To study the impact of genotype on the performance of the 2019 risk model for arrhythmogenic right ventricular cardiomyopathy (ARVC). The study cohort comprised 554 patients with a definite diagnosis of ARVC and no history of sustained ventricular arrhythmia (VA). During a median follow-up of 6.0 (3.1,12.5) years, 100 patients (18%) experienced the primary VA outcome (sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator intervention, aborted sudden cardiac arrest, or sudden cardiac death) corresponding to an annual event rate of 2.6% [95% confidence interval (CI) 1.9–3.3]. Risk estimates for VA using the 2019 ARVC risk model showed reasonable discriminative ability but with overestimation of risk. The ARVC risk model was compared in four gene groups: PKP2 (n = 118, 21%); desmoplakin (DSP) (n = 79, 14%); other desmosomal (n = 59, 11%); and gene elusive (n = 160, 29%). Discrimination and calibration were highest for PKP2 and lowest for the gene-elusive group. Univariable analyses revealed the variable performance of individual clinical risk markers in the different gene groups, e.g. right ventricular dimensions and systolic function are significant risk markers in PKP2 but not in DSP patients and the opposite is true for left ventricular systolic function. The 2019 ARVC risk model performs reasonably well in gene-positive ARVC (particularly for PKP2) but is more limited in gene-elusive patients. Genotype should be included in future risk models for ARVC. ARVC, arrhythmogenic right ventricular cardiomyopathy; AUC, area under the curve; ECHO, echocardiography; CMR, cardiac magnetic resonance; HR, hazard ratio; LGE, late gadolinium enhancement; LV, left ventricular; LVEDD, left ventricular enddiastolic diameter; LVEF, left ventricular ejection fraction; PLAX, parasternal long-axis; PVC, premature ventricular complex; RV, right ventricular; RVEDV, right ventricular end-diastolic volume; RVEF, right ventricular ejection fraction; RVOT, right ventricular outflow tract; TWI, T-wave inversion; VT, ventricular tachycardia.

Aims: Genotype and left ventricular scar on cardiac magnetic resonance (CMR) are increasingly recognized as risk markers for adverse outcomes in non‐ischaemic dilated cardiomyopathy (DCM). We investigated the combined influence of genotype and late gadolinium enhancement (LGE) in assessing prognosis in a large cohort of patients with DCM. Methods and results: Outcomes of 600 patients with DCM (53.3 ± 14.1 years, 66% male) who underwent clinical CMR and genetic testing were retrospectively analysed. The primary endpoints were end‐stage heart failure (ESHF) and malignant ventricular arrhythmias (MVA). During a median follow‐up of 2.7 years (interquartile range 1.3–4.9), 24 (4.00%) and 48 (8.00%) patients had ESHF and MVA, respectively. In total, 242 (40.3%) patients had pathogenic/likely pathogenic variants (positive genotype) and 151 (25.2%) had LGE. In survival analysis, positive LGE was associated with MVA and ESHF (both, p < 0.001) while positive genotype was associated with ESHF ( p = 0.034) but not with MVA ( p = 0.102). Classification of patients according to genotype (G+/G−) and LGE presence (L+/L−) revealed progressively increasing events across L−/G−, L−/G+, L+/G− and L+/G+ groups and resulted in optimized MVA and ESHF prediction ( p < 0.001 and p = 0.001, respectively). Hazard ratios for MVA and ESHF in patients with either L+ or G+ compared with those with L−/G− were 4.71 (95% confidence interval: 2.11–10.50, p < 0.001) and 7.92 (95% confidence interval: 1.86–33.78, p < 0.001), respectively. Conclusion: Classification of patients with DCM according to genotype and LGE improves MVA and ESHF prediction. Scar assessment with CMR and genotyping should be considered to select patients for primary prevention implantable cardioverter‐defibrillator placement.

Dilated cardiomyopathy (DCM) is the third most common cause of heart failure. The multidisciplinary nature of testing — involving genetics, imaging, or cardiovascular techniques — makes its diagnosis challenging. Novel and reliable biomarkers are needed for early identification and tailored personalized management. Peripheral circular RNAs (circRNAs), a leading research topic, remain mostly unexplored in DCM. We aimed to assess whether peripheral circRNAs are expressed differentially among etiology-based DCM. The study was based on a case–control multicentric study. We enrolled 130 subjects: healthy controls (n = 20), idiopathic DCM (n = 30), ischemic DCM (n = 20), and familial DCM patients which included pathogen variants of (i) LMNA gene (n = 30) and (ii) BCL2-associated athanogene 3 (BAG3) gene (n = 30). Differentially expressed circRNAs were analyzed in plasma samples by quantitative RT-PCR and correlated to relevant systolic and diastolic parameters. The pathophysiological implications were explored through bioinformatics tools. Four circRNAs were overexpressed compared to controls: hsa_circ_0003258, hsa_circ_0051238, and hsa_circ_0051239 in LMNA-related DCM and hsa_circ_0089762 in the ischemic DCM cohort. The obtained areas under the curve confirm the discriminative capacity of circRNAs. The circRNAs correlated with some diastolic and systolic echocardiographic parameters with notable diagnostic potential in DCM. Circulating circRNAs may be helpful for the etiology-based diagnosis of DCM as a non-invasive biomarker. Key messages: The limitations of cardiac diagnostic imaging and the absence of a robust biomarker reveal the need for a diagnostic tool for dilated cardiomyopathy (DCM). The circular RNA (circRNA) expression pattern is paramount for categorizing the DCM etiologies. Our peripheral circRNAs fingerprint discriminates between various among etiology-based DCM and correlates with some echocardiographic parameters. We provide a potential non-invasive biomarker for the etiology-based diagnosis of LMNA-related DCM and ischemic DCM.

Aim: Patients with advanced heart failure (AHF) who are not candidates to advanced therapies have poor prognosis. Some trials have shown that intermittent levosimendan can reduce HF hospitalizations in AHF in the short term. In this real-life registry, we describe the patterns of use, safety and factors related to the response to intermittent levosimendan infusions in AHF patients not candidates to advanced therapies. Methods and results: Multicentre retrospective study of patients diagnosed with advanced heart failure, not HT or LVAD candidates. Patients needed to be on the optimal medical therapy according to their treating physician. Patients with de novo heart failure or who underwent any procedure that could improve prognosis were not included in the registry. Four hundred three patients were included; 77.9% needed at least one admission the year before levosimendan was first administered because of heart failure. Death rate at 1 year was 26.8% and median survival was 24.7 [95% CI: 20.4–26.9] months, and 43.7% of patients fulfilled the criteria for being considered a responder lo levosimendan (no death, heart failure admission or unplanned HF visit at 1 year after first levosimendan administration). Compared with the year before there was a significant reduction in HF admissions (38.7% vs. 77.9%; P < 0.0001), unplanned HF visits (22.7% vs. 43.7%; P < 0.0001) or the combined event including deaths (56.3% vs. 81.4%; P < 0.0001) during the year after. We created a score that helps predicting the responder status at 1 year after levosimendan, resulting in a score summatory of five variables: TEER (+2), treatment with beta-blockers (+1.5), Haemoglobin >12 g/dL (+1.5), amiodarone use (−1.5) HF visit 1 year before levosimendan (−1.5) and heart rate >70 b.p.m. (−2). Patients with a score less than −1 had a very low probability of response (21.5% free of death or HF event at 1 year) meanwhile those with a score over 1.5 had the better chance of response (68.4% free of death or HF event at 1 year). LEVO-D score performed well in the ROC analysis. Conclusion: In this large real-life series of AHF patients treated with levosimendan as destination therapy, we show a significant decrease of heart failure events during the year after the first administration. The simple LEVO-D Score could be of help when deciding about futile therapy in this population.

BACKGROUND: Less than 40% of patients with dilated cardiomyopathy (DCM) have a pathogenic/likely pathogenic genetic variant identified. TBX20 has been linked to congenital heart defects; although an association with left ventricular noncompaction (LVNC) and DCM has been proposed, it is still considered a gene with limited evidence for these phenotypes. This study sought to investigate the association between the TBX20 truncating variant ( TBX20tv ) and DCM/LVNC. METHODS: TBX20 was sequenced by next-generation sequencing in 7463 unrelated probands with a diagnosis of DCM or LVNC, 22 773 probands of an internal comparison group (hypertrophic cardiomyopathy, channelopathies, or aortic diseases), and 124 098 external controls (individuals from the gnomAD database). Enrichment of TBX20tv in DCM/LVNC was calculated, cosegregation was determined in selected families, and clinical characteristics and outcomes were analyzed in carriers. RESULTS: TBX20tv was enriched in DCM/LVNC (24/7463; 0.32%) compared with internal (1/22 773; 0.004%) and external comparison groups (4/124 098; 0.003%), with odds ratios of 73.23 (95% CI, 9.90–541.45; P <0.0001) and 99.76 (95% CI, 34.60–287.62; P <0.0001), respectively. TBX20tv was cosegregated with DCM/LVNC phenotype in 21 families for a combined logarythm of the odds score of 4.53 (strong linkage). Among 57 individuals with TBX20tv (49.1% men; mean age, 35.9±20.8 years), 41 (71.9%) exhibited DCM/LVNC, of whom 14 (34.1%) had also congenital heart defects. After a median follow-up of 6.9 (95% CI, 25–75:3.6–14.5) years, 9.7% of patients with DCM/LVNC had end-stage heart failure events and 4.8% experienced malignant ventricular arrhythmias. CONCLUSIONS: TBX20tv is associated with DCM/LVNC; congenital heart defect is also present in around one-third of cases. TBX20tv -associated DCM/LVNC is characterized by a nonaggressive phenotype, with a low incidence of major cardiovascular events. TBX20 should be considered a definitive gene for DCM and LVNC and routinely included in genetic testing panels for these phenotypes.

Background: Despite the high burden of diabetes and cardiovascular risk factors in American Indian communities in the United States, prospective studies of heart failure (HF) in this population group are scarce, and the generalizability of previous HF risk scales may be limited. We developed a parsimonious HF risk prediction equation that accounts for relevant risk factors affecting American Indian communities, focusing on diabetes and kidney damage. Methods and Results: A total of 3059 participants from the SHS (Strong Heart Study) (56±8 years of age, 58% women) were included. Five hundred seven developed HF. Progressively adjusted Cox proportional hazards models were used to identify risk factors for HF and HF subtypes. Predictors of risk at 5 and 10 years included older age (hazard ratio [HR], 1.79 [95% CI, 1.43–2.25]; HR, 1.68 [95% CI, 1.44–1.95]), smoking (HR, 2.26 [95% CI, 1.23–4.13]; HR, 2.08 [95% CI, 1.41–3.06]), macroalbuminuria (HR, 8.38 [95% CI, 4.44–15.83]; HR, 5.20 [95% CI, 3.42–7.9]), microalbuminuria (HR, 2.72 [95% CI, 1.51–4.90]; HR, 1.92 [95% CI, 1.33, 2.78]), and previous myocardial infarction (HR, 6.58 [95% CI, 2.54–17.03]; HR, 3.87 [95% CI, 2.29–6.54]), respectively. These predictors, together with diabetes diagnosis and glycated hemoglobin were significant at 10 and 28 years. High discrimination performance was achieved (C index, 0.81 [95% CI, 0.76–0.84]; C index, 0.78 [95% CI, 0.75–0.81]; and C index, 0.77 [95% CI, 0.74–0.78] at 5, 10, and up to 28 years of follow up, respectively). Some associations varied across HF subtypes, although diabetes, albuminuria, and previous myocardial infarction were associated with all subtypes. Conclusions: This prospective study of HF risk factors in American Indian communities identifies that smoking, body mass index, and indicators of diabetes control and kidney damage (glycated hemoglobin and albuminuria) are major determinants of HF. Our findings can improve HF risk assessment in populations with a high burden of diabetes.