Closed-Loop and Artificial Intelligence-Based Decision Support Systems.

Diabetes Technology & TherapeuticsVol. 25, No. S1 Original ArticlesFree AccessClosed-Loop and Artificial Intelligence–Based Decision Support SystemsRevital Nimri, Moshe Phillip, and Boris KovatchevRevital NimriDiabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.Search for more papers by this author, Moshe PhillipDiabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.Search for more papers by this author, and Boris KovatchevUniversity of Virginia Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA, USA.Search for more papers by this authorPublished Online:20 Feb 2023https://doi.org/10.1089/dia.2023.2505AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionClosed-loop (CL) control of diabetes, known as the “artificial pancreas,” or automated insulin delivery (AID), is no longer a strictly research subject. In the past 5 years, several CL systems have been approved for clinical use in Europe and the United States: Medtronic's MiniMed 670G/770G (1) and Tandem's t:slim X2 pump with Control-IQ technology (2,3) have both the Food and Drug Administration (FDA) for clinical use in the United States and CE (conformity with European standards) marks for clinical use in Europe. Three other systems, Medtronic 780G (referred to as the Advanced Hybrid Closed-Loop System, AHCL) (4), Cambridge's CamAPS FX (5), and Diabeloop's DBLG1 (6) have received CE marking but are still not available in the United States. Insulet's Omnipod 5 (7) was recently approved by the FDA in January 2022, but it is not available to the rest of the world.Results from real-world use of MiniMed 670G (8), Control-IQ Technology (9), and MiniMed 780G (10) were reported in three respective studies. For MiniMed 670G, researchers reported 19,982 users from 13 countries. Of those users, 14,899 (75%) had at least 10 days of data after initiating auto mode, and 880 (4%) had 1-year data after initiating auto mode. For those 880 who had 1 year of data, time in the target range of 70–180 mg/dL (TIR) increased from 62.4% at baseline to 72.1% during CL system use (8). For Control-IQ Technology, researchers reported 9451 users. Of those users, 9010 had more than 75% of their data available over 1 year; virtually all of these users (98.7%) switched to Control-IQ from the previous predictive low-glucose suspend system Basal-IQ. This study accumulated 9415 patient-years of data; the mean TIR increased from 64% on Basal-IQ to 74.4% throughout the year of Control-IQ use (9). For MiniMed 780G (ACHL), researchers reported 6710 users from nine countries. Of those users, 4120 had at least 10 days of data after initiating auto mode; the mean time of observation was 54±32 days per person, and the total observation time was 610 patient-years (10). About 20% of those who recorded more than 10 days of use (n=810) had baseline data as well, and for this subset of users, TIR increased by 12.1% after AHCL initiation (10). Most recently, at the 82nd Scientific Sessions of the American Diabetes Association (ADA), a new large dataset was revealed that included 20,314 people with type 1 (n=19,354) or type 2 (n=960) diabetes who had completed data during 1 month of Basal-IQ use followed by 3 months of Control-IQ technologies use. The results in type 1 and type 2 diabetes were similar, with TIR improvements of 12.1 and 9.4 percentage points, respectively (11,12).Remarkably, in all cases, these large-scale observational studies confirmed the results from randomized controlled trials published previously in high-ranking general medicine journals, such as the New England Journal of Medicine (2,3) and Lancet (4,13). The use of these systems has been further expanded with clinical trials testing CL utility with older adults (14) or those who need dialysis (15) in studies of carbohydrate thresholds that can be handled by a hybrid CL system (16) or with use of ultrarapid-acting insulin, the latter generally yielding results that were noninferior but not better than standard CL insulin therapy (17–19). First trials of fully automated CL not requiring meal announcement have been reported (20), and the general use (21) and economics of mainstream CL utilization have been discussed in the context of the successful use of one system (Control-IQ Technology) with Medicare and Medicaid type 1 and type 2 diabetes populations in the United States (22). The latter is of critical importance for the widespread use of CL technology, at least in the United States. We can therefore conclude that the first-generation CL systems are now mature and well established, enjoying expanding clinical use.Advances in artificial intelligence-based decision support systems are gaining more recognition as technological tools to support personalized health care in many fields of medicine. This article reviews the latest data reported on the use of decision support systems in diabetes. A survey recently published in the United States reported an average wait time for a physician appointment of 26 days in 2022, two days more than in the previous survey in 2017 (23). This reflects a national shortage of physicians and endocrinologists, a pattern also observed in other places of the world, especially in rural areas. Thus, despite the advantage of diabetes technology, the lack of access to specialized care can cause treatment inertia and increase disparities in diabetes care. Decision support systems can provide the level of expert care and overcome barriers to quality care. The American Diabetes Association (ADA) recognizes this gap in current practice, which is probably one of the reasons why only 1 in 4 adults with diabetes meet ADA recommendations for care (24). New strategies are needed to improve the quality of care for people with diabetes. For that, the ADA practice framework was issued and recommended integrating decision support systems into the workflow in order to support providers in clinical decision-making and treatment adjustments and support people with diabetes in self-management (25). A consensus statement by the European Association for the Study of Diabetes (EASD) and ADA stated, “We envision an ongoing role of the EASD, ADA, and other professional medical associations in supporting and expanding the field of diabetes digital health technology in the march to integration and continued automation” (26).Thus, in this article, we focus on clinical trials using decision support systems for people with diabetes who use multiple daily injection (MDI) therapy to support their insulin dosing decisions (27). We also focus on an algorithm to prioritize the team review of patients' continuous glucose monitoring (CGM) data at the clinic (28), on a glucose excursion minimization program based on CGM data to modify lifestyle for newly diagnosed adults with T2DM (29), and on clinical trials extending the use of CL to very young children (30). Furthermore, we examine the psychosocial effects and user experience with CL (5,31,32), the introduction of new combination device-drug therapies (33,34), prediction of success with CL (35), the effects of meal and exercise (36,37), and the proliferation of CL systems around the world (38,39).Key Articles ReviewedImpact of a Novel Diabetes Support System on a Cohort of Individuals with Type 1 Diabetes Treated with Multiple Daily Injections: A Multicenter Randomized StudyBisio A, Anderson S, Norlander L, O'Malley G, Robic J, Ogyaadu S, Hsu L, Levister C, Ekhlaspour L, Lam DW, Levy C, Buckingham B, Breton MDDiabetes Care 2022;45: 186–193Population-Level Management of Type 1 Diabetes via Continuous Glucose Monitoring and Algorithm-Enabled Patient Prioritization: Precision Health Meets Population HealthFerstad 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 DPediatr Diabetes 2021;22: 982–991An Innovative, Paradigm-Shifting Lifestyle Intervention to Reduce Glucose Excursions with the Use of Continuous Glucose Monitoring to Educate, Motivate, and Activate Adults with Newly Diagnosed Type 2 Diabetes: Pilot Feasibility StudyOser TK, Cucuzzella M, Stasinopoulos M, Moncrief M, McCall A, Cox DJJMIR Diabetes 2022;7: e34465Randomized Trial of Closed-Loop Control in Very Young Children with Type 1 DiabetesWare J, Allen JM, Boughton CK, Wilinska ME, Hartnell S, Thankamony A, de Beaufort C, Schierloh U, Fröhlich-Reiterer E, Mader JK, Kapellen TM, Rami-Merhar B, Tauschmann M, Nagl K, Hofer SE, Campbell FM, Yong J, Hood KK, Lawton J, Roze S, Sibayan J, Bocchino LE, Kollman C, Hovorka R from the KidsAP ConsortiumN Engl J Med. 2022;386: 209–219Effect of a Hybrid Closed-Loop System on Glycemic and Psychosocial Outcomes in Children and Adolescents with Type 1 Diabetes: A Randomized Clinical TrialAbraham MB, de Bock M, Smith GJ, Dart J, Fairchild JM, King BR, Ambler GR, Cameron FJ, McAuley SA, Keech AC, Jenkins A, Davis EA, O'Neal DN, Jones TW, Australian Juvenile Diabetes Research Fund Closed-Loop Research GroupJAMA Pediatr 2021;175: 1227–1235Real-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 GPDiabetes Technol Ther 2021;23: 837–843Cambridge Hybrid Closed-Loop Algorithm in Children and Adolescents with Type 1 Diabetes: A Multicentre 6-Month Randomised Controlled TrialWare J, Boughton CK, Allen JM, Wilinska ME, Tauschmann M, Denvir L, Thankamony A, Campbell FM, Wadwa RP, Buckingham BA, Davis N, DiMeglio LA, Mauras N, Besser REJ, Ghatak A, Weinzimer SA, Hood KK, Fox DS, Kanapka L, Kollman C, Sibayan J, Beck RW, Hovorka R on behalf of the DAN05 ConsortiumLancet Digit Health 2022;4: e245–e255Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 DiabetesGarcia-Tirado J, Farhy L, Nass R, Kollar L, Clancy-Oliveri M, Basu R, Kovatchev B, Basu ADiabetes Technol Ther 2022;24: 461–470Empagliflozin Add-On Therapy to Closed-Loop Insulin Delivery in Type 1 Diabetes: A 2 × 2 Factorial Randomized Crossover TrialHaidar A, Lovblom LE, Cardinez N, Gouchie-Provencher N, Orszag A, Tsoukas MA, Falappa CM, Jafar A, Ghanbari M, Eldelekli D, Rutkowski J, Yale JF, Perkins BANat Med 2022;28: 1269–1276Predictors of Time-In-Range (70-180 mg/dL) Achieved Using a Closed-Loop Control SystemSchoelwer MJ, Kanapka LG, Wadwa RP, Breton MD, Ruedy KJ, Ekhlaspour L, Forlenza GP, Cobry EC, Messer LH, Cengiz E, Jost E, Carria L, Emory E, Hsu LJ, Weinzimer SA, Buckingham BA, Lal RA, Oliveri MC, Kollman CC, Dokken BB, Cherñavvsky DR, Beck RW, DeBoer MD and the iDCL Trial Research GroupDiabetes Technol Ther 202l;23: 475–481Dietary Determinants of Postprandial Blood Glucose Control in Adults with Type 1 Diabetes on a Hybrid Closed-Loop SystemVetrani C, Calabrese I, Cavagnuolo L, Pacella D, Napolano E, Di Rienzo S, Riccardi G, Rivellese AA, Annuzzi G, Bozzetto LDiabetologia 2022;65: 79–87A Randomized Crossover Trial Comparing Glucose Control During Moderate-Intensity, High-Intensity, and Resistance Exercise with Hybrid Closed-Loop Insulin Delivery While Profiling Potential Additional Signals in Adults with Type 1 DiabetesPaldus B, Morrison D, Zaharieva DP, Lee MH, Jones H, Obeyesekere V, Lu J, Vogrin S, La Gerche A, McAuley SA, MacIsaac RJ, Jenkins AJ, Ward GM, Colman P, Smart CEM, Seckold R, King BR, Riddell MC, O'Neal DNDiabetes Care 2022;45: 194–203Six-Month Glycemic Control with a Hybrid Closed-Loop System in Type 1 Diabetes Patients in a Latin American CountryProietti A, Raggio M, Paz M, Rubin G, Kabakian M, Saleme A, Grosembacher LDiabetes Technol Ther 2022;24: 220–226Glycemic Outcomes of Advanced Hybrid Closed Loop System in Children and Adolescents with Type 1 Diabetes, Previously Treated with Multiple Daily Injections (MiniMed 780G System in T1D Individuals, Previously Treated with MDI).Petrovski G, Al Khalaf F, Campbell J, Day E, Almajaly D, Hussain K, Pasha M, Umer F, Hamdan M, Khalifa ABMC Endocr Disord 2022;22: 80DECISION SUPPORT SYSTEMSImpact of a Novel Diabetes Support System on a Cohort of Individuals with Type 1 Diabetes Treated with Multiple Daily Injections: a Multicenter Randomized StudyBisio A1, Anderson S1, Norlander L2, O'Malley G3, Robic J1, Ogyaadu S3, Hsu L2, Levister C3, Ekhlaspour L2, Lam DW3, Levy C3, Buckingham B2, Breton MD11Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA; 2School of Medicine, Stanford University, Stanford, CA; 3Icahn School of Medicine at Mount Sinai, New York, NYDiabetes Care 2022;45: 186–193Objective For many people with type 1 diabetes, glycemic control is difficult to optimize, despite the existence of newer management systems, such as continuous glucose monitoring (CGM). Modern management systems produce large volumes of data, but these data are still not being used to a great extent. In this study, we examined the effects of a CGM-based decision support system (DSS) in patients with T1D using multiple daily injections (MDIs).Research Design and MethodsThe studied DSS included real-time dosing advice and retrospective therapy optimization. Adults and adolescents aged >15 years with T1D using MDIs were enrolled at three sites in a 14-week randomized controlled trial of MDI + CGM + DSS versus MDI + CGM. All participants (N=80) used degludec basal insulin and Dexcom G5 CGM. CGM-based and patient-reported outcomes were analyzed. Within the DSS group, ad hoc analysis further contrasted active versus nonactive DSS users.ResultsNo significant differences were detected between experimental and control groups (e.g., time in range [TIR] +3.3% with CGM vs +4.4% with DSS). Participants in both groups reported lower HbA1c (−0.3%; P=.001) with respect to baseline. While TIR may have improved in both groups, it was statistically significant only for DSS; the same pattern was apparent for time spent <60 mg/dL. Compared to nonactive DSS users, active ones showed lower risk of and exposure to hypoglycemia with system use.ConclusionsOur DSS seems to be a feasible option for individuals using MDIs, although the glycemic benefits associated with use need to be further investigated. System design, therapy requirements, and target population should be further refined prior to use in clinical care.CommentsThe use of a CGM-based decision support system (DSS) for real-time insulin dosing for people with T1D using MDI therapy was demonstrated to be feasible and safe. Both study groups showed improvements, but no significant differences were observed in glycemic control and patient reported outcomes (PROs) between the group that used the DSS and the control group.This was a well-designed randomized controlled study, yet it is hard to appreciate the actual additional benefit of the DSS. The use of the DSS was accompanied by significant therapy change in both study groups. This included mainly the introduction of CGM and a switch to insulin analogues with a more stable insulin degludec. These two changes had a significant impact on the TIR in both groups, emphasizing the efficacy of CGM for MDI users. In addition, both groups had frequent contact with the study team (every 2 weeks), which might also have impacted the study outcomes. Furthermore, no insulin data were provided, a factor that limits the ability to evaluate changes in insulin administration between the groups.Nevertheless, the authors demonstrated greater glycemic benefit (reduction in hypoglycemia) in a subgroup of participants who used the DSS recommendations (active users). These findings are in line with the well-known observation that technology helps if you use it. The more intriguing question is why only a third of the participants in the DSS group used the system and not all of them. One of the answers could be the device usability problems that occurred during the study (such as connectivity issues) as mentioned by the authors. It might be that a seamless-use device may increase the number of active users. The device's capabilities include a wide range of interventions, including a bolus calculator, hypoglycemia prediction, exercise and bedtime advice to assess the risk of hypoglycemia, and retrospective insulin dose titration every 2 weeks. It would have been useful to assess which components contributed to the reduction in hypoglycemia observed in the active subgroup who used the DSS.The study demonstrates that DSS is a feasible option for people using MDIs. People who do not want or cannot use pump therapy should have technological tools to support insulin dosing decisions. Evaluating the DSS among different populations for a longer time compared to a regular care group might have provided different outcomes; this is left to be seen.Population-Level Management of Type 1 Diabetes via Continuous Glucose Monitoring and Algorithm-Enabled Patient Prioritization: Precision Health Meets Population HealthFerstad JO1, Vallon JJ1, Jun D1, Gu A2, Vitko A2, Morales DP1, Leverenz J3, Lee MY3, Leverenz B3, Vasilakis C4, Osmanlliu E3,5, Prahalad P3,6, Maahs DM3,6,7, Johari R1,6, Scheinker D1,3,81Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA; 2Department of Computer Science, Stanford University School of Engineering, Stanford, CA; 3Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA; 4Centre for Healthcare Innovation and Improvement (CHI2), School of Management, University of Bath, Bath, UK; 5Department of Pediatrics, Montreal Children's Hospital, McGill University Health Centre, Montreal, Canada; 6Stanford Diabetes Research Center, Stanford University, Stanford, CA; 7Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA; 8Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CAPediatr Diabetes 2021;22: 982–991This manuscript is also discussed in DIA-2023-2506, page S-90.ObjectiveWe aim to develop and scale a patient prioritization system based on an open-source algorithm to manage more patients who have type 1 diabetes (T1D) in a fixed-resource pediatric clinic. We will do this through telemedicine and by reviewing continuous glucose monitoring (CGM) data to remotely monitor patients.MethodsWe 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.ResultsThe 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) glucose levels compared to 25 control patients who did not qualify at 12 months after T1D onset.ConclusionsAsynchronous remote review of T1D patients was prioritized by an algorithm; by treating these patients through asynchronous remote review, providers spent less time per patient. Under this type of care, patients showed improvements in time in range.CommentsIn the present study, the authors have used an open-source algorithm to prioritize the team review of patients' CGM data. In the manuscript, they describe in detail all the stages of the algorithm development and the way they introduced it to their clinic in a research program. Despite the fact the study had relatively small number of participants with a high percentage of newly diagnosed participants with T1D, it is an important step in the right direction.The emerging artificial intelligence–based (AI) technologies and the new generation of sensors with the ability to passively transfer the CGM data to the Cloud, open a new horizon of opportunities for people with diabetes and their health-care providers (HCPs). It is obvious that in a busy T1D or T2D clinic there are people with diabetes (PwD) who need more attention at a certain point of time than others. In most places in the world, the HCPs meet their patients only a few times a year and do not regularly follow them in between visits. The CGM technology and the passive transfer of the data to the Cloud, which make those data accessible to HCPs, can theoretically change that reality. However, it is time consuming to review CGM data in between visits, analyze the data, prioritize the patients, and think of what needs to be changed in a patient's care and what kind of advice should be given to that patient. Handling this level of work is not feasible in most diabetes clinics around the world and especially not in a busy primary care clinic. Therefore, the future of diabetes follow-up visits depends on new technologies that will be able not only to prioritize which patients' data should be reviewed but also to interpret the data and to deliver advice on how to titrate the insulin regimens of those patients while those patients are using a pump, syringe, or a pen and to suggest behavioral changes in the aim to improve the metabolic control. Such an approach will be a game changer in the way we practice medicine globally.An Innovative, Paradigm-Shifting Lifestyle Intervention to Reduce Glucose Excursions with the Use of Continuous Glucose Monitoring to Educate, Motivate, and Activate Adults with Newly Diagnosed Type 2 Diabetes: Pilot Feasibility StudyOser TK1, Cucuzzella M2, Stasinopoulos M1, Moncrief M3, McCall A4, Cox DJ31Department of Family Medicine, University of Colorado School of Medicine, Aurora, CO; 2Department of Family Medicine, West Virginia University School of Medicine, Morgantown, WV; 3Department of Psychiatry and Neurobehavioral Sciences, University of Virginia School of Medicine, Charlottesville, VA; 4Department of Medicine: Endocrinology and Metabolism, University of Virginia School of Medicine, Charlottesville, VAJMIR Diabetes 2022;7: e34465BackgroundType 2 diabetes (T2D) is a problem that has been in increasing in the United States, but in the past 10 years, glycemic control has not improved. Although traditional advice given to new T2D patients is to lose weight, a different approach, glycemic excursion minimization (GEM), instead focuses on reducing postnutrient glucose excursions through lifestyle changes. There is evidence that GEM is superior to routine care when done face to face, and that it is equivalent or superior to the traditional weight loss approach. However, GEM has not been studied in newly diagnosed T2D patients or in those who use a self-administered version of this approach.ObjectiveThis pilot study evaluated the feasibility of a self-administered version of GEM, augmented with continuous glucose monitoring (CGM), to improve metabolic control (hemoglobin A1c [HbA1c]) while diminishing or delaying the need for diabetes medications in adults recently diagnosed with T2D. These primary objectives were hypothesized to be achieved by reducing carbohydrate intake and increasing physical activity to diminish CGM glucose excursions, leading to the secondary benefits of an increase in diabetes empowerment and reduced diabetes distress, depressive symptoms, and body mass index (BMI).MethodsGEM was self-administered by 17 adults recently diagnosed with T2D (mean age, 52±11.6 years; mean T2D duration, 3.9±2.5 months; mean HbA1c levels, 8.0%±1.6%; 40% female; 33.3% non-White), with the aid of a four-chapter pocket guide and diary, automated motivational text messaging, and feedback from an activity monitor, along with CGM and supplies for the 6-week intervention and the 3-month follow-up. Treatment was initiated with one telephone call reviewing the use of the technology and 3 days later with a second call reviewing the use of the GEM pocket guide and intervention.ResultsAt 3-month follow-up, diabetes was in remission for 67% of the participants (HbA1c levels <6.5%), and only one participant started taking diabetes medication. Participants demonstrated a significant reduction in HbA1c levels (−1.8%; P<.001). Participants also experienced significant reductions in the routine consumption of high-glycemic-load carbohydrates, CGM readings that were >140 mg/dL, diabetes distress, depressive symptoms, and BMI. Participants felt that use of the CGM was the most significant single element of the intervention.ConclusionsGEM augmented with CGM feedback may be an effective initial intervention for adults newly diagnosed with T2D. A self-administered version of GEM may provide primary care physicians and patients with a new tool to help people recently diagnosed with T2D achieve remission independent of medication and without weight loss as the primary focus. Future research is needed with a larger and more diverse sample.CommentsIn the present pilot feasibility study, the authors conducted a multicenter trial to investigate the ability of a self-administered version of glucose excursion minimization (GEM) program augmented with CGM to improve metabolic control (HbA1C) in adults who were recently diagnosed with T2DM. The GEM method was developed to empower people with diabetes to better understand the impact of food and exercise on their blood glucose levels. GEM has been administrated as a face-to-face intervention in adults diagnosed with T2DM during the past 10 years and was published in the literature as superior to routine care. The present study was the first attempt to run GEM on newly diagnosed people with T2D in a self-administered format. The authors hoped to achieve a reduction in carbohydrate intake and increase in physical activity and to diminish CGM glucose excursion. Altogether, a small group of patients were enrolled into the study and only 3 months of follow-up data were presented. Mean HbA1c levels were reduced by 1.8% by all participants; there were decreases in diabetes distress, depression symptoms, and BMI. In addition, patients felt more empowered with respect to their diabetes care. All improvements were achieved by the participants themselves, with the use of a CGM and a booklet with instruction and education.It is a pity that the study did not have a control group, had only a small number of participants, and was conducted over a short period of time. Perhaps a decision support system based on artificial intelligence and designed to interact directly with people newly diagnosed with T2D could be helpful. Regardless, before conclusions can be drawn, a prospective randomized control study needs to be conducted with a sufficient number of newly diagnosed people with T2D and for a longer period of time.CLOSED-LOOP SYSTEMSRandomized Trial of Closed-Loop Control in Very Young Children with Type 1 DiabetesWare J2, Allen JM1, Boughton CK1, Wilinska ME1,2, Hartnell S3, Thankamony A2, de Beaufort C6,7, Schierloh U6, Fröhlich-Reiterer E8, Mader JK9, Kapellen TM12, Rami-Merhar B10, Tauschmann M10, Nagl K10, Hofer SE11, Campbell FM4, Yong J4, Hood KK13, Lawton J5, Roze S14, Sibayan J15, Bocchino LE15, Kollman C15, Hovorka R1,2 from the KidsAP Consortium1Wellcome Trust-Medical Research Council (MRC) Institute of Metabolic Science, University of Cambridge, Cambridge, UK; 2Department of Paediatrics (JW, MEW, AT, RH), University of Cambridge, Cambridge, UK; 3Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust Cambridge, UK; 4Department of Paediatric Diabetes, Leeds Children's Hospital, Leeds, UK; 5Usher Institute, University of Edinburgh, Edinburgh, UK; 6Diabetes and Endocrine Care Clinique Pédiatrique, Clinique Pédiatrique, Centre Hospitalier de Luxembourg, Luxembourg; 7Department of Pediatric Endocrinology, Universitair Ziekenhuis Brussel-Vrije Universiteit Brussel, Brussels; 8Department of Pediatric and Adolescent Medicine, Medical University of Graz, Graz, Austria; 9Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria; 10Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria; 11Department of Pediatrics I, Medical University of Innsbruck, Innsbruck, Austria; 12Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany and the Hospital for Children and Adolescents “am Nicolausholz,” Bad Kösen, Germany; 13Division of Pediatric Endocrinology, Stanford University, Stanford, CA; 14Vyoo Agency, Lyon, France; 15Jaeb Center for Health Research, Tampa, FLN Engl J Med. 2022;386: 209–219This manuscript is also discussed in DIA-2023-2508, page S-118.BackgroundIt is unclear whether hybrid closed-loop therapy (i.e., artificial pancreas) is more efficacious than sensor-augmented pump therapy in young children with type 1 diabetes.MethodsThis was a multicenter randomized crossover trial. Eligible participants were children with type 1 diabetes who were between 1 and 7 years of age and were receiving insulin-pump therapy at one of seven centers in Austria, Germany, Luxembourg, or the United Kingdom. Participants received treatment in two 16-week periods, in random order, to compare the closed-loop sys