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Background Traditional cardiac rehabilitation (CR) is effective in improving physical performance and prognosis after myocardial infarction (MI). Anyway, it is not consistently recommended to older adults, and its attendance rate is low. Previous studies suggested that alternative, early and tailored exercise interventions are feasible and effective in improving physical performance in older MI patients. Anyway, the demonstration that they are associated also with a significant reduction of hard endpoints is lacking. Aim To describe rationale and design of the “Physical activity Intervention in Elderly patients with myocardial Infarction” (PIpELINe) trial. Methods The PIpELINe trial is a prospective, randomized, multicentre study with a blinded adjudicated evaluation of the outcomes. Patients aged ≥ 65 years, admitted to hospital for MI and with a low physical performance one month after discharge, as defined as short physical performance battery (SPPB) value between 4 and 9, will be randomized to a multi-domain lifestyle intervention (including dietary counselling, strict management of cardiovascular and metabolic risk factors, and exercise training) or health education. The primary endpoint is the one-year occurrence of the composite of cardiovascular death or re-hospitalization for cardiovascular causes. Results The recruitment started in March 2020. The estimated sample size is 456 patients. The conclusion of the enrolment is planned for mid-2023. The primary endpoint analysis will be available for the end of 2024. Conclusions The PIpELINe trial will show if a multi-domain lifestyle intervention is able to reduce adverse events in older patients with reduced physical performance after hospitalization for MI. Trial registration ClinicalTrials.gov NCT04183465.

Cardiac implantable electronic device infections (CIEDI) are challenging complications, associated with high mortality rate. Transvenous lead extraction (TLE) is the only curative treatment for CIEDI. Albeit continuous improvement in tools and techniques dramatically decreased TLE associated complications, survival after TLE for CIEDI is still poor. Renal failure (RF) is frequently reported in candidates to TLE, but due to variability in its definition, the real prevalence is not well defined.Considering the impact of RF on mortality among patients affected by cardiovascular diseases, we aimed our research at defining the role of RF as a predictor of post-TLE mortality.We will provide the results of a systematic revision of literature on the impact of RF on mortality at different time points after TLE, according to the various definitions adopted for RF. Considering the high variability of literature in this field, we will provide the results of an explorative analysis comparing the different definitions of RF on clinical outcomes in a cohort of candidates to TLE for CIEDI in a high-volume referral center.We discuss the possible reasons of the negative impact of RF after TLE, providing new perspectives for future research.

Atrial fibrillation (AF) is a major cause of cerebral ischemia, and its early detection may impact on health. Both invasive and non-invasive devices can be used for the diagnosis of AF. The aim of our study was to estimate the prevalence of AF using a single-lead ECG device (MyDiagnostickTM) on an adult, asymptomatic population during a screening campaign.A total of 2547 subjects underwent AF screening.The device detected an arrhythmia in 42 subjects (1.65%), and AF was confirmed on 12-lead ECG in 14 (0.55%) of them. The prevalence of confirmed AF increased in subjects over 65 years of age (1.21%) or with a CHA2DS2-VASc score ≥2 in males or ≥3 in females (1.33%). Furthermore, heart failure (odds ratio [OR] 8.62, 95% confidence interval [CI] 1.87-39.6, p=0.006) and diabetes (OR 4.55, 95% CI 1.25-16.5, p=0.021) significantly increased the risk of AF.During a screening campaign, the diagnosis of AF increases when subjects with a high thromboembolic risk are selected.

This work describes an approach to develop a model of driver's distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (Adaptive Neuro Fuzzy Inference System) technique. Further investigations will be carried out by porting the model on real car.

This paper proposes a method which allows user interfaces practitioners to choose the most suitable software tool for the development of a Human Machine Interface (HMI) virtual prototype. In the design process of a User Interface (UI), the prototyping activity represents an important way to detect design errors since the beginning of the development process, saving time and money. The choice has to take care of the technical features, designers' requirements and prototyping characteristics of the tool. The methodology includes four steps: 1) research on the most diffused virtual prototyping software employed in the prototyping activity; 2) benchmark on the technical and virtual prototyping features of each selected tool; 3) creation of a method to asses and classify each prototyping tool considering their specific characteristics; 4) application of the method to a case study.