Subscribe to RSS
Download PDF
DOI: 10.1055/a-2484-0641
Relationship Between Screening-Detected Atrial Fibrillation and Blood Pressure Levels in Elderly Hypertensive Patients: The OMRON Heart Study
Funding This study was financially supported by OMRON Healthcare Co., Ltd.
Abstract
Background
Hypertension is a well-known risk factor for atrial fibrillation (AF) and strokes, but studies assessing screening-detected AF in hypertensive populations and its relationship to the blood pressure (BP) are scarce.
Method
We prospectively recruited hypertensive patients (aged ≥60 years) from all over Japan in a decentralized clinical trial. Participants were asked to measure their electrocardiogram (ECG) and BP at home for 3 months with a BP monitor equipped with ECG.
Results
Between April 2022 and July 2023, 4,078 hypertensive patients from across the country participated in this study. The mean age was 66.3 ± 5.5 years, and the male proportion was 80.3%. After excluding those with no measurement data (n = 258), AF detection was 5.8% (n = 220/3,820), and the time to AF detection was 3 to 109 days (median 28 days). The mean BP at baseline was 133 ± 14/85 ± 9 mmHg in the morning and 125 ± 14/79 ± 9 mmHg in the evening. AF detection did not significantly differ between the baseline BP categories (log rank test, p = 0.54), with hazard ratios (95% confidence interval) of 0.83 (0.57–1.19), 0.79 (0.55–1.14), and 0.99 (0.59–1.68) for systolic BP (SBP) 135 to 144 and/or diastolic BP (DBP) 85 to 89, SBP 145 to 159 and/or DBP 90 to 99, and SBP ≥ 160 and/or DBP ≥ 100, respectively (SBP ≤ 134 and DBP ≤ 84 as a reference). The results did not change when taking into account the impact of the measurement rates and antihypertensive drugs on AF detection during the observation period.
Conclusion
Detection of undiagnosed AF was 5.8% in elderly hypertensives, with no significant differences between the baseline BP categories and no effect of the measurement rate or antihypertensive drugs.
Introduction
Hypertension and atrial fibrillation (AF) are two important public health priorities and often coexist. Some studies[1] [2] have shown that hypertension is a significant risk factor for AF, suggesting that long-term hypertension leads to an increased left atrial pressure and subsequent left atrial enlargement, resulting in “atrial cardiomyopathy.” Both of these problems are known to cause AF, a theory that supports the finding that hypertension causes AF. In AF patients, hypertension is one of the risk factors for the CHA2DS2-VASc score (congestive heart failure, hypertension, age ≥ 75 years, diabetes mellitus, stroke/transient ischemic attack [TIA], vascular disease, age 65–74 years, and sex category), stroke risk score, HASBLED score (hypertension, abnormal renal/liver function, stroke, bleeding tendency or predisposition, labile INR, age ≥ 65, and drugs [concomitant aspirin or NSAIDs] or alcohol), and bleeding risk score. Therefore, early screening for AF in hypertensive patients is critical for a prompt diagnosis and appropriate management to prevent various complications.[3]
Many at-home ECG devices are currently available as tools for screening AF. For example, PPG-based wearable devices, implantable loop recorders, electrocardiogram (ECG)-based wearable devices, PPG-based handheld devices, and ECG-based handheld devices.[4] A home BP monitor equipped with an ECG (Complete, OMRON Healthcare, Kyoto, Japan) can record the ECGs simultaneously with the BP measurements, and the accuracy of the ECG is so high that it is widely used in a variety of settings.[5] [6] [7]
Although there have been many studies on the relationship between AF and a high BP in patients with hypertension,[8] [9] [10] [11] to the best of our knowledge, there have been no studies demonstrating the relationship between screening-detected AF by at-home ECG devices and the BP in hypertensive patients. Therefore, by using the Complete device, we investigated the detection of undiagnosed AF and its relationship to the BP in elderly hypertensive patients (aged ≥60 years) over a 3-month period.
Methods
In selecting our sample, we considered the geographic and demographic factors, and prospectively recruited hypertensive patients from all over Japan, as we considered it important to ensure as much diversity as possible. To accomplish this, we enrolled patients from the decentralized clinical trial (DCT).
Inclusion and Exclusion Criteria
The inclusion criteria were as follows: (1) patients with a history of hypertension and taking antihypertensive medication, (2) patients aged 60 years and over, and (3) patients with consent. The exclusion criteria were as follows: (1) patients currently participating or planning to participate in an interventional trial, (2) patients already with a confirmed diagnosis of AF (self-reported), (3) patients on anticoagulants (self-reported), (4) patients implanted with pacemakers and/or defibrillators, (5) patients residing outside of Japan, and (6) patients who were judged by their physician to be inappropriate to participate in the study.
Study Design
The study was a DCT and the enrolment procedure is described below. During the enrolment period, to recruit participants, in-up messages (invitations to participate in the study) were distributed within the app to app users registered on the OMRON Connect app (https://www.healthcare.omron.co.jp/service/, OMRON Healthcare Corporation, Kyoto, Japan). The OMRON Connect app is a health management app provided by OMRON Healthcare. It allows users to easily transfer and manage data acquired by the company's blood pressure (BP) monitors, body composition analyzers, and pedometers via Bluetooth communication on their smartphones. Because targeting only users of the OMRON Connect app might preferentially include patients with easy access to high-tech technology, volunteers over 60 years old who were not users of the OMRON Connect app were also recruited via the website. Participants reviewed the study outline and implementation details on the guidance website. Those wishing to participate were transferred from the information website to the application website. Within that site, participants were assessed for their eligibility for the study. Participants who were judged eligible reviewed the explanatory document on the website and gave consent on the website. The research office sent the Complete by post in the order of the participants who completed the enrolment. After receiving the device, the participants were asked to record their electrocardiogram and BP for 3 months ([Fig. 1]). The study was approved by the Medical Ethics Review Committee of the Kyoto Prefectural University of Medicine (approval number: ERB-C-1994) and the participants gave their written informed consent.
Blood Pressure and Electrocardiogram Measurements
The BP and ECG were taken using the Complete. Participants were instructed to take their BP and record an ECG for 30 s twice in the morning and twice in the evening, by touching the electrodes on the top face and both sides of the monitor. All participants were asked to sit for 5 minutes with their feet on the floor prior to the BP measurement, and to leave a 30-s interval between measurements. The average of the two readings was calculated.
Follow-Up
Follow-up with the participants during the observation period was carried out by establishing a monitoring server for the participants and keeping track of their queries and the measurement status of the equipment. The participants were automatically notified by the research office via their email address every week, as a weekly report, the weekly ECG measurement rate and number of days remaining until the end of the study. For participants with a measurement rate of 0% for 3 consecutive days, the call center called them and followed up to check for any machine malfunctions. After 3 months of registration, the participants returned the devices and questionnaires to the research office.
Analysis of the Automated ECG Results
Each 30-s ECG recording was analyzed by six independent cardiologists in a blinded fashion and possible AF was classified as sinus rhythm, AF, or uninterpretable (unknown). Three physicians were randomly allocated per ECG and the diagnosis of AF was made only if at least two or more of those three ECG analyses resulted in a diagnosis of AF; if there were discrepancies in the results of the three analyses, the individual ECGs were analyzed again with a discussion among them.
Baseline Hypertension Categories
The baseline BP and pulse rate consisted of the average of the morning or evening BP over the 7 days after the first measurement. For morning measurements, the average of the first two measurements taken within 10 minutes of the earliest time recorded between 4:00 AM and 10:00 AM was used as the measurement for that day. If only one measurement was taken within 10 minutes of the first measurement, the single measurement was taken as the measurement for the day. For evening measurements, the average of the last two measurements taken within 10 minutes of the latest time recorded between 7:00 PM and 2:00 AM the next day was used as the measurement for that day. If only one measurement was taken within 10 minutes of the last measurement, the single measurement was taken as the measurement for that day.
Statistical Analysis
The baseline characteristics of the participants are presented as numbers (percentage) for the categorical variables and as either the mean (±standard deviation) or median (range) for the continuous variables. A comparison of the baseline characteristics by the presence or absence of screen-detected AF was performed by a Welch's t-test for the continuous variables except for the CHA2DS2-VASc score, Wilcoxon rank-sum test for the ordered categorical variables and CHA2DS2-VASc score, and the Pearson's chi-square test for other categorical variables. The probability of screen-detected AF was estimated by the Kaplan-Meier estimator, and the survival curves by groups were tested by the log-rank test. A Cox regression was used to estimate the hazard ratio between the groups. The influence of the baseline BP on the screen-detected AF was assessed nonlinearly by a univariate Cox regression analysis using a restricted cubic spline function. All the statistical tests were two-sided, and a P-value of 0.05 was considered statistically significant. All analyses were performed using SAS version 9.4 software (SAS Institute, Inc. Cary, NC, USA).
Results
Between April 2022 and July 2023, 4,078 hypertensive patients taking antihypertensive medications from across the country participated in this study ([Supplementary Fig. S1], available in the online version). The baseline characteristics of the overall participants are summarized in [Table 1]. The mean age was 66.3 ± 5.5 years, and the proportion of men was 80.3%. As the mean BMI was 24.6 ± 3.3, the proportion of metabolic syndrome was 31.8%. The median CHA2DS2-VASc score was 2.1. Among them, 258 patients were identified to have registered but never had a measurement record after the registration. Of the remaining 3,820, 225 had no morning or evening BP measurements and 25 had AF detected during the first 7-day period. Excluding those participants, the distribution of the systolic BP (SBP) and diastolic BP (DBP) at baseline for 3,570 participants are described in [Supplementary Fig. S2] (available in the online version). The mean BP in the morning was 133 ± 14/85 ± 9 mmHg and in the evening 125 ± 14/79 ± 9 mmHg. Therefore, the SBP was 8 mmHg higher and the DBP was 6 mmHg higher in the morning than in the evening.
Abbreviations: ACEi, angiotensin-converting enzyme inhibitors; AF, atrial fibrillation; ARB, angiotensin II receptor blockers; ARNI, angiotensin receptor-neprilysin inhibitor; BB, beta blocker; CCB, calcium channel blocker; CHA2DS2-VASc score, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke/transient ischemic attack, vascular disease, age 65–74 years and sex category; TIA, transient ischemic attack.
Note: In this study, 4,078 hypertensive patients enrolled, and the number of patients in the analysis set was 3,820 after excluding those with no measurement data (n = 258). Antihypertensive drug use information was available for 3,734 patients. Data are presented as mean (standard deviation) for the continuous variables and number of subjects (%) for the categorical variables. P-value from Welch's t-test for continuous variables except CHA2DS2-VASc score and the number of antihypertensive medications, Wilcoxon rank-sum test for ordered categorical variables, CHA2DS2-VASc score and the number of antihypertensive medications, and Pearson's chi-square test for other categorical variables.
During the 3-month observation period, screening-detected AF was observed in 220 participants (5.8%), with a range of 3 to 109 days and median of 28 days to the event onset among the 3,820 participants after excluding those with no measurement data (n = 258) ([Fig. 2]). Among the participants with AF detected, 50% received notification by day 30 after enrolment, and 90% by day 80 after enrolment. The participants with AF detected were older, more likely to be male, and more likely to have a higher prevalence of a smoking and alcohol history than the non-AF detected group ([Table 1]). As a reference group for the group aged 60 to 64, the AF detection in the group aged 65 to 74 was 1.93 times higher (95% confidence interval [CI]: 1.44–2.58), and for the group aged ≥75, 2.40 times higher (95% CI 1.53–3.77) (p < 0.001). As a reference group for women, the AF detection for men was 2.04 times higher (95% CI: 1.32–3.14) (p = 0.001) ([Supplementary Fig. S3], available in the online version).
A BP analysis set of 3,570 participants was used to investigate the relationship between the BP and AF detection ([Supplementary Fig. S4], available in the online version). The group with SBP ≤ 134 mmHg and DBP ≤ 84 mmHg constituted 37.8% (n = 1,351) of the 3,570 participants, the group with SBP 135 to 144 mmHg and/or DBP 85to 89 mmHg 26.1% (n = 930), the group with SBP 145 to 159 mmHg and/or DBP 90 to 99 mmHg 27.8% (n = 993), and the group with SBP ≥160 mmHg and/or DBP ≥100 mmHg 8.3% (n = 296). The detection of undiagnosed AF according to the baseline BP category is shown in [Fig. 3]. The AF incidence rate did not significantly differ between the baseline BP categories (log rank test, p = 0.54), with a hazard ratio (HR) (95% CI) of 0.83 (0.57–1.19), 0.79 (0.55–1.14), and 0.99 (0.59–1.68) for SBP 135 to 144 and/or DBP 85 to 89, SBP 145 to 159 and/or DBP 90 to 99, and SBP ≥ 160 and/or DBP ≥ 100, respectively (SBP ≤ 134 and DBP ≤ 84 as a reference).
Discussion
To the best of our knowledge, this was the first study on AF screening in which a home BP and ECG were simultaneously recorded over a long period of 3 months in patients with hypertension aged 60 years and over. The detection of undiagnosed AF was 5.8% (n = 220) in the hypertensive participants, with no significant differences between the baseline BP categories.
AF Screening in Hypertension
Several studies[6] [12] have observed screening-detected AF (1.5–1.7%) in hypertensive patients. Although those studies highlighted the feasibility of a systematic or opportunistic screening of AF and that mobile devices could be a promising tool for screening AF in at-risk populations, missed AF existed because those studies were based on a single-shot ECG. Therefore, this study aimed to detect undiagnosed (or missed) AF by recording ECGs for 90 consecutive days. AF was detected by screening in 7.29% of the hypertensive patients aged 65 to 74 years, and particularly in 8.72% of the hypertensive patients aged 75 years and older, making them a notable population for screening. This reinforced what the AF management guidelines[13] [14] state. Those guidelines recommend opportunistic screening for AF in patients aged 65 years or older with at least one comorbidity, including hypertension. The European Society of Cardiology (ESC) guidelines[13] also recommend systematic screening for AF in patients aged 75 years or older or those at high risk of a stroke.
Blood Pressure and the Risk of Screening-Detected AF
An elevated SBP has been associated with an increased risk of AF in several studies. In a case–control population study, the risk of AF doubled in individuals with a SBP of ≥150 mmHg, as compared to patients with SBP levels of 120 to 129 mmHg. However, that excess risk was not significant in the participants with an SBP of 140 to 149 mmHg, whereas there was an increased risk of AF in those with an SBP <120 mm Hg, consistent with a J-curve phenomenon.[9] Other papers reported that the relationship between the BP and risk of AF is linear. For instance, a high normal BP (130–139/85–89 mm Hg) had a 28 to 53% higher risk of incident AF when compared to women with a BP <120/65 mmHg.[10] In our study, no relationship was found between the baseline BP classification and screening-detected AF. We also analyzed the estimated hazard ratios of the baseline morning systolic and diastolic BPs (SBP = 115 or DBP = 75 as reference) for the AF incidence by a univariate Cox regression analysis using a restricted cubic spline function, but no significant association between the baseline BP level and AF could be demonstrated ([Supplementary Fig. S5], available in the online version). An additional analysis investigated BP changes associated with the onset of AF. For the 3,570 participants in the BP analysis set, BP at baseline and on the day of AF onset was compared with paired t-tests, which showed that mean systolic (n = 170) and diastolic (n = 158) BPs on the day of AF onset were significantly lower than those at baseline. However, it is necessary to evaluate the relationship between BP change and AF while controlling for the effects of inter-subject confounding. We therefore performed further analysis using a self-controlled case study to assess the relationship between BP change and AF onset and found that multivariable regression analysis showed that mean SBP was not an important factor independently influencing the AF occurrence ([Supplementary File], available in the online version). Considering the possibility that the measurement rate during the observation period for each BP classification group could influence the AF detection, an additional analysis was performed focusing on the participants with a measurement rate of ≥60%, but no relationship was found between the BP and AF detection ([Supplementary Fig. S6], available in the online version). Furthermore, as the preventive effect of renin-angiotensin system inhibitors (RASis) on the incidence of AF[15] and mortality in AF patients[16] has been previously reported, a sub-analysis of the effect of RASis on the incidence of AF was also conducted. However, our study found no significant difference in the detection of AF by taking RASis or not (the HR for the baseline RASi [+] was 1.26 [95% CI: 0.92–1.73, p = 0.15] with the baseline RASi [−] as a reference). Furthermore, a comparison was made between the detection of AF with and without RASis by the baseline BP category ([Supplementary Table S1], available in the online version), but no clear relationship was found. According to the LOOP sub-study,[17] a high SBP did not affect the overall AF incidence, but it was associated with an increased risk of longer AF episodes of >24 hours. Some of the AF episodes detected in this study may have had a short duration, which may explain the lack of an association with hypertension. However, as single-lead ECGs can only obtain intermittent recordings, the details regarding the duration of AF are unknown. Other possible explanations could be that the hypertensive patients enrolled in the study had a well-controlled BP and there were fewer uncontrolled hypertension patients. Further studies are needed on the association between the BP level and AF burden.
There were several limitations to our study that warrant discussion. First, as the history of AF was self-reported, there was the potential for a higher detection of undiagnosed AF (e.g., people who forget they had a history of AF). Second, all participants in this study were taking antihypertensive medications and the number of participants with uncontrolled hypertension was very low (see [Supplementary Fig. S2], available in the online version). Therefore, the accuracy of the point estimates in the univariate Cox regression analysis using a restricted cubic spline function may have been unsatisfactory. Since there was only one patient with hypotension (SBP < 100 and DBP < 60), the influence of hypotension on the results was determined to be negligible. Third, there were 258 participants who did not know how to participate in the study and withdrew without taking a single measurement, despite having a call center available. The measurement rate during the observation period gradually declined, ranging from 96, 95.7, and 95% between days 1 to 30, 31 to 60, and >60 among the OMRON Connect users, while it was 93.5, 92.8, and 92% among the volunteers from the websites. That was likely due to the differences in the familiarity with mobile devices. Fourth, multimorbidity levels and polypharmacy in the elderly are important issues, but information on such geriatric conditions was not available in this study. Lastly, information on the participants' duration of hypertension was not available because the reliability of obtaining “the duration” of hypertension by a self-reported method cannot be guaranteed.
In summary, in elderly hypertensive patients (aged ≥60), the detection of undiagnosed AF was 5.8%. No significant differences in the AF detection were found between the baseline BP classification, with no influence from the measurement rate or RASis.
What is known about this topic?
-
Several studies have pointed to an association between an elevated systolic BP and increased AF risk in hypertensive patients. However, no study has demonstrated a relationship between screening-detected AF by at-home ECG devices and BP measurements.
What does this paper add?
-
Undiagnosed AF was detected in 5.8% of hypertensive patients aged 60 and above. There were no significant differences in the AF detection based on the baseline BP categories. These results suggest that it will be important to refine the prediction of individual AF in hypertensive patients with sinus rhythm.
Conflict of Interest
K.S. had university research contracts with the OMRON Healthcare company. The other authors have declared no conflicts of interest.
Acknowledgment
We would like to thank Mr. John Martin for his linguistic assistance. The OMRON Healthcare company provided the Complete device and an iPhone (Apple Inc., CA, USA) for this study but was not involved in the study design, analysis, or drafting of the manuscript.
-
References
- 1 Verdecchia P, Reboldi G, Gattobigio R. et al. Atrial fibrillation in hypertension: predictors and outcome. Hypertension 2003; 41 (02) 218-223
- 2 Lau YF, Yiu KH, Siu CW, Tse HF. Hypertension and atrial fibrillation: epidemiology, pathophysiology and therapeutic implications. J Hum Hypertens 2012; 26 (10) 563-569
- 3 Harskamp RE, Lucassen WAM, Lopes RD, Himmelreich JCL, Parati G, Weert HCPMV. Risk of stroke and bleeding in relation to hypertension in anticoagulated patients with atrial fibrillation: a meta-analysis of randomised controlled trials. Acta Cardiol 2022; 77 (03) 191-195
- 4 Hermans ANL, Gawalko M, Dohmen L. et al. Mobile health solutions for atrial fibrillation detection and management: a systematic review. Clin Res Cardiol 2022; 111 (05) 479-491
- 5 Senoo K, Miki T, Okura T. et al. Diagnostic value of atrial fibrillation by built-in electrocardiogram technology in a blood pressure monitor. Circ Rep 2020; 2 (07) 345-350
- 6 Senoo K, Yukawa A, Ohkura T. et al. Screening for untreated atrial fibrillation in the elderly population: a community-based study. PLoS One 2022; 17 (06) e0269506
- 7 Senoo K, Yukawa A, Ohkura T. et al. The impact of home electrocardiograph measurement rate on the detection of atrial fibrillation recurrence after ablation: a prospective multicenter observational study. Int J Cardiol Heart Vasc 2023; 44: 101177
- 8 Magnussen C, Niiranen TJ, Ojeda FM. et al; BiomarCaRE Consortium. Sex differences and similarities in atrial fibrillation epidemiology, risk factors, and mortality in community cohorts: results from the BiomarCaRE Consortium (Biomarker for Cardiovascular Risk Assessment in Europe). Circulation 2017; 136 (17) 1588-1597
- 9 Thomas MC, Dublin S, Kaplan RC. et al. Blood pressure control and risk of incident atrial fibrillation. Am J Hypertens 2008; 21 (10) 1111-1116
- 10 Conen D, Tedrow UB, Koplan BA, Glynn RJ, Buring JE, Albert CM. Influence of systolic and diastolic blood pressure on the risk of incident atrial fibrillation in women. Circulation 2009; 119 (16) 2146-2152
- 11 O'Neal WT, Soliman EZ, Qureshi W, Alonso A, Heckbert SR, Herrington D. Sustained pre-hypertensive blood pressure and incident atrial fibrillation: the Multi-Ethnic Study of Atherosclerosis. J Am Soc Hypertens 2015; 9 (03) 191-196
- 12 Bacchini M, Bonometti S, Del Zotti F. et al. Opportunistic screening for atrial fibrillation in the pharmacies: a population-based cross-sectional study. High Blood Press Cardiovasc Prev 2019; 26 (04) 339-344
- 13 Hindricks G, Potpara T, Dagres N. et al; ESC Scientific Document Group. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J 2021; 42 (05) 373-498
- 14 Joglar JA, Chung MK, Armbruster AL. et al; Peer Review Committee Members. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2024; 149 (01) e1-e156
- 15 Whelton PK, Carey RM, Aronow WS. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2018; 138 (17) e426-e483
- 16 Menichelli D, Poli D, Antonucci E, Palareti G, Pignatelli P, Pastori D. START2 Register Investigators. Renin-angiotensin-aldosterone system inhibitors and mortality risk in elderly patients with atrial fibrillation. Insights from the nationwide START registry. Eur J Intern Med 2024; 119: 84-92
- 17 Xing LY, Diederichsen SZ, Højberg S. et al. Systolic blood pressure and effects of screening for atrial fibrillation with long-term continuous monitoring (a LOOP Substudy). Hypertension 2022; 79 (09) 2081-2090
Address for correspondence
Publication History
Received: 11 June 2024
Accepted: 23 November 2024
Accepted Manuscript online:
25 November 2024
Article published online:
13 December 2024
© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
-
References
- 1 Verdecchia P, Reboldi G, Gattobigio R. et al. Atrial fibrillation in hypertension: predictors and outcome. Hypertension 2003; 41 (02) 218-223
- 2 Lau YF, Yiu KH, Siu CW, Tse HF. Hypertension and atrial fibrillation: epidemiology, pathophysiology and therapeutic implications. J Hum Hypertens 2012; 26 (10) 563-569
- 3 Harskamp RE, Lucassen WAM, Lopes RD, Himmelreich JCL, Parati G, Weert HCPMV. Risk of stroke and bleeding in relation to hypertension in anticoagulated patients with atrial fibrillation: a meta-analysis of randomised controlled trials. Acta Cardiol 2022; 77 (03) 191-195
- 4 Hermans ANL, Gawalko M, Dohmen L. et al. Mobile health solutions for atrial fibrillation detection and management: a systematic review. Clin Res Cardiol 2022; 111 (05) 479-491
- 5 Senoo K, Miki T, Okura T. et al. Diagnostic value of atrial fibrillation by built-in electrocardiogram technology in a blood pressure monitor. Circ Rep 2020; 2 (07) 345-350
- 6 Senoo K, Yukawa A, Ohkura T. et al. Screening for untreated atrial fibrillation in the elderly population: a community-based study. PLoS One 2022; 17 (06) e0269506
- 7 Senoo K, Yukawa A, Ohkura T. et al. The impact of home electrocardiograph measurement rate on the detection of atrial fibrillation recurrence after ablation: a prospective multicenter observational study. Int J Cardiol Heart Vasc 2023; 44: 101177
- 8 Magnussen C, Niiranen TJ, Ojeda FM. et al; BiomarCaRE Consortium. Sex differences and similarities in atrial fibrillation epidemiology, risk factors, and mortality in community cohorts: results from the BiomarCaRE Consortium (Biomarker for Cardiovascular Risk Assessment in Europe). Circulation 2017; 136 (17) 1588-1597
- 9 Thomas MC, Dublin S, Kaplan RC. et al. Blood pressure control and risk of incident atrial fibrillation. Am J Hypertens 2008; 21 (10) 1111-1116
- 10 Conen D, Tedrow UB, Koplan BA, Glynn RJ, Buring JE, Albert CM. Influence of systolic and diastolic blood pressure on the risk of incident atrial fibrillation in women. Circulation 2009; 119 (16) 2146-2152
- 11 O'Neal WT, Soliman EZ, Qureshi W, Alonso A, Heckbert SR, Herrington D. Sustained pre-hypertensive blood pressure and incident atrial fibrillation: the Multi-Ethnic Study of Atherosclerosis. J Am Soc Hypertens 2015; 9 (03) 191-196
- 12 Bacchini M, Bonometti S, Del Zotti F. et al. Opportunistic screening for atrial fibrillation in the pharmacies: a population-based cross-sectional study. High Blood Press Cardiovasc Prev 2019; 26 (04) 339-344
- 13 Hindricks G, Potpara T, Dagres N. et al; ESC Scientific Document Group. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J 2021; 42 (05) 373-498
- 14 Joglar JA, Chung MK, Armbruster AL. et al; Peer Review Committee Members. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2024; 149 (01) e1-e156
- 15 Whelton PK, Carey RM, Aronow WS. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2018; 138 (17) e426-e483
- 16 Menichelli D, Poli D, Antonucci E, Palareti G, Pignatelli P, Pastori D. START2 Register Investigators. Renin-angiotensin-aldosterone system inhibitors and mortality risk in elderly patients with atrial fibrillation. Insights from the nationwide START registry. Eur J Intern Med 2024; 119: 84-92
- 17 Xing LY, Diederichsen SZ, Højberg S. et al. Systolic blood pressure and effects of screening for atrial fibrillation with long-term continuous monitoring (a LOOP Substudy). Hypertension 2022; 79 (09) 2081-2090