Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesions Article Swipe
Marco Lombardi
,
Rocco Vergallo
,
Andrea Costantino
,
Francesco Bianchini
,
Tsunekazu Kakuta
,
Tomasz Pawłowski
,
Antonio Maria Leone
,
Gennaro Sardella
,
Pierfrancesco Agostoni
,
Jonathan Hill
,
Giovanni Luigi De Maria
,
Adrian Banning
,
Tomasz Roleder
,
Anouar Belkacemi
,
Carlo Trani
,
Francesco Burzotta
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ccd.31167
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ccd.31167
Background Fractional flow reserve (FFR) represents the gold standard in guiding the decision to proceed or not with coronary revascularization of angiographically intermediate coronary lesion (AICL). Optical coherence tomography (OCT) allows to carefully characterize coronary plaque morphology and lumen dimensions. Objectives We sought to develop machine learning (ML) models based on clinical, angiographic and OCT variables for predicting FFR. Methods Data from a multicenter, international, pooled analysis of individual patient's level data from published studies assessing FFR and OCT on the same target AICL were collected through a dedicated database to train ( n = 351) and validate ( n = 151) six two‐class supervised ML models employing 25 clinical, angiographic and OCT variables. Results A total of 502 coronary lesions in 489 patients were included. The AUC of the six ML models ranged from 0.71 to 0.78, whereas the measured F1 score was from 0.70 to 0.75. The ML algorithms showed moderate sensitivity (range: 0.68–0.77) and specificity (range: 0.59–0.69) in detecting patients with a positive or negative FFR. In the sensitivity analysis, using 0.75 as FFR cut‐off, we found a higher AUC (0.78–0.86) and a similar F1 score (range: 0.63–0.76). Specifically, the six ML models showed a higher specificity (0.71–0.84), with a similar sensitivity (0.58–0.80) with respect to 0.80 cut‐off. Conclusions ML algorithms derived from clinical, angiographic, and OCT parameters can identify patients with a positive or negative FFR.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ccd.31167
- OA Status
- green
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401274936
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401274936Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/ccd.31167Digital Object Identifier
- Title
-
Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-01Full publication date if available
- Authors
-
Marco Lombardi, Rocco Vergallo, Andrea Costantino, Francesco Bianchini, Tsunekazu Kakuta, Tomasz Pawłowski, Antonio Maria Leone, Gennaro Sardella, Pierfrancesco Agostoni, Jonathan Hill, Giovanni Luigi De Maria, Adrian Banning, Tomasz Roleder, Anouar Belkacemi, Carlo Trani, Francesco BurzottaList of authors in order
- Landing page
-
https://doi.org/10.1002/ccd.31167Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://ora.ox.ac.uk/objects/uuid:708edcf7-5f55-4218-b90d-7b0f2e4563b3Direct OA link when available
- Concepts
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Fractional flow reserve, Medicine, Optical coherence tomography, Gold standard (test), Revascularization, Internal medicine, Cardiology, Radiology, Coronary angiography, Nuclear medicine, 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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53Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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