Telemedicine refers to the practice of remote data exchange between patients and healthcare professionals to facilitate the evaluation, diagnosis, and management of disease (Figure 7.1) [469] . It is used to increase patients’ access to care and provide effective healthcare services at a distance. Technological advances over recent decades have dramatically increased the availability, applicability, range, and quality of telemedicine solutions. There are many potential applications for telemedicine in the management of patients with hypertension [470] . The concept of anticipation medicine presents a unique and exciting challenge in the era of information and communication technology (ICT) and Internet of things (IoT)‐based big data. In addition, video consultation as an alternative to an office visit, blood pressure (BP)‐related telemedicine and telecare could help to reduce the incidence of cardiovascular events and therefore improve patient longevity (Figure 7.2) [172] . Hemodynamic biomarker‐initiated anticipation medicine that can predict BP surge based on individual hemodynamic profiles and trigger early intervention using an ICT‐based real‐time feedback system could prevent or mitigate the onset, recurrence, and aggravation of cardiovascular events. In the context of cardiovascular disease, anticipation medicine is defined as medicine that predicts the time and place of the onset of cardiovascular events, based on a time‐series of data, and provides a patient or doctor with advanced warning of potential risk factors, resulting in proactive, real‐time risk reduction. The field of individualized medicine is currently split into two distinct approaches (Figure 7.3) [156] . One is precision medicine, utilizing population‐based big data such as genomic information, and the other is anticipation medicine, using individual‐based big data such as time‐series data. While there is a huge body of evidence on the relation between hypertension and cardiovascular disease risk based on population‐based big data, there is less‐available information on the use of anticipation medicine to predict the time and place of cardiovascular events in individual subjects using time‐series data for BP and influencing factors. Figure 7.1 Basic telehealth services and their workflow. EHR, electronic health record; IoMT, internet of medical things; Mic, microphone; NFC, near‐field communication; PDA, personal digital assistant. Source: Omboni Front Cardiovasc Med. 2019;6:76 [469] . Figure 7.2 A model of information and communication technology‐based real‐time anticipation medicine of cardiovascular (CV) disease. BP, blood pressure. Source: Reprinted from Kario et al. Prog Cardiovasc Dis. 2017;60:435–449 [172] , with permission from Elsevier. Figure 7.3 Anticipation medicine of cardiovascular (CV) disease. Source: Reprinted from Kario. Prog Cardiovasc Dis. 2016;59:262–281 [156] , with permission from Elsevier. To establish real‐time anticipation medicine, several technical innovations are needed. These include the following: (1) a wearable multisensor device to detect biological and environmental signals; (2) an ICT‐ and IoT‐based platform for real‐time big data transmission and analysis; and (3) pathophysiologic domain‐interactive artificial intelligence (AI). Using these technologies, time‐series big data collection with short‐term intervals could be performed in the context of a prospective study (Figure 7.4) [172] . BP surge‐initiated anticipation medicine, in combination with data on organ damage and psycho‐behavioral, genomic, environmental, and nutritional risk factors, has the potential to achieve a perfect individualized medicine regimen for zero cardiovascular events (Figure 7.5) [156] . Wearable BP monitoring increases the number of serial BP measurements, which in turn increases the accuracy of the diagnostic and target BP levels, and increases the ability to detect various surges with different time phases (Figure 7.6) [471] . The former contributes to guideline‐based medicine, and the latter contributes to anticipation medicine for the prediction of cardiovascular events. Figure 7.4 Technical innovations to establish real‐time anticipation medicine for cardiovascular disease (the key biomarker of real‐time anticipation medicine for cardiovascular disease is blood pressure variability). ICT, information and communication technology; IoT, internet of things. Source: Reprinted from Kario et al. Prog Cardiovasc Dis. 2017;60:435–449 [172] , with permission from Elsevier. Figure 7.5 Hemodynamic biomarker‐initiated anticipation medicine for preventing the onset and aggravation of cardiovascular events. ABI, ankle‐brachial index; AI, artificial intelligence; BP, blood pressure; CAVI, cardio‐ankle vascular index; CKD, chronic kidney disease; ECG, electrocardiography; Echo, cardiac and carotid echography; PWV, pulse wave velocity. Source: Reprinted from Kario. Prog Cardiovasc Dis. 2016;59:262–281 [156] , with permission from Elsevier. Figure 7.6 Time‐series out‐of‐office blood‐pressure (BP)‐based anticipation management of hypertension. ABPM, ambulatory blood pressure monitoring; HBPM, home blood pressure monitoring; ICT, information and communication technology. Source: Kario et al. J Clin Hypertens(Greenwich). 2019;21:344–349 [471] . The management of hypertension should move from BP control to event management. It is a shift away from focusing on the absolute BP level to focusing on BP surge. BP surge management will lead to the effective prevention of cardiovascular event onset, especially in high‐risk subjects with vascular disease. A BP surge management strategy based on the resonance hypothesis consists of the following three goals: (1) minimizing triggers of BP surge; (2) reducing the amplitude of each surge peak; and (3) avoiding the synchronization of peaks with different time phases (Figure 7.7) [172] . The aim of this strategy is to avoid the generation of a large dynamic BP surge that could trigger cardiovascular events. In practical terms, to reduce the amplitude of morning BP surge, it is recommended that patients reduce their alcohol and salt intake at dinner. In winter, abrupt high‐intensity running just after rising without adequate protection against the cold weather should be avoided to attenuate the winter morning BP surge. Regular physical exercise is recommended, especially low‐intensity running, to improve cardiorespiratory fitness, which is associated with a reduction in BP variability due to a decrease in arterial stiffness and BP level [472–474]. Monotherapy or combination therapy with long‐acting antihypertensives, and/or bedtime dosing, is recommended to reduce the peak of BP surge [475] . In addition, renal denervation reduces sympathetic tonus resulting in the suppression of morning and nighttime BP peaks [157, 281, 283, 476], especially in patients with obstructive sleep apnea [282, 459]. Figure 7.7 Blood pressure (BP) variability control strategy based on the synergistic resonance hypothesis, aimed at the prevention of cardiovascular (CV) event onset. Source: Reprinted from Kario et al. Prog Cardiovasc Dis. 2017;60:435–449 [172] , with permission from Elsevier. The new ICT multisensor (IMS)‐ambulatory BP monitoring (ABPM) system uses new ICT/IoT‐based biological and environmental signal monitoring that can simultaneously monitor the environment (temperature, illumination, and humidity) at five different locations in a house (the entryway, bedroom, bathroom, toilet, and living room) and activities measured by using wrist‐type high‐sensitive actigraph to identify the location of patients (Figure 7.8) [172] . Using cloud computing with an IoT gateway and bridges based on hybrid Wi‐SUN, 92 LTE, and Bluetooth Low Energy (BLE) transmission systems, we can collect and store individual time‐series home and ambulatory BP data, waveform data, and data on individual physical activity and environmental signals specific to different rooms in the house (Figure 7.9) [172] . The IoT bridges collect the data from the IMS‐ABPM, wrist‐type actigraph, and body weight scale via BLE transmission. Data are then converted to Wi‐SUN transmission signal format and transmitted to the IoT gateway by a Wi‐SUN multihop transmission system. The Wi‐SUN system is based on IEEE 802.15.4g‐based international new IoT standards 92 and certified by the Wi‐SUN alliance. In the IoT gateway, the received Wi‐SUN signal is also converted to LTE transmission signal format and transmitted to the cloud over a conventional LTE wide‐area network. Moreover, each IoT bridge has devices for environmental monitoring such as a thermometer, illuminometer, and hygroscope. The environment‐monitoring data at each IoT bridge is also transmitted to the cloud. Housing conditions are important for the prevention of cardiovascular disease events, the onset of which exhibits significant seasonal variation with a peak in the winter. The death rate, especially the cardiovascular death, increases in the winter. On the other hand, data show that the winter mortality rate was higher in the north Kanto area (middle of Japan) than in Hokkaido, where winters were severely cold [Figure 2.30]. This inverse phenomenon is considered to be partly due to the increasing prevalence of energy‐saving homes with good thermal insulation performance in colder countries. Our hypothesis is that heterogeneity of room temperatures within the home (i.e. different temperatures between rooms, and even within the same room) could increase the risk of cardiovascular disease events by contributing to exaggerated BP variability. Figure 7.8 Multisensors and real‐time and hybrid Wi‐SUN/Wi‐Fi transmission system of individual biological and environmental signals in the living condition. BLE, bluetooth®, BP, blood pressure; IMS‐ABPM, ICT based multi‐sensor ambulatory BP monitoring. Source: Reprinted from Kario et al. Prog Cardiovasc Dis. 2017;60:435–449 [172] , Copyright (2017), with permission from Elsevier. In the Anticipate study using IMS‐ABPM, mixed‐model analysis of the association between daytime ambulatory systolic blood pressure (SBP) and physical activity, temperature, atmospheric pressure, and season (4699 data points obtained from 79 patients in summer and 72 patients in winter) demonstrated that a 10°C decrease in temperature was associated with a 10.4 mmHg increase in SBP (Figure 7.10) [172] . For example, SBP of 130 mmHg at rest (100 G) in an indoor setting at 25°C will increase to 151 mmHg when the conditions change to walking (1000 G) outdoors at 5°C (Figure 7.10). Figure 7.9 Time trend of multibioenvironmental signals from the information and communication technology multisensor (IMS)‐ambulatory blood pressure monitoring (ABPM) system (IMS‐ABPM) and hybrid transmission system in a 78‐year‐old woman with hypertension living in Tatsuno City, Hyogo, Japan. Time‐trend data gathered by multisensors in the house are successfully transmitted by the hybrid Wi‐SUN/Wi‐Fi transmission system. bpm, beats per minute; DBP, diastolic blood pressure; PR, pulse rate; SBP, systolic blood pressure. Source: Reprinted from Kario et al. Prog Cardiovasc Dis. 2017;60:435–449 [172] , Copyright (2017), with permission from Elsevier. In a prediction model of hypertension, AI increases the prediction accuracy for future‐onset hypertension in normotensive patients [477] . Age, body mass index, the cardio‐ankle vascular index (CAVI), and triglycerides were selected in this algorithm. In the recent Predict study using time series of home BP data and room temperature, AI allowed the prediction of BP levels over the coming two weeks to within approximately 6 mmHg [478] (Figures 7.11 and 7.12). The current status and future direction of out‐of‐office BP monitoring are summarized in Figure 7.13. In future, wearable 24‐hour BP monitoring/guided step 24‐hour BP monitoring could facilitate the achievement of perfect 24‐hour BP management (Figure 7.14). The research and development of continuous beat‐by‐beat “surge” BP monitoring has started, but it is still in the early stages [41, 172]. Figure 7.10 Mixed‐model analysis of the association between daytime ambulatory systolic blood pressure (SBP) and physical activity, temperature, atmospheric pressure and season (4699 data obtained from 79 patients in summer and 72 patients in winter). Source: Reprinted from Kario et al. Prog Cardiovasc Dis. 2017;60:435–449 [172] , Copyright (2017), with permission from Elsevier. There are marked individual differences in the short‐term dynamic BP change caused by various triggers (Figure 7.15) [479] . Wearable noninvasive beat‐by‐beat BP monitoring has been the dream of doctors who manage hypertension. Omron Healthcare Co., Ltd. recently publicized the prototype of a wearable surge BP monitoring (WSP) that uses recent advances in automatically controlled technology and measures absolute values of the maximum peaks of beat‐by‐beat pressure (Figure 7.16). The first prototype (WSP‐1) has two tonometry sensor plates and the angle of the arrayed sensor plate to cover the radial artery is automatically adjusted to obtain effective applanation. This device is being tested and improved in collaboration with Omron, with the goal of developing more accurate beat‐by‐beat WSP (Figure 7.16) [156] . Using the WSP device, continuous beat‐by‐beat BP during sleep was monitored simultaneously with polysomnography (Figure 7.17). Nighttime BP and BP variability were significantly lower during Stages 2 and 3 sleep, and higher in Stage 1 and rapid eye movement (REM) sleep, and during waking hours (Figure 7.18) [235] . Three nighttime BP surges were detected, associated with REM sleep, arousal (unconscious microarousal) (Figure 7.19a), and a sleep apnea episode (Figure 7.19b) [235] . Figure 7.11 Prediction of BP variability using artificial intelligence (AI). Source: Presented at Joint Meeting ESH‐ISH 2021 (on‐air) held on April 11‐14, 2021. Figure 7.12 Typical case of BP prediction by the algorithm (75y, male). Source: Presented at Joint Meeting ESH‐ISH 2021 (on‐air) held on April 11‐14, 2021.
CHAPTER 7
Blood pressure linked telemedicine and telecare
Anticipation medicine
Innovation technology
Concept of “trigger” management
Multisensors and the real‐time hybrid Wi‐SUN/Wi‐Fi transmission system
AI and anticipation models
Development of wearable beat‐by‐beat (surge) BP monitoring
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