This international consensus statement is intended to help cardiologists and other health care professionals involved in the care of adult and pediatric patients with arrhythmogenic cardiomyopathy (ACM), which encompasses a broad range of disorders, by providing recommendations for evaluation and management and supporting shared decision making between health care providers and patients in a document format that is also useful at the point of care.This consensus statement was written by experts in the field chosen by the Heart Rhythm Society (HRS) and collaborating organizations.

Standardized donor‐derived cell‐free DNA (dd‐cfDNA) testing has been introduced into clinical use to monitor kidney transplant recipients for rejection. This report describes the performance of this dd‐cfDNA assay to detect allograft rejection in samples from heart transplant (HT) recipients undergoing surveillance monitoring across the United States. Venous blood was longitudinally sampled from 740 HT recipients from 26 centers and in a single‐center cohort of 33 patients at high risk for antibody‐mediated rejection (AMR). Plasma dd‐cfDNA was quantified by using targeted amplification and sequencing of a single nucleotide polymorphism panel. The dd‐cfDNA levels were correlated to paired events of biopsy‐based diagnosis of rejection. The median dd‐cfDNA was 0.07% in reference HT recipients (2164 samples) and 0.17% in samples classified as acute rejection (35 samples; P = .005). At a 0.2% threshold, dd‐cfDNA had a 44% sensitivity to detect rejection and a 97% negative predictive value. In the cohort at risk for AMR (11 samples), dd‐cfDNA levels were elevated 3‐fold in AMR compared with patients without AMR (99 samples, P = .004). The standardized dd‐cfDNA test identified acute rejection in samples from a broad population of HT recipients. The reported test performance characteristics will guide the next stage of clinical utility studies of the dd‐cfDNA assay.

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Cardiac amyloidosis in the United States is most often due to myocardial infiltration by immunoglobulin protein, such as in AL amyloidosis, or by the protein transthyretin, such as in hereditary and senile amyloidosis. Cardiac amyloidosis often portends a poor prognosis especially in patients with systemic AL amyloidosis. Despite better understanding of the pathophysiology of amyloid, many patients are still diagnosed late in the disease course. This review investigates the current understanding and new research on the diagnosis and treatment strategies in patients with cardiac amyloidosis. Myocardial amyloid infiltration distribution occurs in a variety of patterns. Structural and functional changes on echocardiography can suggest presence of amyloid, but CMR and nuclear imaging provide important complementary information on amyloid burden and the amyloid subtype, respectively. While for AL amyloid, treatment success largely depends on early diagnosis, for ATTR amyloid, new investigational agents that reduce production of transthyretin protein may have significant impact on clinical outcomes. Advancements in the non-invasive diagnostic detection and improvements in early disease recognition will undoubtedly facilitate a larger proportion of patients to receive early therapy when it is most effective.

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Background. We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. The present study aimed to examine the stability of machine-learning algorithms in new biopsies, compare 3AA vs. 4AA algorithms, assess supervised binary classifiers trained on histologic or molecular diagnoses, create a report combining many scores into an ensemble of estimates, and examine possible automated sign-outs. Methods. We studied 889 EMBs from 454 transplant recipients at eight centers: the initial cohort (N=331) and a new cohort (N=558). Published 3AA algorithms derived in cohort 331 were tested in cohort 558; the 3AA and 4AA models were compared; and supervised binary classifiers were created. Results. Algorithms derived in cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (AUCs>0.87) better than histologic rejection (AUCs<0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by one expert showed highly significant agreement with histology (p<0.001), but with many discrepancies as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated sign-outs. Conclusions. Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states.

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