Clinical and angiographic predictors of restenosis after percutaneous coronary intervention: Insights from the Prevention of Restenosis with Tranilast and Its Outcomes (PRESTO) trial

Mandeep Singh, Bernard J. Gersh, Robyn L. McClelland, Kalon K.L. Ho, James T. Willerson, William F. Penny, David Holmes

Research output: Contribution to journalArticle

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Abstract

Background - Restenosis prediction from published studies is hampered by inadequate sample size and incomplete angiographic follow-up. The prediction of restenosis with the existing variables is poor. The aim of the present study was to include the clinical and angiographic variables commonly associated with angiographic restenosis and develop a prediction model for restenosis from the PRESTO database. Methods and Results - This study included 1312 patients with a single lesion enrolled in the angiographic substudy of the PRESTO trial. We constructed 2 risk scores. The first used preprocedural variables (female gender, vessel size [≤2.5 mm, 2.5 to 3 mm, 3 to 3.5 mm, 3.5 to 4 mm, >4 mm], lesion length >20 mm, diabetes, smoking status, type C lesion, any previous percutaneous coronary intervention [PCI], and unstable angina) derived from previous studies. Estimated restenosis rates and corresponding variability for each possible level of the resultant risk score were obtained via bootstrapping techniques. The area under the receiver-operator characteristic (ROC) curve was 0.63, indicating modest discriminatory ability to predict restenosis. The second approach constructed a multiple logistic regression model considering significant univariate clinical and angiographic predictors of restenosis identified from the PRESTO database (treated diabetes mellitus, nonsmoker, vessel size, lesion length, American College of Cardiology/American Heart Association type C lesion, ostial location, and previous PCI). The area under the ROC curve for this risk score was also 0.63. Conclusions - The preprocedural clinical and angiographic variables from available studies and from the PRESTO trial have only modest predictive ability for restenosis after PCI.

Original languageEnglish (US)
Pages (from-to)2727-2731
Number of pages5
JournalCirculation
Volume109
Issue number22
DOIs
StatePublished - Jun 8 2004

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Percutaneous Coronary Intervention
Aptitude
Logistic Models
Databases
Unstable Angina
Sample Size
Diabetes Mellitus
Smoking
tranilast

Keywords

  • Angioplasty
  • Coronary artery disease
  • Restenosis

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)

Cite this

Clinical and angiographic predictors of restenosis after percutaneous coronary intervention : Insights from the Prevention of Restenosis with Tranilast and Its Outcomes (PRESTO) trial. / Singh, Mandeep; Gersh, Bernard J.; McClelland, Robyn L.; Ho, Kalon K.L.; Willerson, James T.; Penny, William F.; Holmes, David.

In: Circulation, Vol. 109, No. 22, 08.06.2004, p. 2727-2731.

Research output: Contribution to journalArticle

Singh, Mandeep ; Gersh, Bernard J. ; McClelland, Robyn L. ; Ho, Kalon K.L. ; Willerson, James T. ; Penny, William F. ; Holmes, David. / Clinical and angiographic predictors of restenosis after percutaneous coronary intervention : Insights from the Prevention of Restenosis with Tranilast and Its Outcomes (PRESTO) trial. In: Circulation. 2004 ; Vol. 109, No. 22. pp. 2727-2731.
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T2 - Insights from the Prevention of Restenosis with Tranilast and Its Outcomes (PRESTO) trial

AU - Singh, Mandeep

AU - Gersh, Bernard J.

AU - McClelland, Robyn L.

AU - Ho, Kalon K.L.

AU - Willerson, James T.

AU - Penny, William F.

AU - Holmes, David

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N2 - Background - Restenosis prediction from published studies is hampered by inadequate sample size and incomplete angiographic follow-up. The prediction of restenosis with the existing variables is poor. The aim of the present study was to include the clinical and angiographic variables commonly associated with angiographic restenosis and develop a prediction model for restenosis from the PRESTO database. Methods and Results - This study included 1312 patients with a single lesion enrolled in the angiographic substudy of the PRESTO trial. We constructed 2 risk scores. The first used preprocedural variables (female gender, vessel size [≤2.5 mm, 2.5 to 3 mm, 3 to 3.5 mm, 3.5 to 4 mm, >4 mm], lesion length >20 mm, diabetes, smoking status, type C lesion, any previous percutaneous coronary intervention [PCI], and unstable angina) derived from previous studies. Estimated restenosis rates and corresponding variability for each possible level of the resultant risk score were obtained via bootstrapping techniques. The area under the receiver-operator characteristic (ROC) curve was 0.63, indicating modest discriminatory ability to predict restenosis. The second approach constructed a multiple logistic regression model considering significant univariate clinical and angiographic predictors of restenosis identified from the PRESTO database (treated diabetes mellitus, nonsmoker, vessel size, lesion length, American College of Cardiology/American Heart Association type C lesion, ostial location, and previous PCI). The area under the ROC curve for this risk score was also 0.63. Conclusions - The preprocedural clinical and angiographic variables from available studies and from the PRESTO trial have only modest predictive ability for restenosis after PCI.

AB - Background - Restenosis prediction from published studies is hampered by inadequate sample size and incomplete angiographic follow-up. The prediction of restenosis with the existing variables is poor. The aim of the present study was to include the clinical and angiographic variables commonly associated with angiographic restenosis and develop a prediction model for restenosis from the PRESTO database. Methods and Results - This study included 1312 patients with a single lesion enrolled in the angiographic substudy of the PRESTO trial. We constructed 2 risk scores. The first used preprocedural variables (female gender, vessel size [≤2.5 mm, 2.5 to 3 mm, 3 to 3.5 mm, 3.5 to 4 mm, >4 mm], lesion length >20 mm, diabetes, smoking status, type C lesion, any previous percutaneous coronary intervention [PCI], and unstable angina) derived from previous studies. Estimated restenosis rates and corresponding variability for each possible level of the resultant risk score were obtained via bootstrapping techniques. The area under the receiver-operator characteristic (ROC) curve was 0.63, indicating modest discriminatory ability to predict restenosis. The second approach constructed a multiple logistic regression model considering significant univariate clinical and angiographic predictors of restenosis identified from the PRESTO database (treated diabetes mellitus, nonsmoker, vessel size, lesion length, American College of Cardiology/American Heart Association type C lesion, ostial location, and previous PCI). The area under the ROC curve for this risk score was also 0.63. Conclusions - The preprocedural clinical and angiographic variables from available studies and from the PRESTO trial have only modest predictive ability for restenosis after PCI.

KW - Angioplasty

KW - Coronary artery disease

KW - Restenosis

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