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
González-Juanatey, Carlos (Hospital Universitario Lucus Augusti (Lugo))
Anguita-Sánchez, Manuel (Hospital Universitario Reina Sofía (Còrdova, Espanya))
Barrios, Vivencio (Hospital Universitario Ramón y Cajal (Madrid))
Núñez-Gil, Iván (Hospital Clínico San Carlos (Madrid))
Gómez-Doblas, Juan José (Hospital Universitario Virgen de la Victoria (Màlaga, Andalusia))
Garcia-Moll, Xavier (Institut d'Investigació Biomèdica Sant Pau)
Lafuente-Gormaz, Carlos (Complejo Hospitalario Universitario de Albacete)
Rollán-Gómez, María Jesús (Hospital Universitario Río Hortega (Valladolid))
Peral-Disdie, Vicente (Hospital Universitari Son Espases (Palma de Mallorca, Balears))
Martínez-Dolz, Luis (Hospital Universitari i Politècnic La Fe (València))
Rodríguez-Santamarta, Miguel (Hospital Universitario de León)
Viñolas, Xavier (Institut d'Investigació Biomèdica Sant Pau)
Soriano-Colomé, Toni (Hospital Universitari Vall d'Hebron)
Muñoz-Aguilera, Roberto (Hospital Universitario Infanta Leonor)
Plaza, Ignacio (Hospital Universitario Infanta Sofía (San Sebastián de los Reyes))
Curcio, Alejandro (Hospital Universitario de Fuenlabrada ( Madrid))
Orts-Soler, Ernesto (Consorci Hospitalari Provincial de Castelló)
Segovia, Javier (Hospital Universitario Puerta de Hierro Majadahonda (Madrid))
Maté, Claudia (Savana)
Cequier, Ángel (Institut d'Investigació Biomèdica de Bellvitge)

Fecha:	2022
Resumen:	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. 72% of the T2D population analyzed; n = 217,632 patients) from 12 representative hospitals in Spain. To access the unstructured clinical information in EHRs, we used the EHRead technology, based on NLP and machine learning. Major adverse cardiovascular events (MACE) were considered: MI, ischemic stroke, urgent coronary revascularization, and hospitalization due to unstable angina. The association between MACE rates and the variables included in this study was evaluated following univariate and multivariate approaches. Results Most patients were male (72. 13%), with a mean age of 70. 5±10 years. Regarding T2D, most patients were non-insulin-dependent T2D (61. 75%) with high prevalence of comorbidities. The median (Q1-Q3) duration of follow-up was 1. 2 (0. 3-4. 5) years. Overall, 35. 66% of patients suffered from at least one MACE during follow up. Using a Cox Proportional Hazards regression model analysis, several independent factors were associated with MACE during follow up: CAD duration (p < 0. 001), COPD/Asthma (p = 0. 021), heart valve disease (p = 0. 031), multivessel disease (p = 0. 005), insulin treatment (p < 0. 001), statins treatment (p < 0. 001), and clopidogrel treatment (p = 0. 039). Conclusions Our results showed high rates of MACE in a large real-world series of PCI-revascularized patients with T2D and CAD with no history of MI or stroke. These data represent a potential opportunity to improve the clinical management of these patients.
Nota:	Altres ajuts: AstraZeneca Spain; Spanish Society of Cardiology.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Electronic Health Records
Publicado en:	PloS one, Vol. 17 Núm. 2 February (february 2022) , p. e0263277, ISSN 1932-6203

DOI: 10.1371/journal.pone.0263277
PMID: 35143527

17 p, 1.3 MB

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