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

Author:

Bobbio Emanuele12ORCID,Eldhagen Per34ORCID,Polte Christian L.25ORCID,Hjalmarsson Clara12ORCID,Karason Kristjan26ORCID,Rawshani Araz12,Darlington Pernilla78ORCID,Kullberg Susanna910ORCID,Sörensson Peder910ORCID,Bergh Niklas12,Bollano Entela12ORCID

Affiliation:

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

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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3