OBJECTIVES: The aim of the present study was to determine the rates of target vessel revascularization (TVR) and to determine predictors of TVR from clinical and angiographic variables available in the Prevention of Restenosis With Tranilast and its Outcomes (PRESTO) database. BACKGROUND: The rates of TVR after percutaneous revascularization procedures, and its prediction with available clinical and angiographic variables, is less well known. METHODS: We studied nine-month TVR in 11,484 patients enrolled in the PRESTO trial. Clinical, lesion-related, and procedural characteristics were analyzed in a logistic regression model. Study data were divided at random into an 80% training set on which the models were developed and a 20% hold-out set on which the model properties were evaluated. RESULTS: A total of 14% (n = 1,609) had ischemic TVR. Clinical variables with increased risk for TVR included younger age; hypertension; diabetes mellitus; nonsmokers; unstable angina; previous coronary artery bypass grafting; peripheral vascular disease; procedure- and lesion-related such as ostial location, multilesion angioplasty, location in the left anterior descending artery, length > or =20 mm, in-stent restenosis at baseline, and use of rotablator. There was significant increase in the risk of ischemic TVR at U.S. treatment sites. Smoking and stent placement were associated with lower risk of ischemic TVR. The mean area (+/- SD) under the receiver-operating characteristic curve of the bootstrap samples was 0.66, indicating a modest ability of the model to discriminate patients who needed TVR on follow-up. CONCLUSIONS: Despite being the largest prospective trial designed to test restenosis, the discriminatory ability of the clinical and angiographic variables to predict TVR is modest.
OBJECTIVES: The aim of the present study was to determine the rates of target vessel revascularization (TVR) and to determine predictors of TVR from clinical and angiographic variables available in the Prevention of Restenosis With Tranilast and its Outcomes (PRESTO) database. BACKGROUND: The rates of TVR after percutaneous revascularization procedures, and its prediction with available clinical and angiographic variables, is less well known. METHODS: We studied nine-month TVR in 11,484 patients enrolled in the PRESTO trial. Clinical, lesion-related, and procedural characteristics were analyzed in a logistic regression model. Study data were divided at random into an 80% training set on which the models were developed and a 20% hold-out set on which the model properties were evaluated. RESULTS: A total of 14% (n = 1,609) had ischemicTVR. Clinical variables with increased risk for TVR included younger age; hypertension; diabetes mellitus; nonsmokers; unstable angina; previous coronary artery bypass grafting; peripheral vascular disease; procedure- and lesion-related such as ostial location, multilesion angioplasty, location in the left anterior descending artery, length > or =20 mm, in-stent restenosis at baseline, and use of rotablator. There was significant increase in the risk of ischemicTVR at U.S. treatment sites. Smoking and stent placement were associated with lower risk of ischemicTVR. The mean area (+/- SD) under the receiver-operating characteristic curve of the bootstrap samples was 0.66, indicating a modest ability of the model to discriminate patients who needed TVR on follow-up. CONCLUSIONS: Despite being the largest prospective trial designed to test restenosis, the discriminatory ability of the clinical and angiographic variables to predict TVR is modest.
Authors: Connie N Hess; Sunil V Rao; David Dai; Megan L Neely; Robert N Piana; John C Messenger; Eric D Peterson Journal: Am Heart J Date: 2014-01-04 Impact factor: 4.749
Authors: G R Iturry-Yamamoto; A C Zago; E H Moriguchi; W C Manfroi; J L Camargo; J L Gross; A J Zago Journal: J Endocrinol Invest Date: 2009-04 Impact factor: 4.256
Authors: Kyu Hwan Park; Ung Kim; Kang Un Choi; Jong Ho Nam; Jung Hee Lee; Chan Hee Lee; Jang Won Son; Jong Seon Park; Dong Gu Shin; Kyu Chang Won; Jun Sung Moon; Yu Kyung Kim; Jang Soo Suh Journal: Diabetes Metab J Date: 2018-04 Impact factor: 5.376
Authors: Carole Decker; Suzanne V Arnold; Olawale Olabiyi; Homaa Ahmad; Elizabeth Gialde; Jamie Luark; Lisa Riggs; Terry DeJaynes; Gabriel E Soto; John A Spertus Journal: Implement Sci Date: 2008-12-31 Impact factor: 7.327