Seung Hun Lee1, Doosup Shin2, Joo Myung Lee3, Adrien Lefieux4, David Molony5, Ki Hong Choi1, Doyeon Hwang6, Hyun-Jong Lee7, Ho-Jun Jang7, Hyun Kuk Kim8, Sang Jin Ha9, Jae-Jin Kwak10, Taek Kyu Park1, Jeong Hoon Yang1, Young Bin Song1, Joo-Yong Hahn1, Joon-Hyung Doh10, Eun-Seok Shin11, Chang-Wook Nam12, Bon-Kwon Koo6, Seung-Hyuk Choi1, Hyeon-Cheol Gwon1. 1. Division of Cardiology, Department of Internal Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 2. Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa. 3. Division of Cardiology, Department of Internal Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Emory University School of Medicine and Emory University Hospital, Atlanta, Georgia. Electronic address: drone80@hanmail.net. 4. Alef Consulting, Montigny-lès-Metz, France. 5. Emory University School of Medicine and Emory University Hospital, Atlanta, Georgia. 6. Department of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul, Republic of Korea. 7. Department of Internal Medicine, Sejong General Hospital, Bucheon, Republic of Korea. 8. Department of Internal Medicine and Cardiovascular Center, Chosun University Hospital, University of Chosun College of Medicine, Gwangju, Republic of Korea. 9. Division of Cardiology, Department of Internal Medicine, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea. 10. Department of Medicine, Inje University Ilsan Paik Hospital, Goyang, Republic of Korea. 11. Department of Cardiology, Ulsan Medical Center, Ulsan, Republic of Korea. 12. Department of Medicine, Keimyung University Dongsan Medical Center, Daegu, Republic of Korea.
Abstract
OBJECTIVES: This study sought to develop an automated algorithm using pre-percutaneous coronary intervention (PCI) fractional flow reserve (FFR) pullback recordings to predict post-PCI physiological results in the pre-PCI phase. BACKGROUND: Both FFR and percent FFR increase measured after PCI showed incremental prognostic implications. However, there is no current method to predict post-PCI physiological results using physiological assessment in the pre-PCI phase. METHODS: An automated algorithm that analyzes instantaneous FFR gradient per unit time (dFFR(t)/dt) was developed from the derivation cohort (n = 30). Using dFFR(t)/dt, the pattern of atherosclerotic disease in each patient was classified into 3 groups (major, mixed, and minor FFR gradient groups) in both the internal validation cohort with constant pullback method (n = 234) and the external validation cohort with nonstandardized pullback methods (n = 252). All patients in the validation cohorts underwent PCI on the basis of pre-PCI FFR ≤0.80. Suboptimal post-PCI physiological results were defined as both post-PCI FFR <0.84 and percent FFR increase ≤15%. From the derivation cohort, cutoffs of dFFR(t)/dt for major and minor FFR gradient were 0.035/s and 0.015/s, respectively. RESULTS: In validation cohorts, dFFR(t)/dt showed significant correlations with percent FFR increase (R = 0.801; p < 0.001) and post-PCI FFR (R = 0.099; p = 0.029). In both the internal and external validation cohorts, the major FFR gradient group showed significantly higher post-PCI FFR and percent FFR increase compared with those in the mixed or minor FFR gradient groups (all p values <0.001). The proportions of suboptimal post-PCI physiological results were significantly different among 3 groups (10.4% vs. 25.8% vs. 45.7% for the major, mixed, and minor FFR gradient groups, respectively; p < 0.001) in validation cohorts. Absence of major FFR gradient lesion (odds ratio: 2.435, 95% [CI]: 1.252 to 4.734; p = 0.009) and presence of minor FFR gradient lesion (odds ratio: 2.756, 95% confidence interval: 1.629 to 4.664; p < 0.001) were independent predictors for suboptimal post-PCI physiological results. CONCLUSIONS: The automated algorithm analyzing pre-PCI pullback curve was able to predict post-PCI physiological results. The incidence of suboptimal post-PCI physiological results was significantly different according to algorithm-based classifications in the pre-PCI physiological assessment. (Automated Algorithm Detecting Physiologic Major Stenosis and Its Relationship with Post-PCI Clinical Outcomes [Algorithm-PCI]; NCT04304677).
OBJECTIVES: This study sought to develop an automated algorithm using pre-percutaneous coronary intervention (PCI) fractional flow reserve (FFR) pullback recordings to predict post-PCI physiological results in the pre-PCI phase. BACKGROUND: Both FFR and percent FFR increase measured after PCI showed incremental prognostic implications. However, there is no current method to predict post-PCI physiological results using physiological assessment in the pre-PCI phase. METHODS: An automated algorithm that analyzes instantaneous FFR gradient per unit time (dFFR(t)/dt) was developed from the derivation cohort (n = 30). Using dFFR(t)/dt, the pattern of atherosclerotic disease in each patient was classified into 3 groups (major, mixed, and minor FFR gradient groups) in both the internal validation cohort with constant pullback method (n = 234) and the external validation cohort with nonstandardized pullback methods (n = 252). All patients in the validation cohorts underwent PCI on the basis of pre-PCI FFR ≤0.80. Suboptimal post-PCI physiological results were defined as both post-PCI FFR <0.84 and percent FFR increase ≤15%. From the derivation cohort, cutoffs of dFFR(t)/dt for major and minor FFR gradient were 0.035/s and 0.015/s, respectively. RESULTS: In validation cohorts, dFFR(t)/dt showed significant correlations with percent FFR increase (R = 0.801; p < 0.001) and post-PCI FFR (R = 0.099; p = 0.029). In both the internal and external validation cohorts, the major FFR gradient group showed significantly higher post-PCI FFR and percent FFR increase compared with those in the mixed or minor FFR gradient groups (all p values <0.001). The proportions of suboptimal post-PCI physiological results were significantly different among 3 groups (10.4% vs. 25.8% vs. 45.7% for the major, mixed, and minor FFR gradient groups, respectively; p < 0.001) in validation cohorts. Absence of major FFR gradient lesion (odds ratio: 2.435, 95% [CI]: 1.252 to 4.734; p = 0.009) and presence of minor FFR gradient lesion (odds ratio: 2.756, 95% confidence interval: 1.629 to 4.664; p < 0.001) were independent predictors for suboptimal post-PCI physiological results. CONCLUSIONS: The automated algorithm analyzing pre-PCI pullback curve was able to predict post-PCI physiological results. The incidence of suboptimal post-PCI physiological results was significantly different according to algorithm-based classifications in the pre-PCI physiological assessment. (Automated Algorithm Detecting Physiologic Major Stenosis and Its Relationship with Post-PCI Clinical Outcomes [Algorithm-PCI]; NCT04304677).
Authors: Doyeon Hwang; Bon-Kwon Koo; Jinlong Zhang; Jiesuck Park; Seokhun Yang; Minsang Kim; Jun Pil Yun; Joo Myung Lee; Chang-Wook Nam; Eun-Seok Shin; Joon-Hyung Doh; Shao-Liang Chen; Tsunekazu Kakuta; Gabor G Toth; Zsolt Piroth; Nils P Johnson; Nico H J Pijls; Abdul Hakeem; Barry F Uretsky; Yohei Hokama; Nobuhiro Tanaka; Hong-Seok Lim; Tsuyoshi Ito; Akiko Matsuo; Lorenzo Azzalini; Massoud A Leesar; Tara Neleman; Nicolas M van Mieghem; Roberto Diletti; Joost Daemen; Damien Collison; Carlos Collet; Bernard De Bruyne Journal: JAMA Netw Open Date: 2022-09-01