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

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.

Keywords

• cardiac sarcoidosis
• endomyocardial biopsy
• heart failure
• inflammatory heart disease
• machine learning
• right ventricular function