Development and external validation of a nomogram prediction model based on quantitative coronary angiography for predicting ischemic lesions: a multi-centre study Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.3389/fcvm.2025.1550550
Objectives Quantitative coronary angiography (QCA) has significantly contributed to the diagnosis of coronary artery disease. This study aimed to construct and validate a QCA-based prediction model, represented as a nomogram, for predicting ischemic lesions defined by invasive fractional flow reserve (FFR) ≤ 0.80. Methods In this multi-centre study, we enrolled 220 patients with 303 interrogated vessels who underwent FFR measurements during clinically indicated invasive coronary angiography. QCA predictors for ischemic lesions were extracted to construct a nomogram model using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis of the development set ( n = 113 patients). An external validation ( n = 107 patients) was performed to assess the nomogram model's discrimination and consistency. Results Lesion length, minimal lumen diameter, stenosis flow reserve, percent diameter stenosis by visual estimation, and weight were included as predictors in the nomogram. The nomogram yielded an area under the curve (AUC) of 0.922 and 0.912 at per-vessel and per-patient levels, respectively, in the development set. In the validation set, it achieved an AUC of 0.915 and 0.912 at per-vessel and per-patient levels, respectively. Per-vessel accuracy, sensitivity, and specificity derived from the nomogram were 86.5%, 88.2%, 86.2% in the development cohort and 84.2%, 85.5%, and 83.1% in the validation cohort. For per-patient analysis, the corresponding values were 85.8%, 85.7%, 86.0% in the development cohort and 82.2%, 83.3%, 81.1% in the validation cohort. Conclusion The nomogram may be useful for predicting ischemic lesions using QCA measurements and the LASSO regression algorithm, with external validation indicating potential predictive value in cardiology care settings.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fcvm.2025.1550550
- https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1550550/pdf
- OA Status
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- References
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4411531874Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fcvm.2025.1550550Digital Object Identifier
- Title
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Development and external validation of a nomogram prediction model based on quantitative coronary angiography for predicting ischemic lesions: a multi-centre studyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-20Full publication date if available
- Authors
-
S. Yang, Shuang Leng, Zhiwei Wang, Jiang Ming Fam, Adrian F. Low, Ru‐San Tan, Ping Chai, Lynette Teo, Chee Yang Chin, John Carson Allen, Mark Y. Chan, Khung Keong Yeo, Aaron Sung Lung Wong, Soo Teik Lim, Qinghua Wu, Liang ZhongList of authors in order
- Landing page
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https://doi.org/10.3389/fcvm.2025.1550550Publisher landing page
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https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1550550/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1550550/pdfDirect OA link when available
- Concepts
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Nomogram, Medicine, Coronary angiography, Cardiology, Internal medicine, Fractional flow reserve, Angiography, Radiology, Myocardial infarctionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Frontiers in Cardiovascular Medicine |
| primary_location.landing_page_url | https://doi.org/10.3389/fcvm.2025.1550550 |
| publication_date | 2025-06-20 |
| publication_year | 2025 |
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