Bolus calculator improves long-term metabolic control and reduces glucose variability in pump-treated patients with Type 1 diabetes

Comparison of Insulin Pump Bolus Calculators Reveals Wide Variation in Dose Recommendations

BACKGROUND

The introduction of insulin pumps with bolus calculators (BCs) has improved glycemic outcomes and quality of life for those with type 1 diabetes. Despite the increased reliance on BCs, the formulas used to derive recommended boluses are not standardized. Our objective was to examine whether recommendations from different pump BCs vary significantly for identical clinical scenarios.

METHODS

Three commercially available insulin pump BCs were programmed with identical settings and then presented with combinations of blood glucose (BG) and carbohydrates (CHOs) to generate a 4-unit bolus. At one- and two-hour time points, while there was insulin-on-board (IOB) present, we simulated various BG and CHO scenarios in order to compare BC-recommended doses.

RESULTS

Differences in suggested doses were noted between BCs, as well as within the same brand. The greatest variation was apparent when BG was below target. Doses suggested by one BC varied depending on whether the IOB resulted from a previous dose given for BG or CHO, while the other two BCs adjusted for total IOB regardless of the source.

CONCLUSIONS

In this simulation study, there were large differences in recommended doses between BCs due to the unique way each manufacturer incorporates IOB into their formulas as well as the pharmacokinetics used to derive the IOB amount. Providers should be aware that identical pump settings will result in a different dose recommendation for each pump brand and advise patients accordingly.

Intelligent Data-Driven Model for Diabetes Diurnal Patterns Analysis

In type 1 diabetes, diurnal activity routines are influential factors in insulin dose calculations. Bolus advisors have been developed to more accurately suggest doses of meal-related insulin based on carbohydrate intake, according to pre-set insulin to carbohydrate levels and insulin sensitivity factors. These parameters can be varied according to the time of day and their optimal setting relies on identifying the daily time periods of routines accurately. The main issues with reporting and adjustments of daily activity routines are the reliance on self-reporting which is prone to inaccuracy and within bolus calculators, the keeping of default settings for daily time periods, such as within insulin pumps, glucose meters, and mobile applications. Moreover, daily routines are subject to change over periods of time which could go unnoticed. Hence, forgetting to change the daily time periods in the bolus calculator could contribute to sub-optimal self-management. In this paper, these issues are addressed by proposing a data-driven model for identification of diabetes diurnal patterns based on self-monitoring data. The model uses time-series clustering to achieve a meaningful separation of the patterns which is then used to identify the daily time periods and to advise of any time changes required. Further improvements in bolus advisor settings are proposed to include week/weekend or even modifiable daily time settings. The proposed model provides a quick, granular, more accurate, and personalized daily time setting profile while providing a more contextual perspective to glycemic pattern identification to both patients and clinicians.

Real-Life Management and Effectiveness of Insulin Pump with or Without Continuous Glucose Monitoring in Adults with Type 1 Diabetes

INTRODUCTION

To describe and compare the routine use of continuous subcutaneous insulin infusion (CSII) in type 1 diabetes (T1D) patients with and without continuous glucose monitoring (CGM) in routine clinical practice and its relationship with glycemic outcomes.

METHODS

Retrospective observational case-control study collecting routine use of CSII and CGM in T1D patients between January 2016 and December 2016. Patients with T1D using sensor augmented pump (SAP) were matched by sex and disease duration in a 1:3 ratio with those treated only with CSII. Patients used a Paradigm Veo or 640G Medtronic-Minimed^® insulin pump with or without a glucose sensor (Enlite, Medtronic-Minimed^®) for at least 12 months.

RESULTS

A total of 160 subjects with T1D were included, 40 using SAP and 120 on CSII (age 47 ± 12 years, 88 women, diabetes duration 29 ± 9.0 years, 10 ± 4.7 years on CSII, HbA_1C 7.6 ± 0.8%). Those in SAP therapy used the sensor 63% of time, performed less self-monitored blood glucose (SMBG)/day (3.3 ± 1.9 vs. 4.5 ± 2.0; p < 0.01), more bolus/day (6.2 ± 3.6 vs. 4.8 ± 1.6; p < 0.05), more basal insulin segment/day (6.5 ± 2.1 vs. 5.9 ± 1.5; p < 0.05), and more suspension time of the pump (97 ± 93 vs. 9.6 ± 20 min/day; p < 0.0001). Regarding metabolic control, SAP therapy patients had lower HbA_1c (7.4 ± 0.7 vs. 7.7 ± 0.9%; p = 0.068), lower average SMBG value (151 ± 32 vs. 163 ± 30 mg/dL; p < 0.05), a lower percentage of SMBG values greater than 180 mg/dL (30 ± 19 vs. 37 ± 16%; p < 0.05) with no differences in SMBG values less than 70 mg/dL (12 ± 8.0 vs. 9.8 ± 9.8%; p = 0.33) compared with patients on CSII. There were no differences in bolus wizard targets or in insulin/carbohydrate ratios per day.

CONCLUSION

In a real-world setting, SAP therapy is associated with more self-adjustments of insulin therapy when compared to CSII alone. This could result in an improvement in glucose control.

Technology in Diabetes Treatment: Update and Future

Worldwide the number of people with diabetes mellitus is increasing. There are estimations that diabetes is one of the leading causes of death. The most important goals for the treatment of diabetes are self-management of the disease and an optimal quality of diabetes control. In the therapy new technologies, like real-time continuous interstitial glucose monitoring, continuous subcutaneous insulin infusion (CSII), electronic tools for the monitoring of therapeutic approaches, automated bolus calculators for insulin and electronic tools for education and information of patients, have become widespread and play important roles. All these efforts are related to the interaction between patients, caregivers, scientists or researchers and industry. The presentation of different aspects of new technological approaches in the present article should give more information about different technologies. However, because of the rather quickly appearance of new technologies, the presentation can only be a spotlight. Further studies are mandatory to analyze the effects and long-term benefits of each technology and electronic device.

Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes

Type 1 diabetes mellitus (T1DM) is a chronic disease. Those who have it must administer themselves with insulin to control their blood glucose level. It is difficult to estimate the correct insulin dosage due to the complex glucose metabolism, which can lead to less than optimal blood glucose levels. This paper presents PepperRec, a case-based reasoning (CBR) bolus insulin recommender system capable of dealing with an unrestricted number of situations in which T1DM persons can find themselves. PepperRec considers several factors that affect glucose metabolism, such as data about the physical activity of the user, and can also cope with missing values for these factors. Based on CBR methodology, PepperRec uses new methods to adapt past recommendations to the current state of the user, and retains updated historical patient information to deal with slow and gradual changes in the patient over time (concept drift). The proposed approach is tested using the UVA/PADOVA simulator with 33 virtual subjects and compared with other methods in the literature, and with the default insulin therapy of the simulator. The achieved results demonstrate that PepperRec increases the amount of time the users are in their target glycaemic range, reduces the time spent below it, while maintaining, or even reducing, the time spent above it.

Bolus Advisors: Sources of Error, Targets for Improvement

Bolus advisors that are designed to improve the accuracy of individual bolus doses relative to a meal's carb content and the current glucose have not substantially changed since they were introduced 15 years ago despite an obvious need for enhancement and innovation. Although some glycemic benefits have been demonstrated, bolus advisors largely ignore the large amounts of clinical data they gather that could have a significant impact on glucose outcomes. Concerns have also been raised regarding the aggressive nature of largely unpublished or poorly explained bolus advisor algorithms. Hypoglycemia and hyperglycemia remain significant risks due to inaccurate bolus advisor settings and the absence of tracking or an inappropriate handling of bolus on board. This review covers common sources for bolus advisor error such as the selection of physiologically inappropriate bolus advisor settings, the use of short duration of insulin action times, poor algorithm logic that tends to cover all carb intake fully, and an excessive reliance on simplistic dosing algorithms. As well as discussing these areas, we provide 21 ways to improve current bolus calculators.

The Calculating Boluses on Multiple Daily Injections (CBMDI) study: A randomized controlled trial on the effect on metabolic control of adding a bolus calculator to multiple daily injections in people with type 1 diabetes

BACKGROUND

Although the insulin bolus calculator is increasingly being used by people with type 1 diabetes (T1D) on multiple daily injection (MDI) therapy, few studies have investigated its effects on glycemic control. The aim of this study was to determine whether adding this device to therapeutic intensification could further improve metabolic control.

METHODS

A 4-month randomized controlled clinical trial was performed comparing subjects undergoing therapeutic intensification and either using the bolus calculator (Cb group) or not (active control [Co] group). Metabolic control, fear of hypoglycemia, and treatment acceptance were evaluated.

RESULTS

In all, 70 people completed the study (42 in the Cb group, 28 in the Co group). There was a significant decrease in HbA1c in both the Cb and Co groups (-7 mmol/mol [-0.7 %] vs -4 mmol/mol [-0.4 %], respectively). There were no significant differences in HbA1c at baseline or the end of the study, or in the decrease in HbA1c, glycemia, or changes in blood glucose levels at the end of the study between the two groups. There was a significant increase in the number of participants with good metabolic control (HbA1c <58 mmol/mol [7.5 %]) in the Cb group (from 16.7 % to 40.5 %), but not in the Co group. The incidence of hypoglycemic events was reduced slightly but significantly only in the Cb group. There was no change in the fear of hypoglycemia at the end of the study. The bolus calculator was well accepted.

CONCLUSIONS

In T1D, adding a bolus calculator to intensive MDI resulted in a significant improvement in metabolic control and slightly decreased the number of hypoglycemic episodes. Metabolic control also improved in the Co group.

A Randomized Controlled Study of an Insulin Dosing Application That Uses Recognition and Meal Bolus Estimations

BACKGROUND

Throughout the insulin pump therapy, decisions of prandial boluses programming are taken by patients individually a few times every day, and, moreover, this complex process requires numerical skills and knowledge in nutrition components estimation. The aim of the study was to determine the impact of the expert system, supporting the patient's decision on meal bolus programming, on the time in range of diurnal glucose excursion in patients treated with continuous subcutaneous insulin infusion (CSII).

METHODS

The crossover, randomized study included 12 adults, aged 19 to 53, with type 1 diabetes mellitus, duration ranging from 7 to 30 years. Patients were educated in complex food counting, including carbohydrate units (CU) and fat-protein units (FPU). Subsequently, they were randomly allocated to the experimental group (A), which used the expert software named VoiceDiab, and the control group (B), using a manual method of meal-bolus estimation.

RESULTS

It was found that 66.7% of patients within the A group statistically reported a relevant increase in the percentage (%) of sensor glucose (SG) in range (TIR 70-180 mg/dl), compared to the B group. TIR (median) reached 53.9% in the experimental group (A) versus 44% within the control group (B), P < .05. The average difference in the number of hypoglycemia episodes was not statistically significant (-0.2%, SD 11.6%, P = .93). The daily insulin requirement in both groups was comparable-the average difference in total daily insulin dose between two groups was 0.26 (SD 7.06 IU, P = .9).

CONCLUSION

The expert system in meal insulin dosing allows improvement in glucose control without increasing the rates of hypoglycemia or the insulin requirement.

Improved A1C Levels in Type 1 Diabetes with Smartphone App Use

OBJECTIVES

Smartphones are a potentially useful tool in diabetes care. We have developed an application (app) linked to a website, Intelligent Diabetes Management (IDM), which serves as both an insulin bolus calculator and an electronic diabetes diary. We have prospectively studied whether patients using this app improved control of their glucose levels.

METHODS

Patients with type 1 diabetes were recruited. There was a 4-week observation period, midway during which we offered to review the participants' records. The app was then downloaded and participants' diabetes regimens entered on the synchronized IDM website. At 2, 4, 8, 12 and 16 weeks of the active phase, their records were reviewed online, and feedback was provided electronically. The primary endpoint was change in levels of glycated hemoglobin (A1C).

RESULTS

Of the 31 patients recruited, 18 completed the study. These 18 made 572±98 entries per person on the IDM system over the course of the study (≈5.1/day). Their ages were 40.0±13.9 years, the durations of their diabetes were 27.3±14.9 years and 44% used insulin pumps. The median A1C level fell from 8.1% (7.5 to 9.0, IQ range) to 7.8% (6.9 to 8.3; p<0.001). During the observation period, glucose records were reviewed for 50% of the participants. In the active phase, review of the glucose diaries took less time on the IDM website than using personal glucose records in the observation period, median 6 minutes (5 to 7.5 IQ range) vs. 10 minutes (7.5 to 10.5 IQ range; p<0.05).

CONCLUSIONS

Our smartphone app enables online review of glucose records, requires less time for clinical staff and is associated with improved glucose control.

Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study

BACKGROUND

The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks.

METHODS

Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia.

RESULTS

Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0-8.2) in week 1 versus 2.0 (0.5-6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study.

CONCLUSION

The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.

Association of HbA1c to BOLUS Scores Among Youths with Type 1 Diabetes

BACKGROUND

Frequency of mealtime insulin bolusing (BOLUS) is a promising new objective assessment of adherence in youths with type 1 diabetes (T1D). As further confirmation of the validity of BOLUS, we compare the associations of glycated hemoglobin (HbA1c) values of T1D youths with the original scoring of BOLUS and two alternative scoring procedures: mean mealtime boluses within a 2-h meal window (2h-BOLUS) and total daily frequency of boluses (TOTAL-BOLUS). In addition, we assess HbA1c associations of these three procedures, including interaction terms for mealtime boluses plus correction boluses.

SUBJECTS AND METHODS

Blood glucose meter data, insulin pump records, and HbA1c levels were collected from a combined clinical and research database for a random sample of 100 youths (mean age, 12.7 ± 4.6 years). Youths' pump records were scored using the published methodology and alternative procedures for evaluating insulin use.

RESULTS

Youths' BOLUS, TOTAL-BOLUS, and mealtime boluses within a 2-h meal window (2h-BOLUS) scores are independently associated with youths' HbA1c level; all measures demonstrated stronger associations with youths' HbA1c than did frequency of glucose monitoring. The strongest association was between youths' BOLUS score and their HbA1c level. In multiple regression analyses, youths' BOLUS score better explains the variations in HbA1c levels than either youths' 2h-BOLUS or TOTAL-BOLUS scores. When combined with BOLUS in the same relationships, 2h-BOLUS and TOTAL-BOLUS were not found to have statistically significant coefficients. None of the bivariate relationships of HbA1c and interaction terms of mealtime and correction boluses was significant.

CONCLUSIONS

The original method for calculating BOLUS appears superior to alternative scoring methods in its association with youths' HbA1c levels.

Development of a New Tool to Assess Bolus Calculation and Carbohydrate Estimation

BACKGROUND

Carbohydrate estimation and bolus calculation are two important skills for handling intensive insulin therapy and effectively using bolus calculators. Structured assessment of both skills is lacking. A new tool for the assessment of skills in carbohydrate estimation and bolus calculation was developed and evaluated.

MATERIALS AND METHODS

A new assessment tool (SMART) was developed that included 10 items for bolus calculation and 12 items for carbohydrate estimation. In total, 411 patients on intensive insulin treatment were recruited. Different parameters of glycemic control were used as validity criteria.

RESULTS

The SMART tool achieved good reliability for the assessment of bolus calculation (Cronbach's α = 0.78) and sufficient reliability for the assessment of carbohydrate estimation (Cronbach's α = 0.67). A good bolus calculation skill was significantly associated with lower glycated hemoglobin values (r = -0.27), lower mean blood glucose levels (r = -0.29), and higher fluctuation of blood glucose control (r = -0.43). A good carbohydrate estimation skill was significantly associated with a lower frequency of severe hyperglycemia (r = -0.27) and a higher frequency of euglycemia (r = 0.26).

CONCLUSIONS

SMART is a reliable and valid tool for the assessment of both skills. Bolus calculation as well as carbohydrate estimation was associated with glycemic control. With the help of SMART, important skills for the management of intensive insulin therapy can be assessed separately. Thus, in clinical practice patients in need of assistance from a bolus calculator can be identified.

An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results

This paper presents the architecture and initial usability results of an advanced insulin bolus calculator for diabetes (ABC4D), which provides personalized insulin recommendations for people with diabetes by differentiating between various diabetes scenarios and automatically adjusting its parameters over time. The proposed platform comprises two main components: a smartphone-based patient platform allowing manual input of glucose and variables affecting blood glucose levels (e.g., meal carbohydrate content and exercise) and providing real-time insulin bolus recommendations; and a clinical revision platform to supervise the automatic adaptations of the bolus calculator parameters. The system implements a previously in silico validated bolus calculator algorithm based on case-based reasoning, which uses information from similar past events (i.e., cases) to suggest improved personalized insulin bolus recommendations and automatically learns from new events. Usability of ABC4D was assessed by analyzing the system usage at the end of a six-week pilot study (n = 10). Further feedback on the use of ABC4D has been obtained from each participant at the end of the study from a usability questionnaire. On average, each participant requested 115 ± 21 insulin recommendations, of which 103 ± 28 (90%) were accepted. The clinical revision software proposed a total of 754 case revisions, where 723 (96%) adaptations were approved by a clinical expert and updated in the patient platform.

Method for automatic adjustment of an insulin bolus calculator: in silico robustness evaluation under intra-day variability

BACKGROUND AND OBJECTIVE

Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters. Although their clinical benefit has been demonstrated, their utilisation has not been widespread and their performance remains suboptimal, mainly because of their lack of flexibility and adaptability. One of the difficulties that people with diabetes, clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements. In this work, we propose a novel method that aims to automatically adjust the parameters of a bolus calculator. Periodic usage of a continuous glucose monitoring device is required for this purpose.

METHODS

To test the proposed method, an in silico evaluation under real-life conditions was carried out using the FDA-accepted Type 1 diabetes mellitus (T1DM) UVa/Padova simulator. Since the T1DM simulator does not incorporate intra-subject variability and uncertainty, a set of modifications were introduced to emulate them. Ten adult and ten adolescent virtual subjects were assessed over a 3-month scenario with realistic meal variability. The glycaemic metrics: mean blood glucose; percentage time in target; percentage time in hypoglycaemia; risk index, low blood glucose index; and blood glucose standard deviation, were employed for evaluation purposes. A t-test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment.

RESULTS

The proposed method statistically improved (p<0.05) all glycemic metrics evaluating hypoglycaemia on both virtual cohorts: percentage time in hypoglycaemia (i.e. BG<70 mg/dl) (adults: 2.7±4.0 vs. 0.4±0.7, p=0.03; adolescents: 7.1±7.4 vs. 1.3±2.4, p=0.02) and low blood glucose index (LBGI) (adults: 1.1±1.3 vs. 0.3±0.2, p=0.002; adolescents: 2.0±2.19 vs. 0.7±1.4, p=0.05). A statistically significant improvement was also observed on the blood glucose standard deviation (BG SD mg/dL) (adults: 33.5±13.7 vs. 29.2±8.3, p=0.01; adolescents: 63.7±22.7 vs. 44.9±23.9, p=0.01). Apart from a small increase in mean blood glucose on the adult cohort (129.9±11.9 vs. 133.9±11.6, p=0.03), the rest of the evaluated metrics, despite showing an improvement trend, did not experience a statistically significant change.

CONCLUSIONS

A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management.

Factors associated with glycemic control in adult type 1 diabetes patients treated with insulin pump therapy

Continuous subcutaneous insulin infusion (CSII) by insulin pump seems to improve glycemia and quality of life as compared to conventional insulin therapy in type 1 diabetes (T1DM). However, while many T1DM subjects achieve excellent glycemic control, some others cannot reach recommended goals. In a retrospective analysis, we searched for factors associated with glycemic control in T1DM patients treated with insulin pump therapy. Data from 192 patients (133 women and 59 men) treated with personal insulin pumps at the Department of Metabolic Diseases, University Hospital, Krakow, Poland were analyzed. Sources of information included medical records, memory read-outs from insulin pumps and data from glucose meters. Univariate, multivariate linear and logistic regression analysis for the association with hemoglobin A1c (HbA1c) level were performed. The mean age of the subjects was 28.9 (±11.2) years, the mean duration of T1DM-14.6 (±7.6) years, mean body mass index-23.5 (±3.1) kg/m2. The mean HbA1c level in the entire study group was 7.4% (57 mmol/mol). In the multivariate linear regression analysis, HbA1c correlated with the mean number of daily blood glucose measurements, number of hypoglycemic episodes per 100 blood glucose measurements, age at the examination, and continuous glucose monitoring system use. Multivariate logistic regression analysis for reaching the therapeutic target of HbA1c<7.0% (53 mmol/mol) showed that the independent predictors of achieving this goal included the same four variables. In a large clinical observation, we identified that patient-related and technological factors associated with glycemic control in adult pump-treated T1DM subjects.

Continuous subcutaneous insulin infusion in Italy: third national survey

BACKGROUND

Continuous subcutaneous insulin infusion (CSII) is increasing worldwide, mostly because of improved technology. The aim of this study was to evaluate the current status of CSII in Italy.

MATERIALS AND METHODS

Physicians from 272 diabetes centers received a questionnaire investigating clinical features, pump technology, and management of patients on CSII.

RESULTS

Two hundred seventeen centers (79.8%) joined the study and, by the end of April 2013, gave information about 10,152 patients treated with CSII: 98.2% with type 1 diabetes mellitus, 81.4% adults, 57% female, and 61% with a conventional pump versus 39% with a sensor-augmented pump. CSII advanced functions were used by 68% of patients, and glucose sensors were used 12 days per month on average. Fifty-eight percent of diabetes centers had more than 20 patients on CSII, but there were differences among centers and among regions. The main indication for CSII was poor glucose control. Dropout was mainly due to pump wearability or nonoptimal glycemic control. Twenty-four hour assistance was guaranteed in 81% of centers. A full diabetes team (physician+nurse+dietician+psychologist) was available in 23% of adult-care diabetes centers and in 53% of pediatric diabetes units.

CONCLUSIONS

CSII keeps increasing in Italy. More work is needed to ensure uniform treatment strategies throughout the country and to improve pump use.

Bolus calculators

Matching meal insulin to carbohydrate intake, blood glucose, and activity level is recommended in type 1 diabetes management. Calculating an appropriate insulin bolus size several times per day is, however, challenging and resource demanding. Accordingly, there is a need for bolus calculators to support patients in insulin treatment decisions. Currently, bolus calculators are available integrated in insulin pumps, as stand-alone devices and in the form of software applications that can be downloaded to, for example, smartphones. Functionality and complexity of bolus calculators vary greatly, and the few handfuls of published bolus calculator studies are heterogeneous with regard to study design, intervention, duration, and outcome measures. Furthermore, many factors unrelated to the specific device affect outcomes from bolus calculator use and therefore bolus calculator study comparisons should be conducted cautiously. Despite these reservations, there seems to be increasing evidence that bolus calculators may improve glycemic control and treatment satisfaction in patients who use the devices actively and as intended.

Severe hypoglycemia and ketoacidosis over one year in Italian pediatric population with type 1 diabetes mellitus: a multicenter retrospective observational study

BACKGROUND AND AIMS

Evaluation of incidence and correlates of severe hypoglycemia (SH) and diabetes ketoacidosis (DKA) in children and adolescents with T1DM.

METHODS AND RESULTS

Retrospective study conducted in 29 diabetes centers from November 2011 to April 2012. The incidence of SH and DKA episodes and their correlates were assessed through a questionnaire administered to parents of patients aged 0-18 years. Incidence rates and incident rate ratios (IRRs) were estimated through multivariate Poisson regression analysis and multilevel analysis. Overall, 2025 patients were included (age 12.4 ± 3.8 years; 53% males; diabetes duration 5.6 ± 3.5 years; HbA1c 7.9 ± 1.1%). The incidence of SH and DKA were of 7.7 and 2.4 events/100 py, respectively. The risk of SH was higher in females (IRR = 1.44; 95%CI 1.04-1.99), in patients using rapid acting analogues as compared to regular insulin (IRR = 1.48; 95%CI 0.97-2.26) and lower for patients using long acting analogues as compared to NPH insulin (IRR = 0.40; 95%CI 0.19-0.85). No correlations were found between SH and HbA1c levels. The risk of DKA was higher in patients using rapid acting analogues (IRR = 4.25; 95%CI 1.01-17.86) and increased with insulin units needed (IRR = 7.66; 95%CI 2.83-20.74) and HbA1c levels (IRR = 1.63; 95%CI 1.36-1.95). Mother's age was inversely associated with the risk of both SH (IRR = 0.95; 95%CI 0.92-0.98) and DKA (IRR = 0.94; 95%CI 0.88-0.99). When accounting for center effect, the risk of SH associated with the use of rapid acting insulin analogues was attenuated (IRR = 1.48; 95%CI 0.97-2.26); 33% and 16% of the residual variance in SH and DKA risk was explained by center effect.

CONCLUSION

The risk of SH and DKA is mainly associated with treatment modalities and strongly depends on the practice of specialist centers.

Use of an insulin bolus advisor facilitates earlier and more frequent changes in insulin therapy parameters in suboptimally controlled patients with diabetes treated with multiple daily insulin injection therapy: results of the ABACUS trial

BACKGROUND

We assessed the impact of using an automated bolus advisor integrated into a blood glucose meter on the timing and frequency of adjusting insulin therapy parameter settings and whether the availability of this technology would increase blood glucose test strip utilization in diabetes patients treated with multiple daily insulin injection (MDI) therapy.

SUBJECTS AND METHODS

The Automated Bolus Advisor Control and Usability Study (ABACUS) trial, a 26-week, prospective, randomized, controlled, multinational study that enrolled 218 type 1 and type 2 diabetes patients, demonstrated that use of an automated insulin bolus advisor helps improve glycemic control in suboptimally controlled, MDI-treated patients. Patient data were assessed to determine when and how often changes in insulin parameter settings occurred during the study. Patient meters were downloaded to determine blood glucose monitoring frequency.

RESULTS

One hundred ninety-three patients completed the study: 93 control arm (CNL) and 100 intervention (experimental) arm (EXP). Significantly more EXP (47.5%) than CNL (30.7%) patients received one or more changes in their insulin sensitivity factor (ISF) settings during the study (P=0.0191). Changes in ISF settings occurred earlier and more frequently in EXP than CNL patients throughout the study. A similar trend was seen in changes in insulin-to-carbohydrate ratios. There were no differences in daily self-monitoring of blood glucose frequency [mean (SD)] between CNL and EXP patients: 4.7 (1.5) versus 4.6 (1.3) (P=0.4085).

CONCLUSIONS

Use of an automated bolus advisor was associated with earlier, more frequent changes in key insulin parameters, which may have contributed to subsequent improvements in glycemic control but without increased glucose test strip utilization.

Use of an insulin bolus advisor improves glycemic control in multiple daily insulin injection (MDI) therapy patients with suboptimal glycemic control: first results from the ABACUS trial

OBJECTIVE

Use of automated bolus advisors is associated with improved glycemic control in patients treated with insulin pump therapy. We conducted a study to assess the impact of using an insulin bolus advisor embedded in a blood glucose (BG) meter on glycemic control and treatment satisfaction in patients treated with multiple daily insulin injection (MDI) therapy. The study goal was to achieve >0.5% A1C reduction in most patients.

RESEARCH DESIGN AND METHODS

This was a 26-week, prospective, randomized, controlled, multinational study that enrolled 218 MDI-treated patients with poorly controlled diabetes (202 with type 1 diabetes, 16 with type 2 diabetes) who were 18 years of age or older. Participants had mean baseline A1C of 8.9% (SD, 1.2 [74 mmol/mol]), mean age of 42.4 years (SD, 14.0), mean BMI of 26.5 kg/m(2) (SD, 4.2), and mean diabetes duration of 17.7 years (SD, 11.1). Control group (CNL) patients used a standard BG meter and manual bolus calculation; intervention group (EXP) patients used the Accu-Chek Aviva Expert meter with an integrated bolus advisor to calculate insulin dosages. Glucose data were downloaded and used for therapy parameter adjustments in both groups.

RESULTS

A total of 193 patients (CNL, n = 93; EXP, n = 100) completed the study. Significantly more EXP than CNL patients achieved >0.5% A1C reduction (56.0% vs. 34.4%; P < 0.01). Improvement in treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire scale) was significantly greater in EXP patients (11.4 [SD, 6.0] vs. 9.0 [SD, 6.3]; P < 0.01). Percentage of BG values <50 mg/dL was <2% in both groups during the study.

CONCLUSIONS

Use of an automated bolus advisor resulted in improved glycemic control and treatment satisfaction without increasing severe hypoglycemia.

ProAct study: new features of insulin pumps improve diabetes management and glycemic control in patients after transition of continuous subcutaneous insulin infusion systems

BACKGROUND

Continuous subcutaneous insulin infusion (CSII) patients experience switches of pump systems on a regular basis. We investigated the impact of transition from older pumps to the Accu-Chek(®) Combo system (Roche Diagnostics Deutschland GmbH, Mannheim, Germany) on a patient's glycemic control and diabetes management.

PATIENTS AND METHODS

In total, 299 patients (172 female, 127 male; mean±SD age, 39.4±15.2 years; CSII duration, 7.0±5.2 years) were enrolled by 61 European sites into this uncontrolled prospective trial. Glycemic control, safety, and diabetes management parameters were measured at baseline and after 3 and 6 months. Changes from baseline were analyzed.

RESULTS

After transition to the new insulin pump, mean±SD hemoglobin A1c (HbA1c) values decreased from 7.8±1.1% (baseline) to 7.7±1.1% (end point). The proportion of patients with HbA1c <7.0% was slightly higher at the end of the study (29.6%) than at baseline (25.2%), whereas the proportion of patients with HbA1c >8.0% decreased (baseline, 36.2%; end point, 32.7%; P<0.05). The number of hypoglycemic episodes (blood glucose<70 mg/dL) improved slightly during the study (baseline, 40.4±34.0 events/quarter; end point, 39.2±33.9 events/quarter). Glycemic control improved significantly in the group with an initial HbA1c >8.0% (-0.46%; P<0.001) and remained solidly stable in the group with an initial HbA1c <7% (+0.04%; not significant). Short-term (<3 years) pump users (n=48) had a larger HbA1c decrease (-0.40%) than long-term (≥3 years) users (n=251) (-0.07%; P<0.05). The number of blood glucose measurements increased (3.7±1.9/day vs. 4.4±1.8/day; P<0.05), whereas the number of insulin boluses decreased (5.1±1.9/day vs. 4.6±1.5/day; P<0.05) during the study.

CONCLUSIONS

Transition from older pump systems to the Accu-Chek Combo system in a large patient population resulted in stable glycemic control with significant improvements in HbA1c in patients with unsatisfactory baseline HbA1c and shorter pump use. Increased frequency of self-monitoring of blood glucose and decrease of bolus frequency could suggest a more confident diabetes management and a reduced need for correction boluses.

Use of Automated Bolus Calculators for Diabetes Management

Fewer than 30 % of patients with diabetes who are on insulin therapy achieve target glycated haemoglobin (HbA1C) levels. Automated bolus calculators (ABCs) are now almost universally used for patients on insulin pump therapy to calculate pre-meal insulin doses. Use of ABCs in glucose monitors and smart phone applications have the potential to improve glucose control in a larger population of individuals with diabetes on insulin therapy by overcoming the fear of hypoglycaemia and assisting those with low numeracy skills.

Glucose meters with built-in automated bolus calculator: gadget or real value for insulin-treated diabetic patients?

Self-monitoring of blood glucose is now widely recognized as efficacious to enhance and facilitate diabetes management. More than just a means of recording and storing data, some blood glucose meters (BGMs) are now designed with an embedded automated bolus calculator (ABC) with the goal to propose patients recommendations about insulin dosage. The growing literature in this field tends to claim that these new smart BGMs make patient's life easier and decision making safer. The main purpose of this review is to verify whether BGMs with a built-in ABC indeed improve the willingness and the ability of insulin-treated patients to make adequate therapeutic decisions and positively impact the metabolic control and the quality of life of ABC users. It appears that, as long as the education provided by caregivers remains a top priority, BGMs with a built-in ABC (more than just electronic gadgets) can be regarded as bringing real value to insulin-treated patients with diabetes.