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Yang W, Lu J, Si SC, Wang WH, Li J, Ma YX, Zhao H, Liu J. Digital health technologies/interventions in smart ward development for elderly patients with diabetes: A perspective from China and beyond. World J Diabetes 2025; 16:103002. [DOI: 10.4239/wjd.v16.i4.103002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 01/22/2025] [Accepted: 02/17/2025] [Indexed: 02/28/2025] Open
Abstract
Diabetes is highly prevalent among the elderly worldwide, with the highest number of diabetes cases in China. Yet, the management of diabetes remains unsatisfactory. Recent advances in digital health technologies have facilitated the establishment of smart wards for diabetes patients. There is a lack of smart wards tailored specifically for older diabetes patients who encounter unique challenges in glycemic control and diabetes management, including an increased vulnerability to hypoglycemia, the presence of multiple chronic diseases, and cognitive decline. In this review, studies on digital health technologies for diabetes in China and beyond were summarized to elucidate how the adoption of digital health technologies, such as real-time continuous glucose monitoring, sensor-augmented pump technology, and their integration with 5th generation networks, big data cloud storage, and hospital information systems, can address issues specifically related to elderly diabetes patients in hospital wards. Furthermore, the challenges and future directions for establishing and implementing smart wards for elderly diabetes patients are discussed, and these challenges may also be applicable to other countries worldwide, not just in China. Taken together, the smart wards may enhance clinical outcomes, address specific issues, and eventually improve patient-centered hospital care for elderly patients with diabetes.
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Affiliation(s)
- Wei Yang
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Juan Lu
- Department of General Practice, The Longzeyuan Community Health Service Center, Beijing 102208, China
| | - Si-Cong Si
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Wei-Hua Wang
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Jing Li
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yi-Xin Ma
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Huan Zhao
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Jia Liu
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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Giorgino F, Bonfanti R, Castaldo F, Irace C, Laurenzi A, Maffeis C, Pappagallo G, Pitocco D, Rabbone I, Zarra E, Scaramuzza AE. The Utility of Smart Multiple Daily Injection Systems in Intensive Insulin-Treated People With Diabetes: An Italian Expert Consensus. J Diabetes Sci Technol 2025:19322968251316577. [PMID: 39927665 PMCID: PMC11811948 DOI: 10.1177/19322968251316577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2025]
Abstract
BACKGROUND Smart systems for multiple daily injections (Smart MDI) integrate continuous glucose monitoring, connected insulin pens, smartphone apps, and cloud-based data storage to provide bolus and corrective dose suggestions, reminders/alerts, automatic tracking and sharing of insulin therapy, and glycemic data to users, caregivers, and providers. This is an expert consensus on the clinical value of Smart MDI and critical points for implementation in adults and children/adolescents with diabetes. METHODS A nominal group technique combined with the estimate-talk-estimate approach was employed to achieve consensus among panel members from the Italian Intersociety Technology and Diabetes Study Group with expertise in pediatric and adult diabetes care. RESULTS The expert consensus indicated that glycemic profiles can be improved by using bolus dose suggestions based on glucose values, planned meals, the insulin-to-carbohydrate ratio, correction factors, and consideration of insulin-on-board. Automatic remote sharing of patient data on glycemia and insulin therapy allows clinicians to make more appropriate and timely therapeutic recommendations based on objective data. Dose tracking, bolus reminders/alerts, and reduced hypoglycemia and associated anxiety achieved through Smart MDI may improve adherence. CONCLUSIONS Smart MDI can reduce treatment burden while improving the daily experiences and glycemic outcomes for adults and children/adolescents with type 1 or type 2 diabetes. However, high-quality clinical data are lacking, and more evidence is needed to compare the effects of Smart MDI and other advanced insulin delivery systems on glycemic and patient-reported outcomes.
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Affiliation(s)
- Francesco Giorgino
- Section of Internal Medicine, Endocrinology, Andrology and Metabolic Diseases, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, Bari, Italy
| | - Riccardo Bonfanti
- Pediatric Diabetes Unit, Department of Pediatrics, Diabetes Research Institute, IRCCS San Raffaele Scientific Institute, Vita Salute San Raffaele University, Milan, Italy
| | - Filomena Castaldo
- Division of Endocrinology and Metabolic Diseases, University of Campania “Luigi Vanvitelli,” Naples, Italy
| | - Concetta Irace
- Department of Health Science, University Magna Græcia, Catanzaro, Italy
| | - Andrea Laurenzi
- IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Claudio Maffeis
- Section of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, Regional Center for Pediatric Diabetes, University of Verona, Veneto, Italy
| | - Giovanni Pappagallo
- School of Clinical Methodology, IRCCS “Sacred Heart—Don Calabria,” Veneto, Italy
| | - Dario Pitocco
- Diabetes Care Unit, UOSD Diabetologia, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Ivana Rabbone
- Division of Pediatrics, Department of Health Sciences, University of Piemonte Orientale, Novara, Italy
| | - Emanuela Zarra
- S.C. Medicina Diabetologia, Dipartimento di Continuità di Cura e Fragilità, ASST Spedali Civili, Brescia, Italy
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Merino-Torres JF, Ilham S, Alshannaq H, Pollock RF, Ahmed W, Norman GJ. Cost-Utility of Real-Time Continuous Glucose Monitoring versus Self-Monitoring of Blood Glucose in People with Insulin-Treated Type 2 Diabetes in Spain. CLINICOECONOMICS AND OUTCOMES RESEARCH 2024; 16:785-797. [PMID: 39525696 PMCID: PMC11549913 DOI: 10.2147/ceor.s483459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 10/18/2024] [Indexed: 11/16/2024] Open
Abstract
Objective Management of advanced type 2 diabetes (T2D) typically involves daily insulin therapy alongside frequent blood glucose monitoring, as treatments such as oral antidiabetic agents are therapeutically insufficient. Real-time continuous glucose monitoring (rt-CGM) has been shown to facilitate greater reductions in glycated hemoglobin (HbA1c) levels and improvements in patient satisfaction relative to self-monitoring of blood glucose (SMBG). This study aimed to investigate the cost-utility of rt-CGM versus SMBG in Spanish patients with insulin-treated T2D.. Methods The analysis was conducted using the IQVIA Core Diabetes Model (CDM V9.5). Baseline characteristics of the simulated patient cohort and treatment efficacy data were sourced from a large-scale, United States-based retrospective cohort study. Costs were obtained from Spanish sources and inflated to 2022 Euros (EUR) where required. A remaining lifetime horizon (maximum 50 years) was used, alongside an annual discount rate of 3% for future costs and health effects. A willingness-to-pay (WTP) threshold of EUR 30,000 per quality-adjusted life year (QALY) was adopted, based on precedent across previous cost-effectiveness studies set in Spain. A Spanish payer perspective was adopted. Results Over patient lifetimes, rt-CGM yielded 9.933 QALYs, versus 8.997 QALYs with SMBG, corresponding to a 0.937 QALY gain with rt-CGM. Total costs in the rt-CGM arm were EUR 2347 higher with rt-CGM versus SMBG (EUR 125,365 versus EUR 123,017). The base case incremental cost-utility ratio was therefore EUR 2506 per QALY gained, substantially lower than the WTP threshold of EUR 30,000 per QALY. The analysis also projected a reduction in cumulative incidence of ophthalmic, renal, neurological, and cardiovascular events in rt-CGM users, with reductions of 16.03%, 13.07%, 7.34%, and 9.09%, respectively. Conclusion Compared to SMBG, rt-CGM is highly likely to be a cost-effective intervention for patients living with insulin-treated T2D in Spain.
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Affiliation(s)
- Juan Francisco Merino-Torres
- Endocrinology and Nutrition Department, Health Research Institute La Fe, University Hospital La Fe. Department of Medicine, University of Valencia, Valencia, Spain
| | | | - Hamza Alshannaq
- Dexcom, San Diego, CA, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Di Molfetta S, Rossi A, Boscari F, Irace C, Laviola L, Bruttomesso D. Criteria for Personalised Choice of a Continuous Glucose Monitoring System: An Expert Opinion. Diabetes Ther 2024; 15:2263-2278. [PMID: 39347900 PMCID: PMC11467157 DOI: 10.1007/s13300-024-01654-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Accepted: 09/09/2024] [Indexed: 10/01/2024] Open
Abstract
Despite the growing evidence supporting the outpatient use of continuous glucose monitoring (CGM) for improving glycaemic control and reducing hypoglycaemia, there is a need for a detailed understanding of the specific features of CGM devices that best meet individual patient needs. This expert opinion, based on a comprehensive literature review and the personal perspectives of clinicians, aims to provide the healthcare professionals (HCPs) with a comprehensive framework for selecting CGM devices. It evaluates the current state of CGM technology, categorizing features into essential features, major drivers of choice, and additional/useful features. Moreover, the practical model presented outlines a patient's journey with CGM, emphasising the importance of aligning device features with patient needs. This includes understanding the patient's lifestyle, clinical conditions, and personal preferences to optimize CGM use and improve diabetes management outcomes.
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Affiliation(s)
- Sergio Di Molfetta
- Department of Precision and Regenerative Medicine and Ionian Area, Section of Internal Medicine, Endocrinology, Andrology and Metabolic Diseases, University of Bari Aldo Moro, 70124, Bari, Italy
| | - Antonio Rossi
- IRCCS Ospedale Galeazzi-Sant'Ambrogio, Department of Biomedical and Clinical Sciences, University of Milan, 20157, Milan, Italy
| | - Federico Boscari
- Unit of Metabolic Diseases, University Hospital of Padua, 35128, Padua, Italy
| | - Concetta Irace
- Department of Health Science, University Magna Græcia Catanzaro, Viale Europa Località Germaneto, 88100, Catanzaro, Italy.
| | - Luigi Laviola
- Department of Precision and Regenerative Medicine and Ionian Area, Section of Internal Medicine, Endocrinology, Andrology and Metabolic Diseases, University of Bari Aldo Moro, 70124, Bari, Italy
| | - Daniela Bruttomesso
- Unit of Metabolic Diseases, University Hospital of Padua, 35128, Padua, Italy
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Manov A, Haddadin R, Chauhan S, Benge E. Retrospective, Longitudinal, One-Group Study on the Implementation of Continuous Glucose Monitoring To Improve Quality of Care for Patients With Type I or II Diabetes Mellitus in an Internal Medicine Residency Continuity Community Clinic. Cureus 2024; 16:e64594. [PMID: 39149659 PMCID: PMC11325259 DOI: 10.7759/cureus.64594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2024] [Indexed: 08/17/2024] Open
Abstract
In this three-year retrospective study, data from 51 patients with type 1 or type 2 diabetes mellitus (DM), receiving a minimum of 3-4 insulin injections per day and self-monitoring their blood glucose (SMBG) four times a day, were derived from our internal medicine residency primary care clinic. The patients were equipped with a continuous glucose monitoring (CGM) device that shared 24-hour glucose data with the clinic. They were assigned to members of our CGM team, which included internal medicine or transitional year medical residents who functioned under the supervision of a board-certified endocrinologist. The residents, in consultation with our endocrinologist, assessed the patients' glucose management data and adjusted their treatment regimens biweekly by calling the patients, and monthly by seeing the patients in the clinic. Significant results from the study include a reduction in HbA1c from 9.9% to 7.6%, an average blood glucose decrement from 242 mg/dL to 169 mg/dL, a reduction in the incidence of mild hypoglycemia from below 70 mg/dL to 54 mg/dL, from 4.68% to 0.76% per day, and a more pronounced hypoglycemia with glucose less than 54 mg/dL from 3.1% per day to 0.2% per day. We observed a significant increase in the time in the range of the blood glucose from 33% to 67% per day. Furthermore, 9.5% of the patients in this study eventually discontinued their daily insulin injections and continued treatment with oral diabetic medications with or without the use of injectable GLP-1 receptors once a week. Our study affirms that CGM devices significantly improve glycemic control compared to SMBG, supporting its efficacy in optimizing glycemic control in real-world clinical practice. The results imply that this can be accomplished in internal medicine residency clinics and not exclusively in specialized endocrine clinics. As far as we know, this is the first study of its kind in a residency clinic in the USA. This study confirms the benefits of widening the application of CGM in DM, along with the challenges that must be overcome to realize the evidence-based benefits of this technology. CGM needs to become a part of routine monitoring for type 1 and type 2 DM.
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Affiliation(s)
- Andrey Manov
- Internal Medicine, MountainView Hospital, Las Vegas, USA
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Papazafiropoulou AK. Diabetes management in the era of artificial intelligence. Arch Med Sci Atheroscler Dis 2024; 9:e122-e128. [PMID: 39086621 PMCID: PMC11289240 DOI: 10.5114/amsad/183420] [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] [Received: 01/21/2024] [Accepted: 01/29/2024] [Indexed: 08/02/2024] Open
Abstract
Artificial intelligence is growing quickly, and its application in the global diabetes pandemic has the potential to completely change the way this chronic illness is identified and treated. Machine learning methods have been used to construct algorithms supporting predictive models for the risk of getting diabetes or its complications. Social media and Internet forums also increase patient participation in diabetes care. Diabetes resource usage optimisation has benefited from technological improvements. As a lifestyle therapy intervention, digital therapies have made a name for themselves in the treatment of diabetes. Artificial intelligence will cause a paradigm shift in diabetes care, moving away from current methods and toward the creation of focused, data-driven precision treatment.
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Marigliano M, Piona C, Mancioppi V, Morotti E, Morandi A, Maffeis C. Glucose sensor with predictive alarm for hypoglycaemia: Improved glycaemic control in adolescents with type 1 diabetes. Diabetes Obes Metab 2024; 26:1314-1320. [PMID: 38177091 DOI: 10.1111/dom.15432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 01/06/2024]
Abstract
AIM Hypoglycaemic events are linked to microvascular and macrovascular complications in people with type 1 diabetes. We aimed to evaluate the efficacy of glucose sensor [real-time continuous glucose monitoring (RT-CGM)] with predictive alarm (PA) in reducing the time spent below the range (%TBR <70 mg/dl) in a group of adolescents with type 1 diabetes (AwD). MATERIALS AND METHODS This was a crossover, monocentric and randomized study. RT-CGM was set with Alarm on Threshold (AoT) at 70 mg/dl) or PA for hypoglycaemia (20 m before threshold). Twenty AwD were enrolled and randomized to either a PA/AoT or AoT/PA treatment sequence, in a 1:1 ratio. The two groups (PA vs. AoT) were compared using two-way repeated measures ANOVA taking account of the carryover effect. RESULTS AwD using PA for hypoglycaemia spent less time in severe hypoglycaemia (%TBR2 <54 mg/dl; 0.32 ± 0.31 vs. 0.91 ± 0.90; p < .02) and hypoglycaemia (%TBR <70 mg/dl; 1.68 ± 1.06 vs. 2.90 ± 2.05; p < .02), with better glycaemia risk index (51.3 ± 11.0 vs. 61.5 ± 12.6; p ≤ .01). CONCLUSION The use of RT-CGM with PA for hypoglycaemia technology in AwD using multiple daily insulin injection treatment could significantly reduce the risk of having hypoglycaemic events resulting in an improved quality of glucose control. CLINICAL TRIAL REGISTRATION NUMBER NCT05574023.
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Affiliation(s)
- Marco Marigliano
- Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy
| | - Claudia Piona
- Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy
| | - Valentina Mancioppi
- Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy
| | - Elisa Morotti
- Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy
| | - Anita Morandi
- Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy
| | - Claudio Maffeis
- Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy
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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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Winkelman J, Nguyen D, vanSonnenberg E, Kirk A, Lieberman S. Artificial Intelligence (AI) in pediatric endocrinology. J Pediatr Endocrinol Metab 2023; 36:903-908. [PMID: 37589444 DOI: 10.1515/jpem-2023-0287] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
Artificial Intelligence (AI) is integrating itself throughout the medical community. AI's ability to analyze complex patterns and interpret large amounts of data will have considerable impact on all areas of medicine, including pediatric endocrinology. In this paper, we review and update the current studies of AI in pediatric endocrinology. Specific topics that are addressed include: diabetes management, bone growth, metabolism, obesity, and puberty. Becoming knowledgeable and comfortable with AI will assist pediatric endocrinologists, the goal of the paper.
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Affiliation(s)
| | - Diep Nguyen
- University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Eric vanSonnenberg
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- From the Departments of Radiology, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Alison Kirk
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Pediatrics, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Steven Lieberman
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Internal Medicine (Division of Endocrinology), University of Arizona College of Medicine Phoenix, Phoenix, USA
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Robinson R, Liday C, Lee S, Williams IC, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care-Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023; 2:e46487. [PMID: 38333424 PMCID: PMC10851077 DOI: 10.2196/46487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/10/2023] [Accepted: 05/14/2023] [Indexed: 02/10/2024]
Abstract
Background Artificial intelligence (AI) is as a branch of computer science that uses advanced computational methods such as machine learning (ML), to calculate and/or predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving healthcare, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in healthcare management. Objectives To support and ensure safe use of AI/ML technologies in healthcare, the team worked to better understand: 1) patient information and training needs, 2) the factors that influence patients' perceived value and trust in AI/ML healthcare applications; and 3) on how best to support safe and appropriate use of AI/ML enabled devices and applications among people living with diabetes. Methods To understand general patient perspectives and information needs related to the use of AI/ML in healthcare, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=40) and interviews with providers (n=6) in Alaska, Idaho, and Virginia. Grounded Theory guided data gathering, synthesis, and analysis. Thematic content and constant comparison analysis were used to identify relevant themes and sub-themes. Inductive approaches were used to link data to key concepts including preferred patient-provider-interactions, patient perceptions of trust, accuracy, value, assurances, and information transparency. Results Key summary themes and recommendations focused on: 1) patient preferences for AI/ML enabled device and/or application information; 2) patient and provider AI/ML-related device and/or application training needs; 3) factors contributing to patient and provider trust in AI/ML enabled devices and/or application; and 4) AI/ML-related device and/or application functionality and safety considerations. A number of participant (patients and providers) recommendations to improve device functionality to guide information and labeling mandates (e.g., links to online video resources, and access to 24/7 live in-person or virtual emergency support). Other patient recommendations include: 1) access to practice devices; 2) connection to local supports and reputable community resources; 3) simplified display and alert limits. Conclusion Recommendations from both patients and providers could be used by Federal Oversight Agencies to improve utilization of AI/ML monitoring of technology use in diabetes, improving device safety and efficacy.
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Affiliation(s)
- Renee Robinson
- College of Pharmacy, Idaho State University, Anchorage, AK, US
| | - Cara Liday
- College of Pharmacy, Idaho State University, Pocatello, ID, US
| | - Sarah Lee
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Ishan C Williams
- School of Nursing, University of Virginia, Charlottesville, VA, US
| | - Melanie Wright
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Sungjoon An
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Elaine Nguyen
- College of Pharmacy, Idaho State University, Meridian, ID, US
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Acciaroli G, Parkin CG, Thomas R, Layne J, Norman GJ, Leone K. G6 continuous glucose monitoring system feature use and its associations with glycaemia in Europe. Diabet Med 2023; 40:e15093. [PMID: 36951684 DOI: 10.1111/dme.15093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 03/24/2023]
Abstract
AIMS Current continuous glucose monitoring (CGM) devices provide features that alert individuals with diabetes about their current and impending adverse glycaemic events. The use of these features has been associated with glycaemic improvements. However, how these features are utilised under real-world conditions has not been well studied. We queried a large database to quantify utilisation of the Dexcom G6 system features and how utilisation impacted glycaemic outcomes within a cohort of European users. METHODS This 6-month retrospective, observational, large database analysis utilised anonymised data from a sample of 47,784 Europe-based G6 users. Primary outcome measures were associations between utilisation and customisation of High/Low threshold alerts, 'urgent low soon' (ULS) alert, and established CGM metrics. RESULTS Users in the Germany, Austria, Switzerland region (n = 20,257), the Nordic countries (n = 10,314), United Kingdom (n = 9006), Italy (n = 4747), France (n = 2130) and Spain (1330) were included. All alert features were utilised by >75% of the cohort across all regions/countries and age groups. Enabling the Low alert and ULS alert was associated with lower percentage of time below range compared to disabling the Low alert (p < 0.001). Enabling the High alert was associated with higher percentage of time in range (%TIR) and lower percentage of time above range (%TAR) %TAR compared to disabling the High alert (p < 0.001). Paediatric patients and older adults tended to set a higher threshold for High/Low alerts, while younger adults tended to use lower threshold values for High/Low alerts. CONCLUSIONS Individuals who utilised the Dexcom G6 features showed better glycaemic control, particularly among those who utilised more sensitive High alert and Low alert settings, than users who did not utilise the system features.
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Affiliation(s)
- Giada Acciaroli
- Dexcom Inc., 6310 Sequence Drive, San Diego, California, 92121, USA
| | - Christopher G Parkin
- CGParkin Communications, 2675 Windmill Pkwy, Ste.2721, Henderson, Nevada, 89074, USA
| | - Roy Thomas
- Dexcom Inc., 6310 Sequence Drive, San Diego, California, 92121, USA
| | - Jennifer Layne
- Dexcom Inc., 6310 Sequence Drive, San Diego, California, 92121, USA
| | - Gregory J Norman
- Dexcom Inc., 6310 Sequence Drive, San Diego, California, 92121, USA
| | - Keri Leone
- Dexcom Inc., 6310 Sequence Drive, San Diego, California, 92121, USA
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Acciaroli G, van der Linden J, Chao C, Walker TC, Oliver N. Longitudinal analysis of users transitioning from the Dexcom G5 to the G6 RT-CGM system in Germany, Sweden and the United Kingdom (2018-2020). Diabet Med 2023; 40:e14946. [PMID: 36053809 PMCID: PMC10087512 DOI: 10.1111/dme.14946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 08/16/2022] [Accepted: 08/25/2022] [Indexed: 01/17/2023]
Abstract
AIMS Regional variations in the adoption of diabetes technology may be reflected in population-level metrics of glycaemic control. In this observational study, we aimed to assess the glycaemic impacts of transitioning from the Dexcom G5 Real-Time Continuous Glucose Monitoring (RT-CGM) System to the Dexcom G6 in three European countries. METHODS Anonymised RT-CGM data (uploaded to the Dexcom Clarity app) were from users in Germany, Sweden, and the United Kingdom (UK) who transitioned from G5 to G6 between 9-12 months after G6 launched in 2018. Primary endpoints were percent time in hypoglycaemia, percent time in range (TIR), user retention rates, device utilisation, and urgent low soon (ULS) alert utilisation. Metrics were computed for 3-month intervals in the 2-year study window. RESULTS In all three countries, the transition from G5 to G6 was associated with a clear decrease in hypoglycaemia. In months 0-3 after transitioning, the median percent time 〈3 mmol/L (54 mg/dL) and 〈3.9 mmol/L (70 mg/dL) decreased by [0.12-0.28] and [0.40-0.43] percentage points, respectively, with another [0.11-0.21] and [0.34-0.65] percentage point decrease in months 3-6 in the three countries analysed. TIR and CGM utilisation were sustained or improved slightly across all countries. At the end of the study window, the retention rate was [88.8-94.8%] and ULS utilization was [83.9-86.9%] in the three countries analysed. CONCLUSIONS Similar RT-CGM trends were observed across Germany, Sweden, and the UK. Improvements in hypoglycaemia occurred in all countries. The high retention of users may lead to sustained glycaemic benefits associated with RT-CGM use.
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Affiliation(s)
| | | | | | | | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
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Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatol Ther (Heidelb) 2022; 12:2637-2651. [PMID: 36306100 PMCID: PMC9674813 DOI: 10.1007/s13555-022-00833-8] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/07/2022] [Indexed: 01/07/2023] Open
Abstract
Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.
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Affiliation(s)
- Konstantinos Liopyris
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
- Dermatology Department, Memorial Sloan Kettering Cancer Center, New York, NY, 10021, USA
| | - Stamatios Gregoriou
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece.
| | - Julia Dias
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
| | - Alexandros J Stratigos
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
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14
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Yeoh E, Png D, Khoo J, Chee YJ, Sharda P, Low S, Lim SC, Subramaniam T. A head-to-head comparison between Guardian Connect and FreeStyle Libre systems and an evaluation of user acceptability of sensors in patients with type 1 diabetes. Diabetes Metab Res Rev 2022; 38:e3560. [PMID: 35728796 DOI: 10.1002/dmrr.3560] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/03/2022] [Accepted: 05/03/2022] [Indexed: 11/11/2022]
Abstract
AIMS A user-calibrated real-time continuous glucose monitoring (rt-CGM) system is compared to a factory-calibrated flash glucose monitoring (FGM) system and assessed in terms of accuracy and acceptability in patients with type 1 diabetes (T1D). METHODS Ten participants with T1D were enroled from a specialist diabetes centre in Singapore and provided with the Guardian Connect with Enlite Sensor (Medtronic, Northridge, CA, USA) and first-generation Freestyle Libre System (Abbott Diabetes Care, Witney, UK), worn simultaneously. Participants had to check capillary blood glucose four times per day. At the end of week 1 and week 2, participants returned for data download and were given a user evaluation survey. RESULTS Accuracy evaluation between Guardian Connect and Freestyle Libre includes the overall mean absolute relative difference value (9.7 ± 11.0% vs. 17.5 ± 10.9%), Clarke Error Grid zones A + B (98.6% vs. 98.1%), sensitivity (78.9% vs. 63.4%), and specificity (93.4% vs. 81.0%). Notably, time below range (<3.9 mmol/L) was 10.5% for FGM versus 2% for rt-CGM. From the evaluation survey, 90% of participants perceived rt-CGM to be accurate versus 40% for FGM, although the majority found both devices to be easy to use, educational, and useful in improving glycaemic control. However, due to the cost of sensors, only 30% were keen to use either device for continuous monitoring. CONCLUSIONS Although rt-CGM was superior to FGM in terms of accuracy, the value of glucose trends in both devices is still useful in diabetes self-management. Patients and clinicians may consider either technology depending on their requirements.
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Affiliation(s)
- Ester Yeoh
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
- Department of Medicine, Division of Endocrinology, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Doanna Png
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
| | - Jonathon Khoo
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Ying Jie Chee
- Department of Medicine, Division of Endocrinology, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Puja Sharda
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
| | - Serena Low
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Su Chi Lim
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Tavintharan Subramaniam
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore
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15
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Melvin RL, Broyles MG, Duggan EW, John S, Smith AD, Berkowitz DE. Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices. Front Digit Health 2022; 4:872675. [PMID: 35547090 PMCID: PMC9081677 DOI: 10.3389/fdgth.2022.872675] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/29/2022] [Indexed: 11/28/2022] Open
Abstract
As implementation of artificial intelligence grows more prevalent in perioperative medicine, a clinician's ability to distinguish differentiating aspects of these algorithms is critical. There are currently numerous marketing and technical terms to describe these algorithms with little standardization. Additionally, the need to communicate with algorithm developers is paramount to actualize effective and practical implementation. Of particular interest in these discussions is the extent to which the output or predictions of algorithms and tools are understandable by medical practitioners. This work proposes a simple nomenclature that is intelligible to both clinicians and developers for quickly describing the interpretability of model results. There are three high-level categories: transparent, translucent, and opaque. To demonstrate the applicability and utility of this terminology, these terms were applied to the artificial intelligence and machine-learning-based products that have gained Food and Drug Administration approval. During this review and categorization process, 22 algorithms were found with perioperative utility (in a database of 70 total algorithms), and 12 of these had publicly available citations. The primary aim of this work is to establish a common nomenclature that will expedite and simplify descriptions of algorithm requirements from clinicians to developers and explanations of appropriate model use and limitations from developers to clinicians.
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Affiliation(s)
- Ryan L. Melvin
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Ryan L. Melvin
| | - Matthew G. Broyles
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Elizabeth W. Duggan
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sonia John
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Andrew D. Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dan E. Berkowitz
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
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16
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Pinheiro SL, Bastos M, Barros L, Melo M, Paiva I. Flash glucose monitoring and glycemic control in type 1 diabetes with subcutaneous insulin infusion. Acta Diabetol 2022; 59:509-515. [PMID: 34786633 DOI: 10.1007/s00592-021-01827-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/30/2021] [Indexed: 10/19/2022]
Abstract
AIMS To analyze the association between scan frequency and glycemic measures in continuous subcutaneous insulin infusion (CSII) treated type 1 diabetes (T1DM) adults. METHODS This retrospective study included 140 patients (> 18 years) with T1DM who used flash glucose monitoring (FGM). For each patient, we analyzed the Ambulatory Glucose Profile data over a period of 90 days. Data regarding glucose management indicator (GMI), time above, below and within range (TIR) and coefficient of variation (CV) were correlated with the number of daily scans. The effect of each additional test on glucose parameters was also evaluated. RESULTS Users performed a mean of 8.6 ± 4.4 scans per day. There was an inverse correlation between scanning frequency and GMI (r = - 0.431, p < 0.001), CV (r = - 0.440, p < 0.001), time above and below range (r = - 0.446, p < 0.001 and r = - 0.200, p = 0.018, respectively). The number of daily scans correlated positively with TIR (r = 0.554, p < 0.001). For each additional scan per day, the mean GMI decreased 0.09% and TIR increased 1.60%. CONCLUSIONS In patients with T1DM and CSII, higher rates of scanning correlated with improved glycemic markers, including reduced GMI and CV and increased TIR. For each test performed, there was a significant effect on the improvement of all glucose parameters.
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Affiliation(s)
- Sara Lomelino Pinheiro
- Department of Endocrinology, Instituto Português de Oncologia de Lisboa, Lisbon, Portugal.
| | - Margarida Bastos
- Department of Endocrinology, Diabetes and Metabolism, Centro Hospitalar Universitário de Coimbra, Coimbra, Portugal
| | - Luísa Barros
- Department of Endocrinology, Diabetes and Metabolism, Centro Hospitalar Universitário de Coimbra, Coimbra, Portugal
| | - Miguel Melo
- Department of Endocrinology, Diabetes and Metabolism, Centro Hospitalar Universitário de Coimbra, Coimbra, Portugal
| | - Isabel Paiva
- Department of Endocrinology, Diabetes and Metabolism, Centro Hospitalar Universitário de Coimbra, Coimbra, Portugal
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Marks BE, Williams KM, Sherwood JS, Putman MS. Practical aspects of diabetes technology use: Continuous glucose monitors, insulin pumps, and automated insulin delivery systems. J Clin Transl Endocrinol 2022; 27:100282. [PMID: 34917483 PMCID: PMC8666668 DOI: 10.1016/j.jcte.2021.100282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/01/2021] [Accepted: 11/27/2021] [Indexed: 02/06/2023] Open
Abstract
There have been tremendous advances in diabetes technology in the last decade. Continuous glucose monitors (CGM), insulin pumps, and automated insulin delivery (AID) systems aim to improve glycemic control while simultaneously decreasing the burden of diabetes management. Although diabetes technologies have been shown to decrease both hypoglycemia and hyperglycemia and to improve health-related quality of life in individuals with type 1 diabetes, the impact of these devices in individuals with cystic fibrosis-related diabetes (CFRD) is less clear. There are unique aspects of CFRD, including the different underlying pathophysiology and unique lived health care experience and comorbidities, that likely affect the use, efficacy, and uptake of diabetes technology in this population. Small studies suggest that CGM is accurate and may be helpful in guiding insulin therapy for individuals with CFRD. Insulin pump use has been linked to improvements in lean body mass and hemoglobin A1c among adults with CFRD. A recent pilot study highlighted the promise of AID systems in this population. This article provides an overview of practical aspects of diabetes technology use and device limitations that clinicians must be aware of in caring for individuals with CF and CFRD. Cost and limited insurance coverage remain significant barriers to wider implementation of diabetes technology use among patients with CFRD. Future studies exploring strategies to improve patient and CF provider education about these devices and studies showing the effectiveness of these technologies on health and patient-reported outcomes may lead to improved insurance coverage and increased rates of uptake and sustained use of these technologies in the CFRD community.
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Affiliation(s)
- Brynn E. Marks
- Division of Endocrinology and Diabetes, Children’s National Hospital, 111 Michigan Ave, NW, Washington, DC 20010, USA
| | - Kristen M. Williams
- Division of Pediatric Endocrinology, Diabetes, and Metabolism, Columbia University Irving Medical Center, 1150 St Nicholas Avenue, New York, NY 10032, United States
| | - Jordan S. Sherwood
- Diabetes Research Center, Division of Pediatric Endocrinology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, United States
| | - Melissa S. Putman
- Division of Endocrinology, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, United States
- Diabetes Research Center, Division of Endocrinology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, United States
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18
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Artificial intelligence perspective in the future of endocrine diseases. J Diabetes Metab Disord 2022; 21:971-978. [PMID: 35673469 PMCID: PMC9167325 DOI: 10.1007/s40200-021-00949-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 11/30/2021] [Indexed: 01/13/2023]
Abstract
In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.
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19
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Masood S, Ghafoor E, Belkhadir J, Sultan M, Sandid M, Baqai S, Shegem N. Availability and accessibility of diabetes-related technologies in IDF-MENA Region. JOURNAL OF DIABETOLOGY 2022. [DOI: 10.4103/jod.jod_117_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
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20
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Diouri O, Cigler M, Vettoretti M, Mader JK, Choudhary P, Renard E. Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments. Diabetes Metab Res Rev 2021; 37:e3449. [PMID: 33763974 PMCID: PMC8519027 DOI: 10.1002/dmrr.3449] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 12/08/2020] [Accepted: 01/28/2021] [Indexed: 02/06/2023]
Abstract
The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.
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Affiliation(s)
- Omar Diouri
- Department of Endocrinology, Diabetes, NutritionMontpellier University HospitalMontpellierFrance
- Department of PhysiologyInstitute of Functional Genomics, CNRS, INSERMUniversity of MontpellierMontpellierFrance
| | - Monika Cigler
- Division of Endocrinology and DiabetologyDepartment of Internal MedicineMedical University of GrazGrazAustria
| | | | - Julia K. Mader
- Division of Endocrinology and DiabetologyDepartment of Internal MedicineMedical University of GrazGrazAustria
| | - Pratik Choudhary
- Department of Diabetes and Nutritional SciencesKing's College LondonLondonUK
- Diabetes Research CentreUniversity of LeicesterLeicesterUK
| | - Eric Renard
- Department of Endocrinology, Diabetes, NutritionMontpellier University HospitalMontpellierFrance
- Department of PhysiologyInstitute of Functional Genomics, CNRS, INSERMUniversity of MontpellierMontpellierFrance
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21
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Gavin JR, Bailey CJ. Real-World Studies Support Use of Continuous Glucose Monitoring in Type 1 and Type 2 Diabetes Independently of Treatment Regimen. Diabetes Technol Ther 2021; 23:S19-S27. [PMID: 34165343 DOI: 10.1089/dia.2021.0211] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Numerous randomized controlled trials (RCTs) have demonstrated the glycemic benefits of continuous glucose monitoring (CGM) in management of type 1 diabetes (T1D) and type 2 diabetes. Although RCTs remain the gold standard clinical study design, findings from these trials do not necessarily reflect the effectiveness of CGM or reveal the feasibility and wider applications for use in broader real-life settings. This review evaluates recent real-world evidence (RWE) demonstrating the value of CGM to improve clinical outcomes, such as avoidance of severe hypoglycemic and hyperglycemic crises, and improved measures of psychological health and quality of life. Additionally, this review considers recent RWE for the role of CGM to enhance health care resource utilization, including prediction of T1D and applications in gestational diabetes, chronic kidney disease, and monitoring during surgery.
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Affiliation(s)
- James R Gavin
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Clifford J Bailey
- Life and Health Sciences, Aston University, Birmingham, United Kingdom
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22
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Hirsch IB, Miller E. Integrating Continuous Glucose Monitoring Into Clinical Practices and Patients' Lives. Diabetes Technol Ther 2021; 23:S72-S80. [PMID: 34546085 DOI: 10.1089/dia.2021.0233] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Numerous studies have demonstrated the clinical benefits of continuous glucose monitoring (CGM) in individuals with diabetes. Within ongoing innovations in CGM technology, individuals now have an expanding array of options that allow them to select the device that meets their individual needs and preferences. Although demand for CGM in primary care continues to grow, many clinicians are reluctant to prescribe this technology due to their unfamiliarity with the various devices, uncertainty about which devices are best suited to each patient and the feasibility of using CGM. This article reviews the features and functionality of the most recent commercially available CGM devices and provides guidance for integrating CGM use into clinical practices.
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Affiliation(s)
- Irl B Hirsch
- Division of Metabolism, Endocrinology and Nutrition, University of Washington School of Medicine, Seattle, Washington, USA
| | - Eden Miller
- Diabetes and Obesity Care, Bend, Oregon, USA
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van der Linden J, Welsh JB, Walker TC. Sustainable Use of a Real-Time Continuous Glucose Monitoring System from 2018 to 2020. Diabetes Technol Ther 2021; 23:508-511. [PMID: 33567233 DOI: 10.1089/dia.2021.0014] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We aimed to describe patterns of continuous glucose monitoring (CGM) system use and glycemic outcomes from 2018 to 2020 in a large real-world cohort by analyzing anonymized data from US-based CGM users who transitioned from the G5 to the G6 System (Dexcom) in 2018. The main end points were persistent use, within-day and between-day utilization, hypoglycemia, time in range (TIR, 70-180 mg/dL [3.9-10 mmol/L]), and use of the optional calibration feature in 2019 and 2020. In a cohort of 31,034 individuals, rates of persistent use were high, with 27,932 (90.0%) and 26,861 (86.6%) continuing to upload data in 2019 and 2020, respectively. Compared with G5 use, G6 use was associated with higher device utilization, less hypoglycemia, higher TIR (in 2020), and >80% fewer calibrations in both 2019 and 2020 (P's < 0.001). High persistence and utilization of the G6 system may contribute to sustainable glycemic outcomes and decreased user burden.
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Affiliation(s)
| | - John B Welsh
- Dexcom, Inc., Global Clinical Initiatives, San Diego, California, USA
| | - Tomas C Walker
- Dexcom, Inc., Global Clinical Initiatives, San Diego, California, USA
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24
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Dagi TF, Barker FG, Glass J. Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021; 89:133-142. [PMID: 34015816 DOI: 10.1093/neuros/nyab170] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- T Forcht Dagi
- Queen's University Belfast and The William J. Clinton Leadership Institute, Belfast, UK
- Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Fred G Barker
- Department of Neurosurgery, Harvard Medical School, Boston, Massachusetts, USA
- The Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jacob Glass
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Abstract
Background: Optional features of continuous glucose monitoring (CGM) systems empower patients and caregivers to understand and manage diabetes in new ways. We examined associations between use of optional features, demographics, and glycemic outcomes. Methods: Retrospective cohort studies were performed with data from US-based users of the G6 CGM System (Dexcom, Inc.). For all cohorts, data included sensor glucose values (SGVs). In separate cohorts, use of alert features (for hyperglycemia, existing hypoglycemia, and impending hypoglycemia), remote data sharing feature (Share), software for retrospective pattern analysis (CLARITY), "virtual assistant" feature that announces the current SGV and trend in response to a spoken request were assessed. Descriptive statistics were used to summarize feature set utilization patterns and relate them to glycemic outcomes. Results: Most individual features were consistently adopted by high proportions of G6 users. Threshold SGVs chosen for activating hyperglycemia and hypoglycemia alerts varied with age and were higher among the youngest and oldest patients. Use of the Share feature was more common among young patients and those with type 1 diabetes. Individuals who used more of the alert and notification features had more favorable glycemic outcomes, including time in range (TIR), than those who used fewer. More extensive engagement with CLARITY notifications was associated with higher TIR. Frequent use of the virtual assistant feature was associated with higher TIR and lower mean SGV. Conclusions: Optional features of the G6 CGM system are acceptable to and appear to benefit patients who use them. Different levels of engagement suggest that demographics and personal circumstances play a role in how patients and caregivers use CGM features to help manage diabetes.
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Affiliation(s)
- Halis Kaan Akturk
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado, USA
- Address correspondence to: H. Kaan Akturk, MD, Barbara Davis Center for Diabetes, University of Colorado, 1775 Aurora Ct, Aurora, CO 80045, USA
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26
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Galindo RJ, Aleppo G. Continuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology. Diabetes Res Clin Pract 2020; 170:108502. [PMID: 33065179 PMCID: PMC7736459 DOI: 10.1016/j.diabres.2020.108502] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Monitoring of glucose levels is essential to effective diabetes management. Over the past 100 years, there have been numerous innovations in glucose monitoring methods. The most recent advances have centered on continuous glucose monitoring (CGM) technologies. Numerous studies have demonstrated that use of continuous glucose monitoring confers significant glycemic benefits on individuals with type 1 diabetes (T1DM) and type 2 diabetes (T2DM). Ongoing improvements in accuracy and convenience of CGM devices have prompted increasing adoption of this technology. The development of standardized metrics for assessing CGM data has greatly improved and streamlined analysis and interpretation, enabling clinicians and patients to make more informed therapy modifications. However, many clinicians many be unfamiliar with current CGM and how use of these devices may help individuals with T1DM and T2DM achieve their glycemic targets. The purpose of this review is to present an overview of current CGM systems and provide guidance to clinicians for initiating and utilizing CGM in their practice settings.
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Affiliation(s)
- Rodolfo J Galindo
- Division of Endocrinology, Metabolism and Lipids, Department of Medicine, Emory University School of Medicine, 69 Jesse Hill Jr. Dr., Glenn Building, Suite 202, Atlanta, GA, 30303, USA.
| | - Grazia Aleppo
- Division of Endocrinology, Metabolism and Molecular Medicine, Feinberg School of Medicine, Northwestern University, 645 N. Michigan Ave, Suite 530, Chicago, IL 60611, USA.
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Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med 2020; 3:118. [PMID: 32984550 PMCID: PMC7486909 DOI: 10.1038/s41746-020-00324-0] [Citation(s) in RCA: 458] [Impact Index Per Article: 91.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 08/13/2020] [Indexed: 02/07/2023] Open
Abstract
At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.
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Affiliation(s)
- Stan Benjamens
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Bertalan Meskó
- The Medical Futurist Institute, Budapest, Hungary
- Department of Behavioural Sciences, Semmelweis University, Budapest, Hungary
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Kim HS, Yoon KH. Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare. Endocrinol Metab (Seoul) 2020; 35:541-548. [PMID: 32981296 PMCID: PMC7520582 DOI: 10.3803/enm.2020.675] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/31/2020] [Indexed: 01/16/2023] Open
Abstract
We live in a digital world where a variety of wearable medical devices are available. These technologies enable us to measure our health in our daily lives. It is increasingly possible to manage our own health directly through data gathered from these wearable devices. Likewise, healthcare professionals have also been able to indirectly monitor patients' health. Healthcare professionals have accepted that digital technologies will play an increasingly important role in healthcare. Wearable technologies allow better collection of personal medical data, which healthcare professionals can use to improve the quality of healthcare provided to the public. The use of continuous glucose monitoring systems (CGMS) is the most representative and desirable case in the adoption of digital technology in healthcare. Using the case of CGMS and examining its use from the perspective of healthcare professionals, this paper discusses the necessary adjustments required in clinical practices. There is a need for various stakeholders, such as medical staff, patients, industry partners, and policy-makers, to utilize and harness the potential of digital technology.
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Affiliation(s)
- Hun-Sung Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Endocrinology and Metabolism, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kun-Ho Yoon
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Endocrinology and Metabolism, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. SENSORS 2020; 20:s20143870. [PMID: 32664432 PMCID: PMC7412387 DOI: 10.3390/s20143870] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/07/2020] [Accepted: 07/07/2020] [Indexed: 12/21/2022]
Abstract
Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1-5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient's data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.
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Puhr S. Response to Dr. Seibold's Comment on Puhr et al. (DOI: 10.1089/dia.2019.0360). Diabetes Technol Ther 2020; 22:430. [PMID: 31724877 DOI: 10.1089/dia.2019.0422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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