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J Thorac Cardiovasc Surg 2001;121:894-901
© 2001 The American Association for Thoracic Surgery

Surgery for Acquired Cardiovascular Disease

Prediction of coronary artery disease in patients undergoing operations for mitral valve degeneration

From the Department of Cardiology,^a the Department of Biostatistics and Epidemiology Research Institute,^b and the Department of Thoracic and Cardiovascular Surgery,^c The Cleveland Clinic Foundation, Cleveland, Ohio.

Supported by grant NCC 9-60, National Aeronautics and Space Administration, Houston, Tex (J.D.T.); grant 9804606, American Heart Association, Northeast Ohio Affiliate, Cleveland, Ohio (S.S.L., M.J.G.).

This study was presented in part at Forty-eighth Annual Scientific Sessions of the American College of Cardiology, March 9, 1999, New Orleans, La.

Received for publication April 3, 2000. Revisions requested July 21, 2000; revisions received Oct 4, 2000. Accepted for publication Oct 20, 2000. Address for reprints: Mario J. Garcia, MD, The Cleveland Clinic Foundation, Desk F-15, 9500 Euclid Ave, Cleveland, OH 44195 (E-mail: garciam{at}ccf.org).

	Abstract

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

 
Objectives: We sought to develop and validate a model that estimates the risk of obstructive coronary artery disease in patients undergoing operations for mitral valve degeneration and to demonstrate its potential clinical utility.
Methods: A total of 722 patients (67% men; age, 61 ± 12 years) without a history of myocardial infarction, ischemic electrocardiographic changes, or angina who underwent routine coronary angiography before mitral valve prolapse operations between 1989 and 1996 were analyzed. A bootstrap-validated logistic regression model on the basis of clinical risk factors was developed to identify low-risk (5%) patients. Obstructive coronary atherosclerosis was defined as 50% or more luminal narrowing in one or more major epicardial vessels, as determined by means of coronary angiography.
Results: One hundred thirty-nine (19%) patients had obstructive coronary atherosclerosis. Independent predictors of coronary artery disease include age, male sex, hypertension, diabetes mellitus,and hyperlipidemia. Two hundred twenty patients were designated as low risk according to the logistic model. Of these patients, only 3 (1.3%) had single-vessel disease, and none had multivessel disease. The model showed good discrimination, with an area under the receiver-operating characteristic curve of 0.84. Cost analysis indicated that application of this model could safely eliminate 30% of coronary angiograms, corresponding to cost savings of $430,000 per 1000 patients without missing any case of high-risk coronary artery disease.
Conclusion: A model with standard clinical predictors can reliably estimate the prevalence of obstructive coronary atherosclerosis in patients undergoing mitral valve prolapse operations. This model can identify low-risk patients in whom routine preoperative angiography may be safely avoided.

	Introduction

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

 
Although a substantial number of patients undergo valvular operations in the United States each year (>200,000 since 1990), there is no validated approach for routine evaluation of possible obstructive coronary artery disease (CAD) in this population. Current American College of Cardiology/American Heart Association practice guidelines for presurgical coronary angiography include the following: male patients 35 years of age or older; female patients who are postmenopausal or premenopausal and 35 years of age or older with coronary risk factors; and patients with chest pain, objective evidence of ischemia, one or more risk factors for CAD, previous CAD, or decreased left ventricular systolic function. ¹ Considering that the estimated mean ages of patients who underwent mitral and aortic valve operations in the United States between 1990 and 1996 were 64 and 68 years of age, respectively, ² with more than half of 31,000 patients undergoing mitral valve operations not requiring concurrent coronary artery bypass grafting, these guidelines recommend coronary angiography for the vast majority who ultimately will not undergo revascularization. Although coronary angiography is generally a low-risk procedure, a validated model for preoperative coronary angiography may be helpful by eliminating the cost and morbidity of unnecessary studies.

The present study was undertaken to (1) construct and validate a model to estimate the risk of obstructive CAD on the basis of clinical predictors in a large series of patients undergoing operations for degenerative mitral valve disease, (2) propose an algorithm for coronary angiography before the operations in these patients, and (3) compare the use of this algorithm with current practice guidelines.

	Methods

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

 
Patient selection
We identified 1178 consecutive patients from the Cleveland Clinic Foundation Cardiovascular Information Registry ³ who underwent operations for degenerative mitral valve disease (998 myocardial mitral valve repairs and 180 replacements) between 1989 and 1996. This prospectively collected and validated database provides comprehensive descriptions of clinical and surgical characteristics of all patients undergoing cardiac operations at the institution since 1971. One hundred three patients who did not undergo coronary angiography at the discretion of the attending staff cardiologist were excluded. In addition, 353 patients with previous myocardial infarction (n = 158), ischemic electrocardiographic changes (n = 99), angina (n = 195), or some combination thereof were excluded from analysis because these patients clearly were reasonable candidates for preoperative coronary angiography. Thus, 722 patients were included in our study.

Clinical data
Angina was limited strictly to symptoms with at least two of the following characteristics: (1) substernal chest discomfort with characteristic quality and duration that is (2) provoked by exertion or emotional stress and (3) relieved by rest or nitroglycerin. ⁴ Ischemic electrocardiographic changes were strictly defined as the presence of diagnostic Q waves or abnormal baseline ST segment changes of 0.1 mV or greater consistent with ischemia unrelated to metabolic abnormalities, drug effects, conduction disturbances, or ventricular hypertrophy. Previous myocardial infarction was defined by means of clinical history, diagnostic Q waves, or thinned akinetic segments on resting echocardiography.

Risk factor selection
Established CAD risk factors, including age, sex, diabetes mellitus, smoking, family history of CAD, hypertension, and hyperlipidemia, were recorded systematically according to standard definitions elucidated in detail elsewhere. ⁵ In addition, abnormal left ventricular function defined as an ejection fraction of 50% or less by means of echocardiography or contrast ventriculography and New York Heart Association class III or IV were also designated a priori for analyses. The presence of obstructive CAD was defined as 50% or greater luminal narrowing in one or more major epicardial vessels by means of coronary angiography.

Statistical analysis

Model development and validation
All designated clinical variables were initially entered into univariable analyses. Predictors of obstructive CAD in the univariable analyses were entered into a forward stepwise multivariable logistic regression model. ⁶ All variables except age were analyzed categorically. Only independent predictors (P < .05) were included in the multivariable model:
Model score (Ln OR) = + x₁ß₁ + x₂ß₂ + x₃ß₃ + x₄ß₄ + ...... + x_kß_k
in which Ln OR is the natural logarithm of the odds ratio; is a constant; x₁,...., and x_k are independent predictors; and ß₁,...., and ß_k represent respective parameter coefficients. A model score based on the sum of parameter coefficient variable products and the term in the model was calculated to identify patients with 5% or lower predicted risk of CAD. On the basis of the regression equation, the odds ratio is related to risk of obstructive CAD by the following equation:
Risk of CAD = e^OR/(e^OR + 1)

A model score of 2.95 corresponds to approximately 5% risk of CAD. This subgroup is defined as having a low risk of CAD.

The multivariable logistic model was validated and further refined by using the bootstrap technique. ⁷ By using this computer-intensive approach, 1000 random resamplings and respective multivariable analyses were performed to assess the stability of odds ratio estimates with sampling variation. The bootstrap-validated 95% confidence interval was defined by the lower 2.5 and upper 97.5 percentile values and was compared with original logistic model estimates.

Model validity was examined by assessing model discrimination and precision. Discrimination reflects the model's ability to distinguish patients with CAD from those without CAD. Receiver-operating characteristic curves were used to compare discrimination of the original and bootstrap models. ^7,8 Precision refers to how closely predicted fit observed outcomes; this was assessed by comparing the predicted versus observed prevalence of CAD according to deciles of predicted risk and quantified by the Hosmer-Lemeslow goodness-of-fit statistic, the coefficient of determination (r₃) with the Spearman rank correlation, and the Brier score.

Comparison with current practice guidelines
Use of the bootstrap model was compared with class I indications from current American College of Cardiology/American Heart Association Practice Guidelines for the Management of Patients with Valvular Heart Disease. ¹ Cost analysis was performed on the basis of 1998 Medicare reimbursement of outpatient coronary angiography ($1424) for the Cleveland area. Cost per patient with obstructive CAD identified was calculated for each strategy relative to the study population.

Continuous variables are expressed as mean values ± SD. Categoric variables are expressed as frequencies, percentage, or both. All statistical analyses were performed with SAS 6.12 software (SAS Inc, Cary, NC).

	Results

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

 
Clinical characteristics
Clinical characteristics of the study population are summarized in Table I. One hundred thirty-nine (19%) patients in the study population had obstructive CAD. Sex-specific prevalence of CAD relative to the unadjusted age in the study cohort is summarized in Fig 1. Both prevalence curves increased in a curvilinear fashion with age, although there was delayed onset of CAD of greater than 10 years in female patients. Of note, the prevalence of obstructive CAD in both male patients 35 years of age or female patients of perimenopausal age range was extremely low.

View larger version (16K): [in this window] [in a new window]   Fig 1. Sex-specific correlation of unadjusted age versus the prevalence of CAD in 772 patients who underwent coronary angiography before operations for degenerative mitral valve disease.

Model development and validation
Table II shows the univariable association between obstructive CAD and the prespecified factors evaluated as potential predictors. All variables, except for abnormal left ventricular function, functional class, and family history, were predictors in univariable analyses. Male sex, age, diabetes mellitus, hyperlipidemia, and hypertension were also predictors in the logistic multivariable analysis and further validated as predictive in the bootstrap validated model as follows:
Model score (Ln OR) = (0.105 x Age) + (1.177 x Male sex) + (1.475 x Diabetes mellitus) + (1.750 x Hyperlipidemia) + (0.483 x Hypertension) – 10.070.

View larger version (15K): [in this window] [in a new window]   Fig 2. Receiver-operating characteristic (ROC) curves from the original and the bootstrap models.

View larger version (28K): [in this window] [in a new window]   Fig 3. Comparison of the observed versus predicted prevalence of CAD according to deciles of predicted risk. r3, Coefficient of determination. Range of probabilities: decile 1, 0.0001-0.014; decile 2, 0.014-0.029; decile 3, 0.029-0.049; decile 4, 0.050-0.074; decile 5, 0.075-0.108; decile 6, 0.108-0.163; decile 7, 0.165-0.229; decile 8, 0.231-0.334; decile 9, 0.343-0.508; and decile 10, 0.525-0.901.

View larger version (18K): [in this window] [in a new window]   Fig 4. Proposed algorithm to risk stratify patients undergoing operations for degenerative mitral valve disease operations. MI, Myocardial infarction; ECG, electrocardiogram.

	Discussion

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

Several previous studies have developed purely clinical models to predict the risk of obstructive angiographic CAD in symptomatic patients with chest pain. ^9-14 Diamond and Forrester ¹³ proposed a rule to predict CAD on the basis of age, sex, and nature of chest pain. Subsequently, Pryor and associates ¹⁰ developed and validated a model based on established risk factor profiles, chest pain characteristics, and electrocardiographic criteria. Application of these models to patients with degenerative mitral valve disease may be constrained by differences in baseline characteristics. Patients with typical or atypical angina were excluded from this analysis, whereas previous studies consisted primarily of patients who presented for evaluation of chest pain. The prevalence of CAD in previous models ranged between 60% and 83%, ^8,9,11-13 which was roughly 3 to 4 times that found in our study. The strength of prediction and respective relationships between predictors derived in previous analyses may be lost in a population with a lower prevalence and lesser severity of CAD.

Predictors of CAD in patients undergoing operations for degenerative mitral valve disease
Previous surgical series have suggested that age of 40 years or greater was a reliable predictor of CAD in patients undergoing valve operations. ^15,16 Chest pain has also been reported as a predictor in several studies. ^15,17-21 In this low-prevalence cohort free of clinical and electrocardiographic evidence of CAD, the risk for obstructive CAD was above low levels (>5%) beyond 56 and 68 years of age for male and female patients, respectively, independent of other predictors. Likewise, family history and smoking were not predictive, although other commonly recognized risk factors, including male sex, hyperlipidemia, hypertension, and diabetes mellitus, remained predictive in the bootstrap model.

Revascularization of CAD during valvular operations
Whether a selective catheterization approach based on risk stratification adversely affects surgical outcome is unclear. Although the presence of coexisting CAD has been clearly linked to a worse prognosis in patients undergoing valvular operations, ^22-25 the incremental benefit from concurrent revascularization in patients with nonsurgical disease is not definitive. ^22,26,27 Although the current practice is to revascularize coexisting CAD in these patients, randomized trials in nonvalve populations have revealed that only patients with multivessel CAD in the presence of proximal left anterior descending artery disease, left ventricular dysfunction, or left main trunk disease derived survival benefit from surgical revascularization. ^28-30 In addition, revascularization of flow-limiting CAD may not prevent myocardial infarction. Angiographic studies have documented that plaque composition and morphology, rather than severity of luminal narrowing, are the major determinants of subsequent acute coronary syndromes. ^31-33 Alternatively, coronary bypass of non-flow-limiting lesions may lead to accelerated venous bypass graft closure ³⁴ or proximal native vessel atherosclerosis. ³⁵ Finally, despite advances in surgical techniques and myocardial protection, ^36,37 combined myocardial revascularization and valvular operations are still associated with a higher operative mortality compared with isolated valvular operations. ²

Comparisons between current guidelines and the model
Although both strategies maintained relatively high negative predictive values for the prediction of CAD, an approach with greater discrimination and cost reduction is an attractive alternative. Application of the derived model would have eliminated nearly one third of 722 angiographic studies, without missing any patients with high-risk CAD. The accuracy was more than 2-fold higher with this approach versus that with existing guidelines, which corresponded to a substantial cost reduction approaching 25% on the basis of conservative measures of cost.

Limitations
This model was developed and validated in patients undergoing operations for degenerative mitral valve disease. For that reason, the size of our study group was limited, and the observed prevalence of obstructive coronary artery disease was low. Although its findings are potentially applicable to those of other valvular populations, given the variability of baseline characteristics among different valvular lesions, extrapolation should be avoided until further validation. Patients in our study who underwent concomitant tricuspid valve operations, aortic valve operations, or both were few and had these listed as secondary conditions. Similar results were obtained, however, when we repeated analysis after excluding those patients. Second, our definition of low risk is arbitrary and not universally accepted. We arrived at a cutoff value of less than 5% on the basis of (1) optimal positive and negative predictive values calculated for cutoffs of 1%, 5%, and 10% and (2) minimal acceptance level agreed on by a group of practicing cardiologists and cardiothoracic surgeons. Nevertheless, by using this cutoff point, not even one patient with high-risk CAD (ie, left main trunk, proximal left anterior descending, or multivessel disease) was missed. Furthermore, the bootstrap validated model allows the clinician to quantify the risk of obstructive CAD by using routine information gathered preoperatively and recommend coronary angiography on the basis of individual definition of low risk. Nevertheless, further validation of our model in an independent patient population is warranted before recommending its widespread adoption in the clinical practice. Finally, despite the substantial reduction in the number of low-yield angiographic studies, there is still a sizeable cohort with intermediate risk who may benefit from additional noninvasive risk stratification (ie, exercise echocardiography). Future studies are required to explore this issue.

	Conclusions

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

 
A model based on routine clinical predictors gathered preoperatively provided reliable estimates of CAD risk in patients undergoing degenerative mitral valve disease operations. Considering the increasing constraint on health care resources, an evidence-based approach of using coronary angiography for patients who have an intermediate or high likelihood of CAD is an attractive and rational alternative. Greater diagnostic yield at a lower cost was achieved with the derived model than with current practice guidelines. Future large-scale prospective studies are required to confirm these results and determine their applicability to other types of nonischemic surgical heart disease.

	Appendix

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

 
Example 1
A 40-year-old man with severe mitral regurgitation presents for surgical evaluation. He is classified as being in functional class I and has hyperlipidemia.
LN (Multivariable odds ratio) = –10.070 + (0.105 x Age) + (1.177 x Male sex) + (1.475 x Diabetes mellitus) + (1.750 x Hyperlipidemia) + (0.483 x Hypertension)
= –10.070 + (0.105 x 40) + (1.177 x 1) + (1.750 x 1) = –2.35
Probability of CAD = e^(–2.35)/e^(–2.35) + 1 = 8.7%.

On the basis of the proposed algorithm, the patient is at higher than low risk and should undergo coronary angiography before mitral valve operations.

Example 2
A 60-year-old woman presents for surgical evaluation of severe mitral regurgitation. She has no clinical predictor for CAD.
LN (Multivariable odds ratio) = –10.070 + (0.105 x 60) = –3.77
Probability of coronary artery disease = e^(–3.77)/e^(–3.77) + 1 = 2.0%.

On the basis of the proposed algorithm, the patient does not require routine coronary angiography before mitral valve operations.

	References

Top Abstract Introduction Methods Results Discussion Conclusions Appendix References

 

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