Insulin Dose Adjustment Research Articles

Helpful or harmful? The impact of the ketogenic diet on eating disorder outcomes in type 1 diabetes mellitus

Introduction Eating disorders (ED) are common complications in people with Type 1 diabetes, (PwT1D) given the rigid focus on food and insulin dose adjustment. Dietary recommendations for T1D match those for the general population, yet many fail to achieve target HbA1c. Evidence suggests that lower carbohydrate meals and thus reduced insulin requirements, may decrease inconsistencies in insulin absorption, maintain euglycemia and weight. Dietary restriction is a recognised risk factor for ED development, and Ketogenic Diets (KD) involve restriction of common family-based foods, thus impacting social normality and microbiome diversity. We reviewed the current literature on PwT1D following a KD to understand effects on ED risks. Areas covered Published data from MEDLINE, Embase and PsycINFO was used. Search terms included: Type 1 diabetes mellites; or insulin dependent diabetes or T1D AND Eating disorders or anorexia or bulimia or disordered eating AND Low-carbohydrate diet or carbohydrate restricted diet or low carb diet or ketogenic diet. Expert opinion Research into the effects of KDs on ED outcomes in PwT1D are limited, given the concerns over risks of diabetic ketoacidosis, hypoglycemia, and dyslipidemia. Longer term studies on the participants’ experience and motivations of adhering or admonishing the diet are needed.

The future of telehealth in type 1 diabetes.

Although telemedicine and telehealth services have been a part of type 1 diabetes (T1D) clinical care for several decades, the expansion of in-home telemedicine during the COVID-19 pandemic significantly increased interest in long-term use as part of routine care. This review highlights the current literature regarding telemedicine in T1D care as well as the benefits and barriers to use in a postpandemic world. Telemedicine has increased patient contact with healthcare providers, allowing for more frequent insulin dose adjustments and improvements in glycemic outcomes. In addition to routine clinical care, T1D device training and mental healthcare have been successful through telemedicine. Significant barriers to continued telemedicine care exist, including patient access and technology knowledge, language, and loss of face-to-face interaction. Healthcare providers additionally face unpredictable reimbursement and loss of continuity across state lines, and lack of resources and training for device downloads and telemedicine software. Telemedicine can be successfully used in T1D care and has the potential to significantly impact glycemic and long-term outcomes. Due to continued interest for in-person visits by people with T1D and providers, it is likely that long-term telemedicine use will include a hybrid format.

Medical Nutrition Therapy Using Carbohydrate Counting

Improved postprandial glucose management requires careful monitoring of carbohydrate counting and consideration of the blood glucose response to dietary carbohydrate. Furthermore, learning how to use insulin-to-carbohydrate ratios for meal planning might help people successfully adjust insulin dose from meal to meal to enhance glycemic control. Regular education to raise knowledge of the link between carbohydrate intake and insulin requirements is critical for those whose meal schedules or carbohydrate consumption are unpredictable. Medical nutrition therapy is an important part of total diabetes management, and each person with diabetes should participate actively in education, self-management, and treatment planning with their health care team, including creation of a personalized food plan.

Efficacy of the mHealth application in patients with type 2 diabetes transitioning from inpatient to outpatient care: A randomized controlled clinical trial

IntroductionNo studies have assessed the efficacy of telemedicine using a platform for recording and adjusting insulin doses in patients with diabetes mellitus type 2 (DM2) transitioning from inpatient to outpatient care. This study aimed to assess, in a population of patients with DM2, discharged from a tertiary referral hospital, whether treatment based on the use of an mHealth application was associated with better glycemic control at the 3-month follow-up, than standard care. MethodsThis open, randomized, controlled clinical trial included adult DM2 patients who were transitioning from inpatient to outpatient care. The efficacy and safety of patient management with and without mHealth was compared at the 3-month follow-up. The primary outcome was the change in the Glycosylated hemoglobin (HbA1c) levels. The secondary outcomes were the rates of hypoglycemic and hyperglycemic events and treatment satisfaction measured using the Insulin Treatment Satisfaction Questionnaire (ITSQ). ResultsIn total, 86 patients (41 using mHealth) were included in the clinical trial. HbA1c levels showed a significant decrease in both groups. The mean HbA1c level was significantly lower in the mHealth group. Patients using mHealth showed decreased incidence rate ratios of hypoglycemia 3.0 mmol/L [<54 mg/dl], hypoglycemia ranging from 3.0 to 3.8 mmol/L [54 to 70 mg/dl] and severe hypoglycemia. The level of satisfaction assessed using the ITSQ was higher in the mHealth group. ConclusionUsing mHealth in patients with DM2 transitioning from inpatient to outpatient care improves metabolic control and may reduce the hypoglycemia rates.

89-OR: Effective Real-World Usage of Artificial Intelligence–Based Decision Support System for Diabetes Management

Background: Artificial intelligence-based decision support system (AI-DSS) for frequent insulin dose adjustment was demonstrated to be effective in improving glycemic control in our previous clinical trials. We aimed to assess the effectiveness of AI-DSS in real world diabetes clinics. Methods: Data was collected and evaluated from routine AI-DSS usage for individuals with type 1 diabetes treated at 21 diabetes clinics (in the USA and 2 in Israel) . Two analyses were made: (1) Insulin dose recommendations were compared for rate of agreement/disagreement between those suggested by the AI-DSS and the approved recommendations by health care providers (HCP) . (2) Glycemic outcomes were compared between baseline and after 3 months of AI-DSS use. Included in this analysis were individuals who had mean baseline glucose level ≥ 182 mg/dL and at least one AI-DSS recommendation within the 3-months. Results: A total of 8 recommendations, provided to 370 individuals, were evaluated. Full agreement on the direction of insulin dose adjustments was observed in 87%, 87%, and 83% of the basal rate, carbs ratio (CR) , and correction factor (CF) pump settings parameters, respectively. Full disagreement on the direction of dose change was observed in only 0.9%, 0.5%, and 1.2% for the basal rate, CR and CF, respectively. Glycemic outcome analysis included 1 eligible individuals (mean of 1.6 recommendations within a 3-month interval) . Average sensor percentage of time in range [70-180 mg/dL] increased by 4% (p=0.003) : 43% increased time in range by more than 5% and 33% increased by 10% or more. Time in hyperglycemia [&amp;gt; 180 mg/dL] was reduced by 5% (p&amp;lt;0.01) while time in severe hypoglycemia [&amp;lt; 54 mg/dL] remained below 0.5%. Conclusion: A high rate of agreement with automated insulin adjustments was observed among HCPs at academic centers who used the AI-DSS in their workflow. Glycemic control was significantly improved for sub-optimally controlled individuals already after 3-months of follow up. Disclosure R.Nimri: Employee; DreaMed Diabetes, Ltd., Research Support; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Medtronic, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk, Stock/Shareholder; DreaMed Diabetes, Ltd. A.Tirosh: Advisory Panel; Abbott Diagnostics, AstraZeneca, Boehringer Ingelheim International GmbH, Merck &amp; Co., Inc., Novo Nordisk, Sanofi, Consultant; Bayer AG, DreaMed Diabetes, Ltd., Research Support; Medtronic, Speaker's Bureau; Eli Lilly and Company. I.Muller: Board Member; DreaMed Diabetes, Ltd. Y.Shtrit: Employee; DreaMed Diabetes, Ltd. M.Phillip: Advisory Panel; Dompé, Insulet Corporation, Medtronic, Pfizer Inc., Board Member; DreaMed Diabetes, Ltd., Consultant; QuLab Medical Ltd., Other Relationship; Dexcom, Inc., Dompé, DreaMed Diabetes, Ltd., Eli Lilly and Company, Medtronic, NG Solutions Ltd, Novo Nordisk, OPKO, Pfizer Inc., Sanofi, Stock/Shareholder; DreaMed Diabetes, Ltd., NG Solutions Ltd.

702-P: Short-Period CGM Data Are More Useful than HbA1c for Treatment Adjustment in Type 2 Diabetes Patients

Current recommendations for type 2 diabetes treatment are based on targeting HbA1c depending on different factors. HbA1c reflects recent 8-12 weeks glucose control, but amount of data show HbA1c insufficiencies. Continuous glucose monitoring (CGM) is used for insulin dose adjustment but also for control assessment and treatment determination. In our study we compare seven-day non-real time CGM data and HbA1c to find out which is more appropriate for determining drug prescription in patients with type 2 diabetes. Our study includes 85 patients with type 2 diabetes (35 women, 50 men; age 43.93±10.87 years, disease duration 21.91±6.years) , on different treatment regimens (31 on oral therapy, 33 treated with premixed insulin, 21 on multiple insulin injections) . Patients performed Non-Real Time Continuous Glucose Monitoring by using iProTM for seven days and HbA1c was measured at the end of this period. No difference in HbA1c level was found in three observed groups. HbA1c showed moderate negative correlation to percentage of time within the limits (r1=-0.48; r2= -0.654; r3= -0.674) . Based on HbA1c assessment 34.84% of type 2 patients on oral medications, 36.36% of patients on premixed insulin and 19.05% of type 2 patients on multiple daily injection are with good control. CGM defined 54.84% of type 2 patients on oral medication, 18.27% of patients on premixed insulin and 17.80% of type 2 patients on intensified regimen as well controlled. CGM was more precise than HbA1c in defining patients with good control (p&amp;lt;0.05) . HbA1c shows bad correlation with overall glucose excursion (r1=-0.096, r2=0.200, r3=0.377) . No difference was found between studied groups in regard to CGM percentage of time spent above and below the limits and to AUC above upper limit. We conclude that CGM data give more precise information than HbA1c about glucose control and are more useful for defining treatment adjustment. Even performing CGM for short period helps physician to choose the most appropriate drug. Disclosure T.Totomirova: n/a. M.Arnaudova: None.

801-P: Effect of Adding Computerized Insulin Dose Adjustment Algorithms (CIDAA) to a Remote Patient Monitoring (RPM) Program on A1C Levels

The purpose of this study was to determine the effect of adding CIDAA to a subpopulation of an HMO that also had an RPM program. Patients taking insulin for &amp;gt;6 months with A1Cs &amp;gt;8.0% were selected from 3 cohorts, RPM + CIDAA (Group A) , RPM alone (Group B) and usual care (Group C) and followed for 6 months. Glucose readings of Group A patients, given a Bluetooth-enabled glucometer, were analyzed with dose adjustment recommendations sent to their providers (2 MDs, 1 PA) that they could accept or modify. Health educators contacted Group A and B patients for each RPM glucose value of &amp;lt;70 or &amp;gt;200 mg/dl and counseled them how to avoid subsequent episodes. Non-insulin drugs were not allowed to be added to Group A and B patients. A1C levels were measured at baseline and 6 months. Patient demographics, their A1C responses and alerts are shown in the Table. There were no differences among baseline A1Cs but significant differences from baseline. Groups A vs. B (P = 0.01) and A vs. C (P = 0.001) were significantly different but not B vs. C. All alerts (P = 0.02) and those &amp;gt;200 mg/dl (P = 0.002) were significantly less in Group A vs. B but alerts &amp;lt;70 mg/dl were not. Conclusion: A1C levels fell twice as much without increases in hypoglycemia in poorly controlled type 2 diabetic patients taking insulin whose providers received recommendations for dose changes based on CIDAA every 2-3 weeks compared with RPM alone or usual care. Disclosure J.S.Pulicharam: None. J.Davidson: None. M.B.Davidson: Other Relationship; Mellitus Health. Funding Mellitus Health, Inc

Closed-Loop Insulin Pump Therapy in Young Type 1 Diabetic Children

Source: Ware J, Allen JM, Boughton CK, et al. Randomized trial of closed-loop control in very young children with type 1 diabetes. N Engl J Med. 2022;386:209-219; doi: 10.1056/NEJMoa2111673Investigators from multiple institutions conducted a crossover randomized trial to compare the effectiveness and safety of a closed-loop system (ie, artificial pancreas) to sensor-augmented insulin pump therapy in young children with type 1 diabetes. Participants were children 1-7 years old with a diagnosis of type 1 diabetes for at least 6 months, recruited from diabetes centers in 4 European countries. The closed-loop system consisted of a continuous glucose sensor and insulin pump connected to a smartphone app. The app included an algorithm that automatically adjusted insulin delivery based on real-time sensor glucose readings. With the sensor-augmented pump, insulin therapy was stopped if an algorithm predicted that sensor glucose levels were likely to drop below a specified threshold. Study children were randomized to receive 16 weeks of treatment with the closed loop system, followed by 16 weeks using the augmented-sensor pump, or vice versa. The primary outcome was percentage of time spent in the target glucose range (70 to 170 mg/dL) during each treatment period. Secondary outcomes included percentage of time spent in a hyperglycemic range (>180 mg/dL) and hypoglycemic range (<70 mg/dL) and mean glucose levels. These outcomes were based on readings from the glucose sensors. Glycated hemoglobin levels were measured at baseline and at the end of each treatment period. Differences in outcomes between the closed-loop and sensor-augmented pump treatment period were assessed with repeated-measures linear regression models. Data on adverse events also were collected.Data were analyzed on 74 children with a mean age of 5.6 ±1.6 years. The mean glycated hemoglobin level at baseline was 7.3 ±0.7%. The mean time spent in the target glucose range were 71.6 ±5.9% during the closed-loop period and 62.9 ±9.0% in the sensor-augmented pump period (adjusted difference, 8.7 percentage points; 95% CI, 7.4, 9.9; P <0.001). The median time spent at glucose level >180 mg/dL was significantly less during the closed-loop period than during the control period (22.9% vs 31.7%; adjusted difference, -8.5 percentage points; 95% CI, -.9.9, -7.1; P <0.001); mean sensor glucose levels (145 ±11.8 mg/dL and 158.1 ±18.5 mg/dL, respectively; P <0.001) and mean glycated hemoglobin levels (6.6 ±0.6% and 7.0 ±0.7%) were significantly lower during the closed-loop period than during treatment with the segmented-sensor pump. There was no difference between treatment periods for percentage of time with glucose <70 mg/dL (P = 0.74). One participant had a serious adverse event (severe hypoglycemia) during their closed-loop treatment period.The authors conclude that a closed-loop system improved glycemic control in young children with type 1 diabetes.Dr Fechner has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.Type 1 diabetes is very difficult to manage in the young child. The therapeutic window for insulin is very narrow, with too much insulin resulting in hypoglycemia and too little insulin resulting in hyperglycemia. The last 5 years have seen tremendous advances in the technology available to children with type 1 diabetes. A 2019 study comparing the insulin pump to multiple daily injections showed that the insulin pump was superior to multiple daily injections. (See AAP Grand Rounds. 2019;42[1]:4.) Continuous glucose monitoring has decreased parental anxiety regarding risk of hypoglycemia and diabetic ketoacidosis;1 however, parents still are responsible for making insulin dose adjustments, and glycemic control overall is not improved.2 The results of a 2020 study of 6- to 13-year-olds with type 1 diabetes demonstrated that glucose levels were in the target range for a longer period of time in those randomized to a closed-loop system than those using a sensor-augmented pump. (See AAP Grand Rounds. 2020;44[6]:69.)3 The current researchers compared a hybrid closed-loop insulin delivery system to sensor-augmented pump therapy in younger children with type I diabetes.The strengths of the current study are the randomized, crossover design using the same algorithm with the closed-loop functionality either abled or disabled; each therapy was 16 weeks, which allowed a meaningful glycated hemoglobin to be obtained. There were no restrictions on previous episodes of hypoglycemia and diabetic ketoacidosis, and the study included a very young children with diabetes, who are more vulnerable to hypo- and hyperglycemia. Weaknesses of this study include underrepresentation of ethnic minorities and a patient population not representative of the typical type 1 diabetes population, as the average glycated hemoglobin level was 7.3% at baseline.4 Decreased patient compliance outside of the confines of a study may be reflected in poorer glucose control regardless of the insulin delivery system used.Hybrid closed-loop insulin therapy in young children with type 1 diabetes has the potential to significantly improve long-term outcomes through improved glycemic control.

Super Bolus: a remedy for a high glycemic index meal in children with type 1 diabetes on insulin pump therapy?\u2014study protocol for a randomized controlled trial

BackgroundPostprandial hyperglycemia (PPH) is a common clinical problem among patients with type 1 diabetes (T1D), which is related to high glycemic index (h-GI) meals. The main problem is linked to high, sharp glycemic spikes following hypoglycemia after h-GI meal consumption. There is a lack of effective and satisfactory solutions for insulin dose adjustment to cover an h-GI meal. The goal of this research was to determine whether a Super Bolus is an effective strategy to prevent PPH and late hypoglycemia after an h-GI meal compared to a Normal Bolus.MethodsA total of 72 children aged 10–18 years with T1D for at least 1 year and treated with continuous subcutaneous insulin infusion for more than 3 months will be enrolled in a double-blind, randomized, crossover clinical trial. The participants will eat a h-GI breakfast for the two following days and receive a prandial insulin bolus in the form of a Super Bolus 1 day and a Normal Bolus the next day. The glucose level 90 min after the administration of the prandial bolus will be the primary outcome measure. The secondary endpoints will refer to the glucose levels at 30, 60, 120, 150, and 180 min postprandially, the area under the blood glucose curve within 180 min postprandially, peak glucose level and the time to peak glucose level, glycemic rise, the mean amplitude of glycemic excursions, and the number of hypoglycemia episodes.DiscussionThere are still few known clinical studies on this type of bolus. A Super Bolus is defined as a 50% increase in prandial insulin dose compared to the dose calculated based on the individualized patient’s insulin-carbohydrate ratio and a simultaneous suspension of basal insulin for 2 h. Our patients reported the best experience with such a combination. A comprehensive and effective solution to this frequent clinical difficulty of PPH after an h-GI meal has not yet been found. The problem is known and important, and the presented solution is innovative and easy to apply in everyday life.Trial registrationClinicalTrials.gov NCT04019821

Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy.

Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo.Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.

COVID-19 vaccination in adolescents and young adults with type 1 diabetes: Glycemic control and side effects.

BackgroundTwo vaccines against SARS‐CoV‐2 are approved by the World Health Organization (WHO) for minors aged 12 years and over. Currently, people with both type 1 diabetes (T1D) and type 2 diabetes (T2D) are prioritized for vaccination.ObjectiveTo evaluate possible glycemic control modification, insulin dose adjustment and adverse effects after COVID‐19 vaccination in young T1D individuals, users of different technology levels.MethodsThirty‐nine T1D individuals, who received a whole vaccination cycle of either Moderna or Pfizer‐ BioNTech vaccines, were enrolled, 24 of whom using advanced hybrid closed loop systems (AHCLs) and 15 using intermittently scanned continuous glucose monitoring (isCGM). Symptoms after each dose and the following variables were considered: time in range 70–180 mg/dl (TIR), time in different glucose ranges, mean glucose levels, coefficient of variation (CV), total daily dose (TDD) and bolus proportionResultsNo significant differences in TIR, time in different glucose ranges, mean glucose levels, TDD, bolus proportion, were observed before and after any dose nor before and after the whole vaccination cycle. CV was significantly lower after the whole vaccination cycle (CV pre‐vaccination 35.1 ± 6.9% vs. CV post‐vaccination 33.5 ± 6.3%; p 0.031) in subjects treated by AHCLs. Side effects after the vaccination were mild and more frequent after the second dose. No severe adverse reactions were reported.ConclusionsCOVID‐19 vaccination was safe and not associated with significant perturbation of glycemic control in adolescents and young adults with T1D. This information could be of clinical use when counseling families about SARS‐CoV‐2 vaccination in young people with T1D.

Teleconsultation for pediatric patients with type 1 diabetes mellitus during the COVID-19 pandemic: experience of a university hospital in Brazil

ObjectiveEvaluate the impact of teleconsultation on glycemic control and prevention of acute complications related to diabetes mellitus in children and adolescents treated in a reference hospital during the COVID-19 pandemic in 2020. MethodDescriptive study of data from pediatric diabetic patients who received teleconsultation between April and September 2020. ResultsDuring this period, 143 diabetic patients were evaluated, with a median of 3.4 teleconsultations per patient in the studied period, requiring adjustment of insulin doses in 84.6% of cases. The hospital admission rate was 8,4% due to diabetic decompensation. The metabolic control (HbA1c) became worse in 46% of the sample and improved in 37%. ConclusionThe teleconsultation promoted health care for patients with diabetes mellitus during the COVID-19 pandemic but was not able to guarantee adequate glycemic control.

Machine Learning and Smart Devices for Diabetes Management: Systematic Review.

(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.

An Opportunity for Improvement: Evaluation of Diabetes Technology Education Among Adult Endocrinology Training Programs.

Despite increases in continuous glucose monitor (CGM) and insulin pump use in adults with diabetes, there is room for expansion. Technology adoption may be influenced by the training environment and fellowship education. However, little is known about adult endocrinology trainee comfort with, understanding of, or methods by which trainees receive education about diabetes technology. Mixed methods, sequential explanatory evaluation using survey and semi-structured interviews of endocrinology trainees and fellowship leadership in Accreditation Council for Graduate Medical Education (ACGME)-accredited adult endocrinology fellowship programs to assess trainee and leadership comfort with, perceived knowledge of, and current methods for diabetes technology education. Seventy-seven respondents completed the survey. The majority of training programs have curricula for training on insulin pumps (74%) and CGM (75.3%); 52% of fellows felt curricula are adequate. First- and second-year fellows were more comfortable with CGM than insulin pump use. Only half of third-year fellows felt comfortable with starting insulin pump therapy or recommending insulin dose adjustments based on CGM rate of change arrows. Qualitative interviews identified the importance of both direct instruction and experiential learning in diabetes technology education. Almost half of trainees feel that curricula for learning to use and manage insulin pumps and CGM are inadequate and feel uncomfortable with critical aspects of technology use, demonstrating the need for increased attention to trainee education in the use of diabetes technology. Based on a better understanding of current and preferred methods for instruction, this study provides direction for future development of initiatives to improve fellow education in this field.

Frequency of diabetes team contacts in children and adolescents using insulin pumps

Background: The purpose of this study is to assess if a relationship exists between A1c within target (£ 7.5%) and frequency of patient-initiated contact with diabetes team, in children with type 1 diabetes (T1DM) on an insulin pump. Additionally, to determine factors impacting frequency of contact. Methods: This was a retrospective study of children with T1DM on an insulin pump. Frequency of contact, type of contact, and A1c were collected. Study participants filled out a questionnaire at study entry. Results: One hundred and seventy-six participants were enrolled, with a mean age of 13 years. The median duration of T1DM was 6 years with a median duration of pump use, 3.6 years. One hundred and sixteen subjects (66%) contacted the diabetes team for insulin dose adjustments between clinic visits with a mean (standard deviation [SD]) of 1.2 (± 1.7) contacts, 90% of which were by e-mail. There was no significant relationship between achieving target A1c and frequency of contact. However, increasing age and longer duration of pump use were associated with decreased frequency of contact. Common barriers to contact included being too busy and technical problems with software. Conclusions: There was no significant relationship between the frequency of patient-initiated contact with diabetes team and A1c. Overall, there was low frequency of contact in the cohort. Older children and children with longer duration of pump use had fewer contacts with low rates of self-directed pump adjustments. These results raise the importance of defining strategies to increase patient engagement and empower diabetes data review.

Systematic literature review of insulin dose adjustments when initiating hemodialysis or peritoneal dialysis.

Chronically uncontrolled hyperglycemia is the leading cause of end stage kidney disease (ESKD) necessitating dialysis. During times of transition to hemodialysis (HD) or peritoneal dialysis (PD), considerations must be given to insulin dosing adjustments for persons with diabetes (PWD) in efforts to maintain glycemic control. However, the literature is sparse with few clear and direct practical clinical recommendations for therapeutic insulin dosing adjustments in PWD and ESKD. The objective of this systematic review was to identify and report the evidence and gaps in the literature for adjustments in therapeutic insulin recommendations when initiating HD or PD in patients with ESKD and diabetes mellitus. A literature search using PubMed, CENTRAL, MEDLINE, CINAHL, Google Scholar, and ClinicalTrials.gov revealed 242 results. After removing duplicates and articles not reaching pre-specified criteria, 29 relevant articles remained for further analysis. Following the exclusion of 18 articles after full-text review due to lack of relevance or inappropriate publication type, 11 articles remained and were included in the review. The most common recommendation regarding HD was to reduce the basal insulin dose up to 25% on HD days to prevent hypoglycemia, although a lack of consensus exists on the percent reduction. Little information was found relating to insulin management with continuous ambulatory PD or automated PD. During PD, insulin may be administered subcutaneously, IP, or with the dialysis fluid. Administration of insulin with dialysate may necessitate a dose increase of up to 30% due to a loss to tubing and dilution. Furthermore, the use of dextrose-based dialysate may require additional insulin to mitigate systemic impact of dextrose absorption on BG. Overall, a gap exists in the primary literature regarding recommendations for prophylactically adjusting insulin therapy when initiating HD or PD, or when switching between the two. More research is needed to clarify ideal alterations in insulin dosing, administration techniques, and product selections for PWD and ESKD undergoing dialysis.

Feasibility and Impact of Remote Glucose Monitoring Among Patients With Newly Diagnosed Type 1 Diabetes: Single-Center Pilot Study.

BackgroundCaregivers of children with newly diagnosed type 1 diabetes (T1D) maintain close contact with providers for several weeks to facilitate rapid adjustments in insulin dosing regimens. Traditionally, patient glucose values are relayed by telephone for provider feedback, but digital health technology can now enable the remote sharing of glucose data via mobile apps.ObjectiveThe aim of this study was to test the feasibility of remote glucose monitoring in a population of children and adolescents with newly diagnosed T1D and to explore whether remote monitoring alters habits for self-review of glucose data or perceived ease of provider contact in this population as compared to a nonrandomized control group.MethodsData were collected from families who chose to participate in remote monitoring (intervention group) as well as from patients receiving usual care (control group). The intervention group received Bluetooth-capable glucose meters and Apple iPod Touch devices. Patient-generated glucose data were passively relayed from the meter to the iPod Touch and then to both the electronic health record (EHR) and a third-party diabetes data platform, Tidepool. The principal investigator reviewed glucose data daily in the EHR and Tidepool and contacted the participants as needed for insulin dose adjustments during the time between hospital discharge and first clinic appointment. Families in the control group received usual care, which involved keeping written records of glucose values and contacting the diabetes team daily by telephone to relay data and receive treatment recommendations. A total of 40 families (20 for the intervention group and 20 for the control group) participated in the study. All families were surveyed at 1 month and 6 months regarding self-review of glucose data and ease of contacting the diabetes team.ResultsPatient-generated glucose data were remotely accessible for 100% of the participants via Tidepool and for 85% via the EHR. Survey data indicated that families in the intervention group were more likely than those in the control group to review their glucose data using mobile health apps after 1 month (P<.001), but by 6 months, this difference had disappeared. Perceived ease of contacting the clinical team for assistance was lower for the intervention group after 6 months (when receiving usual care) in comparison to during the intervention period (P=.48) and compared with a control group who did not have exposure to remote monitoring (P=.03).ConclusionsRemote glucose monitoring is feasible among pediatric patients with newly diagnosed T1D and may be associated with the earlier adoption of mobile health apps for self-management. The use of broadscale remote monitoring for T1D in the future will depend on improved access to Bluetooth-enabled mobile devices for all patients, improved interoperability of mobile health apps to enable data transfer on Android as well as Apple devices, and new provider workflows to handle large-scale panel management based on patient-generated health data.Trial RegistrationClinicalTrials.gov NCT04106440; https://clinicaltrials.gov/ct2/show/NCT04106440

Prevention of exercise-induced hypoglycemia in 12 patients with type 1 diabetes running the Paris Marathon using continuous glucose monitoring: A prospective, single-center observational study

ObjectiveTo investigate the glycemic balance before, during and after the 2016 Paris Marathon using a real-time continuous glucose monitoring (RT-CGM) system in patients with type 1 diabetes mellitus in a prospective single-center observational study. MethodsInclusion criteria were as follows: type 1 diabetes mellitus; age ≥18 years; HbA1c < 9%. Participants performed two 2h-preparatory races (PR) before the Marathon and were monitored with RT-CGM 24h before, during and 72h after each race. Hypoglycemic events were prevented via carbohydrate intake / insulin dose adjustments. The primary outcome was area under the curve (AUC) < 70 and > 200 mg/dl and percentage of time spent in euglycemia, hypoglycemia, and hyperglycemia during the races. ResultsTwelve patients (2F/10M; median HbA1c=6.8%) were included and completed the study. Median AUC < 70 and time spent in hypoglycemia (< 70 mg/dl) during the PRs and Marathon were equal to 0. However, no hypoglycemic episodes occurred during Marathon, while two patients experienced hypoglycemia during PR1 and PR2. There was a significant increase in AUC > 200 mg/dl during races between PR2 and Marathon (P = 0.009) although the median time spent > 200mg/dl was not statistically different in Marathon versus PR2 (48.4% versus 18.4%; P = 0.09). Median time spent in euglycemia (70-200 mg/dl) was lower in Marathon versus PR2 (51.6 versus 58%; P = 0.03). ConclusionOur study proposes a medical support protocol for extreme endurance physical activity in patients with type 1 diabetes mellitus. Our results suggest that RT-CGM, coupled with adjustments in carbohydrate intake and insulin doses, appears to be effective to prevent hypoglycemia during and after exercise.

POSB393 Profile of Patients with Type 1 or Type 2 Diabetes Treated with Insulin Lispro 100 Units/ML Junior Kwikpen in Routine Clinical Practice in Spain

Insulin lispro 100 units /mL Junior KwikPen (JrKwikPen) is a disposable pre-filled pen that delivers 0.5–30 units (U) in increments of 0.5 U, which allows for more accurate insulin dose adjustment and more precise glycaemic control. This study describes sociodemographic, clinical, and treatment characteristics of patients with diabetes who initiated treatment with JrKwikPen in real world practice in Spain. This observational retrospective study, based on IQVIA’s electronic medical records databases, included patients of all ages with type 1 diabetes (T1D) or type 2 diabetes (T2D) who started treatment with JrKwikPen between 1st May 2018 and 31st December 2020. Patient characteristics and concomitant treatment at JrKwikPen initiation (index date) and prior medical/treatment history were analysed descriptively by type of diabetes and age groups. A total of 416 patients were included. Main characteristics for T1D/T2D groups (N=326/90) were, respectively: female, 61.7%/65.6%; mean age (standard deviation [SD]), 32.5 (20.7)/55.5 (16.6) years; time since first diabetes record, 5.8 (5.1)/7.9 (4.2) years; body mass index (BMI), 20.8 (4.2)/24.8 (4.8) kg/m2 (N=106/44); haemoglobin A1c (HbA1c), 8.2 (2.0)%/8.0 (1.4)% (N=67/38); presence of diabetes-associated comorbidities, 27.9%/64.4%. Only 32.8% of patients with T1D were <18 years. Among JrKwikPen users 12.3% (T1D/T2D, 6%/6.3%) were ≥65 years, 20.9% patients were newly diagnosed, and 3.8% were pregnant women. The mean (SD) total insulin dose pre-index for T1D/T2D was 42.4 (23.4)/40.7 (21.6) U/day, respectively. The mean (SD) insulin dose at initiation of JrKwikPen was 26.6 (16.6) and 22.6 (13.6) U/day for T1D/T2D, respectively. JrKwikPen was first prescribed mainly by endocrinologists (58.7%) or pediatricians (22.6%). This is the first study that describes the profile of patients treated with insulin lispro 100 U/mL JrKwikPen in clinical practice in Spain. JrKwikPen users were mainly women, adults, with T1D, low BMI, and more associated comorbidities.

Effect of Incorporating Computerized Insulin Dose Adjustment Algorithms into a Remote Patient Monitoring Program on HbA1c Levels in Patients with Type 2 Diabetes

Aims: To evaluate the effect of Federal Drug Administration-cleared computerized insulin dose adjustment algorithms (CIDAAs) incorporated into a Remote Patient Monitoring (RPM) program on HbA1c levels. Material &amp; Methods: Type 2 patients receiving insulin for &gt;6 months with HbA1c levels ≥8.0% enrolled in a Medicare Advantage plan were recruited into 3 arms; Group A (RPM plus CIDAAs, N=40), Group B (RPM but no CIDAAs, N=42), and Group C (usual care with no RPM or CIDAAs, N=33). In Groups A and B, glucose readings of &gt;200 mg/dL and &lt;70 mg/dL triggered alerts and health educators contacted patients for counseling to avoid future episodes. Results: Baseline HbA1c levels (% ± SD) were not statistically different among Groups A (9.5 ± 1.5), B (9.2 ± 1.1) and C (9.0 ± 0.9). At 6 months, HbA1c levels fell twice as much in Group A (-1.5 ± 1.0) as in Groups B (-0.7 ±1.5) and C (-0.7 ± 1.2) (P &lt;0.001). Alerts &gt;200 mg/dl were significantly less in Group A (N=942) than in B (N=1111) (P = 0.002) but alerts &lt;70 mg/dL were not significantly different (235 vs 209). In Group A, baseline per patient daily insulin doses of 78 units rose 40% to 109 units. Six patients in Group C had emergency room visits for hypoglycemia but none in Groups A and B. Conclusions: The RPM program lessened clinical hypoglycemic risk but required CIDAAs to markedly increase insulin doses effectively and safely to significantly lower HbA1c levels twice as much as either RPM alone or usual care.