1
|
Aghilinejad A, Tamborini A, Gharib M. A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning. Artif Intell Med 2024; 154:102918. [PMID: 38924863 DOI: 10.1016/j.artmed.2024.102918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/02/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024]
Abstract
Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable healthcare electronics. To assess the central aortic pressure using radial-based measurements, there is an essential need to develop mathematical approaches to estimate the central pressure waveform. In this study, we propose a new Fourier-based machine learning (F-ML) methodology to transfer non-invasive radial pressure measurements to the central pressure waveform. To test the method, collection of tonometry recordings of the radial and carotid pressure measurements are used from the Framingham Heart Study (2640 individuals, 55 % women) with mean (range) age of 66 (40-91) years. Method-derived estimates are significantly correlated with the measured ones for three major features of the pressure waveform (systolic blood pressure, r=0.97, p < 0.001; diastolic blood pressure, r=0.99, p < 0.001; and mean blood pressure, r=0.99, p < 0.001). In all cases, the Bland-Altman analysis shows negligible bias in the estimations and error is bounded to 5.4 mmHg. Findings also suggest that the F-ML approach reconstructs the shape of the central pressure waveform accurately with the average normalized root mean square error of 5.5 % in the testing population which is blinded to all stages of machine learning development. The results show that the F-ML transfer function outperforms the conventional generalized transfer function, particularly in terms of reconstructing the shape of the central pressure waveform morphology. The proposed F-ML transfer function can provide accurate estimates for the central pressure waveform, and ultimately expand the usage of non-invasive devices for central hemodynamic assessment.
Collapse
Affiliation(s)
- Arian Aghilinejad
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, United States.
| | - Alessio Tamborini
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, United States
| | - Morteza Gharib
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, United States
| |
Collapse
|
2
|
Rali AS, Butcher A, Tedford RJ, Sinha SS, Mekki P, Van Spall HGC, Sauer AJ. Contemporary Review of Hemodynamic Monitoring in the Critical Care Setting. US CARDIOLOGY REVIEW 2022. [DOI: 10.15420/usc.2021.34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Hemodynamic assessment remains the most valuable adjunct to physical examination and laboratory assessment in the diagnosis and management of shock. Through the years, multiple modalities to measure and trend hemodynamic indices have evolved with varying degrees of invasiveness. Pulmonary artery catheter (PAC) has long been considered the gold standard of hemodynamic assessment in critically ill patients and in recent years has been shown to improve clinical outcomes among patients in cardiogenic shock. The invasive nature of PAC is often cited as its major limitation and has encouraged development of less invasive technologies. In this review, the authors summarize the literature on the mechanism and validation of several minimally invasive and noninvasive modalities available in the contemporary intensive care unit. They also provide an update on the use of focused bedside echocardiography.
Collapse
Affiliation(s)
- Aniket S Rali
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Amy Butcher
- Department of Cardiovascular Anesthesia and Critical Care, Baylor College of Medicine, Houston, TX
| | - Ryan J Tedford
- Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC
| | - Shashank S Sinha
- Division of Cardiology, Inova Heart and Vascular Institute, Inova Fairfax Medical Campus, Falls Church, VA
| | - Pakinam Mekki
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Harriette GC Van Spall
- Department of Medicine, Department of Health Research Methods, Evidence, and Impact, Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - Andrew J Sauer
- Department of Cardiovascular Medicine, University of Kansas Medical Center, Kansas City, KS
| |
Collapse
|
3
|
An Auto Adjustable Transimpedance Readout System for Wearable Healthcare Devices. ELECTRONICS 2022. [DOI: 10.3390/electronics11081181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The objective of this work was to design a versatile readout circuit for patch-type wearable devices consisting of a Transimpedance Amplifier (TIA). The TIA performs Current to Voltage (I–V) conversion, the most widely used technique for amperometry and impedance measurement for various types of electrochemical sensors. The proposed readout circuit employs a digitally controllable feedback resistor (Rf) technique in the TIA to improve accuracy, which can be utilized in a variety of electrochemical sensors within a current range of 0.1 µA–100 µA. It is designed to accommodate multiple sensors simultaneously to track multiple target analytes for high accuracy and versatile usage. The readout circuit consists of low power operational amplifier (op–amp) and digital circuit blocks, is designed and fabricated with Magna 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) technology, which provides low power consumption and a high degree of integration. The design has a small size of 0.282 mm2 and low power consumption of 0.38 mW with a 3.3 V power supply, which are desirable factors in wearable device applications.
Collapse
|
4
|
Mieloszyk R, Twede H, Lester J, Wander J, Basu S, Cohn G, Smith G, Morris D, Gupta S, Tan D, Villar N, Wolf M, Malladi S, Mickelson M, Ryan L, Kim L, Kepple J, Kirchner S, Wampler E, Terada R, Robinson J, Paulsen R, Saponas TS. A Comparison of Wearable Tonometry, Photoplethysmography, and Electrocardiography for Cuffless Measurement of Blood Pressure in an Ambulatory Setting. IEEE J Biomed Health Inform 2022; 26:2864-2875. [PMID: 35201992 DOI: 10.1109/jbhi.2022.3153259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE While non-invasive, cuffless blood pressure (BP) measurement has demonstrated relevancy in controlled environments, ambulatory measurement is important for hypertension diagnosis and control. We present both in-lab and ambulatory BP estimation results from a diverse cohort of participants. METHODS Participants (N=1125, aged 21-85, 49.2% female, multiple hypertensive categories) had BP measured in-lab over a 24-hour period with a subset also receiving ambulatory measurements. Radial tonometry, photoplethysmography (PPG), electrocardiography (ECG), and accelerometry signals were collected simultaneously with auscultatory or oscillometric references for systolic (SBP) and diastolic blood pressure (DBP). Predictive models to estimate BP using a variety of sensor-based feature groups were evaluated against challenging baselines. RESULTS Despite limited availability, tonometry-derived features showed superior performance compared to other feature groups and baselines, yielding prediction errors of 0.329.8 mmHg SBP and 0.547.7 mmHg DBP in-lab, and 0.868.7 mmHg SBP and 0.755.9 mmHg DBP for 24-hour averages. SBP error standard deviation (SD) was reduced in normotensive (in-lab: 8.1 mmHg, 24-hr: 7.2 mmHg) and younger (in-lab: 7.8 mmHg, 24-hr: 6.7 mmHg) subpopulations. SBP SD was further reduced 1520% when constrained to the calibration posture alone. CONCLUSION Performance for normotensive and younger participants was superior to the general population across all feature groups. Reference type, posture relative to calibration, and controlled vs. ambulatory setting all impacted BP errors. SIGNIFICANCE Results highlight the need for demographically diverse populations and challenging evaluation settings for BP estimation studies. We present the first public dataset of ambulatory tonometry and cuffless BP over a 24-hour period to aid in future cardiovascular research.
Collapse
|
5
|
Kędzierski K, Radziejewska J, Sławuta A, Wawrzyńska M, Arkowski J. Telemedicine in Cardiology: Modern Technologies to Improve Cardiovascular Patients’ Outcomes—A Narrative Review. Medicina (B Aires) 2022; 58:medicina58020210. [PMID: 35208535 PMCID: PMC8878175 DOI: 10.3390/medicina58020210] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 12/18/2022] Open
Abstract
The registration of physical signals has long been an important part of cardiological diagnostics. Current technology makes it possible to send large amounts of data to remote locations. Solutions that enable diagnosis and treatment without direct contact with patients are of enormous value, especially during the COVID-19 outbreak, as the elderly require special protection. The most important examples of telemonitoring in cardiology include the use of implanted devices such as pacemakers and defibrillators, as well as wearable sensors and data processing units. The arrythmia detection and monitoring patients with heart failure are the best studied in the clinical setting, although in many instances we still lack clear evidence of benefits of remote approaches vs. standard care. Monitoring for ischemia is less well studied. It is clear however that the economic and organizational gains of telemonitoring for healthcare systems are substantial. Both patients and healthcare professionals have expressed an enormous demand for the further development of such technologies. In addition to these subjects, in this paper we also describe the safety concerns associated with transmitting and storing potentially sensitive personal data.
Collapse
Affiliation(s)
- Kamil Kędzierski
- Department of Medical Emergencies, Wrocław Medical University, ul. K. Parkowa 34, 51-616 Wrocław, Poland;
| | | | - Agnieszka Sławuta
- Department of Internal and Occupational Diseases, Hypertension and Clinical Oncology, Wrocław Medical University, ul Borowska 213, 50-556 Wrocław, Poland;
| | - Magdalena Wawrzyńska
- Center of Preclinical Studies, Wrocław Medical University, ul. K. Bartla 5, 51-618 Wrocław, Poland;
| | - Jacek Arkowski
- Center of Preclinical Studies, Wrocław Medical University, ul. K. Bartla 5, 51-618 Wrocław, Poland;
- Correspondence: ; Tel./Fax: +48-71-330-77-52
| |
Collapse
|
6
|
Maurya MR, Riyaz NUSS, Reddy MSB, Yalcin HC, Ouakad HM, Bahadur I, Al-Maadeed S, Sadasivuni KK. A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring. Med Biol Eng Comput 2021; 59:2185-2203. [PMID: 34611787 DOI: 10.1007/s11517-021-02447-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 09/01/2021] [Indexed: 02/07/2023]
Abstract
Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.
Collapse
Affiliation(s)
- Muni Raj Maurya
- Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
- Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | | | - M Sai Bhargava Reddy
- Center for Nanoscience and Technology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana State, 500085, India
| | | | - Hassen M Ouakad
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman.
| | - Issam Bahadur
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman
| | - Somaya Al-Maadeed
- Department of Computer Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | | |
Collapse
|
7
|
Bayoumy K, Gaber M, Elshafeey A, Mhaimeed O, Dineen EH, Marvel FA, Martin SS, Muse ED, Turakhia MP, Tarakji KG, Elshazly MB. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol 2021; 18:581-599. [PMID: 33664502 PMCID: PMC7931503 DOI: 10.1038/s41569-021-00522-7] [Citation(s) in RCA: 220] [Impact Index Per Article: 73.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 01/31/2023]
Abstract
Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care.
Collapse
Affiliation(s)
- Karim Bayoumy
- Department of Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA
| | - Mohammed Gaber
- Department of Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | | | - Omar Mhaimeed
- Department of Medical Education, Weill Cornell Medicine, Doha, Qatar
| | - Elizabeth H Dineen
- Department of Cardiovascular Medicine, University of California Irvine, Irvine, CA, USA
| | - Francoise A Marvel
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Seth S Martin
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Evan D Muse
- Scripps Research Translational Institute and Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA, USA
| | - Mintu P Turakhia
- Center for Digital Health, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Khaldoun G Tarakji
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Mohamed B Elshazly
- Department of Medical Education, Weill Cornell Medicine, Doha, Qatar.
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
| |
Collapse
|
8
|
Hou C, Zhang F, Chen C, Zhang Y, Wu R, Ma L, Lin C, Guo W, Liu XY. Wearable hydration and pH sensor based on protein film for healthcare monitoring. CHEMICAL PAPERS 2021. [DOI: 10.1007/s11696-021-01627-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|
9
|
Liu J, Pahlevan NM. The underlying mechanism of intersite discrepancies in ejection time measurements from arterial waveforms and its validation in the Framingham Heart Study. Am J Physiol Heart Circ Physiol 2021; 321:H135-H148. [PMID: 34018849 DOI: 10.1152/ajpheart.00096.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Radial applanation tonometry is a well-established method for clinical hemodynamic assessment and is also becoming popular in wrist-worn fitness trackers. The time difference between the foot and the dicrotic notch of the arterial pressure waveform is a well-accepted approximation for the left ventricular ejection time (ET). However, several clinical studies have shown that ET measured from the radial pressure waveform deviates from that measured centrally. In this work, we consider the systolic wave and the dicrotic wave as two independent traveling waves and hypothesize that their wave speed difference leads to the intersite differences of measured ET (ΔET). Accordingly, we derived a mathematical dicrotic wave decomposition model and identified the most influential factors on ΔET via global sensitivity analysis. In our clinical validation on a heterogeneous cohort (N = 5,742) from the Framingham Heart Study (FHS), the local sensitivity analysis results resembled the sensitivity variation patterns of ΔET from model simulations. A regression analysis on FHS data, using morphological features of radial pressure waveforms to estimate the carotid ET, produced a root mean square error of 3.76 ms and R2 of 0.91. The proposed dicrotic wave decomposition model can explain the intersite ET measurement discrepancies observed in the clinical data of FHS and can facilitate the precise identification of ET with radial pressure waveforms. Therefore, the proposed model will improve various physics-based pulse wave analysis methods as well as prospective artificial intelligence methods for tackling the subsequent big data produced from widespread wearable radial pressure monitoring.NEW & NOTEWORTHY Based on a new understanding of pressure wave propagation, we propose a novel dicrotic wave decomposition model considering the dicrotic wave as an independent traveling component. The proposed model can explain the mechanism underlying the intersite discrepancies in ejection time measurement from arterial waveforms and then, in principle, enhance the accuracy of both classical physics-based as well as more contemporary artificial intelligence-based pulse wave analysis methods in clinical and wearable radial blood pressure monitoring applications.
Collapse
Affiliation(s)
- Jing Liu
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California
| | - Niema M Pahlevan
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California.,Division of Cardiovascular Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| |
Collapse
|
10
|
Chang Y, Wang L, Li R, Zhang Z, Wang Q, Yang J, Guo CF, Pan T. First Decade of Interfacial Iontronic Sensing: From Droplet Sensors to Artificial Skins. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2003464. [PMID: 33346388 DOI: 10.1002/adma.202003464] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/16/2020] [Indexed: 05/21/2023]
Abstract
Over the past decade, a brand-new pressure- and tactile-sensing modality, known as iontronic sensing has emerged, utilizing the supercapacitive nature of the electrical double layer (EDL) that occurs at the electrolytic-electronic interface, leading to ultrahigh device sensitivity, high noise immunity, high resolution, high spatial definition, optical transparency, and responses to both static and dynamic stimuli, in addition to thin and flexible device architectures. Together, it offers unique combination of enabling features to tackle the grand challenges in pressure- and tactile-sensing applications, in particular, with recent interest and rapid progress in the development of robotic intelligence, electronic skin, wearable health as well as the internet-of-things, from both academic and industrial communities. A historical perspective of the iontronic sensing discovery, an overview of the fundamental working mechanism along with its device architectures, a survey of the unique material aspects and structural designs dedicated, and finally, a discussion of the newly enabled applications, technical challenges, and future outlooks are provided for this promising sensing modality with implementations. The state-of-the-art developments of the iontronic sensing technology in its first decade are summarized, potentially providing a technical roadmap for the next wave of innovations and breakthroughs in this field.
Collapse
Affiliation(s)
- Yu Chang
- Bionic Sensing and Intelligence Center (BSIC), Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, 518055, China
| | - Liu Wang
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Ruya Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhichao Zhang
- Micro and Nano-Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, CA, 95616, USA
| | - Qi Wang
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Junlong Yang
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Chuan Fei Guo
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Tingrui Pan
- Micro and Nano-Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, CA, 95616, USA
| |
Collapse
|
11
|
Hare AJ, Chokshi N, Adusumalli S. Novel Digital Technologies for Blood Pressure Monitoring and Hypertension Management. CURRENT CARDIOVASCULAR RISK REPORTS 2021; 15:11. [PMID: 34127936 PMCID: PMC8188759 DOI: 10.1007/s12170-021-00672-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW Hypertension is common, impacting an estimated 108 million US adults, and deadly, responsible for the deaths of one in six adults annually. Optimal management includes frequent blood pressure monitoring and antihypertensive medication titration, but in the traditional office-based care delivery model, patients have their blood pressure measured only intermittently and in a way that is subject to misdiagnosis with white coat or masked hypertension. There is a growing opportunity to leverage our expanding repository of digital technology to reimagine hypertension care delivery. This paper reviews existing and emerging digital tools available for hypertension management, as well as behavioral economic insights that could supercharge their impact. RECENT FINDINGS Digitally connected blood pressure monitors offer an alternative to office-based blood pressure monitoring. A number of cuffless blood pressure monitors are in development but require further validation before they can be deployed for widespread clinical use. Patient-facing hubs and applications offer a means to transmit blood pressure data to clinicians. Though artificial intelligence could allow for curation of this data, its clinical use for hypertension remains limited to assessing risk factors at this time. Finally, text-based and telemedicine platforms are increasingly being employed to translate hypertension data into clinical outcomes with promising results. SUMMARY The digital management of hypertension shows potential as an avenue for increasing patient engagement and improving clinical efficiency and outcomes. It is important for clinicians to understand the benefits, limitations, and future directions of digital health to optimize management of hypertension.
Collapse
Affiliation(s)
- Allison J Hare
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Office of the Chief Medical Information Officer, Penn Medicine, Philadelphia, PA USA
- Center for Digital Cardiology, Penn Medicine, Philadelphia, PA USA
| | - Neel Chokshi
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Center for Digital Cardiology, Penn Medicine, Philadelphia, PA USA
- Division of Cardiovascular Medicine, Department of Medicine, Penn Medicine, Philadelphia, PA USA
| | - Srinath Adusumalli
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Office of the Chief Medical Information Officer, Penn Medicine, Philadelphia, PA USA
- Center for Digital Cardiology, Penn Medicine, Philadelphia, PA USA
- Division of Cardiovascular Medicine, Department of Medicine, Penn Medicine, Philadelphia, PA USA
| |
Collapse
|
12
|
Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review. J Am Coll Cardiol 2020; 75:1582-1592. [PMID: 32241375 DOI: 10.1016/j.jacc.2020.01.046] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/20/2020] [Accepted: 01/27/2020] [Indexed: 12/14/2022]
Abstract
Ambulatory monitoring devices are enabling a new paradigm of health care by collecting and analyzing long-term data for reliable diagnostics. These devices are becoming increasingly popular for continuous monitoring of cardiac diseases. Recent advancements have enabled solutions that are both affordable and reliable, allowing monitoring of vulnerable populations from the comfort of their homes. They provide early detection of important physiological events, leading to timely alerts for seeking medical attention. In this review, the authors aim to summarize the recent developments in the area of ambulatory and remote monitoring solutions for cardiac diagnostics. The authors cover solutions based on wearable devices, smartphones, and other ambulatory sensors. The authors also present an overview of the limitations of current technologies, their effectiveness, and their adoption in the general population, and discuss some of the recently proposed methods to overcome these challenges. Lastly, we discuss the possibilities opened by this new paradigm, for the future of health care and personalized medicine.
Collapse
|
13
|
Convertino VA, Schauer SG, Weitzel EK, Cardin S, Stackle ME, Talley MJ, Sawka MN, Inan OT. Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6413. [PMID: 33182638 PMCID: PMC7697670 DOI: 10.3390/s20226413] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 12/21/2022]
Abstract
Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.
Collapse
Affiliation(s)
- Victor A. Convertino
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
| | - Steven G. Schauer
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
- Brooke Army Medical Center, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Erik K. Weitzel
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
- Brooke Army Medical Center, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- 59th Medical Wing, JBSA Lackland, San Antonio, TX 78236, USA
| | - Sylvain Cardin
- Navy Medical Research Unit, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - Mark E. Stackle
- Commander, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - Michael J. Talley
- Commanding General, US Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA;
| | - Michael N. Sawka
- Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.N.S.); (O.T.I.)
| | - Omer T. Inan
- Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.N.S.); (O.T.I.)
| |
Collapse
|
14
|
Li R, Nie B, Zhai C, Cao J, Pan J, Chi YW, Pan T. Telemedical Wearable Sensing Platform for Management of Chronic Venous Disorder. Ann Biomed Eng 2015; 44:2282-91. [PMID: 26530542 DOI: 10.1007/s10439-015-1498-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 10/27/2015] [Indexed: 12/20/2022]
Abstract
Enabled by emerging wearable sensors, telemedicine can potentially offer personalized medical services to long-term home care or remote clinics in the future, which can be particularly helpful in the management of chronic diseases. The wireless wearable pressure sensing system reported in this article provides an excellent example of such an innovation, whereby periodic or continuous monitoring of interface pressure can be obtained to guide routine compression therapy, the cornerstone of chronic venous disorder management. By applying a novel capacitive, iontronic sensing technology, a flexible, ultrathin, and highly sensitive pressure sensing array is seamlessly incorporated into compression garments for the monitoring of interface pressure. The linear pressure sensing array assesses pressure distribution along the limb in a real-time manner (up to a scanning rate of 5 kHz), and the measurement data can be processed and displayed on a mobile device locally, as well as transmitted through a Bluetooth communication module to a remote clinical service. The proposed interface pressure measuring system provides real-time interface pressure distribution data and can be utilized for both clinical and self-management of compression therapy, where both treatment efficacy and quality assurance can be ascertained.
Collapse
Affiliation(s)
- Ruya Li
- Micro-Nano Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, 95616, USA
- Department of Electrical and Computer Engineering, University of California, Davis, 95616, USA
| | - Baoqing Nie
- Micro-Nano Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, 95616, USA
| | - Chengwei Zhai
- Micro-Nano Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, 95616, USA
- College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jennifer Cao
- Micro-Nano Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, 95616, USA
| | - Jian Pan
- Micro-Nano Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, 95616, USA
- College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Yung-Wei Chi
- Division of Cardiovascular Medicine, Vascular Center, UC Davis Health System, Sacramento, USA
| | - Tingrui Pan
- Micro-Nano Innovations (MiNI) Laboratory, Department of Biomedical Engineering, University of California, Davis, 95616, USA.
| |
Collapse
|
15
|
Robotic tilt table reduces the occurrence of orthostatic hypotension over time in vegetative states. Int J Rehabil Res 2015; 38:162-6. [DOI: 10.1097/mrr.0000000000000104] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
|