The primary outcome was Wisdom teeth time spent Automated glucose management primary target-glucose Autoamted between 6. Auto,ated were randomized to Muscle growth pre-workout supplements either the Control-IQ Technology controller installed on a Tandem t:slim X2 insulin pump, or a CGM and insulin pump without the Control-IQ Technology controller. Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P, Bouillon R: Intensive insulin therapy in critically ill patients.

Automated glucose management -

CGMs provide significant, potentially life changing benefits for diabetes management. CGMs are recommended for several reasons because they:. People with type 1 and type 2 diabetes who use a CGM have fewer instances of hypoglycemia and a lower A1C.

One obstacle with CGMs is the cost of access to diabetes technology. Many people with diabetes who have put off getting an insulin pump or CGM, do so because they are too expensive.

Another major obstacle is due to strict Medicaid coverage policies they are not accessible for people who need them. In fact, people with diabetes on Medicaid, especially in minority communities who use Medicaid, are the least likely to use a CGM.

This is concerning since people with diabetes are more than twice as likely to receive their health care from Medicaid as those without diabetes. Individuals who meet the coverage criteria listed in the FAQs below for a CGM and want to learn more about them should talk to their health care provider to ensure it is the right tool for the management of their diabetes.

The American Diabetes Association ® ADA released a new study looking at pharmacy and medical benefit claims for CGMs across commercial insurance plans, Medicare and Medicaid and data on age, race, geography, and diabetes prevalence.

The findings show people of lower income and older people of color who live in states with the highest rates of diabetes prevalence and mortality are the least likely to get access to a CGM. ADA is quite concerned about these findings, given the effect of the COVID pandemic on this population and the importance of tools like CGMs in diabetes management.

Learn more by viewing the study PDF. We are partnering with people with diabetes, health care professionals, advocacy groups, and policy makers to address CGM access for those who use Medicaid.

We need your help in eliminating these systemic barriers to CGMs! Soon, there will be an opportunity to get involved depending on your state with CGM Medicaid regulations and increased access to this technology. If you are interested in providing comments and having your voice heard on behalf of people with diabetes, please provide your contact information below.

Breadcrumb Home Advocacy Overview Continuous Glucose Monitors. Everything you need to know about continuous glucose monitors CGMs. What is a CGM? CGM Resources Learn More. Learn More. Understand the connection between CGM usage and time in range. How CGMs are Shaping the Future of Diabetes Care Watch the videos below to hear patient and practitioner perspectives on how CGMs are shaping the future of diabetes care.

Continuous Glucose Monitors CGMs and Me; The Beauty of Technology. CGMs — The benefits of this life changing diabetes technology. My Life After Continuous Glucose Monitoring. Time spent at greater than 8. A sample hour closed-loop study is shown in Figure 4.

Cumulative distribution of reference glucose values obtained during closed-loop and local treatment protocol. Dashed vertical lines indicate the primary study target range from 6. Vertical fine dashed lines indicate the wider target from 4. An example of the hour closed-loop study.

Darker red continuous line represents sensor glucose. Lighter red squares represent reference glucose measurements used for sensor calibration. Blue line represents insulin infusion. Thin red dashed lines indicate primary target. Dextrose infusion was not required in this study.

The mean glucose level was significantly lower during closed-loop therapy 7. Glucose variability assessed by the standard deviation tended to be lower during the closed-loop therapy, without reaching statistical significance. Reference glucose profiles shown in Figure 6 highlight differences between the two groups.

The closed-loop system administered more insulin during the first study hours Figure 6 , bottom panel , but overall, no statistical difference was found in insulin infusion between the treatments Table 3. Horizontal black line indicates the mean reference glucose in each intervention arm.

Glucose and insulin values during infusion. Top panel: Glucose profiles median and interquartile range during closed-loop and local treatment protocol. Bottom panel: Median insulin infusion rates during closed-loop and local treatment protocol.

The dashed lines indicate the primary target range from 6 to 8 m M. All but one patient received enteral nutrition, according to the local NCCU protocol. One patient received both enteral and parenteral nutrition. The number of calories and carbohydrates as well as the number of feeding interruptions per day was comparable between the two interventions Table 2.

The proportion of patients treated with steroids or inotropes during the hour study period was slightly higher during closed-loop therapy Table 2.

During closed-loop therapy, the number of reference glucose measurements requested by the control algorithm was 9. This translated into an interval between sensor calibrations of to and to minutes during the first and second 24 hours, respectively.

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.

Automated administration of dextrose augmented the ability of closed-loop treatment to avoid low glucose levels. Kavanagh BP, McCowen KC: Clinical practice: glycemic control in the ICU. N Engl J Med.

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Krinsley JS: Glycemic variability: a strong independent predictor of mortality in critically ill patients. Badawi O, Waite MD, Fuhrman SA, Zuckerman IH: Association between intensive care unit-acquired dysglycemia and in-hospital mortality.

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Preiser JC, Devos P, Ruiz-Santana S, Melot C, Annane D, Groeneveld J, Iapichino G, Leverve X, Nitenberg G, Singer P, Wernerman J, Joannidis M, Stecher A, Chiolero R: A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: The Glucontrol Study.

Griesdale DE, de Souza RJ, van Dam RM, Heyland DK, Cook DJ, Malhotra A, Dhaliwal R, Henderson WR, Chittock DR, Finfer S, Talmor D: Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data.

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CAS Google Scholar. Van den Berghe G, Schetz M, Vlasselaers D, Hermans G, Wilmer A, Bouillon R, Mesotten D: Clinical review: intensive insulin therapy in critically ill patients: NICE-SUGAR or Leuven blood glucose target?. J Clin Endocrinol Metab. Van Herpe T, De Moor B, Van den Berghe G: Towards closed-loop glycaemic control.

Best Pract Res Clin Anaesthesiol. Aragon D: Evaluation of nursing work effort and perceptions about blood glucose testing in tight glycemic control. Am J Crit Care. PubMed Google Scholar. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group, Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase HP, Clemons R, Fiallo-Scharer R, Fox LA, Gilliam LK, Hirsch IB, Huang ES, Kollman C, Kowalski AJ, Laffel L, Lawrence JM, Lee J, Mauras N, O'Grady M, Ruedy KJ, Tansey M, Tsalikian E, Weinzimer S, Wilson DM, Wolpert H, Wysocki T, Xing D: Continuous glucose monitoring and intensive treatment of type 1 diabetes.

Pickup JC, Freeman SC, Sutton AJ: Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data.

Corstjens AM, Ligtenberg JJ, van der Horst IC, Spanjersberg R, Lind JS, Tulleken JE, Meertens JH, Zijlstra JG: Accuracy and feasibility of point-of-care and continuous blood glucose analysis in critically ill ICU patients. Crit Care.

Siegelaar SE, Barwari T, Hermanides J, Stooker W, van der Voort PH, DeVries JH: Accuracy and reliability of continuous glucose monitoring in the intensive care unit: a head-to-head comparison of two subcutaneous glucose sensors in cardiac surgery patients. Diabetes Care. Holzinger U, Warszawska J, Kitzberger R, Herkner H, Metnitz PG, Madl C: Impact of shock requiring norepinephrine on the accuracy and reliability of subcutaneous continuous glucose monitoring.

Hovorka R: Closed-loop insulin delivery: from bench to clinical practice. Article CAS Google Scholar. Scott NW, McPherson GC, Ramsay CR, Campbell MK: The method of minimization for allocation to clinical trials.

a review. Control Clin Trials. htm ]. Geoffrey M, Brazg R, Richard W: FreeStyle Navigator Continuous Glucose Monitoring System with TRUstart algorithm, a 1-hour warm-up time. J Diabetes Sci Technol. Bequette B: A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas.

Diabetes Technol Ther. Wilinska ME, Blaha J, Chassin LJ, Cordingley JJ, Dormand NC, Ellmerer M, Haluzik M, Plank J, Vlasselaers D, Wouters PJ, Hovorka R: Evaluating glycemic control algorithms by computer simulations. Am J Physiol Endocrinol Metab. Qaseem A, Humphrey LL, Chou R, Snow V, Shekelle P, Clinical Guidelines Committee of the American College of P: Use of intensive insulin therapy for the management of glycemic control in hospitalized patients: a clinical practice guideline from the American College of Physicians.

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Siegelaar SE, Hermanides J, Oudemans-van Straaten HM, van der Voort PH, Bosman RJ, Zandstra DF, DeVries JH: Mean glucose during ICU admission is related to mortality by a U-shaped curve in surgical and medical patients: a retrospective cohort study.

Critical Care. Balkin M, Mascioli C, Smith V, Alnachawati H, Mehrishi S, Saydain G, Slone H, Alessandrini J, Brown L: Achieving durable glucose control in the intensive care unit without hypoglycaemia: a new practical IV insulin protocol.

Diabetes Metab Res Rev. Goldberg PA, Siegel MD, Sherwin RS, Halickman JI, Lee M, Bailey VA, Lee SL, Dziura JD, Inzucchi SE: Implementation of a safe and effective insulin infusion protocol in a medical intensive care unit.

Kanji S, Singh A, Tierney M, Meggison H, McIntyre L, Hebert PC: Standardization of intravenous insulin therapy improves the efficiency and safety of blood glucose control in critically ill adults. Chase JG, Shaw G, Le Compte A, Lonergan T, Willacy M, Wong XW, Lin J, Lotz T, Lee D, Hann C: Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change.

Davidson PC, Steed RD, Bode BW: Glucommander: a computer-directed intravenous insulin system shown to be safe, simple, and effective in , h of operation. Vogelzang M, Zijlstra F, Nijsten MW: Design and implementation of GRIP: a computerized glucose control system at a surgical intensive care unit.

BMC Med Informat Decision Making. Plank J, Blaha J, Cordingley J, Wilinska M, Chassin L, Morgan C, Squire S, Haluzik M, Kremen J, Svacina S, Toller W, Plasnik A, Ellmerer M, Hovorka R: Pieber T Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients.

Pachler C, Plank J, Weinhandl H, Chassin LJ, Wilinska ME, Kulnik R, Kaufmann P, Smolle KH, Pilger E, Pieber TR, Ellmerer M, Hovorka R: Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients.

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Download references. We are indebted to patients and family members for participating in and consenting to the study.

We thank all staff at the Neurosciences Critical Care Unit NCCU at Addenbrooke's Hospital, Cambridge, UK. We thank Drs Tonny Veenith and Ari Ercole for their help with participant recruitment. Abbott Diabetes Care provided technical support but did not play any role in clinical studies or data analysis.

Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK.

Neurosciences Critical Care Unit, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK. You can also search for this author in PubMed Google Scholar.

Correspondence to Roman Hovorka. LL, SWE, HT, KC, JMA, KK, MEW, MN, JM, and RB have no conflicts of interest. RH reports having received speaker honoraria from Minimed Medtronic, Lifescan, Eli Lilly, and Novo Nordisk, serving on advisory panel for Animas and Minimed Medtronic, receiving license fees from BBraun; and having served as a consultant to BBraun and Profil.

RH conceptualized the study, is the guarantor, and had full access to all the data in the study. RH, LL, RB, SWE, and MLE codesigned the study. LL, HT, SWE, KC, and JMA were responsible for patient screening and enrolment and informed consent.

LL, HT, KC, JMA, and KK provided patient care and contributed to acquisition of data. RH designed and implemented the algorithm. RH, MN, MEW, and JM developed and validated the closed-loop system including the conduct of simulation studies.

LL and MN carried out the data and statistical analyses. LL and RH drafted the manuscript. All authors critically revised the manuscript and approved the final version of the report. Reprints and permissions.

Leelarathna, L.

Continuous glucose managemen Automated glucose management glkcose a manwgement to automatically estimate your Nutritional healing process glucose levelalso called blood sugar, throughout the day and Automatd. You HbAc role in gestational diabetes managemeht what your blood glucose level is at any time. You can also review how your Automated glucose management glucose level changes over a few hours or days and spot trends. Seeing your blood glucose levels in real time can help you make more informed decisions about the food and beverages you consume, the physical activity you do, and the medicines you take. Keeping your blood glucose level in your target range can help prevent other health problems caused by diabetes. A continuous glucose monitor CGM estimates what your glucose level is every few minutes and keeps track of it over time. A CGM has three parts.

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The lower and upper limits of the glucose target range, 5. The standard deviation of glucose was lower during closed-loop than during the control period 3. Total daily insulin doses were similar between interventions.

Closed-loop performance improved from days 1—7 to days 8—20, as shown by an increase in the time spent in the target glucose range by 8. There was no difference in key glycemic outcomes between days 1—7 and days 8—20 during the control period, but measures of glycemic variability increased during days 8—20 compared with days 1—7 inclusive Supplementary Table 2.

Daily trend of the proportion of time when sensor glucose was in the target range between 5. Mean and s. are shown. There were no differences in any glycemic outcomes, including measures of variability between dialysis days and non-dialysis days during either intervention period Table 2.

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Six other serious adverse events were reported Table 3. Two of these occurred during the closed-loop period reduced responsiveness on dialysis requiring hospital admission and COVID infection requiring hospital admissiontwo events occurred during washout or pre-study start one hospital admission for bowel obstruction resulting in death and one hospital admission for diabetic foot-related cellulitis requiring intravenous antibioticsand two events occurred during the control period one below-knee amputation due to diabetic foot ulceration, and one hospital admission with an ischemic stroke.

None of the serious adverse events were deemed related to study devices or study procedures. Nine other adverse events were reported Table 3five of which occurred during closed-loop, two during the control period and two during washout or pre-study arm start. Three of these events were deemed related to study devices or study procedures two skin reactions from the infusion sets and one infusion set failure causing hyperglycemia.

The hypoglycemia confidence score was higher with the closed-loop system than with standard insulin therapy 3. The PAID score in both periods of the study was low 7. Benefits of the closed-loop system reported by study participants included a reduced need for finger-prick glucose checks, less time required to manage diabetes, resulting in more personal time and freedom, and improved peace of mind and reassurance.

Device burden and discomfort wearing the insulin pump and carrying the smartphone were the most common limitations reported by participants Supplementary Table 4.

This study provides evidence that fully closed-loop insulin delivery can improve glucose control and reduce hypoglycemia compared to standard insulin therapy in adults with type 2 diabetes and ESRD requiring dialysis, in an unrestricted home setting. We have shown that the fully closed-loop system has the potential to safely and effectively manage glucose levels in one of the most vulnerable subpopulations with type 2 diabetes where the risk of glycemic complications and diabetes-related adverse events is greatest.

Compared with control therapy, fully closed-loop insulin delivery was associated with over 3. This pattern of incremental improvements in time in range with increasing duration of wear time has been reported previously with this fully closed-loop system in the inpatient setting It is reasonable to postulate that time in target range could improve further with a longer duration of use.

It has previously been reported that 26 days of closed-loop use are required for the proportion of time in target glucose range to plateau, although this is likely to be population-dependent 13 A higher glucose target was applied in the present study median 7.

Higher glucose target settings were associated with less time in target glucose range Extended Data Fig. However, time spent in hypoglycemia did not increase with lower personal glucose targets, suggesting that the glucose target does not need to be unnecessarily elevated.

The reduction in time in hypoglycemia observed with closed-loop is clinically important in this highly vulnerable population with a high burden of comorbidities. Closed-loop was associated with very low time in hypoglycemia 0. Hypoglycemia exposure during the control period was also low, in contrast with the high frequency of hypoglycemia reported in other studies 15 The greatest reductions in hypoglycemia with closed-loop were observed in participants with the highest levels of hypoglycemia during the standard insulin therapy period Fig.

Hypoglycemia is a considerable barrier to optimization of insulin therapy. The risk of hypoglycemia is high in this population, and people on dialysis often have impaired awareness of hypoglycemia Hypoglycemia has been associated with an increased risk of all-cause mortality in those with diabetes on dialysis, but causation has not been established The improved time in target glucose range observed with closed-loop was predominantly due to the reduced time spent in hyperglycemia.

This degree of hyperglycemia is associated with both acute and chronic complications. The closed-loop algorithm was able to manage fluctuations in glucose and insulin kinetics between dialysis and non-dialysis days effectively. There was no difference in glucose outcomes between dialysis and non-dialysis days, but closed-loop insulin delivery was lower on dialysis days than non-dialysis days, an effect that is probably related to the impact of the dialysate glucose concentration on blood glucose concentrations.

Closed-loop insulin delivery was safe in this vulnerable population. No study-related serious adverse events occurred during the closed-loop intervention period, and the commonest study-related adverse events were self-limiting skin reactions.

Closed-loop and sensor glucose usage were high in the study, supporting acceptability of this approach in this population.

All study participants were happy to have glucose levels managed with an automated insulin delivery system and would recommend its use to others. Participants felt more confident in managing hypoglycemia with the closed-loop system, although this could be due to the availability of real-time glucose levels and alarms for hypoglycemia.

Device burden was reported as the main perceived drawback to this approach. The strengths of this study include the multinational randomized crossover design, the fully closed-loop approach adopted and the unrestricted and unsupervised home setting, including dialysis sessions.

Limitations include the smaller sample size than planned due to Brexit-related study sponsorship issues and the COVID pandemic. Device management was performed by the study team to minimize training burden and therefore we cannot comment on the competency of this population to self-manage this treatment modality.

Diabetes therapies during the control period were not standardized or optimized during the trial. We did not evaluate the accuracy of the glucose sensor in the present study; however, because the same sensor was used during both study arms, we believe this is unlikely to have impacted the results.

As this was an exploratory study, no adjustment was made for multiple comparisons in the statistical analysis. We included only one participant receiving peritoneal dialysis, thus limiting interpretation of efficacy and safety in this specific cohort.

Our study evaluated the performance of a fully closed-loop system in an unrestricted outpatient setting in a highly vulnerable population with type 2 diabetes and end-stage renal failure requiring dialysis.

Having demonstrated safety and efficacy in this at-risk population in this exploratory study, larger studies are now required to confirm these findings and to determine if the glycemic improvements observed with closed-loop are associated with a reduction in complications and improved quality of life, as well as whether closed-loop should be targeted towards specific subpopulations for example, those with high hypoglycemic burden or peri-transplant.

We suggest that the fully closed-loop approach may also be beneficial in the wider population of people with type 2 diabetes, and further studies are warranted.

Each intervention period lasted 20 days, separated by two to four weeks of washout using pre-study treatment. The order of the two interventions was random.

: Automated glucose management

Insulin Pumps and Diabetes Management | Tandem Diabetes Care	Managemeny : 24 July Intensive Automatdd Med. Melmer A, Zuger T, Muscle growth pre-workout supplements DM, Leibrand S, Stettler C, Successful weight loss M. Autoamted Charlotte K. Efficacy Muscle growth pre-workout supplements Glucosd 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. Article PubMed CAS Google Scholar Okabayashi T, Kozuki A, Sumiyoshi T, Shima Y: Technical challenges and clinical outcomes of using a closed-loop glycemic control system in the hospital.
What are the benefits of CGM?	The t:slim X2 insulin pump is the only insulin pump capable of remote feature updates. Tandem and Dexcom have created life-changing diabetes management solutions for more than a decade. Check coverage and start the process of getting a pump. Even with advanced systems such as the t:slim X2 insulin pump with Control-IQ technology, users are still responsible for actively managing their diabetes. Control-IQ technology does not prevent all high and low blood glucose events. The system is designed to help reduce glucose variability, but it requires that users accurately input information, such as meals and periods of sleep or exercise. Control-IQ technology will not function as intended unless all system components, including CGM, infusion sets and pump cartridges, are used as instructed. Importantly, the system cannot adjust insulin dosing if the pump is not receiving CGM readings. Because there are situations and emergencies that the system may not be capable of identifying or addressing, users should always pay attention to their symptoms and treat accordingly. Additional training may be required to access certain future software updates. Software updates are only available to customers who are in warranty at the time they update their pump. Charges may apply. Future updates for all or some of Tandem's products may not be developed and may not be offered everywhere. Tandem may discontinue select software and features over time at its discretion. Individual symptoms, situations, circumstances, and results may vary. Please consult your physician or qualified healthcare provider regarding your condition and appropriate medical treatment. Please read the Important Safety Information below before using a Tandem Diabetes Care product. Disconnect the infusion set from your body before flying in an aircraft without cabin pressurization or in planes used for aerobatics or combat simulation pressurized or not. Rapid changes in altitude or gravity can affect insulin delivery and cause injury. As a reminder, avoid exposure of your Tandem pump to temperatures below 40°F 5°C or above 99°F 37°C , as insulin can freeze at low temperatures or degrade at high temperatures. The t:slim X2 insulin pump with Control-IQ technology the System consists of the t:slim X2 insulin pump, which contains Control-IQ technology, and a compatible continuous glucose monitor CGM, sold separately. The t:slim X2 insulin pump is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in people requiring insulin. The t:slim X2 insulin pump can be used solely for continuous insulin delivery and as part of the System. When used with a compatible CGM, the System can be used to automatically increase, decrease, and suspend delivery of basal insulin based on CGM sensor readings and predicted glucose values. The System can also deliver correction boluses when the glucose value is predicted to exceed a predefined threshold. The pump and the System are indicated for use in individuals six years of age and greater. The pump and the System are intended for single user use. The pump and the System are indicated for use with NovoRapid, Admelog, or Humalog U insulin. The System is intended for the management of Type 1 diabetes. Warning: Control-IQ technology should not be used by anyone under the age of six years old. It should also not be used in users who require less than 10 units of insulin per day or who weigh less than 25 kilograms. The System is not indicated for use in pregnant women, people on dialysis, or critically ill users. Do not use the System if using hydroxyurea. The t:slim X2 pump and the CGM transmitter and sensor must be removed before MRI, CT, or diathermy treatment. Visit tandemdiabetes. Other companies operating in this segment include Bayer AG, GlySens Inc. Overall, there is a strong need to lessen the patient burden and for less complex devices that improve accuracy and glycemic control while lowering healthcare costs and improving quality of life. CGMs will play a pivotal role in addressing these needs with CGM companies aggressively competing on affordability, convenience, ease of use, more comfortable sensors and longer sensor wear. However, CGM devices coupled with an insulin pump, offers patients more flexibility because the algorithm in the embedded sensor modulates insulin delivery throughout the day and night, which helps patients stay within set glycemic targets. The global market is highly competitive and undergoing significant change, predominantly marked by four major players and several innovative start-ups. The global market for insulin infusion pumps is not only driven by technological innovation, but also by the diabetes community by pressurizing regulators to approve products faster, and reduce the diabetic care costs by making insurance companies more flexible. The diabetes care industry is changing rapidly, by developing automated systems to achieve better glycemic control by the insulin pump manufacturers. There is a strong need for improved, automated insulin delivery to lessen the complexity, daily burden and potential health risks that result from multiple daily insulin injections. The global insulin pump market has been dominated by key players such as Medtronic plc, Insulet Corp. and Tandem Diabetes Care, Inc. and Valeritas Holdings, Inc. In the coming years, these companies are expected to launch several next-generation integrated hybrid closed-loop systems to collect and interpret data, and manage the disease in this segment. The new artificial pancreas system which automatically monitors and regulates blood glucose levels has found that the new system was more effective than existing treatments at controlling blood glucose levels in people with type 1 diabetes. The Artificial Pancreas Device System APDS closely mimics the glucose regulating function of a healthy pancreas. The system consists of three types of devices already familiar to many people with diabetes: a continuous glucose monitoring system CGM and an insulin infusion pump. A blood glucose device such as a glucose meter is used to calibrate the CGM. An Artificial pancreas device system not only monitors glucose levels in the body, but also automatically adjusts the delivery of insulin to reduce high blood glucose levels hyperglycemia and minimize the incidence of low blood glucose hypoglycemia with little or no input from the patient. A computer-controlled algorithm connects the CGM and insulin infusion pump to allow continuous communication between the two devices. In the coming years, several start-ups, including Bigfoot Biomedical Inc. About Us Analyst Insights Reseach Services Methodology Press Releases Contact Us.
Recent Publications	American Diabetes Association. Lancet Digit Health. Received : 01 March Article CAS Managwment Google Scholar Muscle growth pre-workout supplements KK, Garcia-Willingham N, Glycose S, Tanenbaum ML, Ware J, et al. The control achieved by using the sliding-scale protocol appears suboptimal and reflects the fear of hypoglycemia in the post-NICE-SUGAR era. Kellee MM, Nicole CF, Roy WB, Richard MB, Stephanie ND, Linda AD, et al. Fiasp was used for its properties of faster onset and offset of action, and its potential to enhance closed-loop performance.
Bionic pancreas improves type 1 diabetes management compared to standard insulin delivery methods	The global market is highly competitive and undergoing significant change, predominantly marked by four major players and several innovative start-ups. The global market for insulin infusion pumps is not only driven by technological innovation, but also by the diabetes community by pressurizing regulators to approve products faster, and reduce the diabetic care costs by making insurance companies more flexible. The diabetes care industry is changing rapidly, by developing automated systems to achieve better glycemic control by the insulin pump manufacturers. There is a strong need for improved, automated insulin delivery to lessen the complexity, daily burden and potential health risks that result from multiple daily insulin injections. The global insulin pump market has been dominated by key players such as Medtronic plc, Insulet Corp. and Tandem Diabetes Care, Inc. and Valeritas Holdings, Inc. In the coming years, these companies are expected to launch several next-generation integrated hybrid closed-loop systems to collect and interpret data, and manage the disease in this segment. The new artificial pancreas system which automatically monitors and regulates blood glucose levels has found that the new system was more effective than existing treatments at controlling blood glucose levels in people with type 1 diabetes. The Artificial Pancreas Device System APDS closely mimics the glucose regulating function of a healthy pancreas. The system consists of three types of devices already familiar to many people with diabetes: a continuous glucose monitoring system CGM and an insulin infusion pump. A blood glucose device such as a glucose meter is used to calibrate the CGM. An Artificial pancreas device system not only monitors glucose levels in the body, but also automatically adjusts the delivery of insulin to reduce high blood glucose levels hyperglycemia and minimize the incidence of low blood glucose hypoglycemia with little or no input from the patient. A computer-controlled algorithm connects the CGM and insulin infusion pump to allow continuous communication between the two devices. In the coming years, several start-ups, including Bigfoot Biomedical Inc. About Us Analyst Insights Reseach Services Methodology Press Releases Contact Us. Menu Animal Health Biotechnology Clinical Diagnostics Medical Devices Hospital Equipment Healthcare IT Healthcare Services Pharmaceuticals. Updated Data Matrix Available Market Value Market Volume Epidemiology Clinical Trials Patent Landscape Regulatory Approvals. Artificial intelligence AI technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge. The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. Hypoglycemia is associated with adverse outcomes and may have negated any beneficial effect from intensive glucose control in those patients in whom target glucose levels were achieved. Existing tools for achieving desired glucose levels range from sliding and dynamic scales, and paper-based protocols to computerized protocols that advise the nursing staff [ 19 ]. Safe implementation of insulin therapy requires accurate and frequent glucose measurements, but even hourly glucose measurements may fail to identify hypoglycemia during periods of rapid glucose change. Further, frequent sampling may be inconvenient for the patient and adds to the workload of the nursing staff [ 20 ]. Over the last decade, continuous subcutaneous glucose monitoring CGM has emerged as a valuable tool in the management of diabetes [ 21 , 22 ]. A number of studies have investigated the accuracy of CGM devices in critical illness and have reported acceptable CGM performance [ 23 — 25 ], but the clinical efficacy and effectiveness of CGM devices in daily-life ICU practice is not yet established. Availability of reliable continuous subcutaneous glucose monitoring has led to a rapid expansion of research into closed-loop insulin delivery, documenting superior performance compared with conventional pump therapy in type 1 diabetes [ 26 ]. The objective of the present study was to investigate the feasibility of automated closed-loop glucose control based on continuous subcutaneous glucose measurements in critically ill adults. A separate research nurse was responsible for all study-related activities. Cambridge Central Research Ethics Committee approved the study. Study participants were recruited from May to September All critically ill patients consecutively admitted to NCCU were screened for eligibility. Inclusion criteria were age 18 years and older, stay at NCCU expected of at least 48 hours, and arterial glucose level greater than Exclusion criteria were diabetic ketoacidosis or hyperosmolar state, therapeutic hypothermia, known or suspected allergy to insulin, fatal organ failures, significant abnormalities of blood clotting, pregnancy, and treatment with external cardiac pacemaker. Patients entered into the trial were randomized to an automated closed-loop or local sliding-scale insulin-therapy protocol by using the minimization method [ 27 ], implemented in the Minim program [ 28 ] to balance between group characteristics: Acute Physiology and Chronic Health Evaluation II APACHE II score, glucose at the time of randomization, body mass index, and preexisting diabetes. Randomization was carried out at the time of recruitment by the investigator by using a dedicated study laptop. Apart from glucose control, all other aspects of patient care, including nutritional management and treatment of hypoglycemia and hyperglycemia, were carried out according to local treatment protocols and were identical between treatment arms. Actrapid insulin Novo Nordisk, Bagsværd, Denmark , in a concentration of 50 U in 50 ml of 0. All study-related activities were carried out for a maximum period of 48 hours or until the end of the NCCU stay, whichever came first. Subjects randomized to closed-loop therapy were treated by using an automated closed-loop system comprising a FreeStyle Navigator subcutaneous continuous glucose-monitoring system Abbott Diabetes Care, Alameda, CA, USA , b a laptop computer running a model predictive control MPC algorithm, and c two Alaris CC Plus syringe pumps CareFusion, Basingstoke, UK Figure 1. The CGM system uses CE-marked FreeStyle Navigator Transmitter, and a non-CE-marked investigational receiver device Navigator Companion Abbott Diabetes Care , equivalent in its function and calibration algorithm to CE-marked Navigator Receiver with a 1-hour warm-up time [ 29 ]. The sensor was inserted in either the anterior abdominal wall or the upper arm. The user interface is shown in Figure 2. We used a control algorithm based on the model predictive control approach [ 30 ], optimized and tuned in silico by using a computer-simulation environment validated for glucose control in the critically ill [ 31 ]. The insulin and dextrose pumps were controlled automatically, and no manual intervention was required. The calculations used a compartment model of glucose kinetics [ 32 ], describing the effect of insulin on sensor glucose excursions. The algorithm was initialized by using patient's weight and adapted itself to a particular patient by updating two model parameters: a rapidly changing glucose flux correcting for errors in model-based predictions, and a slowly changing estimate of an insulin rate to maintain euglycemia. The individualized model forecasted plasma glucose excursions over a 1- to 1. Information about enteral or parenteral nutrition was not provided to the algorithm. The algorithm requested a reference glucose measurement every 1 to 6 hours at a sensor level below 3. Reference glucose was used to recalibrate the sensor and to direct insulin and dextrose delivery when sensor levels were not available, such as during the 1-hour warm-up period. We used icuMPC algorithm version 1. Subjects allocated to the local insulin therapy protocol followed the usual care of a paper-based intravenous insulin-administration protocol used in NCCU Table 1. When the patient's glucose control was deemed unsatisfactory, the bedside nurse could initiate a physician-prescribed alteration in the paper-based scale either to increase or to decrease the amount of insulin delivered for a given glucose level, as per usual practice. Similarly, insulin or dextrose boluses were prescribed at the discretion of the treating physician. Arterial blood glucose measurements were made by using an on-site blood gas analyzer Cobas b ; Roche Diagnostics, Burgess Hill, UK at hourly intervals. As previously described in the investigational arm, a subset of reference glucose values was provided as the algorithm dictated, but the remainder of the reference samples did not factor into patient management. In the control arm, however, the hourly reference glucose values were available to the clinical team for insulin-dose adjustments. Demographic and clinical characteristics, including APACHE II scores, were collected at study initiation. Patients were classified as having diabetes on the basis of medical history. Treatment with corticosteroids and inotropes was defined as treatment with these agents during any part of the study, including those subjects already taking these agents at study entry. From the time of randomization to the time of discharge from the ICU or 48 hours after randomization, whichever came first, we recorded all blood glucose measurements, insulin administration, type and volume of all enteral and parenteral nutrition and additional intravenous glucose administered, and corticosteroid and inotrope administration. Investigators agreed on the outcome measures and the analysis plan in advance. The primary outcome was the time spent in primary target-glucose range between 6. Secondary efficacy outcomes were time spent with glucose levels between 4. Utility end points included the number of the reference glucose values requested by the algorithm and CGM availability. As this was a feasibility study, no formal power calculations were performed. All analyses were performed on an intention-to-treat basis. An unpaired t test was used to compare normally distributed variables. Nonnormally distributed variables were compared by using a Mann-Whitney U test. Calculations were carried out by using SPSS Version 19 IBM Software, Hampshire, UK. Outcomes were calculated with GStat software, Version 1. Values are given as mean SD or median interquartile range. In total, 37 patients were screened. Of the 27 randomized subjects, two subjects left the intensive care unit within 24 hours of the study start, and one subject was initiated on therapeutic hypothermia within 24 hours. Efficacy but not safety data from these three subjects were excluded from the data analysis. The baseline characteristics of the two groups were similar Table 2 , with comparable APACHE Il scores, previous diabetes status, and body mass index. The proportion of postsurgical patients was similar between two groups, whereas patients with major trauma were more common in the closed-loop group. The time spent in the primary target glucose range 6. These differences were more pronounced during the first 24 hours, with a fourfold improvement of time spent in the target glucose range These results persisted when the time was spent in a wider target range of 4. Time spent at greater than 8. A sample hour closed-loop study is shown in Figure 4. Cumulative distribution of reference glucose values obtained during closed-loop and local treatment protocol. Dashed vertical lines indicate the primary study target range from 6. Vertical fine dashed lines indicate the wider target from 4. An example of the hour closed-loop study. Darker red continuous line represents sensor glucose. Lighter red squares represent reference glucose measurements used for sensor calibration. Blue line represents insulin infusion. Thin red dashed lines indicate primary target. Dextrose infusion was not required in this study. The mean glucose level was significantly lower during closed-loop therapy 7. Glucose variability assessed by the standard deviation tended to be lower during the closed-loop therapy, without reaching statistical significance. Reference glucose profiles shown in Figure 6 highlight differences between the two groups. The closed-loop system administered more insulin during the first study hours Figure 6 , bottom panel , but overall, no statistical difference was found in insulin infusion between the treatments Table 3. Horizontal black line indicates the mean reference glucose in each intervention arm. Glucose and insulin values during infusion. Top panel: Glucose profiles median and interquartile range during closed-loop and local treatment protocol. Bottom panel: Median insulin infusion rates during closed-loop and local treatment protocol. The dashed lines indicate the primary target range from 6 to 8 m M. All but one patient received enteral nutrition, according to the local NCCU protocol. One patient received both enteral and parenteral nutrition. The number of calories and carbohydrates as well as the number of feeding interruptions per day was comparable between the two interventions Table 2. The proportion of patients treated with steroids or inotropes during the hour study period was slightly higher during closed-loop therapy Table 2. During closed-loop therapy, the number of reference glucose measurements requested by the control algorithm was 9. This translated into an interval between sensor calibrations of to and to minutes during the first and second 24 hours, respectively. 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. Automated administration of dextrose augmented the ability of closed-loop treatment to avoid low glucose levels. Kavanagh BP, McCowen KC: Clinical practice: glycemic control in the ICU. N Engl J Med. Article PubMed CAS Google Scholar. Krinsley JS: Understanding glycemic control in the critically ill: three domains are better than one. Intensive Care Med. Article PubMed Google Scholar. Krinsley JS: Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. Mayo Clin Proc. Bochicchio GV, Joshi M, Bochicchio KM, Pyle A, Johnson SB, Meyer W, Lumpkins K, Scalea TM: Early hyperglycemic control is important in critically injured trauma patients. J Trauma. discussion, Bagshaw SM, Egi M, George C, Bellomo R, Australia New Zealand Intensive Care Society Database Management C: Early blood glucose control and mortality in critically ill patients in Australia. Crit Care Med. NICE-SUGAR Study Investigators, Finfer S, Liu B, Chittock DR, Norton R, Myburgh JA, McArthur C, Mitchell I, Foster D, Dhingra V, Henderson WR, Ronco JJ, Bellomo R, Cook D, McDonald E, Dodek P, Hebert PC, Heyland DK, Robinson BG: Hypoglycemia and risk of death in critically ill patients. Article Google Scholar. Hermanides J, Bosman RJ, Vriesendorp TM, Dotsch R, Rosendaal FR, Zandstra DF, Hoekstra JB, DeVries JH: Hypoglycemia is associated with intensive care unit mortality. Egi M, Bellomo R, Stachowski E, French CJ, Hart G: Variability of blood glucose concentration and short-term mortality in critically ill patients. Krinsley JS: Glycemic variability: a strong independent predictor of mortality in critically ill patients. Badawi O, Waite MD, Fuhrman SA, Zuckerman IH: Association between intensive care unit-acquired dysglycemia and in-hospital mortality. Dungan KM, Braithwaite SS, Preiser JC: Stress hyperglycaemia. Article PubMed CAS PubMed Central Google Scholar. Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P, Bouillon R: Intensive insulin therapy in critically ill patients. Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I, Van Wijngaerden E, Bobbaers H, Bouillon R: Intensive insulin therapy in the medical ICU. NICE-SUGAR Study Investigators, Finfer S, Chittock DR, Su SY, Blair D, Foster D, Dhingra V, Bellomo R, Cook D, Dodek P, Henderson WR, Hebert PC, Heritier S, Heyland DK, McArthur C, McDonald E, Mitchell I, Myburgh JA, Norton R, Potter J, Robinson BG, Ronco JJ: Intensive versus conventional glucose control in critically ill patients. Preiser JC, Devos P, Ruiz-Santana S, Melot C, Annane D, Groeneveld J, Iapichino G, Leverve X, Nitenberg G, Singer P, Wernerman J, Joannidis M, Stecher A, Chiolero R: A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: The Glucontrol Study. Griesdale DE, de Souza RJ, van Dam RM, Heyland DK, Cook DJ, Malhotra A, Dhaliwal R, Henderson WR, Chittock DR, Finfer S, Talmor D: Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data. Article PubMed PubMed Central Google Scholar. Van den Berghe G: Intensive insulin therapy in the ICU: reconciling the evidence. Nature Rev Endocrinol. CAS Google Scholar. Van den Berghe G, Schetz M, Vlasselaers D, Hermans G, Wilmer A, Bouillon R, Mesotten D: Clinical review: intensive insulin therapy in critically ill patients: NICE-SUGAR or Leuven blood glucose target?. J Clin Endocrinol Metab. Van Herpe T, De Moor B, Van den Berghe G: Towards closed-loop glycaemic control. Best Pract Res Clin Anaesthesiol. Aragon D: Evaluation of nursing work effort and perceptions about blood glucose testing in tight glycemic control. Am J Crit Care. PubMed Google Scholar. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group, Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase HP, Clemons R, Fiallo-Scharer R, Fox LA, Gilliam LK, Hirsch IB, Huang ES, Kollman C, Kowalski AJ, Laffel L, Lawrence JM, Lee J, Mauras N, O'Grady M, Ruedy KJ, Tansey M, Tsalikian E, Weinzimer S, Wilson DM, Wolpert H, Wysocki T, Xing D: Continuous glucose monitoring and intensive treatment of type 1 diabetes. Pickup JC, Freeman SC, Sutton AJ: Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data. Corstjens AM, Ligtenberg JJ, van der Horst IC, Spanjersberg R, Lind JS, Tulleken JE, Meertens JH, Zijlstra JG: Accuracy and feasibility of point-of-care and continuous blood glucose analysis in critically ill ICU patients. Crit Care. Siegelaar SE, Barwari T, Hermanides J, Stooker W, van der Voort PH, DeVries JH: Accuracy and reliability of continuous glucose monitoring in the intensive care unit: a head-to-head comparison of two subcutaneous glucose sensors in cardiac surgery patients. Diabetes Care. Holzinger U, Warszawska J, Kitzberger R, Herkner H, Metnitz PG, Madl C: Impact of shock requiring norepinephrine on the accuracy and reliability of subcutaneous continuous glucose monitoring.
Automated Diabetes Management Systems Market	Typically, DIY AID users have been Aktomated educated, with access manxgement disposable income, and have a high level HbAc role in gestational diabetes computer Autoomated [ 1718 HbAc role in gestational diabetes. A higher glucose Holistic herbal extracts was applied in the present study median 7. This paper includes guidance on the use of DIY AID systems, including Loop, OpenAPS, and AndroidAPS, which had supporting evidence available at the time of publication. declare no competing interests associated with this manuscript. Article PubMed PubMed Central Google Scholar Wood JR, Miller KM, Maahs DM, Beck RW, DiMeglio LA, Libman IM, et al. Kazempour-Ardebili, S.

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Peter Attia - On The Causes, Risks, and Signs Of Insulin Resistance