A Processing Algorithm to Address Real-World Data Quality Issues With Continuous Glucose Monitoring Data.

Continuous glucose monitoring (CGM) data stored in data warehouses often include duplicated or time-shifted uploads from the same patient, compromising data quality and accuracy of resulting CGM metrics. We developed a processing algorithm to detect and resolve these errors. We validated the algorithm using two weeks of CGM data from 2038 patients with diabetes.

Distinct Circle of Willis anatomical configurations in healthy preterm born adults: a 3D time-of-flight magnetic resonance angiography study.

Background: Preterm birth (< 37 weeks' gestation) alters cerebrovascular development due to the premature transition from a foetal to postnatal circulatory system, with potential implications for future cerebrovascular health. This study aims to explore potential differences in the Circle of Willis (CoW), a key arterial ring that perfuses the brain, of healthy adults born preterm.

Methods: A total of 255 participants (108 preterm, 147 full-term) were included in the analysis.

A machine learning-based score for precise echocardiographic assessment of cardiac remodelling in hypertensive young adults.

Aims: Accurate staging of hypertension-related cardiac changes, before the development of significant left ventricular hypertrophy, could help guide early prevention advice. We evaluated whether a novel semi-supervised machine learning approach could generate a clinically meaningful summary score of cardiac remodelling in hypertension.

Methods And Results: A contrastive trajectories inference approach was applied to data collected from three UK studies of young adults.