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.

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/m 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.

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.