Francesco Isidori1, Eugenio Lella1, Valeria Marco1, Mario Albertucci1,2, Yukio Ozaki3, Alessio La Manna4, Flavio Giuseppe Biccirè1,2,5, Enrico Romagnoli6, Christos V Bourantas7,8, Giulia Paoletti1,9, Franco Fabbiocchi10, Laura Gatto1,2, Simone Budassi1,2, Alessandro Sticchi1, Francesco Burzotta6, Nevio Taglieri11, Giuseppe Calligaris10, Eloisa Arbustini12, Fernando Alfonso13, Francesco Prati14,15,16. 1. Centro per la Lotta Contro L'Infarto - CLI Foundation, Rome, Italy. 2. Cardiovascular Sciences Department, San Giovanni Addolorata Hospital, Via dell'Amba Aradam 9, 00100, Rome, Italy. 3. Department of Cardiology, Fujita Health University Hospital, Toyoake, Japan. 4. Cardio-Thoracic Vascular Department, Azienda Ospedaliero-Universitaria "Policlinico Vittorio-Emanuele", University of Catania, Catania, Italy. 5. Sapienza University of Rome, Rome, Italy. 6. Departement of Cardiovascular and Thoracic Sciences, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy. 7. Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK. 8. Institure of Cardiovascular Sciences, University College London, London, UK. 9. UniCamillus - Saint Camillus International University of Health Sciences, Rome, Italy. 10. Centro Cardiologico Monzino, IRCCS, Milan, Italy. 11. Cardio-Thoracic Vascular Department, University Hospital of Bologna, Bologna, Italy. 12. Centre for Inherited Cardiovascular Diseases, IRCCS Fondazione Policlinico San Matteo, Pavia, Italy. 13. Department of Cardiology, Hospital Universitario de La Princesa, Madrid, Spain. 14. Centro per la Lotta Contro L'Infarto - CLI Foundation, Rome, Italy. fprati@hsangiovanni.roma.it. 15. Cardiovascular Sciences Department, San Giovanni Addolorata Hospital, Via dell'Amba Aradam 9, 00100, Rome, Italy. fprati@hsangiovanni.roma.it. 16. UniCamillus - Saint Camillus International University of Health Sciences, Rome, Italy. fprati@hsangiovanni.roma.it.
Abstract
PURPOSE: Near infrared spectroscopy-Intravascular ultrasound (NIRS-IVUS) provide a fully automated Lipid Core Burden Index (LCBI). Optical coherence tomography (OCT) is potentially capable of measuring lipid longitudinal extension in a dedicated two-dimensional LCBI spread-out plot. The present study has been designed to validate an automated approach to assess OCT images, able of providing a dedicated LCBI spread-out plot. METHODS: We compared results obtained with conventional (manual) OCT, with those obtained with a novel automated OCT algorithm and with NIRS-IVUS in consecutive 40 patients. Our goal was to calculate the lipid core longitudinal extension in a dedicated two-dimensional LCBI spread-out plot. Three groups were identified according to the studied lesions: (1) culprit lesions in ACS patients (n = 16), (2) non-culprit lesions in ACS patients (n = 12) and (3) lesions in stable patients (n = 12). OCT (either manual and automated) and NIRS-IVUS assessment showed for culprit ACS plaques a more complex anatomy. RESULTS: A strong trend for increased LCBI was found in the culprit ACS group, regardless of the adopted imaging modality (either NIRS-IVUS or automated OCT). A fair correlation was obtained for the maximum 4 mm LCBI measured by NIRS-IVUS and automated OCT (r = 0.75). The sensitivity and specificity of automated OCT to detect significant LCBI (> 400) were 90.5 and 84.2 respectively. CONCLUSION: We developed an OCT automated approach that can provide a dedicated lipid plaque spread-out plot to address plaque vulnerability. The automated OCT software can promote and improve OCT clinical applications for the identification of patients at risk of hard events.
PURPOSE: Near infrared spectroscopy-Intravascular ultrasound (NIRS-IVUS) provide a fully automated Lipid Core Burden Index (LCBI). Optical coherence tomography (OCT) is potentially capable of measuring lipid longitudinal extension in a dedicated two-dimensional LCBI spread-out plot. The present study has been designed to validate an automated approach to assess OCT images, able of providing a dedicated LCBI spread-out plot. METHODS: We compared results obtained with conventional (manual) OCT, with those obtained with a novel automated OCT algorithm and with NIRS-IVUS in consecutive 40 patients. Our goal was to calculate the lipid core longitudinal extension in a dedicated two-dimensional LCBI spread-out plot. Three groups were identified according to the studied lesions: (1) culprit lesions in ACS patients (n = 16), (2) non-culprit lesions in ACS patients (n = 12) and (3) lesions in stable patients (n = 12). OCT (either manual and automated) and NIRS-IVUS assessment showed for culprit ACS plaques a more complex anatomy. RESULTS: A strong trend for increased LCBI was found in the culprit ACS group, regardless of the adopted imaging modality (either NIRS-IVUS or automated OCT). A fair correlation was obtained for the maximum 4 mm LCBI measured by NIRS-IVUS and automated OCT (r = 0.75). The sensitivity and specificity of automated OCT to detect significant LCBI (> 400) were 90.5 and 84.2 respectively. CONCLUSION: We developed an OCT automated approach that can provide a dedicated lipid plaque spread-out plot to address plaque vulnerability. The automated OCT software can promote and improve OCT clinical applications for the identification of patients at risk of hard events.
Authors: Francesco Prati; Enrico Romagnoli; Laura Gatto; Alessio La Manna; Francesco Burzotta; Yukio Ozaki; Valeria Marco; Alberto Boi; Massimo Fineschi; Franco Fabbiocchi; Nevio Taglieri; Giampaolo Niccoli; Carlo Trani; Francesco Versaci; Giuseppe Calligaris; Gianni Ruscica; Alessandro Di Giorgio; Rocco Vergallo; Mario Albertucci; Giuseppe Biondi-Zoccai; Corrado Tamburino; Filippo Crea; Fernando Alfonso; Eloisa Arbustini Journal: Eur Heart J Date: 2020-01-14 Impact factor: 29.983