BACKGROUND: Clinical trials with angiographic end points have been used to assess whether interventions influence the evolution of coronary atherosclerosis because sample size requirements are much smaller than for trials with hard clinical end points. Further studies of the variability of the computer-assisted quantitative measurement techniques used in such studies would be useful to establish better standardized criteria for defining significant change. METHODS AND RESULTS: In 21 patients who had two arteriograms 3-189 days apart, we assessed the reproducibility of repeat quantitative measurements of 54 target lesions under four conditions: 1) same film, same frame; 2) same film, different frame; 3) same view from films obtained within 1 month; and 4) same view from films 1-6 months apart. Quantitative measurements of 2,544 stenoses were also compared with an experienced radiologist's interpretation. The standard deviation of repeat measurements of minimum diameter from the same frame was very low (0.088 mm) but increased to 0.141 mm for measurements from different frames. It did not increase further for films within 1 month but increased to 0.197 mm for films 1-6 months apart. Diameter stenosis measurements were somewhat more variable. Measurement variability for minimum diameter was independent of vessel size and stenosis severity. Experienced radiologists did not systematically overestimate or underestimate lesion severity except for mild overestimation (mean 3.3%) for stenoses > or = 70%. However, the variability between visual and quantitative measurements was two to three times higher than the variability of paired quantitative measurements from the same frame. CONCLUSIONS: Changes of 0.4 mm or more for minimum diameter and 15% or more for stenosis diameter (e.g., 30-45%), measured quantitatively, are recommended as criteria to define progression and regression. Approaches to data analysis for coronary arteriographic trials are discussed.
BACKGROUND: Clinical trials with angiographic end points have been used to assess whether interventions influence the evolution of coronary atherosclerosis because sample size requirements are much smaller than for trials with hard clinical end points. Further studies of the variability of the computer-assisted quantitative measurement techniques used in such studies would be useful to establish better standardized criteria for defining significant change. METHODS AND RESULTS: In 21 patients who had two arteriograms 3-189 days apart, we assessed the reproducibility of repeat quantitative measurements of 54 target lesions under four conditions: 1) same film, same frame; 2) same film, different frame; 3) same view from films obtained within 1 month; and 4) same view from films 1-6 months apart. Quantitative measurements of 2,544 stenoses were also compared with an experienced radiologist's interpretation. The standard deviation of repeat measurements of minimum diameter from the same frame was very low (0.088 mm) but increased to 0.141 mm for measurements from different frames. It did not increase further for films within 1 month but increased to 0.197 mm for films 1-6 months apart. Diameter stenosis measurements were somewhat more variable. Measurement variability for minimum diameter was independent of vessel size and stenosis severity. Experienced radiologists did not systematically overestimate or underestimate lesion severity except for mild overestimation (mean 3.3%) for stenoses > or = 70%. However, the variability between visual and quantitative measurements was two to three times higher than the variability of paired quantitative measurements from the same frame. CONCLUSIONS: Changes of 0.4 mm or more for minimum diameter and 15% or more for stenosis diameter (e.g., 30-45%), measured quantitatively, are recommended as criteria to define progression and regression. Approaches to data analysis for coronary arteriographic trials are discussed.
Authors: F Pelliccia; V Pasceri; A Evangelista; A Pergolini; F Barillà; N Viceconte; G Tanzilli; M Schiariti; C Greco; C Gaudio Journal: Int J Cardiovasc Imaging Date: 2012-07-18 Impact factor: 2.357
Authors: Li Jin Pu; Lin Lu; Rui Yan Zhang; Run Du; Ying Shen; Qi Zhang; Zheng Kun Yang; Qiu Jing Chen; Wei Feng Shen Journal: Diabetes Care Date: 2012-12-10 Impact factor: 19.112
Authors: Carlo Gaudio; Alessandra Tanzilli; Mariachiara Mei; Andrea Moretti; Francesco Barillà; Antonio Varveri; Vincenzo Paravati; Gaetano Tanzilli; Antonio Ciccaglioni; Stefano Strano; Massimo Pellegrini; Paolo Barillari; Francesco Pelliccia Journal: Sci Rep Date: 2019-09-25 Impact factor: 4.379