Abstract
Abstract Objective To develop a clinical risk score for individualized risk stratification of patients with clinically suspected myocardial inflammation. Background Myocardial inflammation is a prominent cause of non-ischaemic dilated cardiomyopathy, heart failure (HF) and sudden cardiac death. Methods This is a prospective multicentre longitudinal study of consecutive patients referred to cardiac magnetic resonance (CMR) with clinically suspected myocardial inflammation between October 2011 and December 2019 as a part of standard diagnostic pathway. Patients were followed up from the date of CMR. The outcome endpoints included major adverse cardiovascular event (MACE, cardiovascular mortality, sudden cardiac death, appropriate device discharge); or death or hospitalisation due to HF). A prognostic model was developed using Cox proportional hazards analysis and validated internally and externally. Results The final dataset included 722 subjects (50 years (40–61); males 422 (58%)). During a follow-up period of median 19 (15–23) months, there were 64 (9%) MACE and 130 (18%) HF events. Ten predictor variables qualified for entry into the prognostic model: age, sex, hematocrit, C-reactive protein, high-sensitive troponin-T (TNT), left and right ventricular ejection fraction, native T1 and T2, and late gadolinium enhancement (LGE). The final multivariable Cox regression model included native T2 (Figure 1A), TNT and LGE (Figure 1B) for the primary (Chi-square: 102.0, p<0.001) and secondary endpoint (Chi-square: 166.9, p<0.001), respectively. Cross-validation as well as external validation of the secondary models revealed good performance and no healthcare system effect. Based on the MyoRISK Score, patients were classified into three risk groups with respective event rates for MACE of 0%, 6.3% and 25.1%, and HF endpoint of 1.8%, 17.3% and 44.2%. TNT≥7 pg/ml allowed to efficiently preselect patients prior to CMR. Conclusions This is the first systematic assessment of outcomes in patients with clinically suspected myocardial inflammation, providing a non-invasive estimation of the probability of adverse events based on a score using readily available clinical parameters. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): DZHK
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