Abstract
Introduction: Sarcoidosis is a systemic disease but cardiac involvement (CS) as first organ manifestation is not uncommon. Hypothesis: Patients with CS as the first manifestation of sarcoidosis has worse outcome compared to patients with known extra cardiac sarcoidosis (ECS) Methods: A retrospective cohort of 141 patients with CS enrolled at two Swedish university hospitals was studied. Presentation, treatment and outcomes of de novo CS and previous-known extra-CS group (ESC) were compared. Survival free time of primary composite outcome (ventricular tachycardia /ventricular fibrillation , heart transplantation , or death) was assessed. Using a data-driven approach with a machine learning (ML) algorithm, we studied the relative importance of several factors Results: 62 de novo CS patients and 79 with known ECS at time of cardiac manifestation. No difference in the demographics was observed between the groups. De novo CS patients showed more advanced NYHA class (p=0.02) as well as higher circulating levels of natriuretic peptides (p<0.001), troponins (p<0.001). Imaging findings were similar between the two groups. During a median (IQR) follow-up time was 28 [16-80] months, the composite end-point occurred in 37% of patients with CS as first clinical manifestation and in 20% of patients with ECS. Kaplan-Meier analysis showed that event-free survival was significantly worse in patients with de novo CS (p<0.001) ( Fig. 1 ). The top four factors to predict event-free survival in order of importance were: tricuspid annular plane systolic excursion (TAPSE), Cardiac Index (CI), Right Ventricular (RV) ejection fraction, previous known ECS Conclusions: Patients presenting with CS as their first recognized organ manifestation of disease display worse outcomes compared to patients with previous-known ECS. Using machine learning, we show that the most important predictor of survival in CS is TAPSE, following by CI, RV ejection fraction and previous known ECS
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