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Kario K, Williams B, Tomitani N, McManus RJ, Schutte AE, Avolio A, Shimbo D, Wang JG, Khan NA, Picone DS, Tan I, Charlton PH, Satoh M, Mmopi KN, Lopez-Lopez JP, Bothe TL, Bianchini E, Bhandari B, Lopez-Rivera J, Charchar FJ, Tomaszewski M, Stergiou G. Innovations in blood pressure measurement and reporting technology: International Society of Hypertension position paper endorsed by the World Hypertension League, European Society of Hypertension, Asian Pacific Society of Hypertension, and Latin American Society of Hypertension. J Hypertens 2024:00004872-990000000-00518. [PMID: 39246139 DOI: 10.1097/hjh.0000000000003827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024]
Abstract
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.
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Affiliation(s)
- Kazuomi Kario
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Bryan Williams
- University College London (UCL) and National Insitute for Health Research UCL Hospitals Biomedical Research Centre, London, United Kingdom
| | - Naoko Tomitani
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Richard J McManus
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Aletta E Schutte
- School of Population Health, University of New South Wales; The George Institute for Global Health, Sydney, Australia
| | - Alberto Avolio
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Daichi Shimbo
- Hypertension Lab, Columbia University Irving Medical Center, New York, NY, USA
| | - Ji-Guang Wang
- Centre for Epidemiological Studies and Clinical Trials, Shanghai Key Laboratory of Hypertension, Department of Hypertension, Ruijin Hospital, The Shanghai Institute of Hypertension, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Nadia A Khan
- Center for Advancing Health Outcomes, Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Dean S Picone
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Isabella Tan
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Michihiro Satoh
- Division of Public Health, Hygiene and Epidemiology, Faculty of Medicine, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Keneilwe Nkgola Mmopi
- Department of Biomedical Sciences, Faculty of Medicine. University of Botswana, Gaborone, Botswana
| | - Jose P Lopez-Lopez
- Masira Research Institute, Medical School, Universidad de Santander, Bucaramanga, Colombia
| | - Tomas L Bothe
- Charité - Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany
| | - Elisabetta Bianchini
- Institute of Clinical Physiology, Italian National Research Council, Pisa, Italy
| | - Buna Bhandari
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Jesús Lopez-Rivera
- Unidad de Hipertension arterial, V departamento, Hospital Central San Cristobal, Tachira, Venezuela
| | - Fadi J Charchar
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat
- Department of Physiology, University of Melbourne, Melbourne, Australia
- Department of Cardiovascular Sciences, University of Leicester, Leicester
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester
- Manchester Royal Infirmary, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - George Stergiou
- Hypertension Center STRIDE-7, National and Kapodistrian University of Athens, School of Medicine, Third Department of Medicine, Sotiria Hospital, Athens, Greece
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Wiens J, Spector-Bagdady K, Mukherjee B. Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care. Annu Rev Genomics Hum Genet 2024; 25:141-159. [PMID: 38724019 DOI: 10.1146/annurev-genom-010323-010230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.
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Affiliation(s)
- Jenna Wiens
- Division of Computer Science and Engineering, College of Engineering, University of Michigan, Ann Arbor, Michigan, USA;
| | - Kayte Spector-Bagdady
- Department of Obstetrics and Gynecology and Center for Bioethics and Social Sciences in Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
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Nkoy FL, Stone BL, Zhang Y, Luo G. A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection. JMIR Med Inform 2024; 12:e56572. [PMID: 38630536 PMCID: PMC11063904 DOI: 10.2196/56572] [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: 01/24/2024] [Revised: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/19/2024] Open
Abstract
Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.
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Affiliation(s)
- Flory L Nkoy
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Bryan L Stone
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Yue Zhang
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
- Division of Biostatistics, Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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Muselli M, Bocale R, Necozione S, Desideri G. Is the response to antihypertensive drugs heterogeneous? Rationale for personalized approach. Eur Heart J Suppl 2024; 26:i60-i63. [PMID: 38867857 PMCID: PMC11167967 DOI: 10.1093/eurheartjsupp/suae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Arterial hypertension represents the most important cardiovascular risk factor with a direct responsibility for a large share of cardiovascular mortality and morbidity in the world. Despite the wide availability of antihypertensive therapies with documented effectiveness, blood pressure control still remains largely unsatisfactory in large segments of the population. Guidelines for the management of arterial hypertension suggest the preferential use of five classes of drugs-angiotensin-converting enzyme inhibitors, angiotensin II type I receptor inhibitors, calcium channel blockers, thiazide/thiazide-like diuretics, and beta-blockers-recommending the use of combination therapy, preferably in pre-established combinations, for the majority of hypertensive patients. The evidence of a non-negligible heterogeneity in the response to different antihypertensive drugs in different patients suggests the opportunity for personalization of treatment. The notable phenotypic heterogeneity of the population of hypertensive patients in terms of genetic structure, behavioural aspects, exposure to environmental factors, and disease history imposes the need to consider all the potential determinants of the response to a specific pharmacological treatment. The progressive digitalization of healthcare systems is making enormous quantities of data available for machine learning systems which will allow the development of management algorithms for truly personalized antihypertensive therapy in the near future.
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Affiliation(s)
- Mario Muselli
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L’Aquila
| | - Raffaella Bocale
- Unit of Endocrinology, Agostino Gemelli University Hospital Foundation Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Catholic University of the Sacred Heart, Rome
| | - Stefano Necozione
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L’Aquila
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Allen MR, Webb S, Mandvi A, Frieden M, Tai-Seale M, Kallenberg G. Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC PRIMARY CARE 2024; 25:42. [PMID: 38281026 PMCID: PMC10821550 DOI: 10.1186/s12875-024-02282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms. METHODS From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents. RESULTS Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship). CONCLUSION Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.
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Affiliation(s)
- Matthew R Allen
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Sophie Webb
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ammar Mandvi
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Marshall Frieden
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ming Tai-Seale
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Gene Kallenberg
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
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