Suarez, Maria Lourdes Gonzalez

Background: Early detection and prediction of arterial hypertension (HTN) are vital to reduce cardiovascular morbidity and mortality. AI has enhanced ECG diagnostic capabilities, enabling detection of latent cardiovascular abnormalities. Aims: This systematic review evaluates current evidence on AI-enhanced ECG for detection and prediction of HTN in in both clinical and subclinical populations. Methods: A search was conducted on April 28, 2025, in PubMed, Scopus, and Web of Science. Gray literature and study reference lists, plus the first 10 pages of Google Scholar, were screened. The review followed PRISMA 2020 guidelines. Bias risk was assessed using PROBAST (for prediction models) and QUADAS-2 (for diagnostic studies). Two reviewers independently performed study selection and data extraction after pilot training. Results: A total of 26 studies were included: 16 focused on detection and 10 on prediction of HTN using AI-enhanced ECG. Overall, 96% reported successful outcomes, underscoring the strong diagnostic and prognostic potential of this approach. Among detection studies, 94% successfully identified hypertensive individuals, classified blood pressure (BP) levels, or evaluated cardiovascular risk. Most were retrospective in design (75%), with internal validation in 44% and external validation in 19%. Sample sizes ranged from 100 to over 120,000, including healthy and cardiovascular patients. Commonly used algorithms included deep Learning, random forest, and ensemble methods, often using single-lead ECG data, highlighting the feasibility of wearable technologies. QUADAS-2 identified 56% of studies had low risk of bias. All prediction studies achieved accurate estimation of systolic/diastolic BP or predicted future HTN onset. Designs were 40% experimental and 50% retrospective. All studies reported internal validation, and 50% included external validation. AI models such as CNNs, ResNet-LSTM, and U-Net were used across clinical and community-based populations, with follow-up periods of up to 6.8 years. Populations were predominantly normotensive or mixed. PROBAST assessments showed 60% low and 40% moderate risk of bias. Conclusion: AI-enhanced ECG is a promising, non-invasive tool for early detection and prediction of HTN. However, external validation and clinical trials are needed to support integration and generalizability.

Cancer therapy-induced hypertension (HTN) is an increasingly recognized complication associated with a wide range of anticancer agents, including vascular endothelial growth factor (VEGF) inhibitors, proteasome inhibitors, tyrosine kinase inhibitors, and alkylating agents. The pathogenesis of HTN in this setting is multifactorial, involving mechanisms such as endothelial dysfunction, nitric oxide (NO) suppression, sympathetic nervous system activation, and vascular remodeling. Additional factors, including paraneoplastic syndromes, poorly controlled pain, mood disturbances, and overlapping cardiovascular risk factors like obesity and diabetes, further contribute to the complexity of diagnosis and management. Despite its prevalence and clinical implications, cancer therapy-induced HTN is often addressed using general population guidelines, with limited oncology-specific protocols available. Accurate blood pressure measurement and individualized treatment plans are critical to optimize outcomes and avoid interruptions to cancer therapy. Antihypertensive agents such as angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARB), and calcium channel blockers have shown efficacy in both blood pressure control and, in some cases, oncologic outcomes. A multidisciplinary approach involving oncologists, cardiologists, and primary care providers is essential to navigate the interplay between cancer treatment and cardiovascular health. Ongoing research is needed to develop targeted guidelines and improve the long-term care of cancer patients affected by treatment-induced HTN.

Key Points Patient-centered care in an interdisciplinary CKD clinic addresses complex needs and supports comprehensive CKD management. Implementation of an interdisciplinary CKD clinic reduced hospital admissions by 26%, emergency department visits by 30%, and increased nephrology consultations. Comprehensive care models with integrated registries enhance tailored interventions, leading to improved CKD management outcomes. Background CKD is a significant public health issue globally. Its progressive nature calls for innovative care models to mitigate disease progression and enhance patient outcomes. An interdisciplinary clinic model may offer comprehensive care tailored to the needs of patients with CKD. The aim of this study was to evaluate the effect of an interdisciplinary CKD clinic on disease progression, health care utilization, and social determinants of health (SDOH). Methods We conducted a retrospective cohort study at the Mayo Clinic in Rochester, Minnesota. The study included 534 patients enrolled in the CKD clinic between March 5, 2021, and May 31, 2022, excluding those who opted out of research. The intervention involved a clinical registry and an interdisciplinary team delivering evidence-based care pathways, patient education, shared decision making, and care coordination. The primary outcomes assessed were CKD progression and health care utilization, while secondary outcomes examined the effect of SDOH. Results At entry, the median age was 73 (interquartile range, 64–79) years, with 60% at stage 4 or lower. Clinic implementation correlated with a 26% decrease in hospital admissions (incidence rate ratio [IRR], 0.74; 95% confidence interval [CI], 0.60 to 0.91; P = 0.004) and a 30% reduction in emergency visits (IRR, 0.70; 95% CI, 0.57 to 0.87; P = 0.001). Nephrology consultations increased by 46% (IRR, 1.46; 95% CI, 1.34 to 1.60; P < 0.001), reflecting enhanced specialized care. Lower exercise frequency and unemployment were linked to increased CKD progression and health care usage. Conclusions An interdisciplinary CKD clinic supported by a registry can potentially reduce health care utilization among patients with CKD, with SDOH playing a critical role in disease management.

This review explores the transformative role of artificial intelligence (AI) in hypertension care, summarizing and analyzing published works from the last three years in this field. Hypertension contributes to a significant healthcare burden both at an individual and global level. We focus on five key areas: risk prediction, diagnosis, education, monitoring, and management of hypertension, supplemented with a brief look into the works on hypertensive disease of pregnancy. For each area, we discuss the advantages and disadvantages of integrating AI. While AI, in its current rudimentary form, cannot replace sound clinical judgment, it can still enhance faster diagnosis, education, prevention, and management. The integration of AI in healthcare is poised to revolutionize hypertension care, although careful implementation and ongoing research are essential to mitigate risks.