Summary. Background: Prophylaxis of venous thromboembolism (VTE) in hospitalized medical patients is largely underused. We sought to assess the value of a simple risk assessment model (RAM) for the identification of patients at risk of VTE. Methods: In a prospective cohort study, 1180 consecutive patients admitted to a department of internal medicine in a 2-year period were classified as having a high or low risk of VTE according to a predefined RAM. They were followed-up for up to 90 days to assess the occurrence of symptomatic VTE complications. The primary study outcome was to assess the adjusted hazard ratio (HR) of VTE in high-risk patients who had adequate in-hospital thromboprophylaxis in comparison with those who did not, and that of VTE in the latter group in comparison with low-risk patients. Results: Four hundred and sixty-nine patients (39.7%) were labelled as having a high risk of thrombosis. VTE developed in four of the 186 (2.2%) who received thromboprophylaxis, and in 31 of the 283 (11.0%) who did not (HR of VTE, 0.13; 95% CI, 0.04-0.40). VTE developed also in two of the 711 (0.3%) low-risk patients (HR of VTE in high-risk patients without prophylaxis as compared with lowrisk patients, 32.0; 95% CI, 4.1-251.0). Bleeding occurred in three of the 186 (1.6%) high-risk patients who had thromboprophylaxis. Conclusions: Our RAM can help discriminate between medical patients at high and low risk of VTE. The adoption of adequate thromboprophylaxis in high-risk patients during hospitalization leads to longstanding protection against thromboembolic events with a low risk of bleeding.

Summary. Background: Little information is available on the long-term clinical outcome of cerebral vein thrombosis (CVT). Objectives and methods:In an international, retrospective cohort study, we assessed the long-term rates of mortality, residual disability and recurrent venous thromboembolism (VTE) in a cohort of patients with a first CVT episode. Results: Seven hundred and six patients (73.7% females) with CVT were included. Patients were followed for a total of 3171 patient-years. Median follow-up was 40 months (range 6, 297 months). At the end of follow-up, 20 patients had died (2.8%). The outcome was generally good: 89.1% of patients had a complete recovery (modified Rankin Score [mRS] 0-1) and 3.8% had a partial recovery and were independent (mRS 2). Eighty-four per cent of patients were treated with oral anticoagulants and the mean treatment duration was 12 months. CVT recurred in 31 patients (4.4%), and 46 patients (6.5%) had a VTE in a different site, for an overall incidence of recurrence of 23.6 events per 1000 patient-years (95% confidence Interval [CI] 17.8, 28.7) and of 35.1 events/1000 patientyears (95% CI, 27.7, 44.4) after anticoagulant therapy withdrawal. A previous VTE was the only significant predictor of recurrence at multivariate analysis (hazard ratio [HR] 2.70; 95% CI 1.25, 5.83). Conclusions: The long-term risk of mortality and recurrent VTE appears to be low in patients who survived the acute phase of CVT. A previous VTE history independently predicts recurrent events.

BACKGROUNDThe prevalence of pulmonary embolism among patients hospitalized for syncope is not well documented, and current guidelines pay little attention to a diagnostic workup for pulmonary embolism in these patients. METHODSWe performed a systematic workup for pulmonary embolism in patients admitted to 11 hospitals in Italy for a first episode of syncope, regardless of whether there were alternative explanations for the syncope. The diagnosis of pulmonary embolism was ruled out in patients who had a low pretest clinical probability, which was defined according to the Wells score, in combination with a negative d-dimer assay. In all other patients, computed tomographic pulmonary angiography or ventilation-perfusion lung scanning was performed. RESULTSA total of 560 patients (mean age, 76 years) were included in the study. A diagnosis of pulmonary embolism was ruled out in 330 of the 560 patients (58.9%) on the basis of the combination of a low pretest clinical probability of pulmonary embolism and negative d-dimer assay. Among the remaining 230 patients, pulmonary embolism was identified in 97 (42.2%). In the entire cohort, the prevalence of pulmonary embolism was 17.3% (95% confidence interval, 14.2 to 20.5). Evidence of an embolus in a main pulmonary or lobar artery or evidence of perfusion defects larger than 25% of the total area of both lungs was found in 61 patients. Pulmonary embolism was identified in 45 of the 355 patients (12.7%) who had an alternative explanation for syncope and in 52 of the 205 patients (25.4%) who did not. CONCLUSIONSPulmonary embolism was identified in nearly one of every six patients hospitalized for a first episode of syncope. (Funded by the University of Padua; PESIT ClinicalTrials.gov number, NCT01797289.)

The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the “Ospedali Riuniti Padova Sud” COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78–0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.

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