Polycystic ovary syndrome (PCOS) is a common endocrine–metabolic condition that carries a higher cardiovascular risk than currently reflected by traditional screening tools. Emerging evidence suggests that resting tachycardia and autonomic dysfunction may serve as early, non-invasive indicators of cardiovascular dysregulation in this population. This review synthesizes current data on resting heart rate (RHR), heart rate variability (HRV), and direct autonomic markers in women with PCOS, drawing from human studies published between 2000 and 2025. Across 32 eligible studies, most reported increased sympathetic activity, reduced parasympathetic tone, elevated RHR, and impaired HRV patterns observed even in normal-weight or metabolically mild PCOS phenotypes. These alterations correlate with endothelial dysfunction, arterial stiffness, and subclinical atherosclerosis, underscoring their cardiovascular relevance. Mechanistic insights highlight the contributions of insulin resistance, hyperandrogenism, inflammation, adipokine imbalance, chemoreflex sensitization, and altered cortisol metabolism to autonomic disruption. Despite consistent findings, methodological variability in HRV protocols and inadequate adjustment for major confounders limit definitive interpretation. RHR, due to its simplicity and accessibility, including through wearable devices, holds promise as a supportive early risk signal; however, it should not be used in isolation. Future studies must adopt standardized autonomic measurements, including diverse cohorts, and evaluate whether modifying autonomic markers translates into improved cardiometabolic outcomes. Integrating RHR and HRV with metabolic and endocrine markers may enhance early cardiovascular risk stratification in women with PCOS.

Background: Heart failure (HF) remains a growing global health problem, with nearly half of all cases attributed to HF with preserved ejection fraction (HFpEF) and its precursor, left ventricular diastolic dysfunction (LVDD). Although echocardiography is the diagnostic gold standard, its high cost and limited availability restrict its use for large-scale screening. In contrast, the electrocardiogram (ECG) is inexpensive and widely accessible. Recent advances in artificial intelligence (AI) have created opportunities to leverage ECG data for the early detection of cardiac dysfunction. The objective of this study was to systematically review and meta-analyze the diagnostic performance of AI-based ECG models for detecting cardiac dysfunction. Methods: The QUADAS-2 tool was used to assess the risk of bias. Pooled sensitivity and specificity were estimated using a bivariate random-effects model, with heterogeneity quantified using the I2 statistic. Pre-specified subgroup analyses were conducted according to clinical endpoint and AI model type. Results: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, nine eligible studies evaluating AI algorithms applied to ECG data for the detection of HFpEF were identified. Considerable methodological and population heterogeneity was observed across studies. Risk of bias was generally low for reference standards, although concerns were noted in patient selection. The pooled specificity of AI-ECG models was high at 0.83 [95% confidence interval (CI): 0.74–0.89], while pooled sensitivity was 0.82 (95% CI: 0.70–0.90). Both estimates demonstrated extremely high heterogeneity (I2 > 96%). Subgroup analyses by endpoint and model type did not explain this variability. Discussion: AI-enhanced ECG models show good diagnostic accuracy, specifically in ruling out cardiac dysfunction due to their high specificity. However, the high and unexplained heterogeneity across these studies limits the immediate generalizability of the results. Large, prospective validation studies across diverse populations are essential before these models can be confidently adopted into routine clinical practice.

Redesigning cardiovascular services at the local level is a pressing task for decentralized health systems facing the rising burden of chronic cardiovascular disease. In northern Modena (Emilia-Romagna, Italy), a post-restructuring reorganization exposed the limits of hospital-centric models and the need for integrated, patient-centered care. In 2021, Santa Maria Bianca Hospital, Mirandola—a first-level, non-interventional facility serving a largely rural population—launched a program to build a digitally integrated, prevention-oriented cardiology network. This review distills that field experience into a scalable framework for organizing peripheral cardiovascular services. The Mirandola Cardiology Network evolved along six operational domains: (1) reactivation of the cardiology unit with community outreach; (2) expansion of outpatient services and telecardiology; (3) a day hospital platform for chronic heart failure management; (4) digital transformation of the echocardiography service; (5) development of an advanced imaging center integrating coronary computed tomography (CT) angiography and planned cardiac magnetic resonance imaging (MRI); and (6) consolidation of professional education, research, and network-wide governance. By combining digital tools, non-invasive imaging, and multidisciplinary collaboration, the model established continuity of care across inpatient, outpatient, and community settings while improving access to diagnostics and appropriateness of care. Although prospective or comparative outcomes are not presented, process indicators and implementation milestones suggest scalability and sustainability, with potential to reduce avoidable admissions and streamline clinical pathways. The Mirandola experience shows that innovation in cardiology is feasible in peripheral settings when investment in technology, governance, and training is aligned with a coherent, value-based vision. It offers actionable guidance for decentralized systems seeking to implement digitally enabled, community-focused cardiology consistent with contemporary recommendations on territorial care and chronic disease management.