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

Abstract

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.

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

“…In line with previous publications, we found that insulin treatment was associated with MACE occurrence [ 10 , 33 ]. Although randomized trials have shown that insulin reduces adverse cardiovascular events [ 27 ], in these patients insulin tends to be prescribed in more advanced phases of the disease, which could explain our findings.…”

“…Our results showed that: (1) T2DM patients who develop CAD are predominantly male with arterial hypertension, hyperlipidemia and obesity as the most prevalent comorbidities; (2) these patients present an increasing probability of developing MACE during the study follow-up; and (3) in this population the risk factors associated with the development of MACE are related with cardiovascular diseases (multivessel or single vessel disease, PCI, transient ischemic attack, heart failure) and T2DM severity (insulin treatment), whilst lipid lowering control strategies (treatment with statins) seems to be a protective factor. The described methodology has been recently used in the same cohort of patients to describe medical management in coronary T2DM patients with previous percutaneous coronary intervention in Spain, where the EHRead ® ability to correctly identify patients’ records containing key variables associated with the study disease is shown [ 10 ].…”

“…Unstructured clinical data were extracted using the EHRead ® technology, which uses NLP, machine learning, and deep learning techniques for extracting free text from de-identified and processed EHR and translates it into a study database. EHRead ® technology have been described elsewhere [ 9 , 10 , 12 , 13 , 15 , 16 , 17 , 18 , 19 ].…”

“…As previously described, we demonstrated a high performance of EHRead ® at identifying records that contain mentions of “Coronary disease” in the target population with a precision, recall, and F1-score of 0.89, 0.73, and 0.80, respectively. Moreover, the evaluation yielded a F1-score of above 90% in most of the variables analyzed [ 9 , 10 ].…”

“…Moreover, this study investigated the probability of developing MACE after presentation of CAD in T2DM patients and the potential risk or protective factors associated with its occurrence within this population. To do so, we used EHRead ® (Madrid, Spain), a technology that applies Natural Language Processing (NLP) and machine learning, to extract, organize, and analyze the unstructured clinical information that health professionals register in patients’ electronic health records (EHRs) [ 8 , 9 , 10 , 11 , 12 , 13 ].…”