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Lombardi M, Vergallo R, Costantino A, Bianchini F, Kakuta T, Pawlowski T, Leone AM, Sardella G, Agostoni P, Hill JM, De Maria GL, Banning AP, Roleder T, Belkacemi A, Trani C, Burzotta F. Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesions. Catheter Cardiovasc Interv 2024. [PMID: 39091119 DOI: 10.1002/ccd.31167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 07/02/2024] [Accepted: 07/20/2024] [Indexed: 08/04/2024]
Abstract
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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Affiliation(s)
- Marco Lombardi
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Rocco Vergallo
- Department of Internal Medicine and Medical Specialties (DIMI), Università di Genova, Genova, Italy
- Interventional Cardiology Unit, Cardiothoracic and Vascular Department (DICATOV), IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Andrea Costantino
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Francesco Bianchini
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Tsunekazu Kakuta
- Department of Cardiovascular Medicine, Tsuchiura Kyodo General Hospital, Tsuchiura, Japan
| | - Tomasz Pawlowski
- Department of Cardiology, Central Hospital of Internal Affairs and Administration Ministry, Postgraduate Medical Education Centre, Warsaw, Poland
| | - Antonio M Leone
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Gennaro Sardella
- Department of Cardiovascular Sciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | | | - Giovanni L De Maria
- Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, UK
| | - Adrian P Banning
- Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, UK
| | - Tomasz Roleder
- Department of Cardiology, Hospital Wroclaw, Wroclaw, Poland
| | | | - Carlo Trani
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
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Vergallo R, Lombardi M, Kakuta T, Pawlowski T, Leone AM, Sardella G, Agostoni P, Hill JM, De Maria GL, Banning AP, Roleder T, Belkacemi A, Trani C, Burzotta F. Optical Coherence Tomography Measures Predicting Fractional Flow Reserve: The OMEF Study. JOURNAL OF THE SOCIETY FOR CARDIOVASCULAR ANGIOGRAPHY & INTERVENTIONS 2024; 3:101288. [PMID: 39130179 PMCID: PMC11307753 DOI: 10.1016/j.jscai.2023.101288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/20/2023] [Accepted: 01/01/2024] [Indexed: 08/13/2024]
Abstract
Background Optical coherence tomography (OCT) allows to carefully characterize coronary plaque morphology and lumen dimensions. We sought to evaluate the value of OCT in predicting fractional flow reserve (FFR). Methods We performed a multicenter, international, pooled analysis of individual patient-level data from published studies assessing FFR and OCT on the same vessel. Data from stable or unstable patients who underwent both FFR and OCT of the same coronary artery were collected through a dedicated database. Predefined OCT parameters were minimum lumen area (MLA), percentage area stenosis (%AS), and presence of thrombus or plaque rupture. Primary end point was FFR ≤0.80. Secondary outcome was the incidence of major adverse cardiac events in patients not undergoing revascularization based on negative FFR (>0.80). Results A total of 502 coronary lesions in 489 patients were included. A significant correlation was observed between OCT-MLA and FFR values (R = 0.525; P < .001), and between OCT-%AS and FFR values (R = -0.482; P < .001). In Receiver operating characteristic analysis, MLA <2.0 mm2 showed a good discriminative power to predict an FFR ≤0.80 (AUC, 0.80), whereas %AS >73% showed a moderate discriminative power (AUC, 0.73). When considering proximal coronary segments, the best OCT cutoff values predicting an FFR ≤0.80 were MLA <3.1 mm2 (AUC, 0.82), and %AS >61% (AUC, 0.84). In patients with a negative FFR not revascularized, the combination of lower MLA and higher %AS had a trend toward worse outcome (which was statistically significant in the analysis restricted to proximal vessels). Conclusions OCT lumen measures (MLA, %AS) may predict FFR, and different cutoffs are needed for proximal vessels.
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Affiliation(s)
- Rocco Vergallo
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Marco Lombardi
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Tsunekazu Kakuta
- Department of Cardiovascular Medicine, Tsuchiura Kyodo General Hospital, Tsuchiura, Japan
| | - Tomasz Pawlowski
- Department of Cardiology, Central Hospital of Internal Affairs and Administration Ministry, Postgraduate Medical Education Centre, Warsaw, Poland
| | - Antonio Maria Leone
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Gennaro Sardella
- Department of Cardiovascular Sciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | | | - Giovanni Luigi De Maria
- Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Adrian P. Banning
- Oxford Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Tomasz Roleder
- Department of Cardiology, Hospital Wroclaw, Wroclaw, Poland
| | | | - Carlo Trani
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica Sacro Cuore, Rome, Italy
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Burzotta F, Leone AM, Aurigemma C, Zambrano A, Zimbardo G, Arioti M, Vergallo R, De Maria GL, Cerracchio E, Romagnoli E, Trani C, Crea F. Fractional Flow Reserve or Optical Coherence Tomography to Guide Management of Angiographically Intermediate Coronary Stenosis. JACC Cardiovasc Interv 2020; 13:49-58. [DOI: 10.1016/j.jcin.2019.09.034] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 09/18/2019] [Accepted: 09/19/2019] [Indexed: 12/16/2022]
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