Hypertension is the leading modifiable risk factor for atrial fibrillation (AF) and is estimated to be present in >70% of AF patients. This Frontiers Review was prepared by 29 expert members of the AF-SCREEN International Collaboration to summarize existing evidence and knowledge gaps on links between hypertension, AF, and their cardiovascular sequelae; simultaneous screening for hypertension and AF; and the prevention of AF through antihypertensive therapy. Hypertension and AF are inextricably connected. Both are easily diagnosed, often silent, and frequently treated inadequately. Together, they additively increase the risk of ischemic stroke, heart failure, and many types of dementia, resulting in greater all-cause mortality, considerable disease burden, and increased health care expenditures. Automated upper arm cuff blood pressure devices with implemented technology can be used to simultaneously detect both hypertension and AF. However, positive screening for AF with an oscillometric blood pressure monitor still requires ECG confirmation. The current evidence suggests that high-risk individuals aged ≥65 years or with treatment-resistant hypertension could benefit from AF screening. Since antihypertensive therapy effectively lowers AF risk, particularly in individuals with left ventricular dysfunction, hypertension should be the key target for AF prediction and prevention rather than merely a comorbidity of AF. Nevertheless, several important gaps in knowledge need to be filled over the next years, including the ideal method and selection of patients for simultaneous screening of hypertension and AF and the optimal antihypertensive drug class and blood pressure targets for AF prevention.

The optimal method for the diagnosis of hypertension among patients on hemodialysis remains a controversial issue. Using 44-h ambulatory blood pressure (BP) monitoring (ABPM) as the reference-standard, we assessed the diagnostic performance of home BP monitoring (HBPM) versus routine dialysis-unit BP recordings in hemodialysis patients. Over a period of 2 weeks, the following methods were used for the assessment of hypertension: (i) routine predialysis and postdialysis BP recordings averaged over 6 consecutive dialysis sessions; (ii) HBPM for 7 days (duplicate morning and evening measurements, Microlife WatchBP Home N); (iii) 44-h ABPM (20-min intervals over an entire interdialytic interval, Microlife WatchBPO3). The study included 70 patients (mean age: 65.3 ± 13.2 years; treated hypertensives: 87.1%; 44-h ambulatory systolic/diastolic BP: 120.6 ± 15.2/66.3 ± 10.1 mmHg). Mean (standard deviation) of the differences between ambulatory daytime systolic BP (SBP) and routine predialysis SBP was -11.4 (13.4) mmHg, routine postdialysis SBP -4.0 (15.1) mmHg and home SBP -8.6 (10.7) mmHg. The area under the receiver-operating-characteristic-curve (AUC) for the detection of an ambulatory daytime SBP ≥ 135 mmHg was higher for home SBP [AUC: 0.934; 95% confidence interval (CI): 0.871-0.996] relative to predialysis SBP (AUC: 0.778; 95% CI: 0.643-0.913) and postdialysis SBP (AUC: 0.766; 95% CI: 0.623-0.909) (P = 0.02 for both comparisons). Home SBP at the cut-off point of 141.0 mmHg provided the best combination of sensitivity (85.7%) and specificity (92.9%) in diagnosing hypertension. In conclusion, the present study shows that among hemodialysis patients, HBPM for 1 week is superior to 2-week averaged routine predialysis or postdialysis BP in predicting ambulatory hypertension.

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence-based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.

Blood pressure variability (BPV) predicts cardiovascular events independent of mean blood pressure. BPV is defined as short-term (24-h), medium or long- term (weeks, months or years). Standard deviation, coefficient of variation and variation independent of the mean have been used to quantify BPV. High BPV is associated with increasing age, diabetes, smoking and vascular disease and is a consequence of premature ageing of the vasculature. Long-term BPV has been incorporated into cardiovascular risk models (QRISK) and elevated BPV confers an increased risk of cardiovascular outcomes even in subjects with controlled blood pressure. Long-acting dihydropyridine calcium channel blockers and thiazide diuretics are the only drugs that reduce BPV and for the former explains their beneficial effects on cardiovascular outcomes. We believe that BPV should be incorporated into blood pressure management guidelines and based on current evidence, long-acting dihydropyridines should be preferred drugs in subjects with elevated BPV.

Kiosk devices for unsupervised self-measurement of blood pressure (BP) are being used in public spaces and healthcare settings in several countries. This statement by the European Society of Hypertension (ESH) Working Group on BP Monitoring and Cardiovascular Variability provides a review of the published evidence on kiosk BP devices and consensus recommendations for their requirements and clinical use. A systematic literature search identified 54 relevant studies. Kiosk BP measurements appeared to be close to office BP [mean difference systolic 0.2 mmHg (95% confidence intervals -1.3 to 1.8); diastolic -0.4 mmHg (-3.5 to 2.7)], and higher than daytime ambulatory and home BP [mean difference 6.0 mmHg (1.6-10.4)/5.0 (2-8) and 8.1 mmHg (-2.6 to 18.9)/0.2 (-9.6 to 10.0), respectively]. Randomized or observational studies using kiosk BP measurements for hypertension screening or for assessing hypertension control were also included, as well as studies investigating users' and healthcare professionals' opinions, acceptability, and perspectives regarding kiosk BP measurements, and validation studies of kiosk BP devices. These studies had considerable heterogeneity in design, setting, methodology, measurement protocol, and sample size. Thus, at present, the clinical utility of kiosk BP measurements is uncertain. This ESH consensus statement acknowledges the potential of kiosk BP measurement as an emerging method for unsupervised self-measurement in the context of opportunistic screening for hypertension in apparently healthy people and the long-term monitoring of people with diagnosed hypertension. Requirements for the design, validation, function, and use of kiosk BP monitors are provided, together with the pending research questions on their optimal implementation in clinical practice.

Blood pressure (BP) is a key contributor to the lifetime risk of preclinical organ damage and cardiovascular disease. Traditional clinic-based BP readings are typically measured infrequently and under standardized/resting conditions and therefore do not capture BP values during normal everyday activity. Therefore, current hypertension guidelines emphasize the importance of incorporating out-of-office BP measurement into strategies for hypertension diagnosis and management. However, conventional home and ambulatory BP monitoring devices use the upper-arm cuff oscillometric method and only provide intermittent BP readings under static conditions or in a limited number of situations. New innovations include technologies for BP estimation based on processing of sensor signals supported by artificial intelligence tools, technologies for remote monitoring, reporting and storage of BP data, and technologies for BP data interpretation and patient interaction designed to improve hypertension management ("digital therapeutics"). The number and volume of data relating to new devices/technologies is increasing rapidly and will continue to grow. This International Society of Hypertension position paper describes the new devices/technologies, presents evidence relating to new BP measurement techniques and related indices, highlights standard for the validation of new devices/technologies, discusses the reliability and utility of novel BP monitoring devices, the association of these metrics with clinical outcomes, and the use of digital therapeutics. It also highlights the challenges and evidence gaps that need to be overcome before these new technologies can be considered as a user-friendly and accurate source of novel BP data to inform clinical hypertension management strategies.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.