In addition, our meta-analysis provides a unique framework for comparing 7 permutations of different technologies. The breadth of these findings provides estimates of treatment effects with particular relevance to clinical decision-making and cost-effectiveness analyses. The application of our results may be illustrated through an approach to device selection.

For example, some devices appeared to offer the greatest potential for improved glycaemia compared to other systems in our sensitivity analyses, although no definite conclusions can be as no head-to-head comparisons were performed. Given the limited knowledge about DIY systems [ 62 ], our analysis provides insight into the potential benefit of openAPS.

Most studies in our analysis did not assess fully automated systems [ 8 , 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ], which still require manual input from the user [ 63 ].

Therefore, the use of such devices in children and adolescents remains a challenge. Previous meta-analyses on paediatric populations, such as a recent one by Michou and colleagues [ 64 ], have shown a reduced risk of hypoglycaemia when assessing RCTs of mostly less than 12 weeks duration.

Nonetheless, our analysis with RCTs of 12 to 96 weeks duration did not show a significantly reduced risk of hypoglycaemia nor coefficient of variation for the paediatric population, which could have been due to several reasons.

For instance, children are more likely to experience hypoglycaemia due to increased physical activity, hormonal changes, varied eating habits and lifestyle, and inability to communicate symptoms appropriately [ 65 ].

Furthermore, considerable proportion of RCTs included have reported system errors and malfunctioning during the longer duration of the trials, potentially having important impacts for children and adolescents who are at a higher risk of hypoglycaemia or those not achieving target control [ 4 ].

These findings have important implications to the design of future paediatric trials, which should consider placing significant focus on patient education, device functioning and type of system used.

Finally, this was the first meta-analysis to assess how long-term use of AID impacts patient-reported outcomes with a considerable number of studies. Although our findings show significantly improved diabetes distress and a tendency for reduced fear of hypoglycaemia, no benefits were seen for treatment satisfaction.

The high cost of AID devices, connectivity problems, automation-related errors, pump glitches, and other issues associated with insulin pumps have been perceived as drawbacks by T1DM patients [ 5 ]. Moreover, most studies included in our analyses use CL algorithms that still require manual bolus input.

Further improvements towards fully AID may result in improved quality of life and treatment satisfaction. Lastly, psychosocial measures varied between trials, limiting the populations of our analyses.

Given that such outcomes have been recently receiving increased attention [ 5 ], future studies may consider using more consistent and widely used measures to aid interpretation of psychosocial impact.

Our study has important limitations. The lack of blinding in the studies, as it is potentially unfeasible to blind patients in such RCTs, reduced the certainty of evidence for our findings. It is important to note that heterogeneity was high for most glycaemic outcomes, especially in the adult and mixed populations.

However, this finding was expected given the highly variable clinical and technical factors involved in studies performed in real-life conditions without supervision. Subgroup analyses of different machines and meta-regression were performed to minimise and interpret such heterogeneities.

Furthermore, we did not search the grey literature, which can increase the risk of publication bias. However, we believe that restricting our research to peer-reviewed sources minimised other sources of bias ensuring a more rigorous evaluation. Unfortunately, no study used outcomes such as mortality or macrovascular and microvascular complications as outcomes.

Therefore, our study relies on surrogate measures for patient-oriented outcomes. Finally, recent bihormonal CL systems were not included as the RCTs on these devices only had a short follow-up period.

Findings also suggest reductions in nocturnal and daily hypoglycaemia as well as patient distress without increasing the risk of DKA and severe hypoglycaemia.

This estimate is beneficial in planning future long-term clinical trials assessing the use of fully automated and bihormonal AID devices. The synthesis of all system subgroups emphasises the potential benefits of certain CL systems, although this finding requires head-to-head comparisons before definitive conclusions can be made.

Our results show that use of CL technology between 12 and 96 weeks has considerable benefits in a variety of clinical settings.

Ultimately, it will be at the discretion of clinicians and patients to understand the potential benefits associated with different CL systems and decide on the most optimal insulin delivery method to improve patient outcomes.

All data are publicly available in the relevant primary and secondary papers from relevant trials as listed in the References.

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Weisman A, Bai JW, Cardinez M, Kramer CK, Perkins BA. Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials.

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Accessed 3 May Schünemann H, Brożek J, Guyatt G, Oxman A, editors. GRADE handbook for grading quality of evidence and strength of recommendations.

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Effect of a hybrid closed-loop system on glycemic and psychosocial outcomes in children and adolescents with type 1 diabetes: a randomized clinical trial. JAMA Pediatr. Choudhary P, Kolassa R, Keuthage W, Kroeger J, Thivolet C, Evans M, Ré R, de Portu S, Vorrink L, Shin J, Habteab A.

Advanced hybrid closed loop therapy versus conventional treatment in adults with type 1 diabetes ADAPT : a randomised controlled study. Tauschmann M, Thabit H, Bally L, Allen JM, Hartnell S, Wilinska ME, Ruan Y, Sibayan J, Kollman C, Cheng P, Beck RW.

Closed-loop insulin delivery in suboptimally controlled type 1 diabetes: a multicentre, week randomised trial. Thabit H, Tauschmann M, Allen JM, Leelarathna L, Hartnell S, Wilinska ME, et al. Home use of an artificial beta cell in type 1 diabetes.

Boughton CK, Hartnell S, Thabit H, Mubita WM, Draxlbauer K, Poettler T, et al. Hybrid closed-loop glucose control compared with sensor augmented pump therapy in older adults with type 1 diabetes: an open-label multicentre, multinational, randomised, crossover study.

Lancet Healthy Longev. Brown SA, Beck RW, Raghinaru D, Buckingham BA, Laffel LM, Wadwa RP, Kudva YC, Levy CJ, Pinsker JE, Dassau E, Doyle FJ III. Glycemic outcomes of use of CLC versus PLGS in type 1 diabetes: a randomized controlled trial.

Burnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SK. Open-source automated insulin delivery in type 1 diabetes. Ware J, Boughton CK, Allen JM, Wilinska ME, Tauschmann M, Denvir L, et al.

Cambridge hybrid closed-loop algorithm in children and adolescents with type 1 diabetes: a multicentre 6-month randomised controlled trial. Lancet Digit Health. Boughton CK, Allen JM, Ware J, Wilinska ME, Hartnell S, Thankamony A, Randell T, Ghatak A, Besser RE, Elleri D, Trevelyan N.

Closed-loop therapy and preservation of C-peptide secretion in type 1 diabetes. Brown SA, Kovatchev BP, Raghinaru D, Lum JW, Buckingham BA, Kudva YC, Laffel LM, Levy CJ, Pinsker JE, Wadwa RP, Dassau E.

Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes. Pinsker JE, Dassau E, Deshpande S, Raghinaru D, Buckingham BA, Kudva YC, et al. Outpatient randomized crossover comparison of zone model predictive control automated insulin delivery with weekly data driven adaptation versus sensor-augmented pump: results from the International diabetes closed-loop trial 4.

Breton MD, Kanapka LG, Beck RW, Ekhlaspour L, Forlenza GP, Cengiz E, Schoelwer M, et al. A randomized trial of closed-loop control in children with type 1 diabetes. Benhamou PY, Franc S, Reznik Y, Thivolet C, Schaepelynck P, Renard E, Guerci B, Chaillous L, Lukas-Croisier C, Jeandidier N, Hanaire H.

Closed-loop insulin delivery in adults with type 1 diabetes in real-life conditions: a week multicentre, open-label randomised controlled crossover trial.

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McAuley SA, Lee MH, Paldus B, Vogrin S, De Bock MI, Abraham MB, et al. Six months of hybrid closed-loop versus manual insulin delivery with fingerprick blood glucose monitoring in adults with type 1 diabetes: a randomized, controlled trial.

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A Pilot randomized trial to examine effects of a hybrid closed-loop insulin delivery system on neurodevelopmental and cognitive outcomes in adolescents with type 1 diabetes.

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Diabetes Obes Metab. Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help doctors and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of CGMs, such as how CGMs could be made more sensitive, reliable, and comfortable to wear. Researchers are also studying how they might be used to manage different types of diabetes or other medical conditions. Find out if clinical studies are right for you.

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials. You can view a filtered list of clinical studies that use CGMs and are federally funded, open, and recruiting at www.

You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe. Always talk with your health care provider before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases NIDDK , part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public.

Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts. NIDDK would like to thank: Jenise C. Wong, M. Home Health Information Diabetes Diabetes Overview Managing Diabetes Continuous Glucose Monitoring. How does a continuous glucose monitor work?

Who can use a continuous glucose monitor? What are the different types of continuous glucose monitors? What are some features of continuous glucose monitors?

What are the benefits of a continuous glucose monitor? What issues could you have while using a continuous glucose monitor? What is an artificial pancreas? How does NIDDK support research on continuous glucose monitors? Clinical Trials on Continuous Glucose Monitors What is continuous glucose monitoring?

Most CGMs send information without using wires to an app on a smartphone. Other differences between CGM models include whether the sensor is placed on the skin or is implanted how often the sensor has to be replaced how long it takes the CGM to warm up how you adjust the program settings For some CGM models, you may need to do a finger-stick test with a standard blood glucose monitor to calibrate the system and make sure the CGM readings are correct.

Many CGMs work with apps that have special features, such as ways to track the food and beverages you consume, your physical activity level, and the medicines you take the ability to download data onto a computer or smart device so you can easily see trends in your glucose levels an alarm that goes off when your glucose level is too low or too high, helping you prevent emergencies For safety, it is important to act quickly if a CGM alarm sounds when your glucose level is too low or too high.

Chapter Headings Introduction—Why Is This Position Paper Needed? Methods What Do We Know About DIY AID Efficacy of DIY AID Special Populations Ethical and Medico-legal Considerations Supporting DIY AID Use in Practice Summary and Key Messages Role of the Community External Reviewers Author Disclosures.

Introduction—Why Is This Position Paper Needed? Methods A diverse group of experts in diabetes technology was convened by the Diabetes Canada Clinical Practice Guidelines CPG Steering Committee to determine the scope and purpose of the position statement.

Individual perspectives DIY AID systems were created by passionate and technologically adept individuals with lived experience out of a deep personal need for more effective and less burdensome solutions for safe glucose management. Textbox 1. Better integration with my phone and Apple watch.

DIY is also more customizable, so better suited for me than commercial systems currently available to me.

The authors of these systems test them on themselves, on their family members and, therefore, create these systems so that they can actually use them.

Efficacy of DIY AID The often cited HCP concern about the lack of high-quality published data [ 5 ] may actually be a constructed rather than a true barrier to use.

Glycemia Improvements in both glycated hemoglobin A1C 0. Quality of life Improved quality of life has been observed in multiple studies that have used validated quantitative scores with the initiation of DIY AID. Safety of DIY AID Despite lower mean glucose levels with DIY AID use, this has not been accompanied by an increase in TBR [ 4 , 24 , 25 , 31—33 , 35 , 38 , 40 , 44 ].

Accessibility and costs Access to insulin pumps and CGM devices varies substantially between Canadian provinces.

Special Populations Pregnancy Pregnancy in PWD is associated with an increased rate of both obstetrical and neonatal adverse outcomes.

In-hospital There have been no published studies on the use of DIY AID while PWD are admitted to hospital. Pediatrics The advantages of DIY AID over SAP for glycemia in a 6-month RCT described earlier applied equally to pediatric individuals aged 7 to 15 years. Textbox 2. Three fictional yet realistic cases Sarah is a year-old mother of 3 living with T1D in Montreal, Quebec.

Ethical and Medico-legal Considerations Medical ethics HCPs have a professional obligation to serve the public good by making the health and well-being of the individuals in their practice their first consideration.

Medico-legal considerations Autonomy and equitable access to health care are overriding legal rights of individuals in Canadian law and jurisprudence. Supporting DIY AID Use in Practice While PWD may independently build their DIY AID systems, the diabetes care team remains essential for core diabetes self-management education and support for DIY AID use.

Summary and Key Messages The evidence supports that DIY AID can provide significant improvements in A1C, TIR, and quality of life. Effectiveness of DIY AID DIY AID can be an effective approach to support individuals with T1D to increase TIR and reduce A1C without increasing hypoglycemia, all while reducing the burden of diabetes self-management and improving quality of life.

The benefits revealed by qualitative studies and lived experience should be carefully considered alongside glycemic benefits when discussing the risks and benefits of DIY AID. A net assessment of total risk should include the goals and concerns of the PWD and not overlook the risks inherent in all methods of insulin delivery.

No comparative studies have evaluated commercial versus DIY AID systems, such that no assumptions can be made about the superiority in either direction. Individual needs and preferences should direct choices. Role of HCPs HCPs have an obligation to discuss all available treatment options that have evidence of benefit with individuals living with T1D.

DIY AID should be discussed alongside commercially approved options. DIY AID may be more accessible to PWD in Canada than commercial systems due to costs, variation in coverage, and slow approval of commercial systems.

Out-of-pocket costs should be considered even for those with private insurance. HCPs should be aware of their biases and the potential for inappropriate gatekeeping e. HCPs making assumptions that an individual or family is not equipped to manage DIY AID.

precise carb counting, frequent finger sticks. HCPs should make every effort to support PWD who choose to use DIY AID and seek to maintain the therapeutic alliance.

It is recommended that PWD should build and install their own AID software. HCPs may support PWD in building their own systems, for example, by providing clinic space for peer-led mentorship programs e.

HCPs should continue to provide ongoing support and education on core diabetes self-management skills, and safe use of insulin pump therapy and CGM. HCPs should assist with optimizing pump settings and coaching on the behaviours linked to improving glycemic outcomes and meeting personal goals.

Role of the Community Where possible, HCPs should connect interested PWD to trusted online, social media, and in-person resources. Peer support has been an effective tool both for people seeking to start using DIY AID and for providing help and support to existing users.

Additional adjustable parameters in newer branches of DIY algorithms may include terminology that does not build on existing CSII settings language, making it difficult for HCPs to support individuals in optimizing settings.

An effort by the community of developers to consult with the diabetes education community would advance translation of DIY AID into clinical practice. External Reviewers Thank you to our external reviewers for their insightful feedback and the lending of their time and expertise: Lois Donovan MD, FRCPC Bruce A.

Perkins MD, FRCPC. Author Disclosures I. References Weisman A, Perkins BA. Attainment of glycemic targets among adults with diabetes in Canada: A cross-sectional national diabetes repository study.

Diabetes ;70 Suppl. American Diabetes Association Professional Practice Committee. Diabetes Technology: Standards of Medical Care in Diabetes Diabetes Care ;45 Suppl.

Braune K, Lal RA, Petruzelkova L, Scheiner G, Winterdijk P, Schmidt S, Raimond L, Hood KK, Riddell MC, Skinner TC, Raile K, Hussain S; OPEN International Healthcare Professional Network and OPEN Legal Advisory Group.

Open-source automated insulin delivery: international consensus statement and practical guidance for health-care professionals. Lancet Diabetes Endocrinol. doi: Epub Nov Erratum in: Lancet Diabetes Endocrinol.

PMID: ; PMCID: PMC Morrison AE, Chong K, Lai V, Senior PA, Lam A. Improved glycemia and quality of life among loop usersa retrospective analysis of real-world data from a single centre.

Diabetes Technol Ther ;A Morrison AE, Farnsworth K, Witteman HO, Lam A, Senior PA. Canadian healthcare providers' attitudes towards automated insulin delivery systems. medRxiv Diabetes Australia. People with type 1 diabetes and do-it-yourself DIY technology solutions. Accessed December 1, Morrison AE, et al.

Do-it-yourself and commercial automated insulin delivery systems in type 1 diabetes: An uncertain area for Canadian healthcare providers. Can J Diabetes Diabetes Canada. Diabetes Canada Clinical Practice Guidelines: Processes manual. A scoping review of do-it-yourself automated insulin delivery system DIY AID use in people with type 1 diabetes.

PLoS One ;e Suttiratana SC, Wong JJ, Lanning MS, Dunlap A, Hanes SJ, Hood KK, Lal RA, Naranjo D. Qualitative Study of User Experiences with Loop, an Open-Source Automated Insulin Delivery System.

Diabetes Technol Ther. Epub May Crocket H, Lewis DM, Burnside M, Faherty A, Wheeler B, Frewen C, Lever C, Jefferies C, Williman J, Sanders O, Wilson R, Paul R, Price S, Jones S, de Bock M. Learning challenges of healthcare professionals supporting open-source automated insulin delivery.

Diabet Med. Epub Dec 7. PMID: Shepard JA, Breton M, Nimri R, Roberts JTF, Street T, Klonoff D, Barnard-Kelly K. User and Healthcare Professional Perspectives on Do-It-Yourself Artificial Pancreas Systems: A Need for Guidelines.

J Diabetes Sci Technol. Epub Oct 1. Dinneen SF, McMorrow L. Re-framing type 1 diabetes care through open-source automated insulin delivery: 'The expert patient will see you now, doctor. Lawton J, Kimbell B, Rankin D, Ashcroft NL, Varghese L, Allen JM, Boughton CK, Campbell F, Randell T, Besser REJ, Trevelyan N, Hovorka R; CLOuD Consortium.

Health professionals' views about who would benefit from using a closed-loop system: a qualitative study. Epub Feb Looped Facebook Group. Accessed May 26, Crocket H. Peer mentoring in the do-it-yourself artificial pancreas system community. J Diabetes Sci Technol ; Farnsworth K, S. M , Drescher O, Racine C, Senior P, Witteman H.

Who is using do-it-yourself artificial pancreas systems and why. Advanced Technologies and Treatment for Diabetes Conference. Apr abstracts Schipp J, Skinner TC, Holloway E, Scibilia R, Langstrup H, Speight J, Hendrieckx C.

Perspectives on the future of artificial pancreas systems by adults in Australia with type 1 diabetes using open-source technologies: A qualitative study.

Epub Oct 7. Diabetes ;68 Suppl. Huhndt A, et al. Barriers to uptake of open-source automated insulin delivery systems: Analysis of socioeconomic factors and perceived challenges of caregivers of children and adolescents with type 1 diabetes from the OPEN survey.

Front Clin Diabetes Healthc Wong JJ, Suttiratana SC, Lal RA, Lum JW, Lanning MS, Dunlap A, Arbiter B, Hanes SJ, Bailey RJ, Hood KK, Naranjo D. Discontinued Use of the Loop Insulin Dosing System: A Mixed-Methods Investigation. Accessed June 1, Lewis D, S. Leibrand, and A. Open, real-world use of open source artificial pancreas systems.

J Diabetes Sci Technol ;XX Lewis DM, Swain RS, Donner TW. Improvements in A1C and time-in-range in DIY closed-loop OpenAPS users. Diabetes ;67 Suppl. Provenzano V, Guastamacchia E, Brancato D, Cappiello G, Maioli A, Mancini R, et al.

Closing the loop with OpenAPS in people with type 1 diabetesexperience from Italy. Braune K, O'Donnell S, Cleal B, Lewis D, Tappe A, Willaing I, Hauck B, Raile K.

In the last 15 years, multiple closed-loop CL systems were developed, such as predictive low-glucose suspend PLGS systems, hybrid closed-loop HCL systems, and fully closed-loop FCL systems, however, their long-term impact on clinical and functional outcomes is still unclear.

Previous randomised controlled trials RCTs have obtained variable conclusions. While some showed no significant difference in mean overnight blood glucose when comparing CL and Sensor-augmented Insulin Pump SAP in adults [ 8 ], adolescents [ 9 ], and children [ 10 ], others showed no difference in time spent in hypoglycaemia [ 11 ].

Recent trials using more advanced AID systems have demonstrated better therapeutic efficacy regarding HbA1c levels and TIR [ 12 ]. During the last decade, several meta-analyses of RCTs have been reported and show encouraging results on the effectiveness of AID devices in optimising glycaemic control, but assessments have only focused on studies with limited time of AID use, mostly hours or days [ 13 ].

To our knowledge, only one published meta-analysis with 11 RCTs has discussed the potential of these devices up to 8 weeks of use [ 14 ]. However, no previous meta-analysis has exclusively assessed studies with over 12 weeks of AID use, which is a more appropriate period of time to properly detect changes in HbA1c levels [ 15 ].

Furthermore, we did not find any meta-analyses assessing the longer use of AID systems according to different age groups compared to usual care UC , which currently represents the use of multiple daily insulin injections MDII , SAP, CGM or PLGS.

Lastly, severe adverse events AEs and psychosocial outcomes, which can influence clinical decisions, have not yet been assessed in the setting of longer and continuous use of AID systems. In this updated systematic review and meta-analysis, our objective was to investigate the impact of AID systems compared to UC on glucose control, as well as treatment satisfaction and distress based on the evidence from RCTs with a duration above 12 weeks.

We aimed to determine whether the use of AID systems improved TIR, HbA1c, and glycaemic variability, reduced AEs, and impacted psychosocial outcomes from a functional perspective. This review was performed in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis PRISMA Statement and recommendations of the Cochrane Collaboration Handbook for Systematic Reviews of Interventions [ 16 ].

The protocol of this meta-analysis was registered on PROSPERO on October 22, ID CRD We systematically searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.

The complete search strategy is available in Supplementary Appendix A. No filters or language restrictions were applied in our search.

Grey literature was not searched. We also utilised a technique of backward snowballing, searching for additional eligible studies through a review of the references from prior publications [ 17 ].

Three authors performed the literature search independently AG, AM, and LH following predefined search criteria. Eventual conflicts were resolved by consensus among the authors.

The research question was defined according to the PICOTT framework and studies were included in the systematic review if they met the following eligibility criteria: 1 enrolling adult or paediatric patient population with T1DM; 2 comparing CL systems with UC; 3 assessing any of the outcomes of interest; 4 RCTs with parallel or crossover designs; and 5 with a minimum duration of at least 12 weeks.

We included both hybrid-loop and fully CL systems in our analysis. UC was considered to include SAP, MDII, CGM, or PLGS. A full description of the current insulin devices can be found in Additional file 1 : Table S1. We excluded studies with overlapping patient populations, understood as derived from overlapping institutions, patients and recruitment periods, and clinical trials with no results after contacting the primary investigator.

In this case, only phase 1 results were included in our HbA1c analysis. If two or more studies with overlapping populations reported different outcomes of interest, they were included if these could be analysed in a non-overlapping manner.

Two authors AG and EMHP extracted outcome data independently using a standardised document and disagreements were resolved by consensus.

Four corresponding authors were contacted for additional data one provided the information. Furthermore, three independent authors IRM, VCSM and ACS extracted additional baseline data for individual studies, including study and patient characteristics Tables 1 , 2.

Participant-level data was not requested. For studies reporting data for paediatric and adult patients separately, we planned to analyse these as separate comparisons. For crossover studies, we planned a priori to analyse group means and standard deviations, assuming no correlation between groups as parallel study designs.

The bias introduced with this assumption is generally conservative [ 18 ]. For missing means data, we used the formula proposed by Wan et al. We collected adjusted mean differences MD as originally reported in each study when available. We assessed the following psychosocial outcomes: Hypoglycaemia Fear Survey HFS [ 20 ]; Diabetes Treatment Satisfaction Questionnaire DTSQ [ 21 ]; treatment distress measured by the scales Diabetes Distress Scale DDS [ 22 ] and Problem Areas in Diabetes PAID [ 23 ].

Safety endpoints included diabetic ketoacidosis DKA and severe hypoglycaemia. Each included study was appraised using the Cochrane Risk of Bias Assessment Tool RoB-2 for RCTs [ 24 ] by at least two independent investigators AG, CH, IS, and CG. Further, the Grading of Recommendations, Assessment, Development and Evaluation GRADE tool was employed by two independent authors IAM and IRM using the GRADEpro Guideline Development Tool [ 25 ] to evaluate the level of certainty of the evidence in this meta-analysis, with categorizations ranging from high to very low [ 26 ].

Any disagreements were discussed and resolved through a consensus. Continuous outcomes were compared with weighted and standardised MDs. We performed sensitivity analyses using the leave-one-out strategy as well as Baujat plots. We further investigated causes of heterogeneity by performing subgroup analyses according to type of AID device.

In addition, a random effect meta-regression analysis was performed to assess the impact of baseline HbA1c and study duration on overall MD. Review Manager 5. There was no funding source for this study. AG and EMHP had full access to all the data in the study and all authors had responsibility for the final publication.

Our search identified a total of unique studies, of which 25 reports from 22 RCTs, including randomised participants, fulfilled the study eligibility criteria Fig.

Of the 25 reports identified, 22 assessed primarily clinical outcomes [ 8 , 12 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], while 3 studies [ 48 , 49 , 50 ] assessed solely patient-reported outcomes.

Characteristics of studies contributing data to this meta-analysis are presented in Tables 1 and 2. The trials were conducted across eight countries spanning three continents. Seventeen studies had a parallel-group design, while five were crossover studies.

Females comprised The mean age of adult participants ranged from 32 to 68 years, and of paediatric participants ranged from 3. The mean duration of T1DM ranged from 1 to 38 years, with a mean Body Mass Index ranging from Duration of CL or UC use ranged from 12 to 96 weeks.

Further detailed findings for age subgroups can be seen in Table 3. As shown in Fig. Meta-analysis of patient-reported outcomes of A diabetes distress measured by Diabetes Distress Survey DDS and Problem Areas in Diabetes PAID , B Diabetes Treatment Satisfaction Questionnaire DTSQ , and C Hypoglycaemia Fear Scale HFS.

The risk of bias assessment of each RCT is provided in the Additional file 1 : Appendix A for clinical Additional file 1 : Figure S3 and functional Additional file 1 : Figure S4 outcomes.

All trials were open-label but used adequate methods for allocating participants and objective measurements of clinical outcomes. Yet, results remained statistically significant to favour CL systems even when each individual study was removed from the analysis Additional file 1 : Figure S7.

To further investigate reasons for the observed heterogeneity of effect for glycaemic control endpoints, we stratified our analyses by type of AID machines Additional file 1 : Table S2.

As seen in Fig. Nonetheless, the openAPS subgroup revealed no significant differences between CL and UC for change in HbA1c. MiniMed G and iLet Pancreas were found to be most effective to improve HbA1c and TIR outcomes Fig.

In addition, we performed a meta-regression based on follow-up duration and baseline HbA1c Additional file 1 : Figure S5. In this systematic review and meta-analysis of 22 RCTs and patients, we compared the use of AID devices versus UC during a period of 12 to 96 weeks.

Achieving glycaemic control of T1DM while also avoiding hypoglycaemia is a challenge for patients [ 51 , 52 ].

Although HbA1c is currently the metric of choice by most endocrinology and diabetes societies [ 54 , 55 ], TIR and HbA1c should be used as complementary parameters to guide care [ 56 ] and allow evaluation in clinical research [ 57 ].

To our knowledge, our study is the most comprehensive meta-analysis of use of AID for 12—96 weeks. Our analysis integrated data from 25 reports and participants, a population that almost tripled compared to a previous meta-analysis [ 14 ]. Furthermore, this is the first analysis with studies over 12 weeks of duration, stratified by age groups and type of AID device used.

Our findings augment the certainty about the beneficial effects of the continuous use of CL systems on HbA1c, TIR, hypoglycaemia, and distress of patients, without increasing the risk of AEs.

Our analyses also show that higher HbA1c levels at baseline are correlated with greater changes in HbA1c after the use of such devices, which may lead to further benefits to certain patient groups. Our findings are similar to the analyses by Weinsman and colleagues [ 13 ], although our results for reduction of time in hypoglycaemia are much smaller.

The longer periodicity of the studies included provides a pragmatic setting for assessment, where greater variables and confounding factors reflect a better real-life picture of treatment impact.

In addition, our meta-analysis provides a unique framework for comparing 7 permutations of different technologies. The breadth of these findings provides estimates of treatment effects with particular relevance to clinical decision-making and cost-effectiveness analyses.

The application of our results may be illustrated through an approach to device selection. For example, some devices appeared to offer the greatest potential for improved glycaemia compared to other systems in our sensitivity analyses, although no definite conclusions can be as no head-to-head comparisons were performed.

Given the limited knowledge about DIY systems [ 62 ], our analysis provides insight into the potential benefit of openAPS. Most studies in our analysis did not assess fully automated systems [ 8 , 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ], which still require manual input from the user [ 63 ].

Therefore, the use of such devices in children and adolescents remains a challenge. Previous meta-analyses on paediatric populations, such as a recent one by Michou and colleagues [ 64 ], have shown a reduced risk of hypoglycaemia when assessing RCTs of mostly less than 12 weeks duration.

Nonetheless, our analysis with RCTs of 12 to 96 weeks duration did not show a significantly reduced risk of hypoglycaemia nor coefficient of variation for the paediatric population, which could have been due to several reasons. For instance, children are more likely to experience hypoglycaemia due to increased physical activity, hormonal changes, varied eating habits and lifestyle, and inability to communicate symptoms appropriately [ 65 ].

Furthermore, considerable proportion of RCTs included have reported system errors and malfunctioning during the longer duration of the trials, potentially having important impacts for children and adolescents who are at a higher risk of hypoglycaemia or those not achieving target control [ 4 ].

These findings have important implications to the design of future paediatric trials, which should consider placing significant focus on patient education, device functioning and type of system used.

Finally, this was the first meta-analysis to assess how long-term use of AID impacts patient-reported outcomes with a considerable number of studies.

Although our findings show significantly improved diabetes distress and a tendency for reduced fear of hypoglycaemia, no benefits were seen for treatment satisfaction. The high cost of AID devices, connectivity problems, automation-related errors, pump glitches, and other issues associated with insulin pumps have been perceived as drawbacks by T1DM patients [ 5 ].

Moreover, most studies included in our analyses use CL algorithms that still require manual bolus input. Further improvements towards fully AID may result in improved quality of life and treatment satisfaction. Lastly, psychosocial measures varied between trials, limiting the populations of our analyses.

Given that such outcomes have been recently receiving increased attention [ 5 ], future studies may consider using more consistent and widely used measures to aid interpretation of psychosocial impact. Our study has important limitations. The lack of blinding in the studies, as it is potentially unfeasible to blind patients in such RCTs, reduced the certainty of evidence for our findings.

It is important to note that heterogeneity was high for most glycaemic outcomes, especially in the adult and mixed populations. However, this finding was expected given the highly variable clinical and technical factors involved in studies performed in real-life conditions without supervision.

Subgroup analyses of different machines and meta-regression were performed to minimise and interpret such heterogeneities.

Furthermore, we did not search the grey literature, which can increase the risk of publication bias. However, we believe that restricting our research to peer-reviewed sources minimised other sources of bias ensuring a more rigorous evaluation.

Unfortunately, no study used outcomes such as mortality or macrovascular and microvascular complications as outcomes. Therefore, our study relies on surrogate measures for patient-oriented outcomes.

Finally, recent bihormonal CL systems were not included as the RCTs on these devices only had a short follow-up period.

Findings also suggest reductions in nocturnal and daily hypoglycaemia as well as patient distress without increasing the risk of DKA and severe hypoglycaemia. This estimate is beneficial in planning future long-term clinical trials assessing the use of fully automated and bihormonal AID devices.

The synthesis of all system subgroups emphasises the potential benefits of certain CL systems, although this finding requires head-to-head comparisons before definitive conclusions can be made.

Our results show that use of CL technology between 12 and 96 weeks has considerable benefits in a variety of clinical settings. Ultimately, it will be at the discretion of clinicians and patients to understand the potential benefits associated with different CL systems and decide on the most optimal insulin delivery method to improve patient outcomes.

All data are publicly available in the relevant primary and secondary papers from relevant trials as listed in the References. Thrower SL, Bingley PJ. Prevention of type 1 diabetes.

Br Med Bull. Article CAS PubMed Google Scholar. International Diabetes Federation. Diabetes atlas. Foster NC, Beck RW, Miller KM, Clements MA, Rickels MR, DiMeglio LA, et al. State of type 1 diabetes management and outcomes from the T1D exchange in — Diabetes Technol Ther.

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Incidences of severe hypoglycaemia and diabetic ketoacidosis and prevalence of microvascular complications stratified by age and glycaemic control in U.

adult patients with type 1 diabetes: a real-world study. Article PubMed Google Scholar. Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.

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Transitioning of people with type 1 diabetes from multiple daily injections and self-monitoring of blood glucose directly to minimed G advanced hybrid closed-loop system: a two-center, randomized. Controll Study Diabetes Care. Hovorka R, Elleri D, Thabit H, et al. Overnight closed-loop insulin delivery in young people with type 1 diabetes: a free-living, randomized clinical trial.

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Positive impact of the bionic pancreas on diabetes control in youth 6—17 years old with type 1 diabetes: a multicenter randomized trial.

Bionic Pancreas Research G, Russell SJ, Beck RW, Damiano ER, El-Khatib FH, Ruedy KJ, et al. Multicenter, randomized Trial of a bionic pancreas in type 1 diabetes.

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Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA. Cochrane handbook for systematic reviews of interventions. Sensor performance was good, with the median absolute deviation of 0.

Overall, sensor unavailability for the entire hour study period during closed-loop therapy was 25 0 to minutes. This translated to 5. Excluding the mandatory first-hour sensor warm-up period, 3.

This occurred mostly during the first 10 hours of sensor use. Two subjects required replacement of sensor because of MRI scanning.

We documented that automated closed-loop glucose control, based on continuous subcutaneous glucose levels, is feasible and may significantly improve glucose levels without increasing the risk of hypoglycemia in critically ill adults.

Compared with local intravenous sliding-scale therapy, closed-loop therapy increased up to fourfold the time spent in the target glucose range and reduced the time spent at higher glucose levels.

Subjects treated with closed-loop therapy achieved consistent results, with a trend toward reduced glucose variability without requiring nurse interventions or decision making on insulin delivery.

Reflecting the current practice recommendations for glucose control in the intensive care unit [ 33 , 34 ], we adopted a moderate glucose target of 6. Based on our simulation work, we were confident of achieving a target between 6. Subjects in the local-treatment protocol were treated with an intravenous sliding-scale protocol intended to maintain glucose in a safe target range of 7 to 10 m M without increasing the risk of hypoglycemia.

We did not change the target range of the usual treatment for two reasons. First, we aimed to compare current local practice with a novel treatment; second, we could not guarantee patient safety by changing the target range of the sliding-scale protocol.

The mean glucose level achieved during closed-loop control was 7. Importantly, during the present study, closed-loop therapy achieved safe glucose levels without increasing the risk of hypoglycemia. Glucose variability, as measured by the standard deviation, tended to be lower during closed-loop without reaching statistical significance.

Because both hypoglycemia and glucose variability have been associated with adverse outcomes, beneficial effects, apart from glucose lowering, may be achieved with closed-loop therapy. Since the introduction of intensive insulin therapy, different algorithms and control systems aiming at effective and safe glucose control have been proposed [ 19 ].

These can range from written guidelines [ 12 , 13 ] and protocols [ 37 — 40 ] to elementary [ 41 , 42 ] and advanced computerized algorithms [ 43 — 48 ].

We used an advanced computer algorithm belonging to the family of model predictive control. The control algorithm and calibration strategy was optimized on a validated computer simulation environment for the critically ill [ 31 ] before study commencement to ensure favorable outcomes.

Our study is the first randomized controlled trial to evaluate fully automated closed-loop glucose control based on subcutaneous continuous glucose monitoring in critically ill patients.

However, this was a retrospective observational study and used the STG system Nikkiso, Tokyo, Japan , which relies on continuous intravenous glucose measurements drawing 2 ml of blood per hour and is expensive [ 51 ], limiting its prolonged and wider use.

We initialized the closed-loop system by using approximate body weight and a reference glucose level. The system did not require information about nutritional intake and was able to respond to rapid changes in caloric and carbohydrate intake, even though a minute lag exists between blood and Navigator sensor glucose levels [ 52 ].

We increased accuracy of the subcutaneous continuous glucose monitor by calibrating with arterial blood glucose at a frequency higher than recommended by the manufacturer. During the first 24 hours, calibration occurred on average every 2. This is comparable with the present nurse workload.

Benefits of subcutaneous glucose monitoring compared with intravenous measurements include reduced invasiveness, obviating the need for dedicated venous placement and a risk of contamination from dextrose or other medications that may interfere with glucose measurements.

The risk of infection and thrombosis is lower with the subcutaneous route. The subcutaneous sensor placement was not associated with any complications. The strengths of our study include the randomized controlled study design, the use of hourly arterial blood glucose to assess outcomes, comparability of the patient groups, and comparable nutrition and treatment modalities.

Study limitations include a small sample size, a single-center study design involving a subspecialized patient population, and short study duration, which limits generalizability but does not affect the main study outcomes.

The control achieved by using the sliding-scale protocol appears suboptimal and reflects the fear of hypoglycemia in the post-NICE-SUGAR era. Comparisons with other standard insulin-infusion protocols would be beneficial. In conclusion, automated closed-loop therapy, based on subcutaneous continuous glucose measurements, is a safe and efficacious approach for glucose control in critically ill adults.

Larger and longer-duration studies are warranted to assess system performance. Apart from providing a tangible treatment option, closed-loop systems may contribute important insights into the ongoing debate about glucose targets by providing the means to achieve uniform and safe outcomes in comparability studies.

Closed-loop treatment provided safe, effective, and consistent glucose control without increasing the risk of hypoglycemia in a small group of patients over a hour period. Nurse intervention is not required during closed-loop treatment, apart from calibrating a subcutaneous glucose monitor.

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