Relationship between hypoglycaemia, body mass index and quality of life among patients with type 1 diabetes: Observations from the DEPICT clinical trial programme.

AIMS: To demonstrate the relationships between hypoglycaemia, body mass index (BMI) and quality of life, and to examine the impact of dapagliflozin on patient-reported treatment satisfaction in patients with type 1 diabetes mellitus (T1DM), using data from the DEPICT (Dapagliflozin Evaluation in Patients With Inadequately Controlled Type 1 Diabetes) clinical trial programme. METHODS: A two-stage modelling approach, using a linear regression framework, was adopted to evaluate the relationship between hypoglycaemia, BMI and quality of life. Hypoglycaemia fear score (HFS) was modelled as a function of hypoglycaemic events (non-severe documented symptomatic and severe) and, subsequently, quality of life (as measured by the EQ-5D questionnaire) was modelled as a function of HFS and BMI. A linked evidence approach correlated the relationship between treatment, hypoglycaemic events and glycated haemoglobin (HbA1c), to the relationships captured within the regression models. The proportion of patients achieving increased patient-reported treatment satisfaction, as measured by the Diabetes Treatment Satisfaction Questionnaire (DTSQ) total score, was compared between study arms. RESULTS: Incident severe hypoglycaemia was associated with significantly higher HFS (coefficient estimate [CE] 14.62, P=0.004). The frequency of symptomatic hypoglycaemic events was associated with a significantly higher HFS (log transposed, CE 1.32, P=0.026). Higher HFS and higher BMI were both independently associated with a significantly lower EQ-5D score (HFS: CE -0.0024, P<0.001; BMI: CE -0.0026, P=0.016). Significantly higher proportions of dapagliflozin-treated patients achieved ≥3-point increases in DTSQ total score compared to patients in the placebo group. CONCLUSION: The results of this study demonstrated that increases in hypoglycaemia and BMI were associated with reduced quality of life in people with T1DM. Dapagliflozin-treated patients achieved a reduction in HbA1c whilst avoiding an increase in hypoglycaemic events. The results also showed that treatment with dapagliflozin was associated with an improvement in treatment satisfaction.

Adverse Changes in HbA1c, Body Weight and Insulin Use in People with Type 1 Diabetes Mellitus Following Dapagliflozin Discontinuation in the DEPICT Clinical Trial Programme.

INTRODUCTION: Dapagliflozin is an orally active inhibitor of sodium-glucose co-transporter 2 (SGLT2) that is indicated for use in adults with type 1 diabetes (T1DM) (with a body mass index (BMI) of at least 27 kg/m2 in Europe, no such BMI limit in Japan), when insulin alone does not provide adequate glycaemic control. The aim of this study was to evaluate changes in glycated haemoglobin (HbA1c), body weight and insulin dose following discontinuation of dapagliflozin for the management of T1DM in the DEPICT clinical trial programme. METHODS: The interrelationship between treatment discontinuation, insulin requirement and outcomes post-discontinuation was evaluated using descriptive summary statistics and linear regression modelling. Data were analysed from individuals with T1DM discontinuing dapagliflozin in DEPICT-1 or DEPICT-2 (unplanned or end of study). HbA1c and body weight were measured over the 56-week study period (consisting of a 52-week treatment period and a 4-week follow-up period) at 4-8 weekly intervals. Following discontinuation of dapagliflozin, 1-year change in HbA1c (%) and weight (kg) following discontinuation of dapagliflozin was estimated; total daily insulin doses were descriptively summarised. RESULTS: Of the 1059 individuals that received dapagliflozin during the DEPICT trials 91 met the eligibility criteria and were included in the analyses of HbA1c and body weight. The mean duration of follow-up was 209 days in both analyses. Following dapagliflozin discontinuation, estimated annualised changes in HbA1c and body weight were + 0.99% (95% CI 0.39, 1.59) and + 3.75 kg (1.65, 5.86), respectively. An increase in insulin dose was observed around the time of discontinuation; insulin dose in the 2-week post-discontinuation was + 3.6 IU and + 4.4 IU higher with dapagliflozin 5 mg and 10 mg than 2 weeks pre-discontinuation, respectively. CONCLUSION: Discontinuation of dapagliflozin is predicted to lead to clinically meaningful increases in HbA1c and body weight, in addition to higher insulin doses. These findings are important in the management of people with T1DM among whom insulin is the only existing pharmacological treatment option.

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12­lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639.