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(Circulation. 2004;109:2727-2731.)
© 2004 American Heart Association, Inc.

Clinical Investigation and Reports

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, MD; Bernard J. Gersh, MB, ChB, DPhil; Robyn L. McClelland, PhD; Kalon K.L. Ho, MD, MSc; James T. Willerson, MD; William F. Penny, MD; David R. Holmes, Jr, MD

From the Division of Internal Medicine and Cardiovascular Diseases (M.S., B.J.G., D.R.H.) and Division of Biostatistics (R.L.M.), Mayo Clinic and Mayo Foundation, Rochester, Minn; Harvard Clinical Research Institute, Boston, Mass (K.K.L.H.); the Texas Heart Institute, Houston, Tex (J.T.W.); and the University of California, San Diego, Calif (W.F.P.).

Correspondence to Dr David R. Holmes, Jr, Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, 200, 2nd St SW, Rochester, MN 55905. E-mail holmes.david{at}mayo.edu

Received November 5, 2003; revision received February 26, 2004; accepted March 15, 2004.

	Abstract

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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.

Key Words: restenosis • angioplasty • coronary artery disease

	Introduction

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Knowledge of risk factors for restenosis may help to refine indications of percutaneous coronary intervention (PCI) and to guide strategies aimed at reducing the frequency of restenosis, including selection of optimal candidates for drug-eluting stents. Many clinical, angiographic, and procedural variables have been studied as predictors for restenosis.^1–7 Most of these studies from which the variables were derived had smaller sample size, retrospective analyses, and different definitions of restenosis. The predictive accuracy of most of the available studies is relatively poor.^2,7 In addition, nonuniformity of variables in these studies complicated restenosis prediction. The merit of the models for restenosis would be enhanced if the prediction of restenosis were made before an intervention is performed.⁶ With this background, our goal was to study the predictors of restenosis in the largest trial performed to date using recent PCI techniques. The purpose of the present analysis was to identify simple clinical and angiographic variables that are historically correlated with risk of restenosis and use their observed values in the Prevention of Restenosis With Tranilast and Its Outcomes (PRESTO) trial to construct a simple risk score for restenosis prediction.

	Methods

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The PRESTO trial was designed to evaluate the effects of tranilast, an oral antiinflammatory agent, on major adverse cardiovascular events (MACE), angiographic, and intravascular (IVUS) end points.⁸ The PRESTO trial has been described previously.⁸ In brief, it was a double-blind, placebo-controlled, parallel group study of patients after PCI. The primary end point was the first occurrence of MACE within 9 months, defined as death, myocardial infarction (MI), and/or ischemia-driven target vessel revascularization (TVR). Restenosis was defined as 50% stenosis in the treated segment at follow-up or at least 50% loss of the original gain in the minimal luminal diameter (MLD). The type of intervention performed was at the investigator’s discretion, with the exclusion of intracoronary radiation.

The present study included all patients with a single lesion enrolled in the prespecified angiographic substudy of the PRESTO trial. Restriction to a single lesion was necessary to associate restenosis with a particular target lesion and hence use lesion-specific characteristics in the analysis. Patients enrolled in the angiographic substudy were required to undergo follow-up angiography at 9 months, or sooner if clinically warranted. Quantitative and qualitative angiography assessments were performed by the core laboratories. Angiograms for quantitative coronary angiography (QCA) were done with the cardiovascular measurement system (Medis Medical Imaging Systems). Each patient received 100 to 200 µg of intracoronary nitroglycerin before all required films. Qualitative assessment for all patients was performed by the investigators as well.

Statistical Analyses
Summary data are expressed as the mean value±SD or as a percentage. Unadjusted comparisons between those who developed restenosis within 9 months and those who did not were performed with ² tests for categorical patient or lesion characteristics and 2-sample t tests for continuous variables.

In the first approach, we included variables that are simple to assess and have frequently been reported to be strong predictors of restenosis risk. The goal of this approach was to construct a simple scoring system that could be used to assess preprocedural risk of restenosis. On the basis of a review of the literature, the following factors were considered as predictors: 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 PCI, and unstable angina.^4,7,9,10 These variables were forced into a logistic regression model. Risk score points (0 to 15+) were assigned on the basis of the coefficients in this model on the linear scale. Estimated restenosis rates and corresponding variability for each possible level of the resultant risk score were obtained via bootstrapping techniques. One thousand bootstrap samples of 1312 patients were drawn with replacement from the original data set. Rates of restenosis for risk score groups were tabulated each time, and the empirical distribution of these estimates was used to obtain the risk and variability estimates. Area under the receiver-operator characteristic (ROC) curve is presented for the risk score.

The second approach was more data driven and provides an internal benchmark against which to compare the predictive ability of the risk-score approach as described above. A multiple logistic regression model was constructed with the following variables considered as candidates: gender, hypertension, treated diabetes mellitus (oral agent or insulin), smoking status, previous coronary artery bypass surgery, previous PCI, previous endarterectomy, history of congestive heart failure (CHF), previous angina, current unstable angina, history of acute coronary syndromes, use of glycoprotein IIb/IIIa inhibitors, nonsteroidal analgesics, angiotensin receptor blockers, American College of Cardiology/American Heart Association (ACC/AHA) type C lesion, preprocedural MLD, and lesion characteristics (vessel size >3 mm, length [<10 mm, 10 to 20 mm, >20 mm], bifurcation, ostial, in-stent restenosis, restenotic, tortuous). Models were constructed using stepwise variable selection; forward selection and backward elimination were found to yield identical results. A probability value of P<0.05 was required for entry into the model and P>0.10 for elimination. No effort was made to construct a simple score out of this model. The area under the ROC curve was calculated on the basis of the estimated risks from the final model.

	Results

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A total of 11 484 patients were enrolled in the PRESTO trial. Informed consent for repeat angiograms was obtained from 2682 patients, and follow-up was terminated when 2018 patient follow-up angiograms were submitted to the core laboratory. We analyzed 1312 single-lesion patients (82% [n=1070] received stents) with complete baseline, procedural, and follow-up angiographic information.

Patient Characteristics
Table 1 compares the baseline characteristics of patients who subsequently developed restenosis (n=601) with those who did not (n=711). Restenosis rates were higher in patients with hypertension, treated diabetes mellitus, previous revascularization or CHF, and current unstable angina. Patients on nonsteroidal analgesics, abciximab, or angiotensin receptor blockers also had higher rates of restenosis. Current smokers had lower restenosis rates. Among the angiographic characteristics (Table 2), lesion length (>20 mm), restenotic lesions, lesions with in-stent restenosis, ostial lesions, and ACC/AHA type C lesions were associated with higher frequency of restenosis. The QCA data demonstrated that smaller reference vessel diameter was associated with higher rates of restenosis (Table 3).

View this table: [in this window] [in a new window]   TABLE 1. Baseline Clinical Characteristics for Single-Lesion Patients

View this table: [in this window] [in a new window]   TABLE 2. Lesion Characteristics for Single-Lesion Patients

View this table: [in this window] [in a new window]   TABLE 3. Lesion Characteristics for Single-Lesion Patients (QCA Data)

Table 4 shows the variables found to be predictors of significant restenosis from previous studies. The integer score (points) assigned to a variable were approximately proportional to the estimated continuous coefficient from the logistic model. Using this risk score, 4 subgroups were constructed with risk score of 0 to 2, 3 to 7, 8 to 14, and 15 (Table 5). The risk score of 0 to 2 (n=57) was associated with 28% (95% CI, 16% to 40%) restenosis, 3 to 7 (n=807) with 40% (95% CI, 37% to 43%), 8 to 14 (n=349) with 63% (95% CI, 58% to 68%), and risk score of 15 (n=11) was associated with 91% (95% CI, 74% to 100%) risk of restenosis. The area under the ROC curve was 0.63, indicating a modest discriminatory ability of the model. The data did not deviate significantly from the logistic model, as indicated by the nonsignificant Hosmer-Lemeshow goodness-of-fit test (P=0.18). Likewise, the Figure plots observed and predicted risks as assessed by the risk scores, and the graph indicates that the model fitted the data well and correctly ranked patient risks.

View this table: [in this window] [in a new window]   TABLE 4. Risk Score Model Derived From Variables Chosen From Previously Published Studies

View this table: [in this window] [in a new window]   TABLE 5. Summary Lookup Table For Risk

View larger version (21K): [in this window] [in a new window]   Observed and predicted risk by risk score.

On the basis of the significant univariate clinical and angiographic variables from the PRESTO trial, we constructed a multivariate model for predictors associated with significant restenosis. We identified treated diabetes mellitus (OR=1.45 [1.06 to 1.98], P=0.02), nonsmoker (OR=1.34 [1.01 to 1.76], P=0.04), vessel size <3 mm versus >3 mm (OR=1.32 [1.04 to 1.68], P=0.02), length of the lesion (>20 mm versus <10 mm, OR=2.36 [1.49 to 3.75], P=0.0003; 10 to 20 versus <10, OR=1.17 [0.91 to 1.50], P=0.22), ostial lesion (OR=1.82 [1.13 to 2.9], P=0.013), and previous PCI (OR=1.41 [1.09 to 1.81], P=0.008) as significant predictors of restenosis. The assigned treatment to tranilast had no effect on angiographic restenosis. The area under the ROC curve was 0.63, indicating only a modest ability of the model to discriminate between patients who developed restenosis and those who did not.

	Discussion

Top Abstract Introduction Methods Results Discussion Conclusions References

 
In the PRESTO study, the largest study designed to prevent restenosis, several clinical and angiographic predictors of coronary restenosis were identified, namely, treated diabetes mellitus, previous PCI, ACC/AHA type C lesion, ostial lesion, and lesion length >20 mm. The prediction model, including clinical and angiographic variables derived from previous restenosis prediction studies, fared as well as a more complex model derived from the PRESTO trial. However, the discriminatory accuracies for restenosis prediction of both these models were low and similar to each other.

Clinical and Angiographic Predictors of Restenosis
Coronary interventions continue to evolve, and it remains important to stratify patients who would derive the most short- and long-term benefit. Although most of the studies on prediction of restenosis antedate the use of stents, there has been no change in the clinical variables predicting restenosis.^3,5,7,11 Diabetes mellitus continues to be a strong clinical predictor of restenosis.^4,12 In the present study, patients with treated diabetes mellitus had a 45% higher risk of restenosis compared with nondiabetics. Conversely, current smokers have less restenosis. This smoker’s paradox has been well described.^9,10,13 The correlation of small vessel size, complex type C lesions, longer length of the lesion, ostial location, and previous angioplasty with risk of restenosis also has been documented.^14–16

Prediction of Restenosis
A primary aim of a study in the present era would be to identify patients at the highest risk for restenosis. This would help in the optimal use of costly drug-eluting stents. The predictive accuracy for restenosis of the previous published studies was poor to modest.^2,4,7 In an analysis of 9120 treated lesions in 8156 patients chosen from a pooled 19 different studies, there was poor ability for clinical variables to discriminate between patients who did and did not develop restenosis (area under the ROC curve=0.51).⁷ It improved slightly with the addition of angiographic and procedural variables (area under the ROC curve=0.63). Quantitative angiographic analysis by Kastrati et al¹¹ in 1349 patients revealed the strongest multivariate predictors for in-stent restenosis to be diabetes mellitus, placement of multiple stents, and poststent MLD <3 mm. This study was performed in the initial stent era; it had incomplete angiographic follow-up and used different poststent antithrombotic regimens.

The present study is the largest in the current era with restenosis as one of the major end points. The predictive accuracy for restenosis was modest, with an area under the ROC curve of 0.63. We initially chose a model that was simple and could be used to assess the patient’s risk of restenosis. The risk score derived from variables chosen from previous published studies could not differentiate low from intermediate risk for restenosis. Moreover, even with almost no risk factors for restenosis, the restenosis rates were 28% (95% CI, 16% to 40%). Conversely, patients with multiple adverse clinical and angiographic variables placed the patient at a very high risk of restenosis, which may be helpful in risk stratifying very-high-risk patients for subsequent restenosis.

The inclusion of patients with previous PCI and patients with in-stent restenosis may increase the restenosis rates even in patients without any clinical and angiographic predictors of restenosis. We did not include any post-PCI angiographic variables that are known to influence the prediction of restenosis. In a study by Mercado et al,⁷ there was only modest gain after inclusion of angiographic and post-PCI variables. In the other 2 studies antedating the present era, there was poor correlation of preprocedural variables with risk of subsequent restenosis.^4,17 The aim of the present study, however, was to predict restenosis solely on the basis of preprocedural clinical and angiographic variables. The low predictive accuracy of the 2 models constructed in the present study is most likely because of noninclusion of postprocedural variables, stent design, and various other genetic, infection, inflammatory, and some unknown factors that can influence restenosis.^14,18,19

Study Limitations
This large prospective study lacked the predictive accuracy to differentiate patients who did and who did not develop restenosis. Solely on the basis of clinical and angiographic variables, we are not able to predetermine the risk of angiographic restenosis. We cannot extrapolate the results of this study to multiple lesions, because we by necessity confined our analysis to single lesions. Although this study is one of the largest and is representative of recent practice, many important subsets, namely, vein grafts, bifurcation, and left main disease, which are known to have higher restenosis, were underrepresented. Also, other features believed to be associated with higher risk of restenosis, eg, chronic total occlusion, and patients with chronic renal failure were underrepresented. Thus, these features could not be evaluated in the present study. The PRESTO trial did not include patients who received intracoronary brachytherapy or drug-eluting stents, and therefore, our analysis cannot be extrapolated to this subset. The angiographic study was prespecified in the trial; however, we cannot totally exclude withdrawal bias.²⁰

	Conclusions

Top Abstract Introduction Methods Results Discussion Conclusions References

 
The present study demonstrates the clinical and angiographic variables associated with risk of restenosis after PCI in a large data set representing recent practice. The predictive accuracy of the model from the commonly identified variables, and also chosen from the trial, is only modest. The relatively low predictive accuracy of the models in this analysis is at first disappointing, given the multiple variables used and the large sample size of the study. Conversely, the results perhaps should not come as a surprise in light of the complexity of the phenomenon of restenosis. Restenosis is a multifactorial biological process involving not only patient and lesion characteristics but also vessel-wall features, blood-borne components, systemic risk factors, and probably a genetic susceptibility. From the perspective of an individual patient, for the present, the prediction of restenosis can be made only in general terms.

	Footnotes

 
Guest Editor for this article was David P. Faxon, MD, University of Chicago, Ill.

	References

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This Article Abstract Full Text (PDF) All Versions of this Article: 109/22/2727    most recent 01.CIR.0000131898.18849.65v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Singh, M. Articles by Holmes, D. R. Search for Related Content PubMed PubMed Citation Articles by Singh, M. Articles by Holmes, D. R., Jr Related Collections Restenosis Catheter-based coronary interventions: stents Chronic ischemic heart disease