Academic Journal
Natural language processing identifies symptoms predicting complete coronary artery occlusion in patients with NSTEMI.
| Τίτλος: | Natural language processing identifies symptoms predicting complete coronary artery occlusion in patients with NSTEMI. |
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| Συγγραφείς: | Dzikowicz DJ; School of Nursing, University of Rochester, 255 Crittenden Blvd, Rochester, NY 14642, USA.; Clinical Cardiovascular Research Center, University of Rochester, 601 Elmwood Ave, Rochester, NY 14642, USA., Lai CJ; Goergen Institute for Data Science, University of Rochester, 500 WIlson Blvd, Rochester, NY 14627, USA., Saoji SB; Goergen Institute for Data Science, University of Rochester, 500 WIlson Blvd, Rochester, NY 14627, USA., Zègre-Hemsey JK; School of Nursing, University North Carolina at Chapel Hill Campus Box 7460, Chapel Hill, NC 27599, USA., DeVon HA; School of Nursing, University of California Los Angeles 700 Tiverton Ave, Los Angeles, CA 90005, USA., Wang L; Rochester Institute of Technology, Computing, and Information Services, 1 Lomb Memorial Lane, Rochester, NY 14623, USA., Zareba W; Clinical Cardiovascular Research Center, University of Rochester, 601 Elmwood Ave, Rochester, NY 14642, USA. |
| Πηγή: | European journal of cardiovascular nursing [Eur J Cardiovasc Nurs] 2026 Jul 16; Vol. 25 (4), pp. 795-807. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: England NLM ID: 101128793 Publication Model: Print Cited Medium: Internet ISSN: 1873-1953 (Electronic) Linking ISSN: 14745151 NLM ISO Abbreviation: Eur J Cardiovasc Nurs Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2021- : [Oxford] : Oxford University Press Original Publication: Amsterdam ; New York : Elsevier, c2002- |
| Ιατρικοί όροι (MeSH): | Non-ST Elevated Myocardial Infarction*/diagnosis , Non-ST Elevated Myocardial Infarction*/complications , Coronary Occlusion*/diagnosis , Symptom Assessment*/methods , Natural Language Processing*, Acute Coronary Syndrome/diagnosis ; Humans ; Male ; Female ; Retrospective Studies ; Aged ; Middle Aged ; Predictive Value of Tests ; Emergency Service, Hospital |
| Περίληψη: | Aims: One in 10 patients present to the emergency department (ED) with symptoms of acute coronary syndrome (ACS). The 13-item ACS Symptom Checklist is a validated tool for rapid ACS symptom assessment. We aimed to evaluate the effectiveness of the 13-item ACS Symptom Checklist in distinguishing NSTEMI patients with and without an occluded artery using natural language processing (NLP). Methods and Results: We retrospectively extracted the 13 symptoms from the 13-item ACS Symptom Checklist for all patients admitted with NSTEMI. The outcome was an occluding coronary artery defined as one requiring revascularization. We applied Chi-square tests to assess the sensitivity and specificity of each symptom for an acutely occluded coronary artery. We used logistic regression models stratified by sex to measure the odds of an occluded artery after controlling for age, obesity, and diabetes. The majority of the 1905 patients were male (63.1%), older (66 ± 12 years), and White (84%). Twenty-three percent of patients require revascularization. Common comorbidities included diabetes (17%) and obesity (42%). Symptoms differentiating patients with and without an occluded artery included palpitations (22.4% vs. 29.0%), arm pain (23.5% vs. 16.5%), unusual fatigue (19.1% vs. 3.3%), and lightheadedness (21.1% vs. 26.5%). Arm pain was associated with 1.532 (95% CI 1.155-2.033) increased odds of an occluded artery, with similar odds in men and women. Conclusion: Arm pain was the primary symptom predicting an occluded coronary artery in NSTEMI patients; of note, there were minimal sex differences. NLP was a useful tool for identifying arm pain and other symptoms from clinical notes. (© The Author(s) 2026. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.) |
| Competing Interests: | Conflict of interest: None declared. |
| Grant Information: | K23 NR017896 United States NR NINR NIH HHS; American Heart Association |
| Contributed Indexing: | Keywords: Acute coronary syndrome; Emergency Medical Services; Myocardial Infarction; NSTEMI; Natural language processing; Symptom assessment |
| Entry Date(s): | Date Created: 20260206 Date Completed: 20260716 Latest Revision: 20260716 |
| Update Code: | 20260717 |
| DOI: | 10.1093/eurjcn/zvag011 |
| PMID: | 41645741 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-1953 |
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| DOI: | 10.1093/eurjcn/zvag011 |