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 < 0.001]. They also received more frequently darunavir/ritonavir, tocilizumab and ventilation support. Furthermore, they faced more bleeding episodes requiring transfusion (15.6% vs. 5.1%, p < 0.001) and non-significantly higher in-hospital mortality (34.6% vs. 22.9%, 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 < 500 ng/mL. 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

BackgroundSeveral 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.Methods and resultsWe 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).ConclusionIn 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.

Heart failure (HF) with preserved left ventricular (LV) ejection fraction (HFpEF) represent today the largest ‘unmet medical need’, because none of the drugs presently available improved survival in this consistent proportion of patients with HF, ∼50% of the total, who have an LV ejection fraction ≥50%. Heart failure with preserved left ventricular ejection fraction is a clinical syndrome that in its classical form, is associated to typical risk factors and comorbidities. The comorbidities represent one of the element contributing to the extreme heterogeneity which characterizes HFpEF. The pathophysiological mechanisms, as well as the clinical presentation, are multifaceted. These factors explain, by and large, the failure of a generalized therapeutic strategy, while build the argument for personalized medicine, designed to address the specific phenotypes, with therapies proven in specific subgroups of patients with HFpEF to reduce mortality and improve ‘surrogate’ outcomes, such as quality of life.

Purpose Antipsychotic long-acting injections (AP-LAIs) are indicated for patients affected by schizophrenia especially those with poor treatment adherence. Patients and Methods To compare paliperidone palmitate 3-monthly (PP3M), paliperidone palmitate one-monthly (PP1M) and haloperidol decanoate (HAL-D) treatment, we enrolled 90 patients with schizophrenia treated in Mental Health Center with one of the three AP-LAIs for at least six months and followed them for another 6 months. At 6 and 12 months of treatment we administered Clinical Global Impression-Severity, Global Assessment of Functioning and World Health Organization Quality of Life-26 items (WHOQOL-BREF). At 1-year treatment, we evaluated relapses (psychiatric hospitalizations and urgent consultations), side effects and drop-outs. Results We did not highlight any statistically significant difference among the three treatments in relapses and scale scores. Weight increase was significantly higher in PP1M and PP3M groups. Twelve patients (13.3%) discontinued AP-LAI. At 1-year AP-LAI treatment, 69% of patients rated quality of life as “good” or “very good” and 71% declared themselves to be “satisfied” or “very satisfied”. Conclusion HAL-D, PP1M and PP3M 1-year treatments were similarly effective in preventing relapses and improving quality of life and health satisfaction. All discontinuations in the new 3-monthly antipsychotic treatment were caused by patient refusal to continue it.

Patients who underwent a successful repair of CoA commonly show asymptomatic longitudinal LVSD associated with worse LV diastolic function in the long-term follow-up.