biostudies-literatureGonzalez-Juanatey CAstraZeneca (Spain)5218https://www.ebi.ac.uk/biostudies/studies/S-EPMC10456002biostudies-literatureUnknown12(16)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. Of the 4072 included patients, 30.9% were younger than 65 years (66.3% male), 34.2% were aged 65-75 years (66.4% male), and 34.8% were older than 75 years (54.3% male). These older patients were more likely to havJournal of clinical medicineImpact 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.PMC10456002n.a.Orts-Soler EGonzalez-Juanatey CBarrios VMunoz-Aguilera RRodriguez-Santamarta MLafuente-Gormaz CCurcio-Ruigomez AVinolas-Prat XSegovia-Cubero JCequier AGarcia-Moll XRollan-Gomez MJGomez-Doblas JJMarin-Corral JFanjul VPlaza ISoriano-Colome TNunez-Gil IAnguita-Sanchez MPeral-Disdier VMartinez-Dolz LSavana Research GroupfalseImpact 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. Of the 4072 included patients, 30.9% were younger than 65 years (66.3% male), 34.2% were aged 65-75 years (66.4% male), and 34.8% were older than 75 years (54.3% male). These older patients were more likely to hav2023-01-01T00:00:00Z2023 Aug2025-04-04T08:53:55.452Z2025-04-04T08:53:55.452ZS-EPMC104560023762926210.3390/jcm12165218