Work

Using natural language processing to extract carotid stenosis severity from clinical notes to create a nationwide veteran cohort

Year: 2025

Type: article

Abstract: Objective: The prevalence of moderate to severe asymptomatic carotid stenosis (ie, atherosclerotic narrowing of the extracranial carotid arteries) is generally approximately 6% and 2%, respectively. Most prior studies of carotid stenosis risk factors have been small. This study describes the development and validation of a natural language processing (NLP) tool to identify carotid stenosis and uses it to identify significant risk factors, presence, and severity of carotid stenosis. Methods: We created an NLP tool to extract the ratio of peak systolic velocity of the internal carotid artery to the common carotid artery (ICA/CCA ratio) in veterans receiving carotid duplex ultrasound examinations in the Veteran's Health Administration from 2001 to 2020. Among those who had at least one valid ICA/CCA ratio, we identified carotid stenosis severity (<50%, 50%-69%, ≥70%) based on the ICA/CCA ratio (<2, ≥2 to <4, ≥4) and assessed the association between presence and severity of carotid stenosis and clinical and demographic characteristics, including age, sex, self-identified race and ethnicity, smoking status, body mass index, systolic and diastolic blood pressures, indicator variables for pre-existing hypertension, coronary heart disease, and type 2 diabetes, and selected laboratory measures (ie, hemoglobin A1c, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, and creatinine). Results: < .001). All patient-level risk factors except high-density lipoprotein cholesterol were significantly associated with carotid stenosis severity. Conclusions: The NLP tool performed well, and the study performed with our NLP-created cohort largely validates the risk factors identified by previous smaller studies, demonstrating the utility of big data and NLP in carotid stenosis research. (JVS-Vascular Insights 2025;3:100302.).

Source: JVS-Vascular Insights

DOI: 10.1016/j.jvsvi.2025.100302