Sudden cardiac death (SCD) remains a major cause of mortality, and risk stratification based on left ventricular ejection fraction (LVEF) has significant limitations. Artificial intelligence (AI) has emerged as a potential approach for modeling complex arrhythmic risk. We conducted a systematic review with prespecified anchor subgroup reporting (without pooling) to evaluate the performance of AI models in predicting SCD and malignant ventricular arrhythmias. We searched MEDLINE/PubMed, Embase, and Scopus from inception to 3 October 2025. Anchor subgroup reporting was restricted to studies reporting area under the receiver-operating characteristic curve (AUROC) with 95% confidence intervals (CIs) within predefined modality/horizon subgroups. Studies without CIs or relying on cross-validation variance were summarized narratively as supplementary evidence. Thirteen studies met the inclusion criteria; five reported AUROC with 95% CIs within prespecified modality/horizon subgroups and were included as anchor subgroup estimates (k=1 per subgroup). Methodological quality and reporting were limited: 54% of studies (7/13) were classified as high risk of bias according to PROBAST+AI, driven by cross-validation-only designs, unit-of-analysis issues, limited external validation, and sparse calibration reporting. Among anchor estimates, deep learning on single-lead ambulatory ECG showed high discrimination for near-term ventricular tachyarrhythmias (AUROC 0.948, 95% CI 0.926-0.967), while 12-lead ECG models achieved good performance for long-term SCD (AUROC 0.820, 95% CI 0.794-0.847). The multimodal anchor estimate (AUROC 0.84) was numerically higher than the CMR radiomics anchor (AUROC 0.70), but this represents an indirect between-study contrast. AI-ECG models show high reported discrimination in near-term risk windows, but cross-study comparisons are indirect, and methodological heterogeneity remains a barrier to translation into clinical practice.

Heart failure (HF) is a highly prevalent condition that contributes significantly to morbidity, mortality, and healthcare burden worldwide. In Portugal, the PORTHOS study estimated that 16.5% of adults aged 50 years and older have HF, underscoring the urgent need for earlier detection and optimized care pathways. N-terminal pro-B-type natriuretic peptide (NT-proBNP) has emerged as a key biomarker for HF screening, diagnosis, and prognosis. The recent reimbursement of NT-proBNP testing in Portuguese primary care represents a critical opportunity to enhance HF detection and management. This position paper provides evidence-based recommendations for clinical pathways following an elevated NT-proBNP result. We advocate for NT-proBNP measurement in individuals over 50 years old with diabetes, diuretic use, or symptoms such as fatigue, dyspnea, or congestion. Age-specific cut-off values are recommended: ≥125 pg/mL for ages 50-79 and ≥250 pg/mL for those aged 80 and older. Confirmatory testing using echocardiography and electrocardiography is essential for diagnosis and management planning. Ensuring smooth transitions between primary and secondary care is vital to improving outcomes. We highlight challenges in continuity of care and emphasize the importance of multidisciplinary collaboration, along with the adoption of digital tools to strengthen communication. Integrating NT-proBNP testing into routine practice will enable earlier diagnosis, timely intervention, and more efficient healthcare delivery. A coordinated approach that combines clinical best practices, public health strategies, and active patient engagement is essential to reducing HF-related morbidity and mortality.