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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

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          Abstract

          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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          Most cited references41

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          Random Forests

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            2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure

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              Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.

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                Author and article information

                Contributors
                emanuele.bobbio@vgregion.se
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                25 July 2023
                01 August 2023
                : 12
                : 15 ( doiID: 10.1002/jah3.v12.15 )
                : e029481
                Affiliations
                [ 1 ] Department of Cardiology Sahlgrenska University Hospital Gothenburg Sweden
                [ 2 ] Institute of Medicine at Sahlgrenska Academy University of Gothenburg Gothenburg Sweden
                [ 3 ] Department of Medicine Solna Karolinska Institutet Stockholm Sweden
                [ 4 ] Unit of Cardiology, Theme Cardiovascular and Neurology Karolinska University Hospital Stockholm Sweden
                [ 5 ] Departments of Clinical Physiology and Radiology Sahlgrenska University Hospital Gothenburg Sweden
                [ 6 ] Department of Transplantation Sahlgrenska University Hospital Gothenburg Sweden
                [ 7 ] Department of Internal Medicine Södersjukhuset Stockholm Sweden
                [ 8 ] Department of Clinical Science and Education Södersjukhuset and Karolinska Institutet Stockholm Sweden
                [ 9 ] Department of Respiratory Medicine, Theme Inflammation and Ageing Karolinska University Hospital Stockholm Sweden
                [ 10 ] Respiratory Medicine Division, Department of Medicine Karolinska Institutet Stockholm Sweden
                Author notes
                [*] [* ]Correspondence to: Emanuele Bobbio, MD, Department of Cardiology, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Email: emanuele.bobbio@ 123456vgregion.se
                Author information
                https://orcid.org/0000-0002-8287-2448
                https://orcid.org/0000-0003-2727-5007
                https://orcid.org/0000-0001-7125-9747
                https://orcid.org/0000-0002-2395-8989
                https://orcid.org/0000-0002-2802-1191
                https://orcid.org/0000-0002-3411-8838
                https://orcid.org/0000-0003-2844-4773
                https://orcid.org/0000-0001-5828-0015
                https://orcid.org/0000-0003-3341-2434
                Article
                JAH38658 JAHA/2023/029481
                10.1161/JAHA.123.029481
                10492974
                37489729
                1233f428-5d5d-4301-967c-6543d99504b7
                © 2023 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://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.

                History
                : 11 January 2023
                : 27 June 2023
                Page count
                Figures: 3, Tables: 2, Pages: 16, Words: 6677
                Funding
                Funded by: ALF , doi 10.13039/100001424;
                Award ID: ALFGBG‐874631
                Award ID: ALFGBG‐971483
                Funded by: Swedish Heart‐Lung Foundation
                Award ID: 20 200 163
                Funded by: Swedish Heart and Lung Association
                Award ID: FA2019:4
                Categories
                Original Research
                Original Research
                Heart Failure
                Custom metadata
                2.0
                01 August 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.2 mode:remove_FC converted:11.08.2023

                Cardiovascular Medicine
                cardiac sarcoidosis,endomyocardial biopsy,heart failure,inflammatory heart disease,machine learning,right ventricular function,cardiomyopathy,mortality/survival

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