Can restenosis after coronary angioplasty be predicted from clinical variables?

• Published in: Journal of the American College of Cardiology Vol. 21; no. 1; pp. 6 - 14
• Main Authors: Weintraub, William S., Kosinski, Andrzej S., Brown, Charles L., King, Spencer B.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01-01-1993; Elsevier Science
• Subjects: Angioplasty, Balloon, Coronary - statistics & numerical data; Biological and medical sciences; Cardiology. Vascular system; Chi-Square Distribution; Coronary Angiography; Coronary Disease - diagnostic imaging; Coronary Disease - epidemiology; Coronary Disease - therapy; Coronary heart disease; Female; Georgia - epidemiology; Heart; Humans; Male; Medical sciences; Middle Aged; Prognosis; Prospective Studies; Recurrence; Regression Analysis; Risk Factors; ROC Curve; Vascular disease; Human; Restenosis; Arteriopathy; Coronary artery; Risk factor; Stenosis; Cardiovascular disease; Instrumental dilatation; Coronary heart disease; Percutaneous route
• ISSN: 0735-1097; 1558-3597
• DOI: 10.1016/0735-1097(93)90711-9

Objectives. The purpose of this study was to determine whether variables shown to correlate with restenosis in one group (learning group) could be shown to predict recurrent stenosis in a second group (validation group).
Background. Restenosis remains a critical limitation after percutaneous transluminal coronary angioplasty. Although several clinical variables have been shown to correlate with restenosis, there are few data concerning attempts to predict recurrent stenosis.
Methods. The source of data was the clinical data bese at Emory University. Patients who had had previous coronary surgery and patients who underwent coronary angioplasty in the setting of acute myocardial Infarction were excluded. A total of 4,006 patients with angiographic restudy after successful angioplisty were identified. They were classified into a learning group of 2,500 patients and a validation group of 1,506 patients. The correlates of restenosis in the learning group were determined by stepwise logistic regression, and a model was developed to predict the probability of restenosis and was tested in the validation group. By using various cut points for the predicted probability of restenosis, a receiver operating characteristic curve was created. Goodness of fit of the model was evaluated by comparing average predicted probabilities with average observed probabilities within subgroups on the basis of risk level determined by linear regression analysis.
Results. In the learning group 1,145 patients had restenosis and 1,355 did not. Correlates of restenosis were severe angina, severe diameter stenosis before angioplasty, left anterior descending coronary artery dilation, diabetes, greater diameter stenosis after angioplasty, hypertension, absence of an intimal tear, eccentric morphology and older patient age. The model derived from the learing group was used to predict restenosis in the validation group. By varying the cut point for the predicted probability of restenosis above which restenosis is diagnosed and below which it is not, a receiver operating characteristic curve was created. The curve was close to the line of identity, reflecting a poor predictive ability. However, the model was shown to fit well with the predicted probability of restenosis correlating well with the observed probability (r = 0.98, p = 0.0001).
Conclusions. Clinical variables provide limited ability to predict definitively whether a particular patient will have restenosis. However, the current model may be used to predict the probability of restenosis, with some uncertainty, at least in well characterized patients who have already had angioplasy.