Impact of Advanced Age on the Incidence of Major Adverse Cardiovascular Events in Patients with Type 2 Diabetes Mellitus and Stable Coronary Artery Disease in a Real-World Setting in Spain.

Patients with type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) without myocardial infarction (MI) or stroke are at high risk for major cardiovascular events (MACEs). We aimed to provide real-world data on age-related clinical characteristics, treatment management, and incidence of major cardiovascular outcomes in T2DM-CAD patients in Spain from 2014 to 2018. We used EHRead technology, which is based on natural language processing and machine learning, to extract unstructured clinical information from electronic health records (EHRs) from 12 hospitals.

Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing.

Patients with Type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) are at high risk of developing major adverse cardiovascular events (MACE). This is a multicenter, retrospective, and observational study performed in Spain aimed to characterize these patients in a real-world setting. Unstructured data from the Electronic Health Records were extracted by EHRead, a technology based on Natural Language Processing and machine learning.

Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing.

Introduction And Objectives: Patients with type 2 diabetes (T2D) and stable coronary artery disease (CAD) previously revascularized with percutaneous coronary intervention (PCI) are at high risk of recurrent ischemic events. We aimed to provide real-world insights into the clinical characteristics and management of this clinical population, excluding patients with a history of myocardial infarction (MI) or stroke, using Natural Language Processing (NLP) technology.

Methods: This is a multicenter, retrospective study based on the secondary use of 2014-2018 real-world data captured in the Electronic Health Records (EHRs) of 1,579 patients (0.