS: Automated in‐line AI‐measured MAPSE and GL‐Shortening can deliver immediate and highly reproducible results during cardiac magnetic resonance scanning. These results have strong associations with adverse outcomes that exceed those of global longitudinal strain and left ventricular ejection fraction. John Wiley and Sons Inc. 2022-02-08 /pmc/articles/PMC9245823/ /pubmed/35132872 http://dx.doi.org/10.1161/JAHA.121.023849 Text en © 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Research
Xue, Hui
Artico, Jessica
Davies, Rhodri H.
Adam, Robert
Shetye, Abhishek
Augusto, João B.
Bhuva, Anish
Fröjdh, Fredrika
Wong, Timothy C.
Fukui, Miho
Cavalcante, João L.
Treibel, Thomas A.
Manisty, Charlotte
Fontana, Marianna
Ugander, Martin
Moon, James C.
Schelbert, Erik B.
Kellman, Peter
Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
title Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
title_full Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
title_fullStr Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
title_full_unstemmed Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
title_short Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
title_sort automated in‐line artificial intelligence measured global longitudinal shortening and mitral annular plane systolic excursion: reproducibility and prognostic significance
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245823/
https://www.ncbi.nlm.nih.gov/pubmed/35132872
http://dx.doi.org/10.1161/JAHA.121.023849
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