Design of a prospective, longitudinal cohort of people living with type 1 diabetes exploring factors associated with the residual cardiovascular risk and other diabetes-related complications: The SFDT1 study.

Type 1 diabetes mellitus (T1DM) is associated with a high risk of cardiovascular (CV) complications, even after controlling for traditional CV risk factors. Therefore, determinants of the residual increased CV morbidity and mortality remain to be discovered. This prospective cohort of people living with T1DM in France (SFDT1) will include adults and children aged over six years living with T1DM, recruited throughout metropolitan France and overseas French departments and territories. The primary objective is to better understand the parameters associated with CV complications in T1DM. Clinical data and biobank samples will be collected during routine visits every three years. Data from connected tools, including continuous glucose monitoring, will be available during the 10-year active follow-up. Patient-reported outcomes, psychological and socioeconomic information will also be collected either at visits or through web questionnaires accessible via the internet. Additionally, access to the national health data system (Health Data Hub) will provide information on healthcare and a passive 20-year medico-administrative follow-up. Using Health Data Hub, SFDT1 participants will be compared to non-diabetic individuals matched on age, gender, and residency area. The cohort is sponsored by the French-speaking Foundation for Diabetes Research (FFRD) and aims to include 15,000 participants.

[Artificial intelligence: Guidelines for internists]. / Des guidelines pour l'intelligence artificielle !

Following the emergence of open public databases and connected objects, big data and artificial intelligence are developing rapidly, especially in medicine, with many opportunities ranging from complex diagnostic assistance to real-time statistical analysis. In order to promote their development and guide their use in the field of internal medicine, guidelines and recommendations are needed. First of all, this article seeks to clarify the concepts of big data and artificial intelligence and the correlations between each other, and then to give an overview of the progress made at European level in this rapidly expanding field.