1
|
Ming W, Guo X, Zhang G, Liu Y, Wang Y, Zhang H, Liang H, Yang Y. Recent advances in the precision control strategy of artificial pancreas. Med Biol Eng Comput 2024; 62:1615-1638. [PMID: 38418768 DOI: 10.1007/s11517-024-03042-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/03/2024] [Indexed: 03/02/2024]
Abstract
The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.
Collapse
Affiliation(s)
- Wuyi Ming
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, 450002, Zhengzhou, China
| | - Xudong Guo
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, 450002, Zhengzhou, China
| | - Guojun Zhang
- Guangdong HUST Industrial Technology Research Institute, 523808, Dongguan, China
| | - Yinxia Liu
- Prenatal Diagnosis Center of Dongguan Kanghua Hospital, 523808, Dongguan, China
| | - Yongxin Wang
- Zhengzhou Phray Technology Co., Ltd, 450019, Zhengzhou, China
| | - Hongmei Zhang
- Zhengzhou Phray Technology Co., Ltd, 450019, Zhengzhou, China
| | - Haofang Liang
- Zhengzhou Phray Technology Co., Ltd, 450019, Zhengzhou, China
| | - Yuan Yang
- Laboratory of Regenerative Medicine in Sports Science, School of Sports Science, South China Normal University, 510631, Guangzhou, China.
| |
Collapse
|
2
|
Jafar A, Pasqua MR. Postprandial glucose-management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence. Diabetes Obes Metab 2024; 26:1555-1566. [PMID: 38263540 DOI: 10.1111/dom.15463] [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: 11/28/2023] [Revised: 01/01/2024] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose-management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre-exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open-loop and closed-loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed-loop systems and decision-support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes-related and external background factors (i.e. behavioural, environmental, biological and abnormal events).
Collapse
Affiliation(s)
- Adnan Jafar
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Melissa-Rosina Pasqua
- Division of Endocrinology, Department of Medicine, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
3
|
Healey E, Tan A, Flint K, Ruiz J, Kohane I. Leveraging Large Language Models to Analyze Continuous Glucose Monitoring Data: A Case Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.06.24305022. [PMID: 38645024 PMCID: PMC11030468 DOI: 10.1101/2024.04.06.24305022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Continuous glucose monitors (CGM) provide patients and clinicians with valuable insights about glycemic control that aid in diabetes management. The advent of large language models (LLMs), such as GPT-4, has enabled real-time text generation and summarization of medical data. Further, recent advancements have enabled the integration of data analysis features in chatbots, such that raw data can be uploaded and analyzed when prompted. Studying both the accuracy and suitability of LLM-derived data analysis performed on medical time series data, such as CGM data, is an important area of research. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. This study used simulated CGM data from 10 different cases. We first evaluated the ability of GPT-4 to compute quantitative metrics specific to diabetes found in an Ambulatory Glucose Profile (AGP). Then, using two independent clinician graders, we evaluated the accuracy, completeness, safety, and suitability of qualitative descriptions produced by GPT-4 across five different CGM analysis tasks. We demonstrated that GPT-4 performs well across measures of accuracy, completeness, and safety when producing summaries of CGM data across all tasks. These results highlight the capabilities of using an LLM to produce accurate and safe narrative summaries of medical time series data. We highlight several limitations of the work, including concerns related to how GPT-4 may misprioritize highlighting instances of hypoglycemia and hyperglycemia. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through CGM analysis, and more broadly, the potential to leverage LLMs for streamlined medical time series analysis.
Collapse
Affiliation(s)
- Elizabeth Healey
- Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Amelia Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Kristen Flint
- Endocrinology Division, Massachusetts General Hospital, Boston, MA
| | - Jessica Ruiz
- Division of Endocrinology, Boston Children's Hospital, Boston, MA
| | - Isaac Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| |
Collapse
|
4
|
Plachy L, Neuman V, Velichova K, Slavenko MG, Santova A, Anne Amaratunga S, Obermannova B, Kolouskova S, Pruhova S, Sumnik Z, Petruzelkova L. Telemedicine maintains good glucose control in children with type 1 diabetes but is not time saving for healthcare professionals: KITES randomized study. Diabetes Res Clin Pract 2024; 209:111602. [PMID: 38437986 DOI: 10.1016/j.diabres.2024.111602] [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: 01/19/2023] [Revised: 02/20/2024] [Accepted: 02/29/2024] [Indexed: 03/06/2024]
Abstract
AIMS To evaluate glucose control non-inferiority and time benefits of telemedicine follow-up in children with type 1 diabetes (CwD). METHODS In a single-center 9-month-long randomized controlled study (clinicaltrials.gov NCT05484427), 50 children were randomized to either telemedicine group (TG) followed-up distantly by e-mail, or to face-to-face group (FFG) attending standard personal visits. The primary endpoint was non-inferiority of HbA1c at final visit (level of non-inferiority was set at 5 mmol/mol). The secondary endpoints were subcutaneous glucose monitoring parameters and time consumption from both study subjects' and the physicians' point of view. RESULTS Non-inferiority of HbA1c in the TG was proven (mean HbA1C 45.8 ± 7.3 [TG] vs. 50.0 ± 12.6 [FFG] mmol/mol, 6.3 vs. 6.7 % DCCT, p = 0.17; between groups HbA1C difference 95 % CI -10.2 to 1.9 mmol/mol). Telemedicine saved time for participants (mean visit duration [MVD] 50 [TG] vs. 247 min [FFG], p < 0.001). There were no other differences between groups neither in CGM parameters nor physician's time consumption (MVD 19 [TG] vs. 20 min [FFG], p = 0.58). CONCLUSIONS Nine-month telemedicine follow-up of the children with well-controlled T1D is not inferior to standard face-to-face visits. Telemedicine visits saved time for the participants but not for their diabetologists.
Collapse
Affiliation(s)
- Lukas Plachy
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Vit Neuman
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Katerina Velichova
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Matvei G Slavenko
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Alzbeta Santova
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Shenali Anne Amaratunga
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Barbora Obermannova
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Stanislava Kolouskova
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Stepanka Pruhova
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Zdenek Sumnik
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic.
| | - Lenka Petruzelkova
- Department of Pediatrics of Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, V Uvalu 84, Prague, 15000, Czech Republic
| |
Collapse
|
5
|
Unsworth R, Avari P, Lett AM, Oliver N, Reddy M. Adaptive bolus calculators for people with type 1 diabetes: A systematic review. Diabetes Obes Metab 2023; 25:3103-3113. [PMID: 37488945 DOI: 10.1111/dom.15204] [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: 03/24/2023] [Revised: 06/08/2023] [Accepted: 06/18/2023] [Indexed: 07/26/2023]
Abstract
AIM To conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D). METHODS Electronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single-arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool. RESULTS Six studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration. CONCLUSIONS There are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in-silico data are promising, further studies are required to support personalized and adaptive management of T1D.
Collapse
Affiliation(s)
- Rebecca Unsworth
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Parizad Avari
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Aaron M Lett
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Monika Reddy
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| |
Collapse
|
6
|
Kompala T, Wong J, Neinstein A. Diabetes Specialists Value Continuous Glucose Monitoring Despite Challenges in Prescribing and Data Review Process. J Diabetes Sci Technol 2023; 17:1265-1273. [PMID: 35403469 PMCID: PMC10563522 DOI: 10.1177/19322968221088267] [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] [Indexed: 11/16/2022]
Abstract
BACKGROUND Diabetes clinicians are key facilitators of continuous glucose monitoring (CGM) provision, but data on provider behavior related to CGM use and CGM generated data are limited. METHODS We conducted a national survey of providers caring for people with diabetes on CGM-related opinions, facilitators and barriers to prescription, and data review practices. RESULTS Of 182 survey respondents, 73.2% worked at academic centers, 70.6% were endocrinologists, and 70.7% practiced in urban settings. Nearly 70% of providers reported CGM use in the majority of their patients with type 1 diabetes. Half of the providers reported CGM use in 10% to 50% of their patients with type 2 diabetes. All respondents believed CGM improved quality of life and could optimize diabetes control. We found no differences in reported rates of CGM use based on providers' years of experience, patient volume, practice setting, or clinic type. Most providers reviewed CGM data each visit (97.7%) and actively involved patients in the data interpretation (98.8%). Only 14.1% of clinicians reported reviewing CGM data without any prompting from patients or their family members outside of visits. Most providers (80.7%) reported their CGM data review was valued by patients although only half reported having adequate time (45.1%) or an efficient process (56.1%) to do so. CONCLUSIONS Despite uniform support for CGM by providers, ongoing challenges related to cost, insurance coverage, and difficulties with prescription were major barriers to CGM use. Increased use of CGM in appropriate populations will necessitate improvements in data access and integration, clearly defined workflows, and decreased administrative burden to obtain CGM.
Collapse
Affiliation(s)
- Tejaswi Kompala
- Division of Endocrinology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jenise Wong
- Division of Endocrinology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Aaron Neinstein
- Division of Endocrinology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Center for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, USA
| |
Collapse
|
7
|
Kalra S, Unnikrishnan AG, Prasanna Kumar KM, Sahay R, Chandalia HB, Saboo B, Annamalai S, Kesavadev J, Shukla R, Wangnoo SK, Baruah MP, Jacob J, Arora S, Singla R, Sharma SK, Damodaran S, Bantwal G. Addendum 1: Forum for Injection Technique and Therapy Expert Recommendations, India. Diabetes Ther 2023; 14:29-45. [PMID: 36380217 PMCID: PMC9880128 DOI: 10.1007/s13300-022-01332-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022] Open
Abstract
With the emerging complexities in chronic diseases and people's lifestyles, healthcare professionals (HCPs) need to update their methods to manage and educate patients with chronic lifestyle disorders, particularly diabetes. The insulin injection technique (IIT), along with various parameters, must also be updated with newer methods. Forum for Injection Technique and Therapy Expert Recommendations (FITTER), India, has updated its recommendations to cover newer ways of detecting hypoglycaemia and lipohypertrophy, preventing needlestick injuries (NSIs), discouraging the reuse of insulin needles and encouraging good disposal. FITTER, India, is also introducing recommendations to calculate insulin bolus dose. These updated recommendations will help HCPs better manage patients with diabetes and achieve improved outcomes.
Collapse
|
8
|
Transforming Evidence Generation for Drug Label Changes: A Case Study. Ann Biomed Eng 2023; 51:137-149. [PMID: 36070049 DOI: 10.1007/s10439-022-03062-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/17/2022] [Indexed: 01/15/2023]
Abstract
Computer Modeling and Simulation (CM&S) provides the opportunity to drastically reduce clinical trial patient burden and advance regulatory decision making. At the suggestion of the US Food and Drug Administration (FDA), MannKind Corporation and Nudge BG submitted an application to the FDA Model-Informed Drug Development (MIDD) pilot program to support a label change for the initial dose of Afrezza® (insulin human), a novel inhalable insulin with a rapid pharmacokinetic and pharmacodynamic profile. The MIDD pilot program demonstrates the FDA's commitment to advancing regulatory science through the adoption of evidence generated by CM&S. A simulation framework was developed based on empirical data. It was used to generate evidence to support the label change. Briefing packages and presentations were prepared for two meetings with the FDA, over a period of four months. The model was thoroughly characterized, determined to be low risk for the question of interest, and submitted along with additional clinical evidence for validation. The FDA found the simulation framework to be helpful in providing insights into the question of interest and provides reasonable glycemic outcome predictions. At the conclusion of the MIDD paired meetings, FDA personnel from the Center for Drug Evaluation and Research review team accepted the simulation and requested additional, traditional clinical evidence to support the proposed label change. In the post-meeting comments, the FDA invited MannKind to submit a proposal for a data package including the CM&S evidence in a Type C meeting for further discussion on the label change. This MIDD pilot experience suggests that CM&S is a credible method for evidence generation. Collaboration between sponsor organizations as well as all stakeholders in the FDA, including proponents of CM&S, can further support regulatory decision-making. The learnings from early participants will allow the program to reach its full potential and thereby ultimately benefit patients, sponsors, and FDA.
Collapse
|
9
|
Nimri R, Tirosh A, Muller I, Shtrit Y, Kraljevic I, Alonso MM, Milicic T, Saboo B, Deeb A, Christoforidis A, den Brinker M, Bozzetto L, Bolla AM, Krcma M, Rabini RA, Tabba S, Gerasimidi-Vazeou A, Maltoni G, Giani E, Dotan I, Liberty IF, Toledano Y, Kordonouri O, Bratina N, Dovc K, Biester T, Atlas E, Phillip M. Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy. Diabetes Technol Ther 2022; 24:564-572. [PMID: 35325567 DOI: 10.1089/dia.2021.0566] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo.Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.
Collapse
Affiliation(s)
- Revital Nimri
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
| | - Amir Tirosh
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
- Division of Endocrinology, Diabetes and Metabolism, Dalia and David Arabov Endocrinology and Diabetes Research Center, Sheba Medical Center, Tel Hashomer, Israel
| | - Ido Muller
- DreaMed Diabetes Ltd., Petah Tikva, Israel
| | | | - Ivana Kraljevic
- Department of Endocrinology and Diabetes, UHC Zagreb, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Montserrat Martín Alonso
- Department of Pediatrics, Children's Endocrinology Unit, University Hospital of Salamanca, Spain
| | - Tanja Milicic
- Clinic for Endocrinology, Diabetes and Metabolic Diseases, University Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Banshi Saboo
- Dia Care Diabetes Care and Hormone Clinic, Ahmedabad, Gujarat, India
| | - Asma Deeb
- Pediatric Endocrine Division, Sheikh Shakhbout Medical City and Khalifa University, Abu Dhabi, United Arab Emirates
| | - Athanasios Christoforidis
- 1st Pediatric Department, Aristotle University of Thessaloniki, Hippokratio General Hospital, Thessaloniki, Greece
| | - Marieke den Brinker
- Division of Pediatric Endocrinology and Diabetology, Department of Pediatrics, Antwerp University Hospital and University of Antwerp, Antwerpen, Belgium
| | - Lutgarda Bozzetto
- Department of Clinical Medicine and Surgery, University of Naples "Federico II," Naples, Italy
| | | | - Michal Krcma
- Diabetes and Endocrinology Unit, Department of Internal Medicine, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
| | - Rosa Anna Rabini
- Department of Diabetology, Hospital Mazzoni, Ascoli Piceno, Italy
| | - Shadi Tabba
- Department of Pediatric Endocrinology, Arnold Palmer Hospital for Children, Orlando, Florida, USA
| | | | - Giulio Maltoni
- Unit of Pediatrics, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Elisa Giani
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Idit Dotan
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
- Rabin Medical Center, Institute of Endocrinology, Beilinson Hospital, Petach Tikva, Israel
| | - Idit F Liberty
- Department of Medicine and Diabetes Unit, Soroka Medical Center, Faculty of Health Sciences, Beer Sheva, Israel
| | - Yoel Toledano
- Division of Maternal Fetal Medicine, Helen Schneider Women's Hospital, Rabin Medical Center, Endocrinology Clinic, Petah Tikva, Israel
| | - Olga Kordonouri
- Diabetes Center for Children and Adolescents, Children's Hospital AUF DER BULT, Hannover, Germany
| | - Natasa Bratina
- Department of Endocrinology, Diabetes and Metabolic Diseases, UMC-University Children's Hospital Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Klemen Dovc
- Department of Endocrinology, Diabetes and Metabolic Diseases, UMC-University Children's Hospital Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Torben Biester
- Diabetes Center for Children and Adolescents, Children's Hospital AUF DER BULT, Hannover, Germany
| | - Eran Atlas
- DreaMed Diabetes Ltd., Petah Tikva, Israel
| | - Moshe Phillip
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
| |
Collapse
|
10
|
Xu NY, Nguyen KT, DuBord AY, Pickup J, Sherr JL, Teymourian H, Cengiz E, Ginsberg BH, Cobelli C, Ahn D, Bellazzi R, Bequette BW, Gandrud Pickett L, Parks L, Spanakis EK, Masharani U, Akturk HK, Melish JS, Kim S, Kang GE, Klonoff DC. Diabetes Technology Meeting 2021. J Diabetes Sci Technol 2022; 16:1016-1056. [PMID: 35499170 PMCID: PMC9264449 DOI: 10.1177/19322968221090279] [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] [Indexed: 11/16/2022]
Abstract
Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.
Collapse
Affiliation(s)
- Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
| | | | | | | | | | | | - Eda Cengiz
- University of California, San
Francisco, San Francisco, CA, USA
| | | | | | - David Ahn
- Mary & Dick Allen Diabetes Center
at Hoag, Newport Beach, CA, USA
| | | | | | | | - Linda Parks
- University of California, San
Francisco, San Francisco, CA, USA
| | - Elias K. Spanakis
- Baltimore VA Medical Center,
Baltimore, MD, USA
- University of Maryland, Baltimore,
MD, USA
| | - Umesh Masharani
- University of California, San
Francisco, San Francisco, CA, USA
| | - Halis K. Akturk
- Barbara Davis Center for Diabetes,
University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Sarah Kim
- University of California, San
Francisco, San Francisco, CA, USA
| | - Gu Eon Kang
- The University of Texas at Dallas,
Richardson, TX, USA
| | - David C. Klonoff
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
| |
Collapse
|
11
|
Kompala T, Neinstein AB. Smart Insulin Pens: Advancing Digital Transformation and a Connected Diabetes Care Ecosystem. J Diabetes Sci Technol 2022; 16:596-604. [PMID: 33435704 PMCID: PMC9294591 DOI: 10.1177/1932296820984490] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
With the first commercially available smart insulin pens, the predominant insulin delivery device for millions of people living with diabetes is now coming into the digital age. Smart insulin pens (SIPs) have the potential to reshape a connected diabetes care ecosystem for patients, providers, and health systems. Existing SIPs are enhanced with real-time wireless connectivity, digital dose capture, and integration with personalized dosing decision support. Automatic dose capture can promote effective retrospective review of insulin dose data, particularly when paired with glucose data. Patients, providers, and diabetes care teams will be able to make increasingly data-driven decisions and recommendations, in real time, during scheduled visits, and in a more continuous, asynchronous care model. As SIPs continue to progress along the path of digital transformation, we can expect additional benefits: iteratively improving software, machine learning, and advanced decision support. Both these technological advances, and future care delivery models with asynchronous interactions, will depend on easy, open, and continuous data exchange between the growing number of diabetes devices. SIPs have a key role in modernizing diabetes care for a large population of people living with diabetes.
Collapse
Affiliation(s)
- Tejaswi Kompala
- Department of Medicine, University of
California, San Francisco, San Francisco, CA, USA
- Tejaswi Kompala, MD, University of
California, San Francisco, 1700 Owens Street, Suite 541, San Francisco, CA
94158, USA.
| | - Aaron B. Neinstein
- Department of Medicine, University of
California, San Francisco, San Francisco, CA, USA
- Center for Digital Health Innovation,
University of California, San Francisco, San Francisco, CA, USA
| |
Collapse
|
12
|
Juneja D, Gupta A, Singh O. Artificial intelligence in critically ill diabetic patients: current status and future prospects. Artif Intell Gastroenterol 2022; 3:66-79. [DOI: 10.35712/aig.v3.i2.66] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Recent years have witnessed increasing numbers of artificial intelligence (AI) based applications and devices being tested and approved for medical care. Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis, to predict and diagnose early complications, increase adherence to therapy, and even motivate patients to manage diabetes and maintain glycemic control. However, these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems. Intensive care units (ICUs) have a dynamic environment generating huge data, which AI can extract and organize simultaneously, thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest. Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia, complicating their ICU course and affecting outcomes. In addition, to maintain glycemic control frequent blood sampling and insulin dose adjustments are required, increasing nursing workload and chances of error. AI has the potential to improve glycemic control while reducing the nursing workload and errors. Continuous glucose monitoring (CGM) devices, which are Food and Drug Administration (FDA) approved for use in non-critically ill patients, are now being recommended for use in specific ICU populations with increased accuracy. AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients. Even though many of these AI applications have shown potential, these devices need to be tested in larger number of ICU patients, have wider availability, show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems, before they become acceptable to ICU physicians for routine use.
Collapse
Affiliation(s)
- Deven Juneja
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
| | - Anish Gupta
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
| | - Omender Singh
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
| |
Collapse
|
13
|
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: 0] [Impact Index Per Article: 0] [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.
Collapse
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
| |
Collapse
|
14
|
Walsh J, Roberts R, Bailey TS, Heinemann L. Insulin Titration Guidelines for Patients With Type 1 Diabetes: It Is About Time! J Diabetes Sci Technol 2022:19322968221087261. [PMID: 35369773 DOI: 10.1177/19322968221087261] [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: 10/18/2022]
Abstract
PURPOSE A proposal that an Insulin Advisory Committee develop insulin titration guidelines 100 years after its discovery. FINDINGS Glucose control metrics remain poor despite significant advances in diabetes technology. SUMMARY A century after the introduction of insulin, health care providers and patients with type 1 diabetes have worldwide access to a variety of insulin delivery devices (IDDs), glucose monitors, bolus calculators (BCs), continuous glucose monitors (CGMs), and automated insulin delivery (AID) systems. However, these advances have not enabled most patients to achieve today's clear A1c and time-in-range goals. Much of this failure arises from the lack of clear insulin titration guidelines for determining appropriate insulin doses. The lack of dosing clarity results in local physicians, clinics, and individual patients managing insulin titrations as they see fit, creating significant inefficiencies for reaching recommended glycemic goals. This review (1) details the widespread problems generated by nonphysiological dose settings in today's BCs, insulin pumps, and AID systems; (2) presents a method to develop and implement optimized total daily doses of insulin to correct the most common problem of hyperglycemia; (3) discusses using large device databases to provide clear insulin titration guidelines that optimize BC settings from an optimized total daily dose (TDD) of insulin for patients with T1D; and (4) recommends the formation of an Insulin Advisory Committee to clarify the steps to take toward universal insulin titration guidelines, optimized BC settings, and a systematic logic for their use in insulin delivery devices.
Collapse
Affiliation(s)
- John Walsh
- Advanced Metabolic Care and Research, Escondido, CA, USA
| | | | | | | |
Collapse
|
15
|
Nimri R, Oron T, Muller I, Kraljevic I, Alonso MM, Keskinen P, Milicic T, Oren A, Christoforidis A, den Brinker M, Bozzetto L, Bolla AM, Krcma M, Rabini RA, Tabba S, Smith L, Vazeou A, Maltoni G, Giani E, Atlas E, Phillip M. Adjustment of Insulin Pump Settings in Type 1 Diabetes Management: Advisor Pro Device Compared to Physicians' Recommendations. J Diabetes Sci Technol 2022; 16:364-372. [PMID: 33100030 PMCID: PMC8861776 DOI: 10.1177/1932296820965561] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
AIMS To compare insulin dose adjustments made by physicians to those made by an artificial intelligence-based decision support system, the Advisor Pro, in people with type 1 diabetes (T1D) using an insulin pump and self-monitoring blood glucose (SMBG). METHODS This was a multinational, non-interventional study surveying 17 physicians from 11 countries. Each physician was asked to provide insulin dose adjustments for the settings of the pump including basal rate, carbohydrate-to-insulin ratios (CRs), and correction factors (CFs) for 15 data sets of pumps and SMBG of people with T1D (mean age 18.4 ± 4.8 years; eight females; mean glycated hemoglobin 8.2% ± 1.4% [66 ± 11mmol/mol]). The recommendations were compared among the physicians and between the physicians and the Advisor Pro. The study endpoint was the percentage of comparison points for which there was an agreement on the direction of insulin dose adjustments. RESULTS The percentage (mean ± SD) of agreement among the physicians on the direction of insulin pump dose adjustments was 51.8% ± 9.2%, 54.2% ± 6.4%, and 49.8% ± 11.6% for the basal, CR, and CF, respectively. The automated recommendations of the Advisor Pro on the direction of insulin dose adjustments were comparable )49.5% ± 6.4%, 55.3% ± 8.7%, and 47.6% ± 14.4% for the basal rate, CR, and CF, respectively( and noninferior to those provided by physicians. The mean absolute difference in magnitude of change between physicians was 17.1% ± 13.1%, 14.6% ± 8.4%, and 23.9% ± 18.6% for the basal, CR, and CF, respectively, and comparable to the Advisor Pro 11.7% ± 9.7%, 10.1% ± 4.5%, and 25.5% ± 19.5%, respectively, significant for basal and CR. CONCLUSIONS Considerable differences in the recommendations for changes in insulin dosing were observed among physicians. Since automated recommendations by the Advisor Pro were similar to those given by physicians, it could be considered a useful tool to manage T1D.
Collapse
Affiliation(s)
- Revital Nimri
- The Jesse Z and Sara Lea Shafer
Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes,
Schneider Children’s Medical Center of Israel, Petah, Tikva, Israel
- Revital Nimri, MD, The Jesse Z and Sara Lea
Shafer Institute for Endocrinology and Diabetes, National Center for Childhood
Diabetes, Schneider Children’s Medical Center of Israel, 14 Kaplan St. Petah
Tikva, 49202, Israel.
| | - Tal Oron
- The Jesse Z and Sara Lea Shafer
Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes,
Schneider Children’s Medical Center of Israel, Petah, Tikva, Israel
| | - Ido Muller
- DreaMed Diabetes Ltd, Petah Tiqva,
Israel
| | - Ivana Kraljevic
- Department of Endocrinology and
Diabetes, UHC Zagreb, School of Medicine, University of Zagreb, Zagreb,
Croatia
| | - Montserrat Martín Alonso
- Department of Pediatrics, Children’s
Endocrinology Unit, University Hospital of Salamanca, Spain
| | - Paivi Keskinen
- Department of Pediatrics, University
Hospital of Tampere, Finland
| | - Tanja Milicic
- Clinic for Endocrinology, Diabetes and
Metabolic Diseases, Clinical Center of Serbia, Faculty of Medicine University of
Belgrade, Serbia
| | - Asaf Oren
- Pediatric Endocrinology and Diabetes
Unit, Dana-Dwek Children’s Hospital, Tel Aviv Sourasky Medical Center, Israel
- Sackler School of Medicine, Tel Aviv
University, Israel
| | - Athanasios Christoforidis
- Pediatric Department, Aristotle
University of Thessaloniki, Hippokratio General Hospital, Thessaloniki, Greece
| | - Marieke den Brinker
- Department of Pediatrics, Division of
Pediatric Endocrinology and Diabetology, Antwerp University Hospital and University
of Antwerp, Belgium
| | - Lutgarda Bozzetto
- Department of Clinical Medicine and
Surgery, University of Naples “Federico II”, Italy
| | | | - Michal Krcma
- Department of Internal Medicine,
Diabetes and Endocrinology Unit, University Hospital Pilsen, Faculty of Medicine in
Pilsen, Charles University, Czech Republic
| | - Rosa Anna Rabini
- Department of Diabetology, Hospital
Mazzoni, Ascoli Piceno, Italy
| | - Shadi Tabba
- Children’s Hospital of the King’s
Daughters, Eastern Virginia Medical School, Norfolk, VA, USA
| | - Lizl Smith
- Department of Internal Medicine,
Division of Endocrinology, University of Pretoria, South Africa
| | - Andriani Vazeou
- A’ Department of Pediatrics, Diabetes
Center, P&A Kyriakou, Athens, Greece
| | - Giulio Maltoni
- Department of Pediatrics, University
Hospital of Bologna Sant’Orsola-Malpighi Polyclinic, Italy
| | - Elisa Giani
- Department of Biomedical Sciences,
Humanitas Clinical and Research Center-IRCCS and Humanitas University, Milan,
Italy
| | - Eran Atlas
- DreaMed Diabetes Ltd, Petah Tiqva,
Israel
| | - Moshe Phillip
- The Jesse Z and Sara Lea Shafer
Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes,
Schneider Children’s Medical Center of Israel, Petah, Tikva, Israel
- Sackler School of Medicine, Tel Aviv
University, Israel
| |
Collapse
|
16
|
Trenfield SJ, Awad A, McCoubrey LE, Elbadawi M, Goyanes A, Gaisford S, Basit AW. Advancing pharmacy and healthcare with virtual digital technologies. Adv Drug Deliv Rev 2022; 182:114098. [PMID: 34998901 DOI: 10.1016/j.addr.2021.114098] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.
Collapse
|
17
|
Ferstad JO, Vallon JJ, Jun D, Gu A, Vitko A, Morales DP, Leverenz J, Lee MY, Leverenz B, Vasilakis C, Osmanlliu E, Prahalad P, Maahs DM, Johari R, Scheinker D. Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health. Pediatr Diabetes 2021; 22:982-991. [PMID: 34374183 PMCID: PMC8635792 DOI: 10.1111/pedi.13256] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 07/28/2021] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review. RESEARCH DESIGN AND METHODS We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review. RESULTS The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 min per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6-16.9 pp) greater time-in-range (70-180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 months after T1D onset. CONCLUSIONS An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range.
Collapse
Affiliation(s)
- Johannes O. Ferstad
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Jacqueline J. Vallon
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Daniel Jun
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Angela Gu
- Department of Computer Science, Stanford University School of Engineering, Stanford, California, USA
| | - Anastasiya Vitko
- Department of Computer Science, Stanford University School of Engineering, Stanford, California, USA
| | - Dianelys P. Morales
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Jeannine Leverenz
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA
| | - Ming Yeh Lee
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA
| | - Brianna Leverenz
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA
| | - Christos Vasilakis
- Centre for Healthcare Innovation and Improvement (CHI), School of Management, University of Bath, Bath, UK
| | - Esli Osmanlliu
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Department of Pediatrics, Montreal Children’s Hospital, McGill University Health Centre, Montreal, Canada
| | - Priya Prahalad
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - David M. Maahs
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA,Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Ramesh Johari
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA,Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, California, USA
| |
Collapse
|
18
|
Liu B, Huang F, Wu X, Xie Y, Xu R, Huang J, Li J, Yang X, Li X, Zhou Z. Poor guideline adherence in type 1 diabetes education in real-world clinical practice: Evidence from a multicentre, national survey. PATIENT EDUCATION AND COUNSELING 2021; 104:2740-2747. [PMID: 33941419 DOI: 10.1016/j.pec.2021.04.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To examine how physicians implement guidelines to deliver insulin dosing education for type 1 diabetes patients in real-world settings. METHODS A nationally representative sample of endocrinologists from top tertiary hospitals in China was obtained by a multistage random sampling method (n = 385). Knowledge, perceptions and practices of insulin dosing were assessed by validated questionnaires. Multivariable logistic regression was used to identify independent determinants of clinical practice and knowledge. RESULTS Only 20.5% of endocrinologists correctly answered> 75% of the items regarding insulin dosing knowledge. Only 37.7% of endocrinologists reported often teaching insulin-to-carbohydrate ratio and insulin sensitivity factor. Practice behaviours were independently associated with guideline familiarity (OR: 5.92, 95% CI: 3.36-10.41), receiving standardized training (OR: 2.00, 95% CI:1.23-3.25), self-reported lack of time (OR: 0.58, 95% CI:0.34-0.99) and insufficient teaching approaches (OR: 0.57, 95% CI:0.33-0.97) CONCLUSIONS: There was a large gap between guidelines and clinical practice in insulin dosing education. Modifiable factors, including self-reported lack of time, unfamiliarity with guidelines, the shortage of medical training and educational tools hinder insulin dosing education. PRACTICE IMPLICATIONS Sufficient medical training and educational tools are important to optimize insulin dosing education. The current care paradigm should also be modified to relieve the burden of physicians.
Collapse
Affiliation(s)
- Bingwen Liu
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Fansu Huang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Department of Nutrition, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xinyin Wu
- Department of Epidemiology and Health Statistics, Xiangya school of Public health, Central South University, Changsha, Hunan, China
| | - Yuting Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Rong Xu
- Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jin Huang
- Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Juan Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xilin Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
| |
Collapse
|
19
|
Rilstone S, Reddy M, Oliver N. A Pilot Study of Flat and Circadian Insulin Infusion Rates in Continuous Subcutaneous Insulin Infusion (CSII) in Adults with Type 1 Diabetes (FIRST1D). J Diabetes Sci Technol 2021; 15:666-671. [PMID: 32081036 PMCID: PMC8120055 DOI: 10.1177/1932296820906195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Initiation of continuous subcutaneous insulin therapy (CSII) in type 1 diabetes requires conversion of a basal insulin dose into a continuous infusion regimen. There are limited data to guide the optimal insulin profile to rapidly achieve target glucose and minimize healthcare professional input. The aim of this pilot study was to compare circadian and flat insulin infusion rates in CSII naïve adults with type 1 diabetes. METHODS Adults with type 1 diabetes commencing CSII were recruited. Participants were randomized to circadian or flat basal profile calculated from the total daily dose. Basal rate testing was undertaken on days 7, 14 and 28 and basal rates were adjusted. The primary outcome was the between-group difference in absolute change in insulin basal rate over 24 hours following three rounds of basal testing. Secondary outcomes included the number of basal rate changes and the time blocks. RESULTS Seventeen participants (mean age 33.3 (SD 8.6) years) were recruited. There was no significant difference in absolute change in insulin basal rates between groups (P = .85). The circadian group experienced significant variation in the number of changes made with the most changes in the morning and evening (P = .005). The circadian group received a greater reduction in total insulin (-14.1 (interquartile range (IQR) -22.5-12.95) units) than the flat group (-7.48 (IQR -11.90-1.23) units) (P = .021). CONCLUSION The initial insulin profile does not impact on the magnitude of basal rate changes during optimization. The circadian profile requires changes at specific time points. Further development of the circadian profile may be the optimal strategy.
Collapse
Affiliation(s)
- Siân Rilstone
- Department of Nutrition and Dietetics,
Imperial College Healthcare NHS Trust, St Mary’s Hospital, London, UK
- Siân Rilstone, MSc, RD, Department of
Nutrition and Dietetics, Imperial College Healthcare NHS Trust, St Mary’s
Hospital, Praed Street, London W2 1NY, UK.
| | - Monika Reddy
- Diabetes and Endocrinology, Imperial
College Healthcare NHS Trust, St Mary’s Hospital, London, UK
| | - Nick Oliver
- Imperial College London, Hammersmith
Campus, London, UK
| |
Collapse
|
20
|
Avari P, Leal Y, Herrero P, Wos M, Jugnee N, Arnoriaga-Rodríguez M, Thomas M, Liu C, Massana Q, Lopez B, Nita L, Martin C, Fernández-Real JM, Oliver N, Fernández-Balsells M, Reddy M. Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study. Diabetes Technol Ther 2021; 23:175-186. [PMID: 33048581 DOI: 10.1089/dia.2020.0301] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9-10.0 mmol/L [70-180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years, and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1-67.8] % vs. 58.4 [49.6-64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness. Clinical trial registration: ClinicalTrials.gov NCT03849755.
Collapse
Affiliation(s)
- Parizad Avari
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Yenny Leal
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Pau Herrero
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Marzena Wos
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Narvada Jugnee
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - María Arnoriaga-Rodríguez
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Maria Thomas
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Chengyuan Liu
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
- Centre for Aerospace Manufactuiring, University of Nottingham, London, United Kingdom
| | - Quim Massana
- eXiT Research Group, Institut d'Informàtica i Aplicacions, University of Girona, Girona, Spain
| | - Beatriz Lopez
- eXiT Research Group, Institut d'Informàtica i Aplicacions, University of Girona, Girona, Spain
| | - Lucian Nita
- Department of Research & Development, RomSoft SRL, Iasi, Romania
| | - Clare Martin
- School of Engineering, Computing, and Mathematics, Oxford Brookes University, Oxford, United Kingdom
| | - José Manuel Fernández-Real
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
- Department of Medical Sciences, Faculty of Medicine, University of Girona, Girona, Spain
- CIBEROBN Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Mercè Fernández-Balsells
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Monika Reddy
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| |
Collapse
|
21
|
Abstract
There has been a rapid advancement in the pace of development of new diabetes technologies and therapies for the management of type 1 diabetes over the past decade. The Diabetes Control and Complications Trial conclusively established that tight glycemic control with intensive insulin therapy decreases the rates of diabetes complications in proportion to glycemic control, and diabetes technologies have accordingly been developed to help patients reach these goals. In this review, the authors discuss new diabetes therapeutics and technologies, including new insulin analogues, insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems."
Collapse
Affiliation(s)
- Jordan S Sherwood
- Diabetes Research Center, Massachusetts General Hospital, 50 Staniford Street, Suite 301, Boston, MA 02114, USA
| | - Steven J Russell
- Diabetes Research Center, Massachusetts General Hospital, 50 Staniford Street, Suite 301, Boston, MA 02114, USA
| | - Melissa S Putman
- Diabetes Research Center, Massachusetts General Hospital, 50 Staniford Street, Suite 301, Boston, MA 02114, USA.
| |
Collapse
|
22
|
Fathi AE, Kearney RE, Palisaitis E, Boulet B, Haidar A. A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy. IEEE Trans Biomed Eng 2020; 68:1208-1219. [PMID: 32915722 DOI: 10.1109/tbme.2020.3023555] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D) including basal insulin doses to keep glucose levels constant during fasting conditions and bolus insulin doses with meals. Optimal insulin dosing is critical to achieving satisfactory glycemia but is challenging due to inter- and intra-individual variability. Here, we present a novel model-based iterative algorithm that optimizes insulin doses using previous-day glucose, insulin, and meal data. METHODS Our algorithm employs a maximum-a-posteriori method to estimate parameters of a model describing the effects of changes in basal-bolus insulin doses. Then, parameter estimates, their confidence intervals, and the goodness of fit, are combined to generate new recommendations. We assessed our algorithm in three ways. First, a clinical data set of 150 days (15 participants) were used to evaluate the proposed model and the estimation method. Second, 60-day simulations were performed to demonstrate the efficacy of the algorithm. Third, a sample 6-day clinical experiment is presented and discussed. RESULTS The model fitted the clinical data well with a root-mean-square-error of 1.75 mmol/L. Simulation results showed an improvement in the time in target (3.9-10 mmol/L) from 64% to 77% and a decrease in the time in hypoglycemia (< 3.9 mmol/L) from 8.1% to 3.8%. The clinical experiment demonstrated the feasibility of the algorithm. CONCLUSION Our algorithm has the potential to improve glycemic control in people with T1D using MDI. SIGNIFICANCE This work is a step forward towards a decision support system that improves their quality of life.
Collapse
|
23
|
Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med 2020; 26:1380-1384. [DOI: 10.1038/s41591-020-1045-7] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 08/03/2020] [Indexed: 01/29/2023]
|
24
|
El Fathi A, Palisaitis E, von Oettingen JE, Krishnamoorthy P, Kearney RE, Legault L, Haidar A. A pilot non-inferiority randomized controlled trial to assess automatic adjustments of insulin doses in adolescents with type 1 diabetes on multiple daily injections therapy. Pediatr Diabetes 2020; 21:950-959. [PMID: 32418302 DOI: 10.1111/pedi.13052] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 04/15/2020] [Accepted: 05/11/2020] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Multiple daily injections (MDI) therapy for type 1 diabetes involves basal and bolus insulin doses. Non-optimal insulin doses contribute to the lack of satisfactory glycemic control. We aimed to evaluate the feasibility of an algorithm that optimizes daily basal and bolus doses using glucose monitoring systems for MDI therapy users. METHODS We performed a pilot, non-inferiority, randomized, parallel study at a diabetes camp comparing basal-bolus insulin dose adjustments made by camp physicians (PA) and a learning algorithm (LA), in children and adolescents on MDI therapy. Participants wore a glucose sensor and underwent 11 days of daily dose adjustments in either arm. Algorithm adjustments were reviewed and approved by a physician. The last 7 days were examined for outcomes. RESULTS Twenty-one youths (age 13.3 [SD, 3.7] years; 13 females; HbA1c 8.6% [SD, 1.8]) were randomized to either group (LA [n = 10] or PA [n = 11]). The algorithm made 293 adjustments with a 92% acceptance rate from the camp physicians. In the last 7 days, the time in target glucose (3.9-10 mmol/L) in LA (39.5%, SD, 20.7) was similar to PA (38.4%, SD, 15.6) (P = .89). The number of hypoglycemic events per day in LA (0.3, IQR, [0.1-0.6]) was similar to PA (0.2, IQR, [0.0-0.4]) (P = .42). There was no incidence of severe hypoglycemia nor ketoacidosis. CONCLUSIONS In this pilot study, glycemic outcomes in the LA group were similar to the PA group. This algorithm has the potential to facilitate MDI therapy, and longer and larger studies are warranted.
Collapse
Affiliation(s)
- Anas El Fathi
- Department of Electrical and Computer Engineering, McGill University, Montreal, Canada
| | - Emilie Palisaitis
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Julia E von Oettingen
- Montreal Children's Hospital, Pediatric Endocrinology, Montréal, Canada.,The Research Institute of McGill University Health Center, Montréal, Canada
| | | | - Robert E Kearney
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Laurent Legault
- Montreal Children's Hospital, Pediatric Endocrinology, Montréal, Canada
| | - Ahmad Haidar
- Department of Biomedical Engineering, McGill University, Montreal, Canada.,The Research Institute of McGill University Health Center, Montréal, Canada
| |
Collapse
|
25
|
Ho C, Ng NBH, Lee YS. Caring for Pediatric Patients with Diabetes amidst the Coronavirus Disease 2019 Storm. J Pediatr 2020; 223:186-187. [PMID: 32387113 PMCID: PMC7199676 DOI: 10.1016/j.jpeds.2020.04.067] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 04/28/2020] [Accepted: 04/28/2020] [Indexed: 12/24/2022]
Affiliation(s)
- Cindy Ho
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore; Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Nicholas Beng Hui Ng
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore; Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yung Seng Lee
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore; Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| |
Collapse
|
26
|
Taylor KA, Forlenza GP. Use of Machine Learning and Hybrid Closed Loop Insulin Delivery at Diabetes Camps. Diabetes Technol Ther 2020; 22:535-537. [PMID: 32058821 DOI: 10.1089/dia.2020.0026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
27
|
Tyler NS, Mosquera-Lopez CM, Wilson LM, Dodier RH, Branigan DL, Gabo VB, Guillot FH, Hilts WW, El Youssef J, Castle JR, Jacobs PG. An artificial intelligence decision support system for the management of type 1 diabetes. Nat Metab 2020; 2:612-619. [PMID: 32694787 PMCID: PMC7384292 DOI: 10.1038/s42255-020-0212-y] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 04/23/2020] [Indexed: 11/08/2022]
Abstract
Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)1,2. Errors in dosing can lead to life-threatening hypoglycaemia events (<70 mg dl-1) and hyperglycaemia (>180 mg dl-1), increasing the risk of retinopathy, neuropathy, and nephropathy. Machine learning (artificial intelligence) approaches are being harnessed to incorporate decision support into many medical specialties. Here, we report an algorithm that provides weekly insulin dosage recommendations to adults with T1D using MDI therapy. We employ a unique virtual platform3 to generate over 50,000 glucose observations to train a k-nearest neighbours4 decision support system (KNN-DSS) to identify causes of hyperglycaemia or hypoglycaemia and determine necessary insulin adjustments from a set of 12 potential recommendations. The KNN-DSS algorithm achieves an overall agreement with board-certified endocrinologists of 67.9% when validated on real-world human data, and delivers safe recommendations, per endocrinologist review. A comparison of inter-physician-recommended adjustments to insulin pump therapy indicates full agreement of 41.2% among endocrinologists, which is consistent with previous measures of inter-physician agreement (41-45%)5. In silico3,6 benchmarking using a platform accepted by the United States Food and Drug Administration for evaluation of artificial pancreas technologies indicates substantial improvement in glycaemic outcomes after 12 weeks of KNN-DSS use. Our data indicate that the KNN-DSS allows for early identification of dangerous insulin regimens and may be used to improve glycaemic outcomes and prevent life-threatening complications in people with T1D.
Collapse
Affiliation(s)
- Nichole S Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA.
| | - Clara M Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Robert H Dodier
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Deborah L Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Virginia B Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Florian H Guillot
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Wade W Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA.
| |
Collapse
|
28
|
Abstract
BACKGROUND Advances in pump technology have increased the popularity of this treatment modality among patients with type 1 diabetes and recently also among patients with type 2 diabetes. AREAS OF UNCERTAINTY Four decades after the incorporation of the insulin pump in clinical use, questions regarding its efficacy, occurrence rate of short-term complications as hypoglycemia and diabetes ketoacidosis, timing of pump initiation, and selected populations for use remain unanswered. DATA SOURCES A review of the literature was performed using the PubMed database to identify all articles published up till December 2018, with the search terms including insulin pump therapy/continuous subcutaneous insulin delivery. The Cochrane database was searched for meta-analysis evaluating controlled randomized trials. Consensuses guidelines published by the International Society for Pediatric and Adolescent Diabetes, American Diabetes Association, and Advanced Technologies and Treatments for Diabetes year books were additionally reviewed for relevant cited articles. THERAPEUTIC ADVANCES Insulin pump therapy offers flexible management of diabetes. It enables adjustment of basal insulin to daily requirements and circadian needs, offers more precise treatment for meals and physical activity, and, when integrated with continuous glucose monitoring, allows glucose responsive insulin delivery. The ability to download and transmit data for analysis allow for treatment optimization. Newer pumps are simple to operate and increase user experience. Studies support the efficacy of pump therapy in improving glycemic control and reducing the occurrence of hypoglycemia without increasing episodes of diabetes ketoacidosis. They also improve quality of life. Recent evidence suggests a role for pump therapy in reducing microvascular and macrovascular diabetes-related complications. CONCLUSIONS Insulin pump therapy appears to be effective and safe in people with T1D regardless of age. Future advancements will include incorporation of closed loop and various decision support systems to aid and improve metabolic control and quality of life.
Collapse
|
29
|
Tyler NS, Jacobs PG. Artificial Intelligence in Decision Support Systems for Type 1 Diabetes. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3214. [PMID: 32517068 PMCID: PMC7308977 DOI: 10.3390/s20113214] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 12/16/2022]
Abstract
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.
Collapse
Affiliation(s)
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
| |
Collapse
|
30
|
Abstract
Optimal glycemic control remains challenging in individuals with type 1 diabetes. With the comprehensive clinical evidence on safety and efficiency, the adoption of continuous glucose monitoring (CGM), insulin pumps, and control algorithms merging the two into closed-loop systems is rapidly increasing. Particularly the CGM and intermittently scanned CGM improved diabetes management outcomes in large populations. A meaningful translation from clinical trials in highly controlled settings to numerous evaluations of closed-loop technology in the unrestricted home environment ended with its commercialization and use in routine clinical practice. Although it is still not a cure, the closed-loop currently seems to be the most promising advancement in the treatment of diabetes, with promising results also reported from routine clinical practice in children and adults with type 1 diabetes. We summarize different aspects of a technological approach to diabetes care, list currently available devices and systems in the pipeline, and the key supporting clinical evidence for their use. We consider human factors associated with technology use and the importance of health economics to support implementation and reimbursement.
Collapse
Affiliation(s)
- Klemen Dovc
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, Ljubljana, Slovenia.,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadej Battelino
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, Ljubljana, Slovenia - .,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| |
Collapse
|
31
|
Abstract
Technological innovations have fundamentally changed diabetes care. Insulin pump use and continuous glucose monitoring are associated with improved glycemic control along with a better quality of life; automated insulin-dosing advisors facilitate and improve decision making. Glucose-responsive automated insulin delivery enables the highest targets for time in range, lowest rate and duration of hypoglycemia, and favorable quality of life. Clear targets for time in ranges and a standard visualization of the data will help the diabetes technology to be used more efficiently. Decision support systems within and integrated cloud environment will further simplify, unify, and improve modern routine diabetes care.
Collapse
Affiliation(s)
- Klemen Dovc
- Department of Paediatric Endocrinology, Diabetes and Metabolic Diseases, UMC - University Children's Hospital, University Medical Centre Ljubljana, Bohoriceva 20, Ljubljana SI-1000, Slovenia; Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadej Battelino
- Department of Paediatric Endocrinology, Diabetes and Metabolic Diseases, UMC - University Children's Hospital, University Medical Centre Ljubljana, Bohoriceva 20, Ljubljana SI-1000, Slovenia; Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| |
Collapse
|
32
|
Sheng T, Offringa R, Kerr D, Clements M, Fischer J, Parks L, Greenfield M. Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management. J Diabetes Sci Technol 2020; 14:271-276. [PMID: 32116024 PMCID: PMC7196866 DOI: 10.1177/1932296819873641] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. METHODS We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from "best" to "worst"). We also asked HCPs to endorse features of CGM data that were important in making such assessments. RESULTS In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall's W = .52, P < .001). Rankings were similar between endocrinologists and educators (R2 = .90, Cohen's kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). CONCLUSIONS Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.
Collapse
Affiliation(s)
- Tong Sheng
- Glooko, Inc., Mountain View, CA,
USA
- Tong Sheng, PhD, Glooko, Inc., 303 Bryant
St, Mountain View, CA 94041, USA.
| | | | - David Kerr
- Sansum Diabetes Research Institute,
Santa Barbara, CA, USA
| | | | | | | | | |
Collapse
|
33
|
Palisaitis E, El Fathi A, Von Oettingen JE, Krishnamoorthy P, Kearney R, Jacobs P, Rutkowski J, Legault L, Haidar A. The Efficacy of Basal Rate and Carbohydrate Ratio Learning Algorithm for Closed-Loop Insulin Delivery (Artificial Pancreas) in Youth with Type 1 Diabetes in a Diabetes Camp. Diabetes Technol Ther 2020; 22:185-194. [PMID: 31596127 DOI: 10.1089/dia.2019.0270] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background: Optimizing programmed basal rates and carbohydrate ratios may improve the performance of the artificial pancreas. We tested, in a diabetes camp, the efficacy of a learning algorithm that updates daily basal rates and carbohydrate ratios in the artificial pancreas. Materials and Methods: We conducted a randomized crossover trial in campers and counselors aged 8-21 years with type 1 diabetes on pump therapy. Participants underwent 2 days of artificial pancreas alone and 6 days of artificial pancreas with learning. During the artificial pancreas with learning, programmed basal rates and carbohydrate ratios were updated daily based on the learning algorithm's recommendations. All algorithm recommendations were reviewed for safety by camp physicians. The primary outcome was the time in target range (3.9-10 mmol/L) of the last 2 days of each intervention. Results: Thirty-four campers (age 13.9 ± 3.9, hemoglobin A1c 8.3% ± 0.2%) were included. Ninety-six percent of algorithm recommendations were approved by the camp physicians. Participants were in closed-loop mode 74% of the time. There was no difference between interventions in time in target (55%-55%; P = 0.71) nor in hypoglycemia events (0.8-0.9 events per day; P = 0.63). This was despite changes in programmed basal rate ranging from -21% to +117%, and changes in breakfast, lunch, and supper carbohydrate ratios from -17% to +40%, -36% to +37%, and -35% to +63%, respectively. Morever, postprandial hyperglycemia and hypoglycemia did not decrease in participants whose carbohydrate ratios were decreased (more insulin boluses) and increased (less insulin boluses), respectively. Conclusions: In camp settings, despite adjustments to programmed basal rates and carbohydrate ratios, the learning algorithm did not change glycemia, which may point toward limited effect of these adjustments in environments with large day-to-day variability in insulin needs. Longer randomized studies in real-world settings are required to further assess the efficacy of automatic adjustments of programmed basal rates and carbohydrate ratios.
Collapse
Affiliation(s)
- Emilie Palisaitis
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
| | - Anas El Fathi
- Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada
| | - Julia E Von Oettingen
- Department of Pediatric Endocrinology, McGill University Health Centre, Montreal Children's Hospital, Montreal, Canada
- The Research Institute of McGill University Health Centre, Montreal, Canada
| | - Preetha Krishnamoorthy
- Department of Pediatric Endocrinology, McGill University Health Centre, Montreal Children's Hospital, Montreal, Canada
| | - Robert Kearney
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
| | - Peter Jacobs
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon
| | - Joanna Rutkowski
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
| | - Laurent Legault
- Department of Pediatric Endocrinology, McGill University Health Centre, Montreal Children's Hospital, Montreal, Canada
- The Research Institute of McGill University Health Centre, Montreal, Canada
| | - Ahmad Haidar
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
- The Research Institute of McGill University Health Centre, Montreal, Canada
| |
Collapse
|
34
|
|
35
|
Guzman Gómez GE, Burbano Agredo LE, Martínez V, Bedoya Leiva OF. Application of Artificial Intelligence Techniques for the Estimation of Basal Insulin in Patients with Type I Diabetes. Int J Endocrinol 2020; 2020:7326073. [PMID: 33204261 PMCID: PMC7655245 DOI: 10.1155/2020/7326073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 10/02/2020] [Accepted: 10/15/2020] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence techniques have been positioned in the resolution of problems in various areas of healthcare. Clinical decision support systems developed from this technology have optimized the healthcare of patients with chronic diseases through mobile applications. In this study, several models based on this methodology have been developed to calculate the basal insulin dose in patients with type I diabetes using subcutaneous insulin infusion pumps. Methods. A pilot experimental study was performed with data from 56 patients with type 1 diabetes who used insulin infusion pumps and underwent continuous glucose monitoring. Several models based on artificial intelligence techniques were developed to analyze glycemic patterns based on continuous glucose monitoring and clinical variables in order to estimate the basal insulin dose. We used neural networks (NNs), Bayesian networks (BNs), support vector machines (SVMs), and random forests (RF). We then evaluated the agreement between predicted and actual values using several statistical error measurements: mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), Pearson's correlation coefficient (R), and determination coefficient (R 2). Results. Twenty-four different models were obtained, one for each hour of the day, with each chosen technique. Correlation coefficients obtained with RF, SVMs, NNs, and BNs were 0.9999, 0.9921, 0.0303, and 0.7754, respectively. The error increased between 06:00 and 07:00 and between 13:00 and 17:00. Conclusions. The performance of the RF technique was excellent and got very close to the actual values. Intelligence techniques could be used to predict basal insulin dose. However, it is necessary to explore the validity of the results and select the target population. Models that allow for more accurate levels of prediction should be further explored.
Collapse
Affiliation(s)
- Guillermo Edinson Guzman Gómez
- Fundación Valle del Lili, Departamento de Endocrinología, Cali, Colombia
- Universidad Icesi, Facultad de Ciencias de la Salud, Cali, Colombia
| | | | - Veline Martínez
- Universidad Icesi, Facultad de Ciencias de la Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | |
Collapse
|
36
|
Prahalad P, Zaharieva DP, Addala A, New C, Scheinker D, Desai M, Hood KK, Maahs DM. Improving Clinical Outcomes in Newly Diagnosed Pediatric Type 1 Diabetes: Teamwork, Targets, Technology, and Tight Control-The 4T Study. Front Endocrinol (Lausanne) 2020; 11:360. [PMID: 32733375 PMCID: PMC7363838 DOI: 10.3389/fendo.2020.00360] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 05/07/2020] [Indexed: 12/12/2022] Open
Abstract
Many youth with type 1 diabetes (T1D) do not achieve hemoglobin A1c (HbA1c) targets. The mean HbA1c of youth in the USA is higher than much of the developed world. Mean HbA1c in other nations has been successfully modified following benchmarking and quality improvement methods. In this review, we describe the novel 4T approach-teamwork, targets, technology, and tight control-to diabetes management in youth with new-onset T1D. In this program, the diabetes care team (physicians, nurse practitioners, certified diabetes educators, dieticians, social workers, psychologists, and exercise physiologists) work closely to deliver diabetes education from diagnosis. Part of the education curriculum involves early integration of technology, specifically continuous glucose monitoring (CGM), and developing a curriculum around using the CGM to maintain tight control and optimize quality of life.
Collapse
Affiliation(s)
- Priya Prahalad
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- *Correspondence: Priya Prahalad
| | - Dessi P. Zaharieva
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - Ananta Addala
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - Christin New
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - David Scheinker
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
| | - Manisha Desai
- Quantitative Sciences Unit, Division of Biomedical Informatics Research, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford, CA, United States
| | - Korey K. Hood
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford, CA, United States
| | - David M. Maahs
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford, CA, United States
| |
Collapse
|
37
|
Forlenza GP. Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy. Diabetes Technol Ther 2019; 21:S24-S28. [PMID: 31169433 PMCID: PMC6551985 DOI: 10.1089/dia.2019.0077] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Gregory P. Forlenza
- University of Colorado Denver, Barbara Davis Center, Pediatric Endocrinology, Aurora, Colorado
- Address correspondence to: Gregory P. Forlenza, MD, Barbara Davis Center, University of Colorado Denver, 1775 Aurora CT, MS A140, Aurora, CO 80045
| |
Collapse
|
38
|
Abstract
PURPOSE OF REVIEW To emphasize the current unmet needs for patients with diabetes and evaluate the recent technological advances in the diabetes field and summarize upcoming technologies in diabetes care. This review highlights emerging diabetes technologies and patient-centered diabetes management. RECENT FINDINGS A review of the literature showed that there is a clear benefit of using diabetes technologies in diabetes care. Recently, the US Food and Drug Administration (FDA) created a new category of Class II integrated continuous glucose monitoring (iCGM) devices and announced new guidelines to accelerate the approval of future products. With the first-generation hybrid-closed loop, a new era opened in automated insulin delivery systems. Diabetes coaching, apps, and remote monitoring technologies eased access to the providers and increased patient's self-confidence for diabetes management. SUMMARY Improvements in diabetes technologies will hopefully overcome unmet needs for patients with diabetes and improve health outcomes. Patients will benefit from the upcoming technologies in their day-to-day diabetes management while providers may monitor patients remotely with ease and efficiently. These developments will decrease diabetes burden, improve quality of life, and open a new era of personalized diabetes care.
Collapse
Affiliation(s)
- Halis Kaan Akturk
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus
- University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Satish Garg
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus
- University of Colorado School of Medicine, Aurora, Colorado, USA
| |
Collapse
|