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Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study

BACKGROUND: Cardiac involvement can be an initial manifestation in sarcoidosis. However, little is known about the association between various clinical phenotypes of cardiac sarcoidosis (CS) and outcomes. We aimed to analyze the relation of different clinical manifestations with outcomes of CS and t...

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Autores principales: Bobbio, Emanuele, Eldhagen, Per, Polte, Christian L., Hjalmarsson, Clara, Karason, Kristjan, Rawshani, Araz, Darlington, Pernilla, Kullberg, Susanna, Sörensson, Peder, Bergh, Niklas, Bollano, Entela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492974/
https://www.ncbi.nlm.nih.gov/pubmed/37489729
http://dx.doi.org/10.1161/JAHA.123.029481
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author Bobbio, Emanuele
Eldhagen, Per
Polte, Christian L.
Hjalmarsson, Clara
Karason, Kristjan
Rawshani, Araz
Darlington, Pernilla
Kullberg, Susanna
Sörensson, Peder
Bergh, Niklas
Bollano, Entela
author_facet Bobbio, Emanuele
Eldhagen, Per
Polte, Christian L.
Hjalmarsson, Clara
Karason, Kristjan
Rawshani, Araz
Darlington, Pernilla
Kullberg, Susanna
Sörensson, Peder
Bergh, Niklas
Bollano, Entela
author_sort Bobbio, Emanuele
collection PubMed
description BACKGROUND: Cardiac involvement can be an initial manifestation in sarcoidosis. However, little is known about the association between various clinical phenotypes of cardiac sarcoidosis (CS) and outcomes. We aimed to analyze the relation of different clinical manifestations with outcomes of CS and to investigate the relative importance of clinical features influencing overall survival. METHODS AND RESULTS: A retrospective cohort of 141 patients with CS enrolled at 2 Swedish university hospitals was studied. Presentation, imaging studies, and outcomes of de novo CS and previously known extracardiac sarcoidosis were compared. Survival free of primary composite outcome (ventricular arrhythmias, heart transplantation, or death) was assessed. Machine learning algorithm was used to study the relative importance of clinical features in predicting outcome. Sixty‐two patients with de novo CS and 79 with previously known extracardiac sarcoidosis were included. De novo CS showed more advanced New York Heart Association class (P=0.02), higher circulating levels of NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) (P<0.001), and troponins (P<0.001), as well as a higher prevalence of right ventricular dysfunction (P<0.001). During a median (interquartile range) follow‐up of 61 (44–77) months, event‐free survival was shorter in patients with de novo CS (P<0.001). The top 5 features predicting worse event‐free survival in order of importance were as follows: impaired tricuspid annular plane systolic excursion, de novo CS, reduced right ventricular ejection fraction, absence of β‐blockers, and lower left ventricular ejection fraction. CONCLUSIONS: Patients with de novo CS displayed more severe disease and worse outcomes compared with patients with previously known extracardiac sarcoidosis. Using machine learning, right ventricular dysfunction and de novo CS stand out as strong overall predictors of impaired survival.
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spelling pubmed-104929742023-09-11 Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study Bobbio, Emanuele Eldhagen, Per Polte, Christian L. Hjalmarsson, Clara Karason, Kristjan Rawshani, Araz Darlington, Pernilla Kullberg, Susanna Sörensson, Peder Bergh, Niklas Bollano, Entela J Am Heart Assoc Original Research BACKGROUND: Cardiac involvement can be an initial manifestation in sarcoidosis. However, little is known about the association between various clinical phenotypes of cardiac sarcoidosis (CS) and outcomes. We aimed to analyze the relation of different clinical manifestations with outcomes of CS and to investigate the relative importance of clinical features influencing overall survival. METHODS AND RESULTS: A retrospective cohort of 141 patients with CS enrolled at 2 Swedish university hospitals was studied. Presentation, imaging studies, and outcomes of de novo CS and previously known extracardiac sarcoidosis were compared. Survival free of primary composite outcome (ventricular arrhythmias, heart transplantation, or death) was assessed. Machine learning algorithm was used to study the relative importance of clinical features in predicting outcome. Sixty‐two patients with de novo CS and 79 with previously known extracardiac sarcoidosis were included. De novo CS showed more advanced New York Heart Association class (P=0.02), higher circulating levels of NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) (P<0.001), and troponins (P<0.001), as well as a higher prevalence of right ventricular dysfunction (P<0.001). During a median (interquartile range) follow‐up of 61 (44–77) months, event‐free survival was shorter in patients with de novo CS (P<0.001). The top 5 features predicting worse event‐free survival in order of importance were as follows: impaired tricuspid annular plane systolic excursion, de novo CS, reduced right ventricular ejection fraction, absence of β‐blockers, and lower left ventricular ejection fraction. CONCLUSIONS: Patients with de novo CS displayed more severe disease and worse outcomes compared with patients with previously known extracardiac sarcoidosis. Using machine learning, right ventricular dysfunction and de novo CS stand out as strong overall predictors of impaired survival. John Wiley and Sons Inc. 2023-07-25 /pmc/articles/PMC10492974/ /pubmed/37489729 http://dx.doi.org/10.1161/JAHA.123.029481 Text en © 2023 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
Bobbio, Emanuele
Eldhagen, Per
Polte, Christian L.
Hjalmarsson, Clara
Karason, Kristjan
Rawshani, Araz
Darlington, Pernilla
Kullberg, Susanna
Sörensson, Peder
Bergh, Niklas
Bollano, Entela
Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study
title Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study
title_full Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study
title_fullStr Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study
title_full_unstemmed Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study
title_short Clinical Outcomes and Predictors of Long‐Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study
title_sort clinical outcomes and predictors of long‐term survival in patients with and without previously known extracardiac sarcoidosis using machine learning: a swedish multicenter study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492974/
https://www.ncbi.nlm.nih.gov/pubmed/37489729
http://dx.doi.org/10.1161/JAHA.123.029481
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