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- Adolfsson, Peter, 1963, et al.
(författare)
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Improved Glycemic Control Observed in Children with Type 1 Diabetes Following the Introduction of Smart Insulin Pens: A Real-World Study
- 2022
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Ingår i: Diabetes Therapy. - : Springer Science and Business Media LLC. - 1869-6953 .- 1869-6961. ; 13, s. 43-56
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Ahrén, Bo, et al.
(författare)
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Postprandial Glucagon Reductions Correlate to Reductions in Postprandial Glucose and Glycated Hemoglobin with Lixisenatide Treatment in Type 2 Diabetes Mellitus : A Post Hoc Analysis
- 2016
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Ingår i: Diabetes Therapy. - : Springer Science and Business Media LLC. - 1869-6953 .- 1869-6961. ; 7:3, s. 583-590
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The extent to which postprandial glucagon reductions contribute to lowering of postprandial glucose in patients with type 2 diabetes mellitus (T2DM) is currently unknown. The aim of this analysis was to determine whether a reduction in postprandial glucagon following treatment with the glucagon-like peptide-1 receptor agonist lixisenatide correlates with a reduction in postprandial glucose and glycated hemoglobin (HbA1c) in patients with T2DM. Methods: A post hoc analysis was performed on pooled data from the modified intent-to-treat populations of two lixisenatide Phase 3 trials: GetGoal-M (lixisenatide versus placebo as add-on to metformin) and GetGoal-S (lixisenatide versus placebo as add-on to sulfonylurea [SU] ± metformin). Glucagon levels were assessed 2 h after a standardized meal test performed at baseline and Week 24 and were examined for correlation with changes in 2-h postprandial glucose and HbA1c. Results: Lixisenatide reduced 2-h postprandial glucagon at Week 24 compared with placebo (P < 0.00001). The mean change in postprandial glucagon significantly correlated with reductions in postprandial glucose (P < 0.00001) and HbA1c (P < 0.00001). Conclusion: A reduction in postprandial glucagon following lixisenatide administration correlated with a decrease in postprandial glucose and HbA1c in patients with T2DM insufficiently controlled on metformin and/or SU. This suggests that lowering of postprandial glucagon contributes to the overall glycemic improvement observed with lixisenatide. Funding: Sanofi. Clinical Trial Numbers: NCT00712673 (GetGoal-M) and NCT00713830 (GetGoal-S).
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| 5. |
- Bernfort, Lars, et al.
(författare)
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Disease Burden and Healthcare Costs for T2D Patients With and Without Established Cardiovascular Disease in Sweden: A Retrospective Cohort Study
- 2020
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Ingår i: Diabetes Therapy. - : SPRINGER HEIDELBERG. - 1869-6953 .- 1869-6961. ; 11:7, s. 1537-1549
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Tidskriftsartikel (refereegranskat)abstract
- IntroductionType 2 diabetes (T2D) is a complex chronic disease with an increasing prevalence worldwide. It is commonly associated with complications, such as cardiovascular disease (CVD). Patients with both T2D and established CVD are exposed to increased risk of further cardiovascular events, which means increased healthcare costs and impairments to quality of life and survival. To determine the added burden of CVD for T2D patients, we have analyzed the consumption and costs of healthcare and mortality in two T2D patient cohorts, with and without established CVD, respectively, during a 5-year follow-up in a Swedish region.MethodsPatients with T2D on 1 January 2012 were identified using the administrative database of Region ostergotland and the Swedish National Diabetes Register. Established CVD was defined as the presence of a CVD-related healthcare visit in the period 2002-2011. Identified T2D patients were then followed retrospectively for 5 years (2012-2016) and data collected on utilization of healthcare resources, healthcare costs, and survival. Data pertinent to the study were retrieved from regional databases and national registries.ResultsOn the index date (1 January 2012) there were 19,731 patients with T2D (prevalence 4.5%) in Region ostergotland, of whom 5490 had established CVD. Those patients with established CVD were older, more often men, and had longer diabetes duration and worse kidney function than those without. Compared to T2D patients without CVD, those with CVD had a significantly higher healthcare consumption, experienced higher costs, and had lower survival during the follow-up.ConclusionThis study confirms that established CVD is common among patients with T2D (approximately 30%). Established CVD has negative effects on the utilization of healthcare resources, healthcare costs, and mortality. It is therefore very important to improve the treatment strategy of this patient group.
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| 6. |
- Bernfort, Lars, et al.
(författare)
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Implementation of Empagliflozin in Patients with Diabetes Mellitus Type 2 and Established Cardiovascular Disease : Estimation of 5-Year Survival and Costs in Sweden
- 2020
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Ingår i: Diabetes Therapy. - : Springer Berlin/Heidelberg. - 1869-6953 .- 1869-6961. ; 11, s. 2921-2930
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Tidskriftsartikel (refereegranskat)abstract
- IntroductionCardiovascular disease (CVD) affects approximately 30% of patients with diabetes mellitus type 2 (T2D) and leads to increased morbidity, decreased survival and increased healthcare utilization. The aim of this study was to estimate the impact of treating these patients with the sodium–glucose cotransporter 2 (SGLT2) inhibitor empagliflozin on survival and healthcare utilization.MethodsActual survival and healthcare utilization data from a 5-year retrospective cohort study on patients with T2D and CVD in the Region of Östergötland, Sweden were used as a starting point. Actual data were adjusted in accordance with risk reductions for mortality and CV events related to empagliflozin treatment as reported in the EMPA-REG OUTCOME study.ResultsApplying the risk reductions related to empagliflozin treatment on the cohort of patients with T2D and CVD in Östergötland resulted in an increase in 5-year survival of 96 days per patient and reduced costs for healthcare and drugs other than empagliflozin. Including the cost of empagliflozin, treatment led to an increased net cost per patient of approximately SEK 18,000 over 5 years.ConclusionEmpagliflozin treatment would reduce mortality and healthcare utilization in the patient group. The treatment strategy should be considered cost-effective, supporting a broad implementation of empagliflozin for patients with T2D and established CVD, in line with current national and international guidelines.
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| 9. |
- Blissett, D. B., et al.
(författare)
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Analysis Estimating the Potential Cost Impact of Utilizing Flash Glucose Monitoring with Optional Alarms in Swedish Adults with Diabetes with Impaired Awareness of Hypoglycaemia, Using Intensive Insulin
- 2021
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Ingår i: Diabetes Therapy. - : Springer Science and Business Media LLC. - 1869-6953 .- 1869-6961. ; 12:8, s. 2179-2193
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Tidskriftsartikel (refereegranskat)abstract
- Introduction The objective of this analysis was to estimate the costs associated with using flash glucose monitoring with optional alarms as a replacement for either traditional continuous glucose monitoring (CGM) or routine self-monitoring of blood glucose (SMBG) in adults with diabetes and impaired awareness of hypoglycaemia (IAH) who use intensified insulin therapy, from a Swedish payer perspective, applying assumptions to simulate hypothetical scenarios. Methods A simple two-state cohort Markov model was used to calculate the cost per patient treated over a 3-year period, capturing the risk of severe hypoglycaemic events requiring medical assistance and non-adherence using quarterly Markov cycles. The costs considered were those for glucose monitoring and resource use to treat severe hypoglycaemic events. Cost inputs were sourced from Swedish price lists, manufacturer data and resource use reported in the control arm of the HypoDE study. Targeted literature searches were run in PubMed to source the clinical inputs. Uncertainty in the model was considered through one-way sensitivity analysis and scenario analysis. Results Over 3 years, flash monitoring with optional alarms resulted in potential cost-savings of Swedish krona (SEK) 7708 and SEK 69,908 per patient when compared to routine SMBG or CGM respectively. Sensitivity and scenario analyses were largely supportive of this conclusion with respect to SMBG, and large cost-savings were consistent across all sensitivity and scenario analyses with respect to CGM. Conclusion Utilizing flash monitoring with optional alarms is potentially a cost-saving treatment strategy compared to routine SMBG or traditional CGM in adults with diabetes using intensive insulin and IAH from a Swedish payer perspective. Future studies in the IAH population will help to assess more precisely the relative cost impact of flash glucose monitoring with optional alarms compared with SMBG and traditional CGM.
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| 10. |
- Cederblad, Lars, et al.
(författare)
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Classification of Hypoglycemic Events in Type 1 Diabetes Using Machine Learning Algorithms
- 2023
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Ingår i: Diabetes Therapy. - : Springer Nature. - 1869-6953 .- 1869-6961. ; 14:6, s. 953-965
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Tidskriftsartikel (refereegranskat)abstract
- IntroductionTo improve the utilization of continuous- and flash glucose monitoring (CGM/FGM) data we have tested the hypothesis that a machine learning (ML) model can be trained to identify the most likely root causes for hypoglycemic events.MethodsCGM/FGM data were collected from 449 patients with type 1 diabetes. Of the 42,120 identified hypoglycemic events, 5041 were randomly selected for classification by two clinicians. Three causes of hypoglycemia were deemed possible to interpret and later validate by insulin and carbohydrate recordings: (1) overestimated bolus (27%), (2) overcorrection of hyperglycemia (29%) and (3) excessive basal insulin presure (44%). The dataset was split into a training (n = 4026 events, 304 patients) and an internal validation dataset (n = 1015 events, 145 patients). A number of ML model architectures were applied and evaluated. A separate dataset was generated from 22 patients (13 ‘known’ and 9 ‘unknown’) with insulin and carbohydrate recordings. Hypoglycemic events from this dataset were also interpreted by five clinicians independently.ResultsOf the evaluated ML models, a purpose-built convolutional neural network (HypoCNN) performed best. Masking the time series, adding time features and using class weights improved the performance of this model, resulting in an average area under the curve (AUC) of 0.921 in the original train/test split. In the dataset validated by insulin and carbohydrate recordings (n = 435 events), i.e. ‘ground truth,’ our HypoCNN model achieved an AUC of 0.917.ConclusionsThe findings support the notion that ML models can be trained to interpret CGM/FGM data. Our HypoCNN model provides a robust and accurate method to identify root causes of hypoglycemic events.
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