Search

In patients admitted for acute heart failure (HF) indication for drugs which reduce the heart rate (HR) is debated. The multicentre prospective study Reduction of heart Rate in Heart Failure (RedRate-HF) was designed to analyse the hemodynamic effects of an early reduction of HR in acute HF.Hemodynamic parameters were recorded by using the bioimpedance technique, which was shown to be accurate, highly reproducible and sensitive to intra-observer changes. Lowering HR was obtained by ivabradine 5 mg bd, given 48-72 h after admission on the top of optimized treatment. Patients were followed at 24, 48, 72 h after drug assumption and at hospital discharge.Twenty patients of a mean age of 67 ± 15 years, BNP at entry 1348 ± 1198 pg/ml were enrolled. Despite a clinical stabilization, after 48-72 h from admission, HR was persistently70 bpm. Ivabradine was well tolerated in all patients with a significant increase in RR interval from 747 ± 69 ms at baseline to 948 ± 121 ms at discharge, P 0.0001. Change in HR was associated with a significant increase in stroke volume (baseline 73 ± 22 vs. 84 ± 19 ml at discharge, P = 0.03), and reduction in left cardiac work index (baseline 3.6 ± 1.2 vs. 3.1 ± 1.1 kg/m2 at discharge, P = 0.04). Other measures of heart work were also significantly affected while cardiac output remained unchanged.The strategy of an early lowering of HR in patients admitted for acute HF on top of usual care is feasible and safe. The HR reduction causes a positive increase in stroke volume and may contribute to saving energy without affecting cardiac output.

Background: Glucocorticoid therapy has emerged as an effective therapeutic option in hospitalized patients with coronavirus disease 2019 (COVID-19). This study aimed to focus on the impact of relevant clinical and laboratory factors on the protective effect of glucocorticoids on mortality. Methods: A sub-analysis was performed of the multicenter Cardio-COVID-Italy registry, enrolling consecutive patients with COVID-19 admitted to 13 Italian cardiology units between 01 March 2020 and 09 April 2020. The primary endpoint was in-hospital mortality. Results: A total of 706 COVID-19 patients were included (349 treated with glucocorticoids, 357 not treated with glucocorticoids). After adjustment for relevant covariates, use of glucocorticoids was associated with a lower risk of in-hospital mortality (adjusted HR 0.44; 95% CI 0.26–0.72; p = 0.001). A significant interaction was observed between the protective effect of glucocorticoids on mortality and PaO2/FiO2 ratio on admission (p = 0.042), oxygen saturation on admission (p = 0.017), and peak CRP (0.023). Such protective effects of glucocorticoids were mainly observed in patients with lower PaO2/FiO2 ratio (100 mg/L). Conclusions: The protective effects of glucocorticoids on mortality in COVID-19 were more evident among patients with worse respiratory parameters and higher systemic inflammation.

Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission.We studied an Italian cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 who were hospitalized in 13 cardiology units during Spring 2020. The Lasso procedure was used to select the most relevant covariates. The dataset was randomly divided into a training set containing 80% of the data, used for estimating the model, and a test set with the remaining 20%. A Random Forest modeled in-hospital mortality with the selected set of covariates: its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity and related 95% confidence interval (CI). This model was then compared with the one obtained by the Gradient Boosting Machine (GBM) and with logistic regression. Finally, to understand if each model has the same performance in the training and test set, the two AUCs were compared using the DeLong's test. Among 701 patients enrolled (mean age 67.2 ± 13.2 years, 69.5% male individuals), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9-24) days. Variables selected by the Lasso procedure were: age, oxygen saturation, PaO2/FiO2, creatinine clearance and elevated troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, lower creatinine clearance levels and higher prevalence of elevated troponin (all P 0.001). The best performance out of the samples was provided by Random Forest with an AUC of 0.78 (95% CI: 0.68-0.88) and a sensitivity of 0.88 (95% CI: 0.58-1.00). Moreover, Random Forest was the unique model that provided similar performance in sample and out of sample (DeLong test P = 0.78).In a large COVID-19 population, we showed that a customizable machine learning-based score derived from clinical variables is feasible and effective for the prediction of in-hospital mortality.

Ruxolitinib is an anti-inflammatory drug that inhibits the Janus kinase-signal transducer (JAK-STAT) pathway on the surface of immune cells. The potential targeting of this pathway using JAK inhibitors is a promising approach in patients affected by coronavirus disease 2019 (COVID-19). Ruxolitinib was provided as a compassionate use in patients consecutively admitted to our institution for severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection. Inclusion criteria were oxygen saturation less than or equal to 92%, signs of interstitial pneumonia, and no need of mechanical ventilation. Patients received 5mg b.i.d. of ruxolitinib for 15days, data were collected at baseline and on days 4, 7, and 15 during treatment. Two main targets were identified, C-reactive protein (CRP) and PaO2/FiO2 ratio. In the 31 patients who received ruxolitinib, symptoms improved (dyspnea scale) on day 7 in 25 of 31 patients (80.6%); CRP decreased progressively from baseline (79.1±73.4mg/dl) to day 15 (18.6±33.2, p=0.022). In parallel with CRP, PO2/FiO2 ratio increased progressively during the 3 steps from 183±95 to 361±144mmHg (p&nbsp

INTRODUCTION: The role of sex compared to comorbidities and other prognostic variables in patients with coronavirus disease (COVID-19) is unclear. METHODS: This is a retrospective observational study on patients with COVID-19 infection, referred to 13 cardiology units. The primary objective was to assess the difference in risk of death between the sexes. The secondary objective was to explore sex-based heterogeneity in the association between demographic, clinical and laboratory variables, and patients' risk of death. RESULTS: Seven hundred and one patients were included: 214 (30.5%) women and 487 (69.5%) men. During a median follow-up of 15 days, deaths occurred in 39 (18.2%) women and 126 (25.9%) men. In a multivariable Cox regression model, men had a nonsignificantly higher risk of death vs. women (P = 0.07).The risk of death was more than double in men with a low lymphocytes count as compared with men with a high lymphocytes count [overall survival hazard ratio (OS-HR) 2.56, 95% confidence interval (CI) 1.72-3.81]. In contrast, lymphocytes count was not related to death in women (P = 0.03).Platelets count was associated with better outcome in men (OS-HR for increase of 50 × 103 units: 0.88 95% CI 0.78-1.00) but not in women. The strength of association between higher PaO2/FiO2 ratio and lower risk of death was larger in women (OS-HR for increase of 50 mmHg/%: 0.72, 95% CI 0.59-0.89) vs. men (OS-HR: 0.88, 95% CI 0.80-0.98; P = 0.05). CONCLUSIONS: Patients' sex is a relevant variable that should be taken into account when evaluating risk of death from COVID-19. There is a sex-based heterogeneity in the association between baseline variables and patients' risk of death.

Data concerning the combined prognostic role of natriuretic peptide (NP) and troponin in patients with COVID-19 are lacking. The aim of the study is to evaluate the combined prognostic value of NPs and troponin in hospitalized COVID-19 patients. From March 1, 2020 to April 9, 2020, consecutive patients with COVID-19 and available data on cardiac biomarkers at admission were recruited. Patients admitted for acute coronary syndrome were excluded. Troponin levels were defined as elevated when greater than the 99th percentile of normal values. NPs were considered elevated if above the limit for ruling in acute heart failure (HF). A total of 341 patients were included in this study, mean age 68 +/-& nbsp;13 years, 72% were men. During a median follow-up period of 14 days, 81 patients (24%) died. In the Cox regression analysis, patients with elevated both NPs and troponin levels had higher risk of death compared with those with normal levels of both (hazard ratio 2.94; 95% confidence interval 1.31 to 6.64; p = 0.009), and this remained significant after adjustment for age, gender, oxygen saturation, HF history, and chronic kidney disease. Interestingly, NPs provided risk stratification also in patients with normal troponin values (hazard ratio 2.86; 95% confidence interval 1.21 to 6.72; p = 0.016 with high NPs levels). These data show the combined prognostic role of troponin and NPs in COVID-19 patients. NPs value may be helpful in identifying patients with a worse prognosis among those with normal troponin values. Further, NPs' cut-point used for diagnosis of acute HF has a predictive role in patients with COVID-19.

Aims Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Prediction models are needed to optimize clinical management and to early stratify patients at a higher mortality risk. Machine learning (ML) algorithms represent a novel approach to identify a prediction model with a good discriminatory capacity to be easily used in clinical practice. Methods and results The Cardio-COVID is a multicentre observational study that involved a cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 [by real time reverse transcriptase—polymerase chain reaction (RT-PCR)] who were hospitalized in 13 Italian cardiology units from 1 March to 9 April 2020. Patients were followed-up after the COVID-19 diagnosis and all causes in-hospital mortality or discharge were ascertained until 23 April 2020. Variables with more than 20% of missing values were excluded. The Lasso procedure was used with a λ = 0.07 for reducing the covariates number. Mortality was estimated by means of a Random Forest (RF). The dataset was randomly divided in two subsamples with the same percentage of death/alive people of the entire sample: training set contained 80% of the data and test set the remaining 20%. The training set was used in the calibration procedure where a RF models in-hospital mortality with the covariates selected by Lasso. Its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity, and related 95% confidence interval (CI) computed with 10 000 stratified bootstrap replicates. From the RF the relative Variable Importance Measure (relVIM) was extracted to understand which of the selected variables had the greatest impact on outcome, providing a ranking from the most (relVIM = 100) to the less important variable. The model obtained was compared with the Gradient Boosting Machine (GBM) and with the logistic regression, where the predictions were cross validated. Finally, to understand if each model has the same performance in sample (training) and out of sample (test), the two AUCs were compared by means of the DeLong’s test. Among 701 patients enrolled (mean age 67.2 ± 13.2 years, 69.5% males), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9–24) days. Variables selected by the Lasso were: age, Oxygen saturation, PaO2/FiO2, Creatinine Clearance and elevated Troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, a lower creatinine clearance levels and higher prevalence of elevated Troponin (all P Conclusions In a large COVID-19 population, we showed that a customizable ML-based score derived from clinical variables, is feasible and effective for the prediction of in-hospital mortality.

Background Pulmonary embolism (PE) has been described in coronavirus disease 2019 (COVID-19) critically ill patients, but the evidence from more heterogeneous cohorts is limited. Methods Data were retrospectively obtained from consecutive COVID-19 patients admitted to 13 Cardiology Units in Italy, from March 1st to April 9th, 2020, and followed until in-hospital death, discharge, or April 23rd, 2020. The association of baseline variables with computed tomography-confirmed PE was investigated by Cox hazards regression analysis. The relationship between d-dimer levels and PE incidence was evaluated using restricted cubic splines models. Results The study included 689 patients (67.3 ± 13.2 year-old, 69.4% males), of whom 43.6% were non-invasively ventilated and 15.8% invasively. 52 (7.5%) had PE over 15 (9–24) days of follow-up. Compared with those without PE, these subjects had younger age, higher BMI, less often heart failure and chronic kidney disease, more severe cardio-pulmonary involvement, and higher admission d-dimer [4344 (1099–15,118) vs. 818.5 (417–1460) ng/mL, p p p = 0.06). In multivariate regression, only d-dimer was associated with PE (HR 1.72, 95% CI 1.13–2.62; p = 0.01). The relation between d-dimer concentrations and PE incidence was linear, without inflection point. Only two subjects had a baseline d-dimer Conclusions PE occurs in a sizable proportion of hospitalized COVID-19 patients. The implications of bleeding events and the role of d-dimer in this population need to be clarified. Graphic abstract

Aims Myocardial injury (MI) in coronavirus disease‐19 (COVID‐19) is quite prevalent at admission and affects prognosis. Little is known about troponin trajectories and their prognostic role. We aimed to describe the early in‐hospital evolution of MI and its prognostic impact. Methods and results We performed an analysis from an Italian multicentre study enrolling COVID‐19 patients, hospitalized from 1 March to 9 April 2020. MI was defined as increased troponin level. The first troponin was tested within 24 h from admission, the second one between 24 and 48 h. Elevated troponin was defined as values above the 99th percentile of normal values. Patients were divided in four groups: normal, normal then elevated, elevated then normal, and elevated. The outcome was in‐hospital death. The study population included 197 patients; 41% had normal troponin at both evaluations, 44% had elevated troponin at both assessments, 8% had normal then elevated troponin, and 7% had elevated then normal troponin. During hospitalization, 49 (25%) patients died. Patients with incident MI, with persistent MI, and with MI only at admission had a higher risk of death compared with those with normal troponin at both evaluations (P

Aims To assess the clinical relevance of a history of atrial fibrillation (AF) in hospitalized patients with coronavirus disease 2019 (COVID-19). Methods and results We enrolled 696 consecutive patients (mean age 67.4 ± 13.2 years, 69.7% males) admitted for COVID-19 in 13 Italian cardiology centres between 1 March and 9 April 2020. One hundred and six patients (15%) had a history of AF and the median hospitalization length was 14 days (interquartile range 9–24). Patients with a history of AF were older and with a higher burden of cardiovascular risk factors. Compared to patients without AF, they showed a higher rate of in-hospital death (38.7% vs. 20.8%; P 0.05 for both) and was not related to stroke or bleeding events. Conclusion Among hospitalized patients with COVID-19, a history of AF contributes to worse clinical course with a higher mortality and in-hospital events including new-onset AF, acute HF, and multiorgan failure. The mortality risk remains significant after adjustment for variables associated with COVID-19 severity and comorbidities.

Aims: To assess the prognostic value of a history of heart failure (HF) in patients with coronavirus disease 2019 (COVID-19). Methods and results: We enrolled 692 consecutive patients admitted for COVID-19 in 13 Italian cardiology centres between 1 March and 9 April 2020. Mean age was 67.4± 13.2 years, 69.5% of patients were males, 90 (13.0%) had a history of HF, median hospitalization length was 14 days (interquartile range 9–24). In-hospital death occurred in 37 of 90 patients (41.1%) with HF history vs. 126 of those with no HF history (20.9%). The increased risk of death associated with HF history remained significant after adjustment for clinical variables related to COVID-19 and HF severity, including comorbidities, oxygen saturation, lymphocyte count and plasma troponin [adjusted hazard ratio (HR) for death: 2.25; 95% confidence interval (CI) 1.26–4.02; P= 0.006 at multivariable Cox regression model including 404 patients]. Patients with a history of HF also had more in-hospital complications including. acute HF (33.3% vs. 5.1%, P< 0.001), acute renal failure (28.1% vs. 12.9%, P< 0.001), multiorgan failure (15.9% vs. 5.8%, P= 0.004) and sepsis (18.4% vs. 8.9%, P= 0.006). Other independent predictors of outcome were age, sex, oxygen saturation and oxygen partial pressure at arterial gas analysis/fraction of inspired oxygen ratio (PaO2/FiO2). In-hospital treatment with corticosteroids and heparin had beneficial effects (adjusted HR for death: 0.46; 95% CI 0.29–0.74; P= 0.001; n= 404 for corticosteroids, and adjusted HR 0.41; 95% CI 0.25–0.67; P< 0.001; n= 364 for heparin). Conclusions: Hospitalized patients with COVID-19 and a history of HF have an extremely poor outcome with higher mortality and in-hospital complications. HF history is an independent predictor of increased in-hospital mortality.

Importance: Myocardial injury, detected by elevated plasma troponin levels, has been associated with mortality in patients hospitalized with coronavirus disease 2019 (COVID-19). However, the initial data were reported from single-center or 2-center studies in Chinese populations. Compared with these patients, European and US patients are older, with more comorbidities and higher mortality rates. Objective: To evaluate the prevalence and prognostic value of myocardial injury, detected by elevated plasma troponin levels, in a large population of White Italian patients with COVID-19. Design, Setting, and Participants: This is a multicenter, cross-sectional study enrolling consecutive patients with laboratory-confirmed COVID-19 who were hospitalized in 13 Italian cardiology units from March 1 to April 9, 2020. Patients admitted for acute coronary syndrome were excluded. Elevated troponin levels were defined as values greater than the 99th percentile of normal values. Main Outcomes and Measures: Clinical characteristics and outcomes stratified as elevated or normal cardiac troponin levels at admission, defined as troponin T or troponin I at a level greater than the 99th percentile of normal values. Results: A total of 614 patients with COVID-19 were included in this study (mean age [SD], 67 [13] years; 70.8% male), of whom 148 patients (24.1%) died during the hospitalization. Elevated troponin levels were found in 278 patients (45.3%). These patients were older (mean [SD] age, 64.0 [13.6] years vs 71.3 [12.0] years; P