Aims Data regarding impact of COVID‐19 in chronic heart failure (CHF) patients and its potential to trigger acute heart failure (AHF) is lacking. The aim of this work was to study characteristics, cardiovascular outcomes and mortality in patients with confirmed COVID‐19 infection and prior diagnosis of HF. Also, to identify predictors and prognostic implications for AHF decompensations during hospital admission and to determine whether there was a correlation between withdrawal of HF guideline‐directed medical therapy (GDMT) and worse outcomes during hospitalization. Methods and results A total of 3080 consecutive patients with confirmed COVID‐19 infection and at least 30‐day follow‐up were analyzed. Patients with previous history of CHF (152, 4.9%), were more prone to develop AHF (11.2% vs 2.1%; p<0.001) and had higher levels of NT‐proBNP. Also, previous CHF group had higher mortality rates (48.7% vs 19.0%; p<0.001). In contrast, 77 patients (2.5%) were diagnosed of AHF and the vast majority (77.9%) developed in patients without history of HF. Arrhythmias during hospital admission and CHF were main predictors of AHF. Patients developing AHF had significantly higher mortality (46.8% vs 19.7%; p<0.001). Finally, withdrawal of beta‐blockers, mineralocorticoid antagonists and ACE/ARB inhibitors was associated with a significant increase of in‐hospital mortality. Conclusions Patients with COVID‐19 have a significant incidence of AHF, entity that carries within a very high mortality. Moreover, patients with history of CHF are prone to develop acute decompensation after COVID‐19 diagnosis. Withdrawal of GDMT was associated with higher mortality.

Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

Aims Extensive research regarding the association of troponin and prognosis in coronavirus disease 2019 (COVID‐19) has been performed. However, data regarding natriuretic peptides are scarce. N‐terminal pro B‐type natriuretic peptide (NT‐proBNP) reflects haemodynamic stress and has proven useful for risk stratification in heart failure (HF) and other conditions such as pulmonary embolism and pneumonia. We aimed to adequately characterize NT‐proBNP concentrations using a large cohort of patients with COVID‐19, and to investigate its association with prognosis. Methods and results Consecutive patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection and available NT‐proBNP determinations, from March 1st to April 20th, 2020 who completed at least 1‐month follow‐up or died, were studied. Of 3080 screened patients, a total of 396 (mean age 71.8 ± 14.6 years, 61.1% male) fulfilled all the selection criteria and were finally included, with a median follow‐up of 53 (18–62) days. Of those, 192 (48.5%) presented NT‐proBNP levels above the recommended cut‐off for the identification of HF. However, only 47 fulfilled the clinical criteria for the diagnosis of HF. Patients with higher NT‐proBNP during admission experienced more frequent bleeding, arrhythmias and HF decompensations. NT‐proBNP was associated with mortality both in the whole study population and after excluding patients with HF. A multivariable Cox model confirmed that NT‐proBNP was independently associated with mortality after adjusting for all relevant confounders (hazard ratio 1.28, 95% confidence interval 1.13–1.44, per logarithmic unit). Conclusion NT‐proBNP is frequently elevated in COVID‐19. It is strongly and independently associated with mortality after adjusting for relevant confounders, including chronic HF and acute HF. Therefore, its use may improve early prognostic stratification in this condition.

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