Background: We sought to identify the comorbidities associated with heart failure (HF) in a non-selected cohort of patients, and its influence on mortality and rehospitalization. Design and methods: Data were obtained from the ‘Registro de Insuficiencia Cardiaca’ (RICA) of the Spanish Society of Internal Medicine. The registry includes patients prospectively admitted in Internal Medicine units for acute HF. Variables included in Charlson Index (ChI) were collected and analysed according to age, gender, left ventricular ejection fraction (LVEF) and Barthel Index. The primary end point of study was the likelihood of rehospitalization and death for any cause during the year after discharge. Results: We included 2051 patients, mean age 78 and 53% females. LVEF was ⩾ 50% in 59.1% of the cohort. There was a high degree of dependency as measured by Barthel Index (14.8 % had an index ≤ 60). Mean ChI was 2.91 (SD ± 2.4). The most frequent comorbidities included in ChI were diabetes mellitus (44.3%), chronic renal impairment (30.8%) and chronic obstructive pulmonary disease (COPD) (27.4%). Age, myocardial infarction, peripheral artery disease, dementia, COPD, chronic renal impairment and diabetes with target-organ damage were all identified as independent prognostic factors for the combined end point of rehospitalization and death at 1 year. However, if multivariate analysis was done including ChI, only this remained as an independent prognostic factor for the combined end point ( P < 0.001). Conclusions: HF is a comorbid condition. ChI is a simple and feasible tool for estimating the burden of comorbidities in such population. We believe that a holistic approach to HF would improve prognosis and the relief the pressure exerted on public health services.

Heart failure (HF) is becoming increasingly prevalent and affects both men and women. However, women have traditionally been underrepresented in HF clinical trials. In this study, we aimed to analyze sex differences in the comorbidity, therapy, and health services’ use of HF patients. We conducted a cross-sectional study in Aragón (Spain) and described the characteristics of 17,516 patients with HF. Women were more frequent (57.4 vs. 42.6%, p < 0.001) and older (83 vs. 80 years, p < 0.001) than men, and presented a 33% lower risk of 1-year mortality (p < 0.001). Both sexes showed similar disease burdens, and 80% suffered six or more diseases. Some comorbidities were clearly sex-specific, such as arthritis, depression, and hypothyroidism in women, and arrhythmias, ischemic heart disease, and COPD in men. Men were more frequently anti-aggregated and anti-coagulated and received more angiotensin-converting-enzyme (ACE) inhibitors and beta-blockers, whereas women had more angiotensin II antagonists, antiinflammatories, antidepressants, and thyroid hormones dispensed. Men were admitted to specialists (79.0 vs. 70.6%, p < 0.001), hospital (47.0 vs. 38.1%, p < 0.001), and emergency services (57.6 vs. 52.7%, p < 0.001) more frequently than women. Our results highlight the need to conduct future studies to confirm the existence of these differences and of developing separate HF management guidelines for men and women that take into account their sex-specific comorbidity.

Background/Objectives: Heart failure (HF) is usually accompanied by other comorbidities, which, altogether, have a major impact on patients and healthcare systems. Our aim was to analyse the demographic and clinical characteristics of incident HF patients and the effect of comorbidities on one-year health outcomes. Methods: This was an observational, retrospective, population-based study of incident HF patients between 2014 and 2018 in the EpiChron Cohort, Spain. The included population contained all primary and hospital care patients with a diagnosis of HF. All chronic diseases in their electronic health records were pooled into three comorbidity clusters (cardiovascular, mental, other physical). These comorbidity groups and the health outcomes were analysed until 31 December 2018. A descriptive analysis was performed. Cox regression models and survival curves were calculated to determine the hazard risk (HR) of all-cause mortality, all-cause and HF-related hospital admissions, hospital readmissions, and emergency room visits for each comorbidity group. Results: In total, 13,062 incident HF patients were identified (mean age = 82.0 years; 54.8% women; 93.7% multimorbid; mean of 4.52 ± 2.06 chronic diseases). After one-year follow-up, there were 3316 deaths (25.3%) and 4630 all-cause hospitalisations (35.4%). After adjusting by gender, age, and inpatient/outpatient status, the mental cluster was associated (HR; 95% confidence interval) with a higher HR of death (1.08; 1.01–1.16) and all-cause hospitalisation (1.09; 1.02–1.16). Conclusions: Cardiovascular comorbidities are the most common and studied ones in HF patients; however, they are not the most strongly associated with negative impacts on health outcomes in these patients. Our findings suggest the importance of a holistic and integral approach in the care of HF patients and the need to take into account the entire spectrum of comorbidities for improving HF management in clinical practice.

Background: Risk stratification of COVID-19 patients is fundamental to improving prognosis and selecting the right treatment. We hypothesized that a combination of lung ultrasound (LUZ-score), biomarkers (sST2), and clinical models (PANDEMYC score) could be useful to improve risk stratification. Methods: This was a prospective cohort study designed to analyze the prognostic value of lung ultrasound, sST2, and PANDEMYC score in COVID-19 patients. The primary endpoint was in-hospital death and/or admission to the intensive care unit. The total length of hospital stay, increase of oxygen flow, or escalated medical treatment during the first 72 h were secondary endpoints. Results: a total of 144 patients were included; the mean age was 57.5 ± 12.78 years. The median PANDEMYC score was 243 (52), the median LUZ-score was 21 (10), and the median sST2 was 53.1 ng/mL (30.9). Soluble ST2 showed the best predictive capacity for the primary endpoint (AUC = 0.764 (0.658–0.871); p = 0.001), towards the PANDEMYC score (AUC = 0.762 (0.655–0.870); p = 0.001) and LUZ-score (AUC = 0.749 (0.596–0.901); p = 0.002). Taken together, these three tools significantly improved the risk capacity (AUC = 0.840 (0.727–0.953); p ≤ 0.001). Conclusions: The PANDEMYC score, lung ultrasound, and sST2 concentrations upon admission for COVID-19 are independent predictors of intra-hospital death and/or the need for admission to the ICU for mechanical ventilation. The combination of these predictive tools improves the predictive power compared to each one separately. The use of decision trees, based on multivariate models, could be useful in clinical practice.