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The use of increasingly complex cardiac implantable electronic devices (CIEDs) has increased exponentially in recent years. One of the most serious complications in terms of mortality, morbidity and financial burden is represented by infections involving these devices. They may affect only the generator pocket or be generalised with lead-related endocarditis. Modifiable and non-modifiable risk factors have been identified and they can be associated with patient or procedure characteristics or with the type of CIED. Pocket and systemic infections require a precise evaluation and a specialised treatment which in most cases involves the removal of all the components of the device and a personalised antimicrobial therapy. CIED retention is usually limited to cases where infection is unlikely or is limited to the skin incision site. Optimal re-implantation timing depends on the type of infection and on the results of microbiological tests. Preventive strategies, in the end, include antibiotic prophylaxis before CIED implantation, the possibility to use antibacterial envelopes and the prevention of hematomas. The aim of this review is to investigate the pathogenesis, stratification, diagnostic tools and management of CIED infections.

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.

Introduction: This study analyzes the usefulness of the CHA2DS2-VASc score for mortality prediction in patients with acute coronary syndromes (ACSs) and evaluates if the addition of renal functional status could improve its predictive accuracy. Methods: CHA2DS2-VASc score was calculated by using both the original scoring system and adding renal functional status using 3 alternative renal dysfunction definitions (CHA2DS2-VASc-R1: eGFR Results: Nine hundred and eight ACS patients (median age 68 years, 30% female, 51% ST-elevation) composed the study population. Of the 871 patients discharged, 865 (99%) completed a 12-month follow-up. The IHM rate was 4.1%. The CHA2DS2-VASc score demonstrated a good discriminative performance for IHM (C-statistic 0.75). Although all the eGFR-modified risk models showed higher C-statistics than the original model, a statistically significant difference was observed only for CHA2DS2-VASc-R3. The PDM rate was 4.5%. The CHA2DS2-VASc C-statistic for PDM was 0.75, and all the modified risk models showed significantly higher C-statistics values than the original model. The NRI analysis showed similar results. Conclusions: CHA2DS2-VASc score demonstrated a good predictive accuracy for IHM and PDM in ACS patients. The addition of renal dysfunction to the original score has the potential to improve identification of patients at the 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.

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.

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

Although the overall survival of patients with dilated cardiomyopathy (DCM) has improved significantly in the last decades, a non-negligible proportion of DCM patients still shows an unfavorable prognosis. DCM patients not only need imaging techniques that are effective in diagnosis, but also suitable for long-term follow-up with frequent re-evaluations. The exponential growth of echocardiography’s technology and performance in recent years has resulted in improved diagnostic accuracy, stratification, management and follow-up of patients with DCM. This review summarizes some new developments in echocardiography and their promising applications in DCM. Although nowadays cardiac magnetic resonance (CMR) remains the gold standard technique in DCM, the echocardiographic advances and novelties proposed in the manuscript, if properly integrated into clinical practice, could bring echocardiography closer to CMR in terms of accuracy and may certify ultrasound as the technique of choice in the follow-up of DCM patients. The application in DCM patients of novel echocardiographic techniques represents an interesting emergent research area for scholars in the near future.

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 Implantable cardioverter defibrillator (ICD) is an effective treatment to reduce mortality in patients with symptomatic heart failure and left ventricular ejection fraction (LVEF) 35% or less. LVEF presents a low sensitivity for predicting arrhythmic events. Aim of this study was to identify predictors of sustained ventricular arrhythmias (SVAs), overall and according to the cause of heart failure. METHODS Single-center, retrospective, cohort study of 193 patients (51 nonischemic and 142 ischemic) with chronic heart failure and LVEF less than 35% who had received ICD for primary prevention of sudden cardiac death. We collected clinical data, echocardiographic parameters and SVAs detected by the ICD. RESULTS During a median follow-up of 1440 days, 32 (16.2%) patients had SVAs. SVAs incidence was similar in patients with nonischemic (15.6%) and ischemic cause of heart failure (16.9%). Hypertension, diabetes, chronic renal failure, atrial fibrillation, chronic obstructive pulmonary disease, New York Heart Association class at least III were predictors at univariate analysis of SVAs. A clinical score, assigning one point to each of these variables, was associated with a significantly increased risk of SVAs [odds ratio for each point increase = 1.92, 95% confidence interval 1.40-2.65, P

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