ArticleAmerican journal of preventive cardiology2022
Leveraging structured and unstructured electronic health record data to detect reasons for suboptimal statin therapy use in patients with atherosclerotic cardiovascular disease.
Article in American journal of preventive cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Obicetrapib as an Adjunct to Stable Statin Therapy in Japanese Subjects: Results from a Randomized Phase 2 Trial.Journal of atherosclerosis and thrombosis · 2024Trial
- Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease.Briefings in bioinformatics · 2025Review
- Novel Interventions to Improve Adherence to Guideline-Directed Medical Therapy in Claudicants.Journal of clinical medicine · 2025Review
- Achievement of guideline-based lipid goals among very-high-risk patients with atherosclerotic cardiovascular disease and type 2 diabetes: results in 213,380 individuals from the cvMOBIUS2 registry.American journal of preventive cardiology · 2025Article
- Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?Current atherosclerosis reports · 2024Review
- Artificial intelligence in clinical workflow processes in vascular surgery and beyond.Seminars in vascular surgery · 2023Review
- Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research.Journal of cardiovascular translational research · 2023Review
- Highlights of Cardiovascular Disease Prevention Studies Presented at the 2023 American College of Cardiology Conference.Current atherosclerosis reports · 2023Review
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveTo determine whether natural language processing (NLP) of unstructured medical text can improve identification of ASCVD patients not using high-intensity statin therapy (HIST) due to statin-associated side effects (SASEs) and other reasons.
methodsReviewers annotated reasons for not prescribing HIST in notes of 1152 randomly selected patients from across the VA healthcare system treated for ASCVD but not receiving HIST. Developers used reviewer annotations to train the Canary NLP tool to detect and extract notes containing one or more of these reasons. Negative predictive value (NPV), sensitivity, specificity and Area Under the Curve (AUC) were used to assess accuracy at detecting documents containing reasons when using structured data, NLP-extracted unstructured data, or both data sources combined.
resultsAt least one documented reason for not prescribing HIST occurred in 47% of notes. The most frequent reasons were SASEs (41%) and general intolerance (20%). When identifying notes containing any documented reason for not using HIST, adding NLP-extracted, unstructured data significantly (
conclusionsNLP extraction of data from unstructured text can improve identification of reasons for patients not being on HIST over structured data alone. The additional information provided through NLP of unstructured free text should help in tailoring and implementing system-level interventions to improve HIST use in patients with ASCVD.
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Registered trials
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