Electronic health records features associated to coronary artery disease in patients with chest pain
Authors: Garcia Olea A , Jojoa Acosta M , Diez Gonzalez I , Garcia Zapirain M B , Fernandez De La Prieta I , Maeztu Rada M , Ormaetxe Merodio J M , Idiazabal Rodriguez U , Pereiro Lili I , Ruiz Rodriguez A , Amuriza De Luis E 
Published: October 2021

Abstract

Introduction
Cardiovascular diseases remain the main cause of death in most European countries and coronary artery disease (CAD) follows an ascendent tendency. Current guidelines on chronic coronary syndromes recommend to estimate the CAD pre-test probability based on age, sex and symptoms in patients with suspected CAD. Besides, they underline the importance of adapting this likelihood with the patient information, although the risk the physician attributes to the medical history might not be concordant among colleagues. Electronic health records (EHR) are the main patient-level data collection tools in clinical care and data capture systems compile this clinical information into research-oriented datasets. We can analyze which variables in EHR have stronger associations with CAD in this context to facilitate a more concise and personalized adjustment of the pre-test CAD risk.

Purpose
To analyze the association between features in EHR with the development of CAD in patients who attended the local public health system for thoracic pain in the 2016–2018 period.

Methods
We queried the system for patients who required medical attendance for thoracic pain or equivalent symptoms from 2016 to 2018 in the public health system. We extracted data within this subset of the potentially predictive features according to a literature review. We investigated whether patients developed CAD in the year following their attendance. A database cleaning process removed subjects without further diagnostic tests. We used parametric tests to assess the distribution differences of these features between patients who developed CAD and those who did not and we applied logistic regression to document their prediction potential.

Results
The EHR dataset extracted from the system contained information about 2,199,711 individuals, out of which 43,835 required medical attention for thoracic pain in the selected period. 10,463 patients had no further diagnostic tests, so were excluded. Among the remaining 33,372 patients, 5,379 (16.1%) developed CAD in the follow-up period. Heart failure and traditional cardiovascular risk factors were predominant in the CAD group, and so were related blood test features and therapies. Any history of CAD diagnostic test was significantly associated to CAD presentation. Ischemic etiology of the chest pain was more unlikely in patients with anxiety history and benzodiazepine prescription. Proton pump inhibitors use and hearth rhythm disorders did not increase CAD risk in adjusted models.

Conclusions
Medical history, basal blood tests and treatment information available in EHR are related to CAD risk in patients with chest pain. Considering this information seems valuable for a personalized risk adjustment of the CAD pre-test probability in symptomatic patients. Further studies with multivariable models and machine learning techniques applied to this dataset might entail more specific predictive algorithms.

Funding Acknowledgement
Type of funding sources: None.