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Epidemiology of Acute Cardiovascular and Cerebrovascular Events in the Lombardy Region: A Population-Based Study (2015–2021)
No full textIntroduction
Cardiovascular and cerebrovascular diseases represent one of the leading causes of morbidity and mortality in Italy [1]. Acute events such as myocardial infarction and ischemic stroke have a significant clinical, economic, and social impact. Each year, approximately 150,000 new cases of myocardial infarction and 200,000 strokes are registered in Italy, with a substantial healthcare burden especially among the elderly [2,3]. In Lombardy, in 2020, diseases of the circulatory system were the leading cause of death, accounting for about 34,000 deaths, corresponding to 25% of the total [4].
Despite advances in prevention and treatment strategies, territorial and sociodemographic disparities in the distribution of events persist [5]. Temporal and spatial analysis of acute event incidence, particularly at the sub-regional level, represents an essential tool to guide healthcare planning and to evaluate the effectiveness of public health policies.
The analysis of acute event incidence is therefore a key instrument to monitor the effectiveness of prevention strategies and healthcare responses. Observing trends over an extended time frame allows detection of significant trends and potential inequalities in access to care or in the distribution of risk factors [6].
Objective
This study aims to describe the epidemiology of acute cardiovascular and cerebrovascular events in the Lombardy Region between 2015 and 2021, analysing temporal trends and differences by sex, age, and Local Health Protection Agencies (ATS). Additionally, it seeks to assess the presence of significant differences between ATS in terms of in-hospital mortality for these events.
Methods
The study population includes all residents of Lombardy aged ≥45 years who were hospitalized for an acute cardiovascular or cerebrovascular event between 2015 and 2021.
To identify incident events, new cases were selected for each year by excluding individuals who had experienced the same type of event in the five years prior to the hospitalization date.
Events were identified using regional administrative healthcare databases, specifically hospital discharge records (SDO), based on selected ICD-9-CM codes for myocardial infarction, unstable angina, acute heart failure, ischemic and haemorrhagic stroke, and transient ischemic attack.
Annual incidence was estimated by calculating crude rates, stratified by sex and age group, using person-time denominators.
Temporal trends were analysed using Poisson regression models to estimate the annual rate variation and assess its statistical significance.
To compare geographic differences, age- and sex-standardized incidence rates were calculated for each ATS through direct standardization, using the Lombardy population as the standard.
Results are presented as rates per 100,000 population with 95% confidence intervals (CIs).
To explore geographic heterogeneity in in-hospital mortality for the studied acute events, a multilevel logistic regression model was implemented, adjusted for age, sex, and comorbidities. Subsequently, a fixed-effects model was used to assess whether there were statistically significant differences in in-hospital mortality across the different ATS.
Results
A total of 260,725 residents of Lombardy aged ≥45 years who experienced an acute cardiovascular or cerebrovascular event were included. Of these, 42.5% were female, and the overall median age was 76 years (IQR: 65–83).
The average annual rate was higher in men than in women across all age groups. In the ≥75 age group, the rate was 2,162 per 100,000 population (95% CI: 2,138–2,187) in men and 1,236 per 100,000 (95% CI: 1,222–1,231) in women (p<.001). Marked differences (p<.001) were also observed in the 45–59 age group: 293 per 100,000 (95% CI: 289–297) in men vs. 167 (95% CI: 165–170) in women.
Age and sex standardized incidence rates varied across ATS from a minimum of 622 per 100,000 population (95% CI: 600–663) to a maximum of 771 (95% CI: 739–783), highlighting geographic differences.
Finally, the multilevel logistic model showed a random-effect variance between ATS of 0.007, indicating limited geographic heterogeneity in in-hospital mortality. However, based on the fixed-effects model, only one ATS showed a significantly lower in-hospital mortality probability compared to the others.
Conclusions
The study showed a significant temporal reduction in the incidence of acute cardiovascular and cerebrovascular events in Lombardy between 2015 and 2021, with higher rates in men and older age groups. Although substantial territorial variability in in-hospital mortality at the ATS level was not observed, some localized differences point to the need for targeted investigations and interventions to address potential territorial health inequalities
Insights from the EXPOSITION Study: Exposome-related microRNA Expression and Clinical Outcomes in People with multiple sclerosis
No full textIntroductionMicroRNAs (miRNAs) are emerging as promising biomarkers of neuroinflammation and may capture the influence of lifestyle and environmental exposures in people with multiple sclerosis (pwMS). The EXPOSITION study[1] aims to elucidate relationships between internal exposome markers including miRNA profiles and clinical, demographic, and lifestyle factors in pwMS.
Objective(s)
To assess the associations between the relative expression of five candidate miRNAs and clinical, demographic, and lifestyle variables, with a particular focus on the exposome and functional and psychological outcomes in pwMS.
MethdsIn this cross-sectional analysis, we included 139 pwMS (median age 45 years [IQR: 35–56], 65% female) from the provinces of Pavia and Milan in the Lombardy region of Italy. Relative expression levels of five candidate miRNAs (mir30, mir146, mir330, mir574, mir664) were quantified and compared across clinical and lifestyle categorical variables groups using the Mann-Whitney U test (for binary variables) or the Kruskal-Wallis test (for variables with more than two categories). Spearman correlation analyses were conducted to assess the relationship between each miRNA and continuous variables, including age, BMI, EDSS, dietary inflammatory index, and quality of life scores.
ResultsThere were no statistically significant differences in microRNA expression between EDSS disability groups or across most clinical or lifestyle variables. Notably, mir146 expression was significantly higher in participants with a pro-inflammatory dietary pattern compared to those with an anti-inflammatory pattern (p = 0.0187), and mir146 was positively correlated with the mental health component of the quality of life MSQoL-29 questionnaire (rho = 0.336, p = 0.0174) [Table 1]. In contrast, higher disability status (EDSS >4) was significantly associated with older age (median 51 vs. 44 years, p = 0.047), more frequent prior relapses (94% vs. 69%, p = 0.040), and lower physical (p = 0.039) and mental (p = 0.034) quality of life. Significant group differences were also observed for MS type (p = 0.008), MS stage (p = 0.034), and occupational status (p = 0.063, trend) between the two EDSS groups. No significant associations were identified between disability status and microRNA expression, diet category, physical activity, or MRI lesion status.
ConclusionsIn this preliminary analysis, higher disability among pwMS was more strongly linked to clinical history, age, and quality of life than to lifestyle factors or circulating miRNA levels. Mir146 may act as a molecular intermediary between dietary inflammation, mental health, and neuroinflammatory processes in MS. These findings highlight the need for replication and longitudinal validation in larger, independent cohorts
Risk Scores Validation: An Example in Cardiology
No full textIntroduction: Clinicians often make important decisions about patient care by estimating the likelihood of a particular disease, condition or event occurring. Prediction models are useful in this context. Development studies aim to develop a prediction model by selecting clinically relevant predictors and statistically combining them in a multivariable logistic or cox model [1]. Once a model has been developed, its performance must be assessed in the same cohort (internal validation) and in a new cohort (external validation). The performance of a model can be assessed in terms of calibration (comparison between the observed and predicted proportions of events) and discrimination (ability to predict patients who will or will not have the event of interest) [2]. There are several calibration methods: calibration in large, calibration curve associated with the Hosmer-Lemeshow test and calibration slope. Discrimination can be assessed by the area under the ROC curve (AUC) or the Harrel-c index, depending on the regression model used for development [3]. The development and validation of predictive scores are useful in cardiology: a study was conducted to investigate whether the ECG acquired after return of spontaneous circulation (ROSC) could play a prognostic role for 30-days mortality in patients surviving from out-of-hospital cardiac arrest (OHCA), defined as sudden cessation of cardiac function with loss of consciousness and circulatory signs occurring in an out-of-hospital setting. The study was conducted considering post-ROSC ECGs related to OHCA patients from 2015 to 2018 in the populations of Lugano, Vienna and Pavia. Multivariable cox regression was performed and age ≥62 years, female, ECG acquisition time≥8 min, presence of >1 segment with ST elevation, a QRS≥120 msec and the diagnostic pattern for Brugada syndrome resulted associated with higher 30-days mortality. The coefficient of each variable was multiplied by 10 and rounded to the nearest whole number and, by summing the rounded coefficients, a score between 0 and 26 was created. The study showed that the risk of death increased as the score increased. Furthermore, by dividing the population according to score tertiles, 30-days mortality risk classes were identified: low (score 0-4), intermediate (score 5-7) and high (score 8-26) [4].
Aim: The aim of this study is to validate a post-ROSC ECG score in predicting mortality risk and stratifying 30-days mortality risk after OHCA in a new cohort.
Methods: This is a multicenter, prospective, score validation study to predict 30-days mortality in OHCA survivors. Post-ROSC ECGs of patients enrolled in the LombardiaCARe registry from 01/01/2015 to 31/12/2023 and ECGs of OHCA patients admitted to Saint-Pierre Hospital, Brussels, from 01/01/2017 to 31/12/2023 were collected. The same outcome and the same predictors of the previous work were considered. Categorical variables were described as numbers and percentages and compared using the chi-squared test or the Fisher exact test, depending on the expected frequencies. Continuous variables were described as mean ± standard deviation and compared with the t-test or described as median and interquartile range (IQR) and compared with the Mann-Whitney test and according to their normal distribution, tested with the Shapiro-Wilk test. The risk score and mortality risk groups were identified according to previous work [4]. Univariable cox regression was performed with 30-days mortality risk category as the independent variable. The assumption of hazard proportionality was tested using the Shoenfeld test. Calibration was assessed by plotting the observed proportions of events against the predicted probabilities, while the c-index was assessed for discrimination. Moreover, the prognostic index (PI) was calculated from the cox regression model with the same predictors of the previous work [4] as covariates and Kaplan–Meier (KM) curves of PI tertiles were plotted. Long-rank test was used to test the difference between the three curves. All values p<0.05 were considered statistically significant. Statistical analyses were performed using Stata17.
Results: A total of 1167 ECGs were collected in the two centres and score calculation was possible for 1075 of them. Of these patients, 431 (40.1%) were alive at 30 days. The median score was 10.0 (6.0-12.0) and 175 (16.35%) patients were classified as low risk, 300 (27.9%) as intermediate risk and 600 (55.8%) as high risk. Cox regression showed that patients in the intermediate risk group had a higher risk of death compared with those in the low risk group (HR: 1.3 [95% CI: 1.1-1.9]; p-value: 0.049), as did patients in the high risk group (HR: 1.9 [95% CI: 1.4-2.5]; p-value<0.001). The harrel-C of the model is C:0.56 [95% CI: 0.54-0.59].
Conclusion: The discrimination is lower compared to the original model (Harrel-c: 0.66 [95% CI, 0.57-0.76]), though acceptable. Indeed, the model retains the ability to discriminate patients at low, intermediate and high risk of 30-days mortality. Figure 1A shows the KM curves of the PI tertiles (p-value<0.001) and the graph suggests that the model discriminates better patients at hight risk rather than low and intermediate risk, as expected considering the harrel-c. Figure 1B shows similar predicted and observed survival probabilities in all groups, confirming good calibration of the model. A limitation of the study is the non-homogeneous distribution of patients in the 3 risk groups. Our results suggest that the post-ROSC ECG score can predict the risk of 30-days mortality after OHCA. This provides a possibility for risk stratification in post-cardiac arrest care, assisting clinicians in clinical decision making and underlining the prognostic role of ECG
Counterfactual Estimates of Pneumococcal Disease Incidence in England after Vaccine Introduction
No full textIntroduction:
Streptococcus pneumoniae is a leading cause of serious bacterial infections worldwide, including pneumonia, meningitis, and sepsis, especially in young children. The World Health Organization estimates that it is responsible for approximately 5% of global infant deaths [1]. Pneumococcal conjugate vaccines (PCVs) have been developed to protect against the most clinically relevant serotypes and introduced into infant immunization programs across multiple countries. PCV7, targeting seven serotypes, was followed by PCV13, extending protection to thirteen. These vaccines have substantially reduced vaccine-type invasive pneumococcal disease (VT-IPD). Nevertheless, over 80 additional serotypes remain uncovered [2; 3]. In recent years, several settings have reported increases in non-vaccine-type (NVT) IPD, suggesting possible serotype replacement. In England, this phenomenon has been particularly marked, raising concerns about whether the population-level benefits of PCVs might be offset. However, interpreting post-vaccination trends in IPD is challenging. Observed changes in disease incidence may reflect not only biological responses to vaccination but also coincident changes in surveillance systems, healthcare-seeking behaviour, diagnostic practices, or case definitions. Traditional before–after comparisons may misattribute such secular trends to vaccine effects, especially in ecological designs where randomized controls are absent.
Objectives:
We aim to estimate the causal impact of PCV7 and PCV13 introduction on IPD incidence in England, focusing on both direct reductions in VT-IPD and potential increases in NVT-IPD. A key goal is to disentangle true serotype replacement from surveillance-driven artifacts by constructing a data-driven counterfactual using unaffected pathogens as controls.
Methods:
We analysed monthly national IPD surveillance data in England from 2000 to 2018, covering the introduction of PCV7 in 2006 and PCV13 in 2010. To estimate the impact of vaccination, we employed a Bayesian structural time series (BSTS) model [4], a causal inference framework designed for population-level interventions without randomized control groups. The model accounts for seasonality, underlying trends, and time-varying confounders. To adjust for secular changes unrelated to PCVs, we used time series of other bacterial infections (H. influenzae, E. coli, S. aureus, P. aeruginosa, and others) as control outcomes. These pathogens share similar diagnostic pathways and reporting mechanisms but are unaffected by pneumococcal vaccination. The model included a spike-and-slab prior for Bayesian variable selection, allowing only those control series with high predictive value in the pre-intervention period to inform post-intervention counterfactuals. This synthetic control design improves robustness over simple before–after approaches and helps isolate vaccine effects from unrelated system-level changes.
Results
We estimate a 60% overall reduction in IPD incidence following the introduction of PCV7 and PCV13, comparing the pre-vaccine (2000–2006) and post-PCV13 (2011–2018) periods. The greatest reductions occurred among children under five (−73%). Specifically, PCV7-type IPD fell by 92% across age groups, and PCV13-type IPD declined by 42% following its introduction in 2010. These effects were consistent across subpopulations and robust to alternative model specifications. In contrast, NVT-IPD incidence increased by 36.5% after PCV7 and by 31.8% after PCV13 in raw surveillance data. However, when adjusted for confounding trends using control pathogens, the estimated increase in NVT-IPD was attenuated to +16% overall, with wide credible intervals and non-significant effects in most age groups. This suggests that previous unadjusted analyses may have overestimated the magnitude of serotype replacement by not accounting for coincident improvements in detection and reporting.
Conclusions
Our findings demonstrate that PCVs have had a substantial and sustained public health impact, dramatically reducing IPD caused by vaccine-covered serotypes. While serotype replacement is evident, much of the apparent increase in NVT-IPD can be explained by concurrent changes in surveillance and diagnostic practices rather than biological displacement alone. By leveraging control pathogens and a Bayesian synthetic control approach, we provide more credible causal estimates than conventional time series analyses. These results are important for public health planning and support continued investment in pneumococcal immunization, particularly as higher-valency PCVs are developed. Future evaluations of vaccine impact should incorporate similar causal modelling strategies to avoid misinterpretation of surveillance-based trends
Structural Equation Modeling and Monte Carlo Simulation in Clinical and Nursing Research: Insights into Sample Size, Opportunities, and Challenges
No full textINTRODUCTIONStructural Equation Modeling (SEM) is widely adopted in behavioral, economic, and sociological sciences, and sample size calculations in this regard typically involve Monte Carlo simulation. SEM simultaneously integrates measurement and structural components, enabling both the evaluation of construct validity and the testing of specific hypotheses. Its application is particularly relevant when analyzing self-report data or complex theoretical frameworks, which require testing hypotheses as the primary aim, involving more than one dependent variable in the same model. Clinicians are often unfamiliar with these methods, and studies involving SEM tend to employ convenience sampling without a proper sample size calculation. A critical discussion regarding opportunities and challenges in this regard could facilitate the adoption of best practices when implementing SEM, including appropriate sample size calculations.
OBJECTIVESTo highlight the potential and challenges of adopting SEM in clinical research and nursing science and to discuss the contribution of Monte Carlo simulation for sample size planning in such contexts by using a real-case application as the context of the critical discourse.
METHODSA real-case application is presented from a randomized controlled trial (RCT) involving Health Easy, a digital ecosystem designed to enhance engagement and health literacy among adolescents with congenital heart disease. The ecosystem integrates medical term simplification (SIMPLE), a patient-centered health search engine (FACILE), and a balanced learning interface (ULearn). The pilot study informed the primary endpoint (self-care behavioral score improvement of 10% after 3 months, on a scale of 0–100, which is Cohen’s d = 0.67). While classical power-based sample size calculation was applied to the primary outcome, Monte Carlo simulation was used to evaluate the statistical power to test the hypothesized structural paths underpinning the Health Easy model. These included the mediating role of health literacy in the relationship between the intervention (Health Easy) and improvements in patient engagement, where health literacy is hypothesized to mediate and moderate the effects of the digital ecosystem on self-care behaviors and empowerment.
RESULTSMonte Carlo simulation enabled simulation-based validation of the hypothesized structural paths within the Health Easy conceptual framework, particularly those involving latent variables and indirect effects. The simulation assessed the power and estimation precision for each path, including mediating effects of health literacy and moderated pathways influencing patient engagement and self-care behaviors, by generating multiple synthetic datasets (1000 replications) under specified model parameters. The simulation output supported the stability of parameter estimates and standard errors across replications, reinforcing the robustness of the SEM design.
A traditional power analysis was conducted to detect a 10-point mean difference in behavioral scores between two independent groups (Cohen’s d = 0.67, α = 0.05, power = 0.90), indicating that a total sample of 96 participants (48 per group) would be sufficient for this specific comparison. However, when this same sample size was evaluated within the Monte Carlo simulation framework, it yielded an empirical power of only 0.49 to detect the hypothesized small-to-moderate indirect and moderated effects typical of SEM (Cohen’s d ≈ 0.3). To achieve adequate power (≥0.80) for testing the full model, a substantially larger sample, approximately 344 participants, was required. These findings demonstrate that when the objective is to test a conceptual model rather than a simple group difference, traditional power analysis may be misleading. Simulation-based approaches, such as Monte Carlo methods, are therefore essential for planning the appropriate sample size in SEM-driven clinical research.
CONCLUSIONSSEM and Monte Carlo simulation represent valuable yet underutilized tools in clinical research. Their application allows for the rigorous evaluation of complex intervention models, particularly when outcomes are mediated by constructs such as health literacy and patient engagement. Unlike traditional power analysis, these methods accommodate the analytical complexity of real-world frameworks and offer more accurate guidance for study design. Integrating SEM and simulation-based approaches into clinical trial methodology may enhance the interpretability, precision, and validity of intervention research. To support broader adoption, the development of a collaborative research network focused on advancing and disseminating these methods is encouraged
Predicting Mortality using Frailty Index and Latent Class Approaches: AUC-Based Evaluation in Simulated Data
No full textBACKGROUND
The global population is aging rapidly, creating significant challenges for healthcare systems [1]. Traditional disease-centered models often fail to meet the complex needs of older adults, who frequently have multiple chronic conditions. In geriatric medicine, frailty has become a key concept [2-3], representing increased vulnerability due to a decline in physiological reserves and functional capacity. Frailty is a multidimensional syndrome that includes physical, cognitive, psychological, and social impairments, making its identification vital for improving patient outcomes and healthcare resource allocation. The Frailty Index (FI) is a widely used tool that quantifies frailty by measuring the ratio of health deficits to the total number of health variables [4]. This approach allows for practical and scalable assessments across various settings.
However, while the FI offers a comprehensive assessment, it remains an observed composite measure that may not fully capture the underlying latent nature of frailty. Frailty can be conceptualized as a latent [5-6] construct. Studying frailty as a latent variable enables a deeper understanding of its structure and heterogeneity. It allows researchers to explore whether distinct frailty phenotypes [7] exist and whether they differ in their relationship to key outcomes such as mortality or functional decline. Moreover, latent variable models can uncover hidden patterns that are not evident from single observed measures like the FI, potentially offering more nuanced tools for risk stratification and clinical decision-making.
OBJECTIVES
To compare the predictive accuracy of mortality models based on a continuous FI and latent
class approaches through simulation, using the area under the curve (AUC) as the evaluation metric.
METHODS
The simulation uses real-world data on 50 binary and ordinal items related to HyperFrail, focusing on marginal probabilities and empirical correlations. Data simulation began by generating multivariate normal data using the empirical correlation matrix for a sample of 1,000 individuals with 50 variables each. These continuous values were transformed into uniform probabilities and mapped to discrete response levels based on specified marginal distributions. A dataset was created with one row per individual and the calculated FI as the sum of the 50-item responses, normalized by the number of items. Five domain-specific subscores were derived: activity (items 1-16), health (items 17-20), psychological (items 21-25), comorbidity (items 26-43), and cognitive (items 44-50). Age was simulated based on the frailty index (FI) levels. Individuals with an FI of less than 0.12 were assigned a random age between 20 and 50 years. For those with an FI of 0.12 or greater, age was sampled between 50 and 80 years, following an exponentially increasing distribution to reflect the greater frailty commonly observed in older adults. Mortality was simulated conditionally based on age: individuals under 50 were assigned a death status of 0 (indicating no death), while those aged 50 and above were assigned a death status with a probability ranging from 30% to 80% (in 5% increments) to represent various levels of risk. Three models were developed to predict binary death outcomes. The first model utilized logistic regression, employing the continuous Frailty Index (FI) on a scale from 0 to 100. The second model applied Latent Class Analysis (LCA) using the poLCA [8] package on 50 item variables to identify various frailty classes, followed by logistic regression where frailty class served as a categorical predictor. The third model used Gaussian Mixture Modeling (GMM) with the Mclust [9] package, analyzing five domain-specific summary scores to identify latent clusters. The Area Under the Curve (AUC) was calculated for each model to evaluate discrimination performance. Finally, a simulation study was conducted to assess model performance across different mortality scenarios.
RESULTS
Latent class analysis using Bayesian Information Criterion (BIC) identified nine distinct frailty classes across five domains. Class 1 represented "low frailty" with minimal deficits, while Class 4 showed "high activity limitation" mainly affecting the activity domain. Class 6 had a "cognitive-predominant" phenotype, marked by significant cognitive impairment. Class 8 displayed a "multi-domain severe" pattern with high frailty scores in activity, health, and comorbidity. Finally, Class 9 exhibited extreme frailty with the highest burden, especially relating to comorbidity and health status. The comparison of the AUC showed that the continuous FI consistently outperformed both latent variable approaches in all mortality probability scenarios. The FI displayed superior discriminative ability, particularly at higher mortality probabilities. It was followed closely by the GMM, while the LCA demonstrated the lowest predictive performance.
CONCLUSION
The continuous FI showed better predictive accuracy for mortality outcomes, while the latent class approach identified significant frailty phenotypes with distinct patterns in specific domains that may have important clinical implications. These findings indicate that, although the comprehensive nature of the FI provides more effective discrimination of mortality risk, understanding frailty as a latent variable offers valuable insights into the diverse characteristics of this syndrome
From Polygenic Scores to Phenotypic Screening: A Multi-Trait Framework for Cost-Free Risk Stratification in Endometriosis
No full textIntroduction: Endometriosis is a chronic inflammatory condition affecting approximately 10% of women of reproductive age and is often diagnosed late due to nonspecific symptoms, overlap with common conditions such as primary dysmenorrhea, and the reliance on invasive laparoscopy [1,2]. Early detection could reduce patient burden and long-term complications, but current diagnostic tools remain limited. Genome-wide association studies (GWAS) have identified several genetic risk variants [3], yet their individual effects are modest. Polygenic risk scores (PRSs), which aggregate the effects of multiple variants, show promise but still lack the accuracy required for clinical application due to limited replication, small effect sizes, and population-specific variability [4,5]. Recent findings suggest that endometriosis is linked to a range of genetically influenced traits—such as immune, metabolic, and psychiatric characteristics—pointing toward the potential of multi-trait approaches to improve early, non-invasive risk stratification.
Objectives: This study aims to develop a non-invasive, cost-effective strategy for endometriosis risk stratification using a genetics-informed, two-phase approach. First, we evaluated whether polygenic scores (PRSs) related to a broad spectrum of complex traits could predict disease risk and reveal genetically defined subgroups among patients. Next, we identified the most informative traits associated with these genetic risk profiles and translated them into a targeted phenotypic questionnaire. We then assessed whether this phenotype-only model could accurately classify endometriosis cases, offering a feasible alternative to genetic testing for early detection in real-world settings.
Methods: We analyzed 1,996 genotyped women (862 cases, 1,134 controls) and computed 4,490 PRSs across complex traits. After filtering and trait mapping, 645 scores were retained; one per trait was selected via bootstrap logistic regression (218), then reduced to 40 via LASSO. Supervised machine learning models (logistic regression, random forest, XGBoost, neural networks) [6,7] were trained to evaluate the predictive performance of the PRS-based model. Top-ranking PRSs from the best-performing model were used to cluster endometriosis cases, identifying genetically defined subgroups. Traits linked to these PRSs were used to design a targeted phenotypic questionnaire. The questionnaire was tested in an independent cohort (n = 506), where curated phenotypic features were used to train classification models. The best model was then used to generate a non-invasive, phenotype-only risk score for endometriosis stratification.
Results: The multi-PRS model significantly outperformed the endometriosis-specific PRS (AUC = 0.636 vs. 0.546, p < 0.001), with key contributions from traits related to height, early menarche, schizophrenia, and autoimmune disorders. Clustering based on the most informative PRSs identified two genetically defined subgroups with distinct clinical characteristics, including differences in endometrioma prevalence, gastrointestinal symptoms, and disease stage. A phenotype-only model trained on questionnaire data demonstrated high discriminative ability (AUC = 0.904), with CA125, fatigue, gynecological symptoms, and muscle pain emerging as the most informative features, supporting its potential as a cost-effective and non-invasive tool for early risk stratification.
Conclusions: Our results demonstrate that leveraging polygenic information to identify trait-level predictors enables the development of accurate, phenotype-based models for endometriosis risk stratification. The use of AI-driven approaches allows robust prediction from a minimal set of non-invasive, low-cost clinical features—reducing reliance on genetic testing and supporting more accessible, early diagnostic strategies within precision gynecology.
 
A proposito di Dino Campana, “L’opera in prosa e in versi”, a cura di Gianni Turchetta, Mondadori, Milano 2024 [pp. 313-328]
No full textDigital educational pacts: A possible response to technological challenges?
No full textIl contributo mira ad approfondire il ruolo dei Patti educativi digitali come strumenti capaci di implementare l’alleanza educativa tra scuola, famiglie, istituzioni e territorio, in un contesto segnato dalla pervasiva presenza delle tecnologie digitali nella vita quotidiana, soprattutto in quella delle giovani generazioni.
In un siffatto scenario, i Patti educativi – nella loro accezione più ampia – si configurano come risposte generative alla necessità di ricostruire forme di dialogo tra generazioni e un tessuto di corresponsabilità educativa, capace di interpretare i mutamenti in atto nelle forme del sapere, della relazione e della cittadinanza attiva.
La società iperconnessa richiede, infatti, nuovi paradigmi educativi, che non si limitino a interventi emergenziali o repressivi (o comunque di carattere meramente regolatorio), ma siano in grado di accompagnare bambini e adolescenti nella costruzione di un rapporto equilibrato, consapevole e critico con la rete.
I Patti educativi digitali si collocano in questa cornice come strumenti con implicazioni sia sul versante pedagogico-educativo, sia sul versante del diritto, in grado di favorire il dialogo tra attori diversi del mondo educante (ma anche tra generazioni), promuovendo un approccio fondato sulla corresponsabilità, sull’ascolto e sulla costruzione condivisa di regole e obiettivi educativi.The contribution aims to explore the potential of Digital Educational Pacts to strengthen the educational alliance between schools, families, institutions, and the wider community, in a context that is increasingly shaped by the pervasiveness of digital technologies in everyday life, particularly among younger generations.
In this scenario, Educational Pacts – in their broadest sense – emerge as a generative response to the need to rebuild forms of intergenerational dialogue and shared educational responsibility, capable of interpreting the ongoing transformations in knowledge, relationships, and active citizenship.
Indeed, a hyperconnected society calls for new educational paradigms that go beyond emergency or repressive measures, as well as merely regulatory approaches, in order to support children and adolescents in developing a balanced, conscious, and critical relationship with the digital world.
Within this framework, Digital Educational Pacts serve as tools with both pedagogical-educational and legal implications. They foster dialogue among the various actors in the educational ecosystem (as well as between generations), promoting an approach grounded in shared responsibility, active listening, and the co-construction of rules and educational goals
SOUTH AMERICA: ASPECTS OF SECURITY: EDITORIAL
No full textThis section of Glocalism focuses on security challenges of or related to South America. It studies security using a widened security agenda, based on the sectoral theory of the Copenhagen School, including not only military security, but also political, economic, societal and environmental security sectors