Real‐World Diabetes Technology: What Do We Have? Who Are We Missing?
2022; Mary Ann Liebert, Inc.; Volume: 24; Issue: S1 Linguagem: Inglês
10.1089/dia.2022.2510
ISSN1557-8593
AutoresLaurel H. Messer, Stuart A. Weinzimer,
Tópico(s)Mobile Health and mHealth Applications
ResumoDiabetes Technology & TherapeuticsVol. 24, No. S1 Original ArticlesFree AccessReal‐World Diabetes Technology: What Do We Have? Who Are We Missing?Laurel H. Messer and Stuart A. WeinzimerLaurel H. MesserBarbara Davis Center, University of Colorado Anschutz, COCollege of Nursing, University of Colorado Anschutz, COSearch for more papers by this author and Stuart A. WeinzimerDepartment of Pediatrics, Yale University, New Haven, CTSearch for more papers by this authorPublished Online:25 Apr 2022https://doi.org/10.1089/dia.2022.2510AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionThis is the fourth ATTD Yearbook article on practical implementation of diabetes technology. It serves as an important moment to benchmark outcomes in 2021 that are achieved outside of clinical trials, and in real‐world use. The difference between trial outcomes and real‐world outcomes are dependent on the social‐ecological domains of influence, including individual factors (knowledge, behavior); interpersonal (networks, support); organizational (healthcare access); community (cultural values, norms); and policy (insurance coverage, policy).Benchmarking is necessarily influenced by technology diffusion patterns across subpopulations. Technology (innovation) diffusion theory explains how technology adaption occurs across predictable trajectories of individuals from higher to lower socioeconomic status (SES), education, and risk tolerance levels (1). Last year's Yearbook article (2) highlighted a study that examined the diffusion of diabetes technology across different levels of education and socioeconomic status in Norway (3). The disparity in technology uptake was highest when technologies were newer, and slowly became less apparent as time went on, though it persisted through the 20‐year study period. This is important context for examining benchmarks of real‐world success for diabetes technologies. In addition to asking, “What are the real‐world outcomes using diabetes technologies?,” the more important question becomes, “Who do these outcomes represent, and who are missing?”This article highlights two aspects of real‐world diabetes technology use—first, we present important benchmarking data related to outcomes achieved in the real world, with perspective on study population. Second, we highlight important disparities in technology use across different national contexts. Included in this article are original research articles retrieved from PubMed, published between July 2020 and June 2021, using search terms related to diabetes technologies, including insulin pump, continuous subcutaneous insulin infusion (CSII), hybrid closed loop (HCL), continuous glucose monitor (CGM), flash/intermittently scanned CGM (isCGM), and real‐time CGM (rtCGM). Important context terms included “disparities,” “real‐world use,” “barriers,” “discontinuation,” “practical,” “clinical care,” and “education.” Over 300 article titles were reviewed for pertinence and possible inclusion in this article. Of these, 46 abstracts were reviewed, and 16 selected for inclusion in this article.Key Articles Reviewed for the ArticleTime in Range in Children with Type 1 Diabetes Using Treatment Strategies Based on Nonautomated Insulin Delivery Systems in the Real WorldCherubini V, Bonfanti R, Casertano A, De Nitto E, Iannilli A, Lombardo F, Maltoni G, Marigliano M, Bassi M, Minuto N, Mozzillo, Rabbone I, Rapini N, Rigamonti A, Salzano G, Scaramuzza A, Schiaffini R, Tinti D, Toni S, Zagaroli L, Zucchini S, Maffeis C, Gesuita RDiabetes Technol Ther 2020;22: 509–515Real‐World Improvements in Hypoglycemia in an Insulin‐Dependent Cohort with Diabetes Mellitus Pre/Post Tandem Basal‐IQ Technology Remote Software UpdatePinsker JE, Leas S, Müller M, Habif SEndocr Pract 2020;26: 714–721Real‐World Performance of Hybrid Closed-Loop in Youth, Young Adults, and Older Adults with Type 1 Diabetes: Identifying a Clinical Target for Hybrid Closed-Loop UseBerget C, Akturk HK, Messer LH, Vigers T, Pyle L, Snell‐Bergeon J, Driscoll KA, Forlenza GPDiabetes Obes Metab 2021;23: 2048–2057Real‐World Performance of the MiniMed™ 670G System in EuropeDa Silva J, Bosi E, Jendle J, Arrieta A, Castaneda J, Grossman B, Cordero TL, Shin J, Cohen ODiabetes Obes Metab 2021;23: 1942–1949One Year Real‐World Use of the Control‐IQ Advanced Hybrid Closed‐Loop TechnologyBreton MD, Kovatchev BPDiabetes Technol Ther 2021; 23: 601–608Real‐World Use of a New Hybrid Closed Loop Improves Glycemic Control in Youth with Type 1 DiabetesMesser LH, Berget C, Pyle L, Vigers T, Cobry E, Driscoll KA, Forlenza GP2021; 23: 837–843A Real‐World Prospective Study of the Safety and Effectiveness of the Loop Open Source Automated Insulin Delivery SystemLum JW, Bailey RJ, Barnes‐Lomen V, Naranjo D, Hood KK, Lal RA, Arbiter B, Brown AS, DeSalvo DJ, Pettus J, Calhoun P, Beck RWDiabetes Technol Ther 2021;23: 367–375Racial‐Ethnic Disparities in Diabetes Technology Use Among Young Adults with Type 1 DiabetesAgarwal S, Schechter C, Gonzalez J, Long JADiabetes Technol Ther 2021;23: 306–313Insulin Pump Use in Children with Type 1 Diabetes: Over a Decade of DisparitiesLipman TH, Willi SM, Lai CW, Smith JA, Patil O, Hawkes CPJ Pediatr Nurs 2020;55: 110–115Racial and Ethnic Disparities in Rates of Continuous Glucose Monitor Initiation and Continued Use in Children with Type 1 DiabetesLai CW, Lipman TH, Willi SM, Hawkes CPDiabetes Care 2021;44: 255–257A Decade of Disparities in Diabetes Technology Use and HbA1c in Pediatric Type 1 Diabetes: A Transatlantic ComparisonAddala A, Auzanneau M, Miller K, Maier W, Foster N, Kapellen T, Walker A, Rosenbauer J, Maahs DM, Holl RWDiabetes Care 2021;44: 133–140Heterogeneity of Access to Diabetes Technology Depending on Area Deprivation and Demographics Between 2016 and 2019 in GermanyAuzanneau M, Rosenbauer J, Maier W, von Sengbusch S, Hamann J, Kapellen T, Freckmann G, Schmidt S, Lilienthal E, Holl RW on behalf of the DPV InitiativeJ Diabetes Sci Technol 2021; 15: 1059–1068Provider Implicit Bias Impacts Pediatric Type 1 Diabetes Technology Recommendations in the United States: Findings from The Gatekeeper StudyAddala A, Hanes S, Naranjo D, Maahs DM, Hood KKJ Diabetes Sci Technol 2021; 15: 1027–1033Barriers and Facilitators to Accessing Insulin Pump Therapy by Adults with Type 1 Diabetes Mellitus: A Qualitative StudyGajewska KA, Biesma R, Bennett K, Sreenan SActa Diabetol 2021;58: 93–105Barriers to Technology Use and Endocrinology Care for Underserved Communities with Type 1 DiabetesWalker AF, Hood KK, Gurka MJ, Filipp SL, Anez‐Zabala C, Cuttriss N, Haller MJ, Roque X, Naranjo D, Aulisio G, Addala A, Konopack J, Westen S, Yabut K, Mercado E, Look S, Fitzgerald B, Maizel J, Maahs DMDiabetes Care 2021;44: 1480–1490“I didn't really have a choice”: Qualitative Analysis of Racial‐Ethnic Disparities in Diabetes Technology Use Among Young Adults with Type 1 DiabetesAgarwal S, Crespo‐Ramos G, Long JA, Miller VADiabetes Technol Ther 2021; 23: 616–622BENCHMARKING IN THE REAL WORLDTime in Range in Children with Type 1 Diabetes Using Treatment Strategies Based on Nonautomated Insulin Delivery Systems in the Real WorldCherubini V1, Bonfanti R2, Casertano A3, De Nitto E4, Iannilli A1, Lombardo F5, Maltoni G6, Marigliano M7, Bassi M8, Minuto N8, Mozzillo3, Rabbone I9, Rapini N10, Rigamonti A2, Salzano G5, Scaramuzza A11, Schiaffini R10, Tinti D9, Toni S4, Zagaroli L1, Zucchini S6, Maffeis C7, Gesuita R121Department of Women's and Children's Health, Azienda Ospedaliero Universitaria Ospedali Riuniti di Ancona Umberto I G M Lancisi G Salesi, Ancona, Italy; 2Department of Pediatrics, Pediatric Diabetology Unit, Diabetes Research Institute, Scientific Institute Hospital San Raffaele, Milan, Italy; 3Department of Translational Medical Science, Section of Pediatrics, University of Naples Federico II School of Medicine and Surgery, Napoli, Italy; 4Pediatric Endocrinology and Diabetology Unit, Meyer Children's Hospital, Firenze, Italy; 5Department of Pediatrics, University of Messina Faculty of Medicine and Surgery, Messina, Italy; 6Department of Pediatrics, University Hospital of Bologna Sant'Orsola‐Malpighi Polyclinic, Bologna, Italy; 7Pediatric Diabetes and Metabolic Disorders Unit, University of Verona School of Medicine and Surgery, Verona, Italy; 8Department of Pediatrics, Giannina Gaslini Children's Hospital, Genova, Italy; 9Department of Pediatrics, University of Turin Faculty of Medicine and Surgery, Torino, Italy; 10Diabetes Unit – Bambino Gesù Children's Hospital – Roma, Italy; 11ASST Cremona, Cremona, Italy; 12Center of Epidemiology, Biostatistics, and Medical Informatics, Università Politecnica delle Marche, Ancona, ItalyDiabetes Technol Ther 2020;22: 509–515BackgroundGlucose sensors consist of real‐time continuous glucose monitoring (rtCGM) and intermittently scanned CGM (isCGM). Their clinical use has been widely increasing during the past 5 years. The aim of this study is to evaluate percentage of time in range (TIR) in a large group of children with type 1 diabetes (T1D) using glucose sensors with nonautomated insulin delivery systems, in a real‐world setting.MethodsAn 11‐center cross‐sectional study was conducted during January–May 2019. Children with T1D 1 year, treated with multiple daily injections (MDI) or nonautomated insulin pump (IP), were recruited consecutively. Clinical data, HbA1c measurement, and CGM‐downloaded data were collected by each center and included in a centralized database for the analysis. Glucose metrics of four treatment strategies were analyzed: isCGM‐MDI, rtCGM‐MDI, isCGM‐IP, and rtCGM‐IP.ResultsData from 666 children with T1D (51% male and 49% female), median age 12 years, diabetes duration 5 years, were analyzed. An rtCGM was used by 51% of the participants, and a nonautomated IP by 46%. For isCGM‐MDI, rtCGM‐MDI, isCGM‐IP, and rtCGM‐IP, the median TIR 70–180 mg/dL values were 49%, 56%, 56%, and 61% (P<0.001), respectively; HbA1c 7.6%, 7.5%, 7.3%, and 7.3% (P<0.001), respectively. Use of rtCGM was associated with significant lower time below target range 180 mg/dL and lower HbA1c. If there are no barriers, an upgrade of the treatment strategy with a higher performing technology should be offered to all children who do not achieve blood glucose metrics within the suggested range.Real‐World Improvements in Hypoglycemia in an Insulin‐Dependent Cohort with Diabetes Mellitus Pre/Post Tandem Basal‐IQ Technology Remote Software UpdatePinsker JE1, Leas S2, Müller M3, Habif S41Sansum Diabetes Research Institute, Santa Barbara, CA; 2Tandem Diabetes Care, Information Technology, San Diego, CA; 3University of California San Diego, Design Lab, La Jolla, CA; 4Tandem Diabetes Care, Behavioral Sciences, San Diego, CAEndocr Pract 2020;26: 714–721ObjectiveSoftware‐updatable insulin pumps, such as the t:slim X2 pump from Tandem Diabetes Care, enable access to new technology as soon as it is commercialized. The remote software update process allows for minimal interruption in therapy compared to purchasing a new pump; however, little quantitative data exist on the software update process or on pre/post therapeutic outcomes. We examined real‐world usage and impact of a remote software‐updatable predictive low‐glucose suspend (PLGS) technology designed to reduce hypoglycemic events in people with insulin‐dependent diabetes.MethodsSince its commercial release, approximately 15,000 U.S. Tandem pump users remotely updated their t:slim X2 software to Basal‐IQ PLGS technology. We performed a retrospective analysis of users who uploaded at least 21 days of pre/post PLGS update usage data to the Tandem t:connect web application between August 28, 2018, and October 21, 2019 (N=6,170). The authors analyzed insulin delivery and sensor glucose values per recent international consensus and American Diabetes Association guidelines. They also assessed software update performance.ResultsThe median time to update software was 5.36 minutes. Overall glycemic outcomes for pre- and post-software update showed a decrease in sensor time <70 mg/dL from 2.14 to 1.18% (−1.01; 95% confidence interval [CI], −0.97, −1.05; P<0.001), with overall sensor time 70 to 180 mg/dL increasing from 57.8 to 58.5% (0.64; 95% CI, 0.04, 1.24; P<0.001). At 3, 6, and 9 months after the update, these improvements were sustained.ConclusionSustained reductions of hypoglycemia resulted from the introduction of a software‐updatable PLGS algorithm for the Tandem t:slim X2 insulin pump.Real‐World Performance of Hybrid Closed-Loop in Youth, Young Adults, and Older Adults with Type 1 Diabetes: Identifying a Clinical Target for Hybrid Closed-Loop UseBerget C1, Akturk HK1, Messer LH1, Vigers T2, Pyle L1,2, Snell‐Bergeon J1, Driscoll KA3, Forlenza GP11University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO; 2Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO; 3Department of Clinical and Health Psychology, University of Florida, Gainesville, FLDiabetes Obes Metab 2021;23: 2048–2057AimThe aim of the study was to describe real‐world hybrid closed loop (HCL) use and glycemic outcomes across the lifespan and identify a clinical threshold for HCL use associated with meeting the internationally recommended target of 70% sensor glucose time in range (TIR; 70–180 mg/dL).Materials and methodsThe authors used mixed models to examine MiniMed 670G HCL use and glycemic outcomes in 276 people with type 1 diabetes from four age groups: youth (aged <18 years), young adults (18–25 years), adults (26–49 years), and older adults (≥50 years) for 1 year. ROC analysis identified the minimum percentage HCL use associated with meeting the TIR goal of 70%.ResultsAt month 1, HCL use was 70.7%±2.9% for youth, 71.0%±3.8% for young adults, 78.9%±2.1% for adults, and 84.7%±3.8% in older adults. At 12 months, HCL use declined significantly to 49.3%±3.2% in youth (P 8.0%) and well‐controlled (GMI <7.0%) glycemia pre‐Auto Mode initiation.ResultsUsers (N=14,899) spent a mean of 81.4% of the time in Auto Mode and achieved a mean GMI of 7.0%±0.4%, TIR of 72.0%±9.7%, TBR less than 3.9 mmol/L of 2.4%±2.1%, and TAR more than 10 mmol/L of 25.7%±10%, after initiating Auto Mode. When compared with pre‐Auto Mode initiation, GMI was reduced by 0.3%±0.4% and TIR increased by 9.6%±9.9% (P 2 weeks of continuous glucose monitoring (CGM) data pre‐ and >12 months post‐Control‐IQ technology initiation were included in the analysis.ResultsIn total, 9,451 users met the inclusion criteria, 83% had type 1 diabetes, and the rest had type 2 or other forms of diabetes. The mean age was 42.6±20.8 years, and 52% were female. Median percent time in automation was 94.2% [interquartile range, IQR: 90.1%–96.4%] for the entire 12‐month duration of observation, with no significant changes over time. Of these users, 9,010 (96.8%) had ≥ 75% of their CGM data available, that is, sufficient data for reliable computation of CGM‐based glycemic outcomes. At baseline, median percent time in range (70–180 mg/dL) was 63.6 (IQR: 49.9%–75.6%) and increased to 73.6% (IQR: 64.4%–81.8%) for the 12 months of Control‐IQ technology use with no significant changes over time. Median percent time <70 mg/dL remained consistent at ∼1% (IQR: 0.5%–1.9%).ConclusionIn this real‐world use analysis, Control‐IQ technology retained, and to some extent exceeded, the results obtained in randomized controlled trials, showing glycemic improvements in a broad age range of people with different types of diabetes.Real‐World Use of a New Hybrid Closed Loop Improves Glycemic Control in Youth with Type 1 DiabetesMesser LH1, Berget C1, Pyle L1, Vigers T1, Cobry E1, Driscoll KA2, Forlenza GP11Barbara Davis Center for Diabetes, University of Colorado Anschutz, Aurora, CO; 2Diabetes Institute, University of Florida, Gainesville, FLDiabetes Technol Ther 2021; 23: 837–843ObjectiveTo describe real‐world outcomes for youth using the Tandem t:slim X2 insulin pump with Control‐IQ technology (“Control‐IQ”) for 6 months at a large pediatric clinic.MethodsYouth with type 1 diabetes, who started Control‐IQ for routine care, were prospectively followed. Data on system use and glycemic control were collected before Control‐IQ start and at 1, 3, and 6 months after start. Mixed models assessed change across time; interactions with baseline HbA1c and age were tested.ResultsIn 191 youth (median age 14, 47% female, and median HbA1c 7.6%), percent time with glucose levels 70–180 mg/dL (time in range [TIR]) improved from 57% at baseline to 66% at 6 months (P 70%) doubled from 23.5% at baseline to 47.8% at 3 months, sustaining at 46.7% at 6 months (P<0.001). Glucose management indicator (approximation of HbA1c) improved from 7.5% at baseline to 7.1% at 3 months and 7.2% at 6 months (P<0.001). Those with higher baseline HbA1c experienced the most substantial improvements in glycemic control. Percent time using the Control‐IQ feature was 86.4% at 6 months, and <4% of cohort discontinued use.ConclusionThe Control‐IQ system clinically and significantly improved glycemic control in a large sample of youth. System use was high at 6 months, with only a small proportion discontinuing use, indicating potential for sustaining results long term.A Real‐World Prospective Study of the Safety and Effectiveness of the Loop Open Source Automated Insulin Delivery SystemLum JW1, Bailey RJ1, Barnes‐Lomen V1, Naranjo D2, Hood KK2, Lal RA2, Arbiter B3, Brown AS3, DeSalvo DJ4, Pettus J5, Calhoun P1, Beck RW11Jaeb Center for Health Research, Tampa, FL; 2Department of Pediatrics and Medicine, Stanford University School of Medicine, Stanford, CA; 3Tidepool, Palo Alto, CA; 4Section of Pediatric Diabetes and Endocrinology, Baylor College of Medicine, Houston, TX; 5Division of Endocrinology, University of California, San Diego, San Diego, CADiabetes Technol Ther 2021;23: 367–375ObjectiveTo evaluate the safety and effectiveness of the Loop Do‐It‐Yourself automated insulin delivery system.Research design and methodsA prospective real‐world observational study was conducted, which included 558 adults and children (age range 1–71 years, mean HbA1c 6.8%±1.0%) who initiated Loop either on their own or with community‐developed resources and provided data for 6 months.ResultsMean time in range 70–180 mg/dL (TIR) increased from 67%±16% at baseline (before starting Loop) to 73%±13% during the 6 months (mean change from baseline 6.6%, 95% confidence interval [CI] 5.9%–7.4%; P<0.001). TIR increased in both adults and children, across the full range of baseline HbA1c, and in participants with both high‐ and moderate‐income levels. Median time <54 mg/dL was 0.40% at baseline and changed by −0.05% (95% CI −0.09% to −0.03%, P<0.001). Mean HbA1c was 6.8%±1.0% at baseline and decreased to 6.5%±0.8% after 6 months (mean difference=−0.33%, 95% CI −0.40% to −0.26%, P 1 year Youth 12 months Control-IQ use. Individuals who provided data and had sufficient CGM dataN=945194.2%73.6%(IQR: 64.4–81.8%)1% (IQR: 0.5%–1.9%)GMI: 6.9 (IQR 5.6–7.3) Control-IQ (9)US6-month follow-up. Large peds clinic academic center, data provided at 4 time points, study participants, 100% T1D, 48% female, 88% non-Hispanic white, 96% previous pump users 80% privately insuredn=19186.4 (SE 1.3)66.2%(SE 1.2)1.8% (SE 0.2)GMI: 7.2% (SE 0.1) DIY Loop (10)US6-month follow-up. Mean age 23 years, 57% female, 91% non-Hispanic white. Baseline HbA1c 6.8%, 94% privately insured.n=558unknown73%+/−132.8% (CI: 1.3%, 4.7%)6.5%48 +/−8.7 mmol/mol Who were the participants included in this cross‐section of “real‐world studies”? Some of the studies (5, 7, 8) used industry‐owned databases of glucose data, which produce large sample sizes and a wealth of data, but by the nature of personal health information protection, anonymizes the sample population. It is assumed the individuals had capacity to upload data from home and were either incentivized (internally or externally) to upload on a routine basis, or uploaded as part of routine clinical care. Further, these individuals were able to afford the upkeep of diabetes technology costs over time. Prospective observational trials provide opportunity to know more about real‐world participants, although Cherubini and colleagues did not report personal characteristics beyond % female. They point out, however, that “. . . glucose sensors and [insulin pumps] are accessible to all children with T1D in Italy,” indicating a potentially diverse population of participants (4). The participants in Berget et al. and Messer et al., which are from the same large academic center, report majority non‐Hispanic white participants (85% and 88%, respectively), which is associated with higher SES in the United States (6, 9). Further, over 80% had private insurance, a more direct proxy for socioeconomic advantage in the United States. In the Do‐it‐Yourself Loop study by Lum et al., 91% were non‐Hispanic white individuals, 94% had private health insurance, and 70% had >$100,000 USD annual household income, indicating a homogenous sample of wealthy, high SES participants (10).Notably, these “real‐world” participant demographics mimic randomized clinical trial participant demographics, with 84.5% of recent diabetes technology study participants identifying as non‐Hispanic white in the United States, which is disproportionately higher than the overall demographics of people living with type 1 diabetes (11). It would be expected that real‐world studies would include a broader representation of the population. The reason many of these studies do not reflect increased diversity is likely technology diffusion rates across subpopulations. Hybrid closed‐loop systems (like 670G, Control‐IQ, and DIY Loop) are new technologies, and contemporaneous data reflect the use of these systems by “early adopters.” According to technology diffusion, these individuals tend to have higher SES, higher levels of attained education, and have closer contact with HCPs (1). It is further reported that many of these individuals previously used other advanced technologies, minimizing the training and learning needed for a new system (5, 6, 8).It is not a criticism of the study design of these real‐world trials that the majority of participants report indicators of greater economic advantage. It is important to continue to report these metrics, however, so the results can be contextualized within the broader diabetes community.So, who are we missing in the broader diabetes community? As reflected in the representative articles featured in this article, this year has been the year of identification and documentation of the pervasive disparities in use of diabetes technologies: primarily across socioeconomic lines, but also, and more insidiously, across racial and ethnic lines that cannot be explained by simple financial and insurance factors alone.DISPARITIES IN DIABETES TECHNOLOGY UPTAKERacial‐Ethnic Disparities in Diabetes Technology Use Among Young Adults with Type 1 DiabetesAgarwal S1, Schechter C2, Gonzalez J1,3, Long JA4,51Fleischer Institute of Diabetes and Metabolism, New York‐Regional Center for Diabetes Translation Research, Division of Endocrinology, Albert Einstein College of Medicine, Bronx, NY; 2Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY; 3Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY; 4University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; 5Corporal Michael J. Crescenz VA Medical Center, Philadelphi
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