Associations of bolus insulin injection frequency and smart pen engagement with glycaemic control in people living with type 1 diabetes.

AIM: To evaluate whether both bolus insulin injection frequency and smart pen engagement were associated with changes in glycaemic control, using real-world data from adults with type 1 diabetes (T1D). MATERIALS AND METHODS: Adults using a smart pen (NovoPen 6) to administer bolus insulin (fast-acting insulin aspart or insulin aspart) alongside continuous glucose monitoring were eligible for inclusion. Smart pen engagement was characterized by number of days with pen data uploads over the previous 14 days. Glycaemic control was evaluated by analysing glucose metrics. RESULTS: Overall, data from 1194 individuals were analysed. The number of daily bolus injections was significantly associated with time in range (TIR; 3.9-10.0 mmol/L [70-180 mg/dL]; P < 0.0001). Individuals administering, on average, three daily bolus insulin injections had an estimated 11% chance of achieving >70% TIR. The probability of achieving >70% TIR increased with the mean number of daily bolus injections. However, the percentage of TIR was lower on days when individuals administered higher-than-average numbers of injections. The observed mean number of daily bolus injections administered across the study population was lower than the optimal number required to reach glycaemic targets (4.8 injections vs. 6-8 injections). Smart pen engagement was significantly associated with improved TIR. CONCLUSIONS: Glycaemic control was associated with daily bolus insulin injection frequency and smart pen engagement. A treatment regimen combining an optimal bolus injection strategy, and effective smart pen engagement, may improve glycaemic control among adults with T1D.

Smart Pen Exposes Missed Basal Insulin Injections and Reveals the Impact on Glycemic Control in Adults With Type 1 Diabetes.

BACKGROUND: Adherence to basal insulin injections and the effects of missed basal insulin injections in adults with type 1 diabetes (T1D) were investigated using data from continuous glucose monitoring (CGM) and smart insulin pen devices in a real-world study. METHODS: This was a post hoc analysis of a prospective, real-world study conducted in Sweden. Adults with T1D who were using CGM received a smart insulin pen device (NovoPen 6) for insulin injections. Missed basal insulin doses (≥40 hours between doses) were evaluated over 14-day periods, and the probability of missing basal insulin doses was estimated. Associations between missed basal insulin doses and glycemic outcomes were also explored. RESULTS: Thirty-two patients with 4410 acceptable CGM days (315 14-day periods) were included. The number of missed basal insulin doses ranged from 0 to 4 over 315 14-day periods. The estimated probability of missing at least one basal insulin dose over any given 14-day period was 22% (95% confidence interval: 10%-40%). Missed basal insulin doses were significantly associated with higher mean glycemic levels, higher glucose management indicator, and lower time in range (70-180 mg/dL [3.9-10.0 mmol/L]). Similar results were observed when adjusted for missed bolus insulin doses; age and sex had no statistically significant effect on any glycemic parameter. CONCLUSIONS: This is the first study, based on accurate real-world injection data, to demonstrate the challenge of adherence to basal insulin injections in patients with T1D, and document that just one missed basal injection per week can result in clinically significant changes in glycemic control.

Improved Glycemic Control Observed in Children with Type 1 Diabetes Following the Introduction of Smart Insulin Pens: A Real-World Study.

INTRODUCTION: Many challenges are associated with optimizing glycemic control in pediatric patients with type 1 diabetes (T1D); combining data from smart insulin pens and continuous glucose monitoring (CGM) could mitigate some of these obstacles. METHODS: This one-arm, prospective, observational study investigated the effects of introducing a smart pen on glycemic control in pediatric patients with T1D who were using CGM. Children and adolescents with T1D who had been prescribed a smart pen for basal and/or bolus insulin injections were enrolled from three clinics in Sweden. Outcomes compared between baseline and follow-up (≥ 12 months) included: mean numbers of daily (over 24 h) and nocturnal hypoglycemic or hyperglycemic events; time above range (TAR; > 180 mg/dL); time below range (TBR; level 1: 54 to < 70 mg/dL; level 2: < 54 mg/dL); time in range (TIR; 70-180 mg/dL); and missed bolus-dose (MBD) meals. RESULTS: Overall, 39 patients were included. Mean numbers of daily hypoglycemic events (- 31.4%; p = 0.00035) and nocturnal hypoglycemic events (- 24.4%; p = 0.043) were significantly reduced from baseline to follow-up. Mean daily TBR level 2 was reduced from 2.82% at baseline to 2.18% at follow-up (- 0.64 percentage points; p = 0.025). There were no statistically significant changes in number of daily hyperglycemic events, MBD meals, TIR, TAR, or TBR level 1. CONCLUSIONS: Introducing smart insulin pens was associated with a reduced number of hypoglycemic events and decreases in TBR level 2, demonstrating a potential benefit for glycemic control in pediatric patients.

Smart Insulin Pens are Associated with Improved Clinical Outcomes at Lower Cost Versus Standard-of-Care Treatment of Type 1 Diabetes in Sweden: A Cost-Effectiveness Analysis.

INTRODUCTION: Real-world evidence has demonstrated improved glycemic control and insulin management following introduction of smart insulin pens in a Swedish type 1 diabetes (T1D) population. To understand the implications for healthcare costs and expected health outcomes, this analysis evaluated the long-term cost-effectiveness of introducing smart insulin pens to standard-of-care T1D treatment (standard care) from a Swedish societal perspective. METHODS: Clinical outcomes and healthcare costs (in 2018 Swedish krona, SEK) were projected over patients' lifetimes using the IQVIA CORE Diabetes Model to estimate cost-effectiveness. Clinical data and baseline characteristics for the simulated cohort were informed by population data and a prospective, noninterventional study of a smart insulin pen in a Swedish T1D population. This analysis captured direct and indirect costs, mortality, and the impact of diabetes-related complications on quality of life. RESULTS: Over patients' lifetimes, smart insulin pen use was associated with per-patient improvements in mean discounted life expectancy (+ 0.90 years) and quality-adjusted life expectancy (+ 1.15 quality-adjusted life-years), in addition to mean cost savings (direct, SEK 124,270; indirect, SEK 373,725), versus standard care. A lower frequency and delayed onset of complications drove projected improvements in quality-adjusted life expectancy and lower costs with smart insulin pens versus standard care. Overall, smart insulin pens were a dominant treatment option relative to standard care across all base-case and sensitivity analyses. CONCLUSIONS: Use of smart insulin pens was projected to improve clinical outcomes at lower costs relative to standard care in a Swedish T1D population and represents a good use of healthcare resources in Sweden.

Increased Time in Range and Fewer Missed Bolus Injections After Introduction of a Smart Connected Insulin Pen.

Background: This observational study investigated whether the connected NovoPen® 6 could influence insulin regimen management and glycemic control in people with type 1 diabetes (T1D) using a basal-bolus insulin regimen and continuous glucose monitoring in a real-world setting. Methods: Participants from 12 Swedish diabetes clinics downloaded pen data at each visit (final cohort: n = 94). Outcomes included time in range (TIR; sensor glucose 3.9-10.0 mmol/L), time in hyperglycemia (>10 mmol/L), and hypoglycemia (L1: 3.0- <3.9 mmol/L; L2: <3.0 mmol/L). Missed bolus dose (MBD) injections were meals without bolus injection within -15 and +60 min from the start of a meal. Outcomes were compared between the baseline and follow-up periods (≥5 health care professional visits). Data were analyzed from the first 14 days following each visit. For the TIR and total insulin dose analyses (n = 94), a linear mixed model was used, and for the MBD analysis (n = 81), a mixed Poisson model was used. Results: TIR significantly increased (+1.9 [0.8; 3.0]95% CI h/day; P < 0.001) from baseline to follow-up period, with a corresponding reduction in time in hyperglycemia (-1.8 [-3.0; -0.6]95% CI h/day; P = 0.003) and L2 hypoglycemia (-0.3 [-0.6; -0.1]95% CI h/day; P = 0.005), and no change in time in L1 hypoglycemia. MBD injections decreased by 43% over the study (P = 0.002). Change in MBD injections corresponded to a decrease from 25% to 14% based on the assumption that participants had three main meals per day. Conclusions: Our study highlights the potential benefit on glycemic control and dosing behavior when reliable insulin dose data from a connected pen contribute to insulin management in people with T1D.

Chronic kidney disease in patients with the Philadelphia-negative chronic myeloproliferative neoplasms.

BACKGROUND: The progression of kidney function and frequency of chronic kidney disease (CKD) in patients with the Philadelphia-negative myeloproliferative neoplasms (MPN) is unknown, although CKD is linked to increased mortality. METHODS: This longitudinal retrospective study evaluates the estimated glomerular filtration rate (eGFR) in 143 MPN patients over a period of 9 years. RESULTS: 29% of patients had CKD stage 3 or 4 at time of diagnosis. 20% of patients had a rapid annual loss of eGFR (>3mL/min/1.73m(2)) and eGFR was negatively correlated to monocyte and neutrophil counts. CONCLUSION: Kidney impairment might contribute to the increased mortality observed in MPN patients.

Model identification using stochastic differential equation grey-box models in diabetes.

BACKGROUND: The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. METHODS: An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the "time to peak of meal response" parameter. RESULTS: We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the "peak time of meal absorption" parameter showed that the absorption rate varied according to meal type. CONCLUSION: This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development.