Importance Patients with hypertrophic cardiomyopathy (HCM) are prone to body weight increase and obesity. Whether this predisposes these individuals to long-term adverse outcomes is still unresolved. Objective To describe the association of body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) with long-term outcomes in patients with HCM in terms of overall disease progression, heart failure symptoms, and arrhythmias. Design, Setting, and Participants In this cohort study, retrospective data were analyzed from the ongoing prospective Sarcomeric Human Cardiomyopathy Registry, an international database created by 8 high-volume HCM centers that includes more than 6000 patients who have been observed longitudinally for decades. Records from database inception up to the first quarter of 2018 were analyzed. Patients were divided into 3 groups according to BMI class (normal weight group, 30). Patients with 1 or more follow-up visits were included in the analysis. Data were analyzed from April to October 2018. Exposures Association of baseline BMI with outcome was assessed. Main Outcome and Measures Outcome was measured against overall and cardiovascular mortality, a heart failure outcome (ejection fraction less than 35%, New York Heart Association class III/IV symptoms, cardiac transplant, or assist device implantation), a ventricular arrhythmic outcome (sudden cardiac death, resuscitated cardiac arrest, or appropriate implantable cardioverter-defibrillator therapy), and an overall composite outcome (first occurrence of any component of the ventricular arrhythmic or heart failure composite end point, all-cause mortality, atrial fibrillation, or stroke). Results Of the 3282 included patients, 2019 (61.5%) were male, and the mean (SD) age at diagnosis was 47 (15) years. These patients were observed for a median (interquartile range) of 6.8 (3.3-13.3) years. There were 962 patients in the normal weight group (29.3%), 1280 patients in the preobesity group (39.0%), and 1040 patients in the obesity group (31.7%). Patients with obesity were more symptomatic (New York Heart Association class of III/IV: normal weight, 87 [9.0%]; preobesity, 138 [10.8%]; obesity, 215 [20.7%];P < .001) and more often had obstructive physiology (normal weight, 201 [20.9%]; preobesity, 327 [25.5%]; obesity, 337 [32.4%];P < .001). At follow-up, obesity was independently associated with the HCM-related overall composite outcome (preobesity vs normal weight: hazard ratio [HR], 1.102; 95% CI, 0.920-1.322;P = .29; obesity vs normal weight: HR, 1.634; 95% CI, 1.332-1.919;P < .001) and the heart failure composite outcome (preobesity vs normal weight: HR, 1.192; 95% CI, 0.930-1.1530;P = .20; obesity vs normal weight: HR, 1.885; 95% CI, 1.485-2.393;P < .001) irrespective of age, sex, left atrium diameter, obstruction, and genetic status. Obesity increased the likelihood of atrial fibrillation but not of life-threatening ventricular arrhythmias. Conclusions and Relevance Obesity is highly prevalent among patients with HCM and is associated with increased likelihood of obstructive physiology and adverse outcomes. Strategies aimed at preventing obesity and weight increase may play an important role in management and prevention of disease-related complications.

Background: Over the last 50 years, the epidemiology of hypertrophic cardiomyopathy (HCM) has changed because of increased awareness and availability of advanced diagnostic tools. We aim to describe the temporal trends in age, sex, and clinical characteristics at HCM diagnosis over >4 decades. Methods: We retrospectively analyzed records from the ongoing multinational Sarcomeric Human Cardiomyopathy Registry. Overall, 7286 patients with HCM diagnosed at an age ≥18 years between 1961 and 2019 were included in the analysis and divided into 3 eras of diagnosis (2010). Results: Age at diagnosis increased markedly over time (40±14 versus 47±15 versus 51±16 years, P <0.001), both in US and non-US sites, with a stable male-to-female ratio of about 3:2. Frequency of familial HCM declined over time (38.8% versus 34.3% versus 32.7%, P <0.001), as well as heart failure symptoms at presentation (New York Heart Association III/IV: 18.1% versus 15.8% versus 12.6%, P <0.001). Left ventricular hypertrophy became less marked over time (maximum wall thickness: 20±6 versus 18±5 versus 17±5 mm, P 30 mm Hg: 31.9% versus 39.3% versus 39.0%, P =0.001). Consistent with decreasing phenotypic severity, yield of pathogenic/likely pathogenic variants at genetic testing decreased over time (57.7% versus 45.6% versus 38.4%, P <0.001). Conclusions: Evolving HCM populations include progressively greater representation of older patients with sporadic disease, mild phenotypes, and genotype-negative status. Such trend suggests a prominent role of imaging over genetic testing in promoting HCM diagnoses and urges efforts to understand genotype-negative disease eluding the classic monogenic paradigm.

Objectives: Several physiological abnormalities that develop during COVID-19 are associated with increased mortality. In the present study, we aimed to develop a clinical risk score to predict the in-hospital mortality in COVID-19 patients, based on a set of variables available soon after the hospitalisation triage. Setting: Retrospective cohort study of 516 patients consecutively admitted for COVID-19 to two Italian tertiary hospitals located in Northern and Central Italy were collected from 22 February 2020 (date of first admission) to 10 April 2020. Participants: Consecutive patients≥18 years admitted for COVID-19. Main outcome measures: Simple clinical and laboratory findings readily available after triage were compared by patients’ survival status (‘dead’ vs ‘alive’), with the objective of identifying baseline variables associated with mortality. These were used to build a COVID-19 in-hospital mortality risk score (COVID-19MRS). Results: Mean age was 67±13 years (mean±SD), and 66.9% were male. Using Cox regression analysis, tertiles of increasing age (≥75, upper vs <62 years, lower: HR 7.92; p<0.001) and number of chronic diseases (≥4 vs 0–1: HR 2.09; p=0.007), respiratory rate (HR 1.04 per unit increase; p=0.001), PaO ₂ /FiO ₂ (HR 0.995 per unit increase; p<0.001), serum creatinine (HR 1.34 per unit increase; p<0.001) and platelet count (HR 0.995 per unit increase; p=0.001) were predictors of mortality. All six predictors were used to build the COVID-19MRS (Area Under the Curve 0.90, 95% CI 0.87 to 0.93), which proved to be highly accurate in stratifying patients at low, intermediate and high risk of in-hospital death (p<0.001). Conclusions: The COVID-19MRS is a rapid, operator-independent and inexpensive clinical tool that objectively predicts mortality in patients with COVID-19. The score could be helpful from triage to guide earlier assignment of COVID-19 patients to the most appropriate level of care.