Also in Mediterranean countries, which are considered a low risk population for cardiovascular disease (CVD), the increase in body mass index (BMI) has become a public health priority. To evaluate the feasibility of a CVD and obesity surveillance network, forty General Practitioners (GPs) were engaged to perform a screening to assess obesity, cardiovascular risk, lifestyle habits and medication use.

Background: Proximal humeral fractures (PHFs) are common, especially in the elderly, and account for 4% to 10% of all fractures, with women more often affected than men. Treatments include conservative methods, internal fixation and arthroplasty, with surgical approaches increasingly being used due to technological advancements. This study analyzes the evolution of PHF treatments in Italy from 2001 to 2022, using data from the Italian Hospital Discharge Records (HDRs) Database, and includes a stratified analysis by age and sex. Methods: Using HDR data from 2001 to 2022, records with ICD9-CM codes for proximal humeral fractures (812.0 and 812.1) among diagnoses were selected and categorized into three treatment groups: arthroplasty, fixation and conservative. Time series were analyzed with stratification by sex and age. Results: The extracted data included 486,368 records of PHFs, with 223,742 cases treated surgically (arthroplasty or internal fixation) and 262,626 treated conservatively; the average patient age was 66.6 years, with a higher proportion of women, especially among arthroplasty patients. Over time, the use of fixation and arthroplasty increased from 20% of treatments in 2001 to over 60% in 2022, with fixation becoming the most common treatment method by 2014 and arthroplasty significantly increasing among women, particularly in the 65–74 and 75–84 age groups. Conclusions: The study shows that in Italy, over the past two decades, treatment for PHFs has shifted from conservative methods to a preference for internal fixation and increasingly for arthroplasty, particularly among women and patients aged 65–84, reflecting evolving trends and technological improvements.

Background/Objectives: Cochlear implants (CIs) and other implantable hearing devices are crucial to treat hearing loss. The aim of this study was to analyze the temporal trends of implantation for hearing devices in Italy between 2001 and 2023, with stratification by age. Methods: This population-based study explored Hospital Discharge Records and used codes from the International Classification of Diseases, 9th revision—Clinical Modification (ICD9-CM) to identify cochlear and non-cochlear implants. Patients were partitioned into six age classes: 80; and time series for counts and incidence rates (IRs) per 1,000,000 inhabitants with confidence intervals (CI_95%) were explored overall and by age class. Trends were assessed by incidence rate ratio and Cox–Stuart test with a significance threshold for p-values at 0.05. Results: 22,850 (83.6%) records for cochlear and 4476 (16.4%) for non-cochlear implants were extracted. Cochlear implants volume shifted from 537 procedures in 2001 to 1595 in 2023 (p < 0.01), while IR increased (p < 0.01) from 9.4 (CI_95%: 9.7, 10.3) in 2001 to 27 (CI_95%: 25.7, 28.4) in 2023. The volumes of implanted CIs increased in children and adults. Volumes for non-cochlear implants increased between 2001 and 2010, from 62 to 254, and remained stable afterwards. IR shifted from 1.1 (CI_95%: 0.8, 1.4) in 2001 to 4.1 (CI_95%: 3.6, 4.7) in 2023. Conclusions: Those trends highlight the importance of monitoring efficacy and safety of hearing devices, and the establishment of the Italian Implantable Hearing Device Registry at the Italian National Institute of Health is a first step in such a direction.

Background: Atherothrombosis is a systemic disease that may affect one or more than one vascular bed. Data on the impact of polyvascular disease (PVD) on the long-term prognosis of patients with coronary artery disease (CAD) are still scarce. Aim: To assess the prevalence of symptomatic PVD in a cohort of patients with a new episode of acute coronary syndrome (ACS) and to investigate the impact of multiple vascular beds involvement on long-term outcomes. Methods: We analysed a nationwide, comprehensive administrative database of consecutive patients aged ≥ 40 years admitted for a new episode of ACS in Italy in 2017–2018. Patients with ACS were stratified according to the presence of peripheral artery disease (PAD) only; cerebrovascular disease (CeVD) only; PAD+CeVD; or neither (no PAD/noCeVD, i.e., ACS only). A multivariate Cox proportional hazards model was used to assess the impact of PAD only; CeVD only and PAD+CeVD on 5-year MACCE. Results: A total of 342,052 patients hospitalised with ACS were identified. Among them, 24,727 (7.2%) were patients with PAD only, 16,887 (4.9%) with CeVD only, and 5810 (1.7%) with PAD+CeVD. After adjusting for age, sex, and comorbidities, the hazard ratio (HR) for 5-year MACCE was 1.37 (95% CI: 1.35–1.40), 1.36 (95% CI: 1.33–1.39), and 1.45 (95% CI: 1.40–1.50) in patients with PAD only, CeVD only, and PAD+CeVD, respectively, compared with patients with ACS only. Conclusions: In patients with ACS, the involvement of a second vascular bed increases the risk of long-term outcomes; the simultaneous involvement of three vascular beds further increases the risk of long-term outcomes.

Joint replacement registries are essential for post-market surveillance; however, medical device (MD) classification dictionaries often rely on manual processes and heterogeneous data sources, limiting efficiency and standardization. The aim of this study was to design and validate, as a proof of concept (PoC), an artificial intelligence (AI) classification system for hip and knee implants, leveraging registry taxonomies and manufacturer technical documents to overcome the above limitations. A PoC study was conducted using a custom-built dataset derived from the Italian Arthroplasty Registry (RIAP) MD-Dictionary, the UK National Joint Registry MD-Classification, and 49 manufacturer documents. A convolutional neural network (CNN) and a transformer-based model (BioBERT) were evaluated. Model performance was assessed on a validation dataset (49 documents) and an external test set (25 documents) using standard metrics and a comparison with manual extraction. BioBERT outperformed the CNN across main evaluation metrics (precision 94.62% vs. 67.18%, recall 88.45% vs. 75.82%, and F1-score 91.43% vs. 71.24%). Processing times were reduced from over 22 h for manual extraction to 4 min for CNN and 27 min for BioBERT. The results show that this AI module-PoC enables the extraction of structured data from technical documents and enhances data completeness and surveillance capabilities by integrating NLP-based automation into MD registries.