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Medical education is widely known to be demanding and stressful, with elevated levels of anxiety and depression among medical students. This study aims at examining well-being, perceived stress and quality of life in a sample of Italian first-year medical students in order to investigate the relations between these variables and individual characteristics and behaviours. An anonymous cross-sectional questionnaire was administered to a sample of 407 Italian first-year medical students. Psychological well-being, perceived stress and quality of life were assessed using standardised questionnaires PGWBI, PSS, SF-12. Data were analysed with multivariate statistical methods. Significant differences in PGWBI, PSS and SF-12 scores were found between males and females (with poorer scores for females), between smokers and non-smokers (with poorer scores for smokers) and between students who reported cannabis use in their lifetime and students who did not (with poorer scores for those who smoked cannabis). Logistic regression showed that gender (OR 0.38, 95% CI 0.24–0.61), reported cannabis use (OR 1.82, 95% CI 1.07–3.09) and regular physical exercise (OR 0.54, 95% CI 0.34–0.86) were significant predictors of distress. In summary, this study shows that female medical students and students who reported negative health behaviours and sporadic drug use reported lower psychological well-being and quality of life and higher stress, while positive health behaviours might prevent distress.

Background Patients with atrial fibrillation (AF) experience a high mortality rate despite optimal antithrombotic treatment. Characteristics of AF patients at higher mortality risk have been barely described so far and no risk score has been specifically developed at this aim. Furthermore, a clinical approach based on risk scores present some limits such as to not consider some important risk factors for mortality, and many available scores have poor predictive value. Cluster analysis may play a role in overcoming limitations of risk scores, especially in the case of overlapping risk factors. Purpose To identify of clinical phenotypes by using an unbiased statistical approach, such as the cluster analysis. Methods Cluster analysis was used to identify clinical phenotypes of AF patients associated with all-cause mortality in 5,171 AF patients from the START registry. Clinical variables used for the analysis were age, sex, diabetes, previous cerebrovascular events, previous cardiovascular events, heart failure, peripheral artery disease, use of non-vitamin K oral anticoagulants, cancer, pulmonary disease, smoking habit, previous major bleeding. The risk of all-cause mortality in each cluster was analyzed. Results We identified 4 clusters (Figure 1). Cluster 1 was composed by youngest patients, with obesity and paroxysmal AF; Cluster 2 by patients with low cardiovascular risk factors and high proportion of cancer; Cluster 3 by men with diabetes and coronary and peripheral artery disease, a high proportion of thrombocytopenia, and a high use of aspirin, proton pump inhibitors, and statins; Cluster 4 included the oldest patients, mainly women, with previous cerebrovascular disease, persistent/ permanent AF, heart failure, kidney disease and anemia. In this cluster there was the highest use of digoxin and NOACs. During 9856,84 patient/years of observation, 386 deaths (3.92%/year) occurred. Mortality rates significantly increased across clusters: 0.42%/year (cluster 1, reference group), 2.12%/year (cluster 2, adjusted hazard ratio [aHR] 3.306, 95% confidence interval [CI] 1.204–9.077, p=0.020), 4.41%/year (cluster 3, aHR 6.702, 95% CI 2.433–18.461, p Conclusions We identified different clinical phenotypes of AF patients by cluster analysis which were specifically associated with mortality. This approach may help identify patients at higher risk of mortality. Figure 1 Funding Acknowledgement Type of funding source: None

Background Statins are guidelines recommended in patients with peripheral artery disease (PAD) for the prevention of cardiovascular (CV) events. Comprehensive meta-data on the impact of statins on major adverse limb events (MALE) in PAD patients are lacking. We examined the association of statin use with MALE in patients with PAD. Methods We performed a systematic review (registered at PROSPERO: number CRD42019137111) and metanalysis of studies retrieved from PubMed (via MEDLINE) and Cochrane (CENTRAL) databases addressing the impact of statin on MALE including amputation and graft occlusion/revascularization. Secondary endpoints were all-cause death, composite CV endpoints, CV death, and stroke. Results We included 51 studies with 138,060 PAD patients, of whom 48,459 (35.1%) were treated with statins. The analysis included 2 randomized controlled trials, 20 prospective, and 29 retrospective studies. Overall, 11,396 MALE events, 21,624 deaths, 4,852 composite CV endpoints, 4,609 CV deaths, and 860 strokes were used for the analysis. Statins reduced MALE incidence by 30% (pooled hazard ratio [HR]: 0.702; 95% confidence interval [CI]: 0.605–0.815) and amputations by 35% (HR: 0.654; 95% CI: 0.522–0.819), all-cause mortality by 39% (pooled HR: 0.608, 95% CI: 0.543–0.680), CV death by 41% (HR: 0.594; 95% CI: 0.455–0.777), composite CV endpoints by 34% (pooled HR: 0.662; 95% CI: 0.591–0.741) and ischemic stroke by 28% (pooled HR: 0.718; 95% CI: 0.620–0.831). Conclusion Statins reduce the incidence of MALE, all-cause, and CV mortality in patients with PAD. In PAD, a high proportion of MALE events and deaths could be prevented by implementing a statin prescription in this patient population.

In recent years, there has been a widespread cross-fertilization between Medical Statistics and Machine Learning (ML) techniques.