Computer probability estimates of angiographic coronary artery disease: transportability and comparison with cardiologists' estimates

VALUE OF SPECIFIC PARAMETERS AND INTEGRATIVE INDICES OF TREADMILL TEST FOR THE ASSESSMENT OF CORONARY STENOSIS SEVERITY

Aim.To assess the value of specific parameters and integrated indices (II; such as Duke Index (DI), Centre for Preventive Medicine Index (CPMI), and modified CPMI) of the treadmill test in the diagnostics of coronary stenosis severity among patients with stable coronary heart disease (CHD).

Material and methods.The study included all patients (260 permanent residents of Moscow City or Moscow Region) who were admitted to the State Research Centre for Preventive Medicine with the CHD diagnosis and who underwent coronary angiography (CAG) and treadmill test in the period between January 1st 2004 and December 31st 2007.

Results.There were statistically significant associations between the main treadmill test parameters and the severity of coronary artery (CA) atherosclerosis. The larger number of stenosis-affected CA was associated with a higher prevalence of chest pain and treadmill tests with positive results and ST segment depression >1 mm, as well as with a decreased total duration of treadmill test. Similarly, the increased risk, as assessed by treadmill test indices (DI, CPMI, and modified CPMI), was linked to an increased number of stenosis-affected CA. Modified CPMI demonstrated the highest diagnostic value for the assessment of coronary atherosclerosis severity.

Conclusion.The treadmill test parameters which demonstrated their diagnostic value for the assessment of CHD severity included the following: positive test results, retrosternal chest pain as the reason for test discontinuation, ST segment depression >1mm, and short total duration of the test. Overall, all II demonstrated their high value in CHD diagnostics. Modified CPMI was the most effective II in the assessment of CA atherosclerosis severity. 

A risk score for predicting coronary artery disease in women with angina pectoris and abnormal stress test finding

Women with angina pectoris and abnormal stress test findings commonly have no epicardial coronary artery disease (CAD) at catheterization. The aim of the present study was to develop a risk score to predict obstructive CAD in such patients. Data were analyzed from 337 consecutive women with angina pectoris and abnormal stress test findings who underwent cardiac catheterization at our center from 2003 to 2007. Forward selection multivariate logistic regression analysis was used to identify the independent predictors of CAD, defined by ≥50% diameter stenosis in ≥1 epicardial coronary artery. The independent predictors included age ≥55 years (odds ratio 2.3, 95% confidence interval 1.3 to 4.0), body mass index <30 kg/m(2) (odds ratio 1.9, 95% confidence interval 1.1 to 3.1), smoking (odds ratio 2.6, 95% confidence interval 1.4 to 4.8), low high-density lipoprotein cholesterol (odds ratio 2.9, 95% confidence interval 1.5 to 5.5), family history of premature CAD (odds ratio 2.4, 95% confidence interval 1.0 to 5.7), lateral abnormality on stress imaging (odds ratio 2.8, 95% confidence interval 1.5 to 5.5), and exercise capacity <5 metabolic equivalents (odds ratio 2.4, 95% confidence interval 1.1 to 5.6). Assigning each variable 1 point summed to constitute a risk score, a graded association between the score and prevalent CAD (ptrend <0.001). The risk score demonstrated good discrimination with a cross-validated c-statistic of 0.745 (95% confidence interval 0.70 to 0.79), and an optimized cutpoint of a score of ≤2 included 62% of the subjects and had a negative predictive value of 80%. In conclusion, a simple clinical risk score of 7 characteristics can help differentiate those more or less likely to have CAD among women with angina pectoris and abnormal stress test findings. This tool, if validated, could help to guide testing strategies in women with angina pectoris.

Observer bias in the interpretation of dobutamine stress echocardiography

BACKGROUND AND HYPOTHESIS

Blinded image analysis is typically utilized in published studies evaluating the accuracy of dobutamine stress echocardiography (DSE). However, in clinical settings, practical considerations may limit the use of blinded interpretations and thus the potential for observer bias arises. This study evaluated the relationships between clinical and blinded interpretations of DSE.

METHODS

Wall motion analysis from clinical and blinded DSE interpretations were compared and factors associated with their concordance were investigated in 115 consecutive patients with known or suspected coronary artery disease.

RESULTS

Clinical and blinded interpretations agreed on the presence or absence of inducible ischemia in 102 of 115 cases (88.7%: k = 0.76, p < 0.00001). In studies in which the clinical and blinded interpretations were in agreement, there was greater ST-segment depression (STD) in echocardiographically positive compared with negative studies (mean STD 0.73 +/- 0.65 vs. 0.42 +/- 0.67 mm; p = 0.008). In contrast, studies in which there was disagreement had significantly less ST-segment changes (mean STD 0.19 +/- 0.56 mm; p = 0.012) despite comparable results on blinded wall motion analysis. Multiple logistic regression for factors related to the results of clinical and blinded wall motion analysis disclosed that angina pectoris and ST-segment changes were related to clinical interpretations, whereas only angina pectoris was related to the findings on blinded analysis.

CONCLUSIONS

Clinical interpretations of echocardiographic images during DSE overall demonstrate good agreement with the results of blinded analysis. Ancillary testing data may influence the analysis of wall motion abnormalities, and thus the potential for observer bias exists unless these interpretations are performed blinded to other clinical data.

A comparison of treadmill scores to diagnose coronary artery disease

BACKGROUND

Recently, several treadmill scores have been proposed as means for improving the diagnostic accuracy of the exercise treadmill test (ETT). Questions remain regarding the diagnostic accuracy of treadmill scores when applied to a different patient population than that from which they were derived; furthermore, many treadmill scores have not been compared with one another in the same population.

HYPOTHESIS

The diagnostic accuracy of treadmill scores may not be the same.

METHODS

A retrospective analysis of data collected prospectively was performed on consecutive patients referred for evaluation of chest pain. All patients underwent a standard ETT followed by coronary angiography. Using angiographic evidence of coronary artery disease (CAD) as a reference, the area under the curve (AUC) of receiver operator characteristic (ROC) plots of the ST response alone, the Duke Treadmill Score (DTS), the Morise score, the Detrano score, the VA score, and a Consensus score consisting of the Morise, Detrano, and VA scores together were calculated and compared. The predictive accuracies of the DTS and the Consensus score to stratify patients for the likelihood of CAD were calculated and compared.

RESULTS

In all, 1,282 patients without a prior myocardial infarction had an ETT and coronary angiography. The AUC (+/- standard error) was 0.67+/-0.01 for the ST response, 0.73+/-0.01 for DTS, 0.76+/-0.01 for Detrano score, 0.77+/-0.01 for Morise score, 0.78+/-0.01 for VA score, and 0.78+/-0.01 for Consensus score. The AUC for each treadmill score was significantly higher (z-score > 1.96) than for the ST response alone. The AUC of DTS was significantly lower than all other treadmill scores (z-score > 1.96). The predictive accuracy (+/-95% confidence interval) of the DTS to risk stratify patients into high and low likelihood for CAD was 71 (65-77)%, versus 80 (74-86)% for the Consensus score (p < 0.0001).

CONCLUSION

In this population, the DTS remains useful for diagnosing CAD and stratifying for the likelihood of CAD, although it is less accurate than other treadmill scores.

What percent luminal stenosis should be used to define angiographic coronary artery disease for noninvasive test evaluation?

BACKGROUND

There has been controversy over what is the best angiographic luminal dimension criterion associated with ischemia for evaluating diagnostic tests. If one assumes that ST-segment depression or scores are indicators of ischemia, then whatever angiographic criteria best discriminates those with ischemic and nonischemic responses would be the best angiographic marker for ischemia. To study this, we calculated the area under the ROC curves for ST depression and scores at different angiographic cut-points in order to determine the best angiographic cut-point for defining ischemia-producing coronary disease.

METHODS

Twelve hundred and seventy-six consecutive males without prior MI with a mean age of 59 +/- 11 years who had undergone exercise testing and coronary angiography were analyzed in this study. We calculated the number of patients of this population that would be considered to have coronary artery disease at different cut-points for angiographic luminal stenosis. For example, 59% of the patients had significant CAD when disease was defined as 50% or greater coronary lumen stenosis of any coronary vessel while 49% of the patients had significant CAD when disease was defined as 70% or greater coronary lumen stenosis. Cut-points were considered between 40 to 100% coronary lumen stenosis. ROC analysis was then performed comparing ST depression and treadmill scores at each of these cut-points.

RESULTS

The cut-point for coronary lumen stenosis that returned the highest AUC for ST depression and scores was between 70 and 80% coronary luminal stenosis. However, the difference between the 50% and 75% luminal stenosis criteria was minimal.

CONCLUSION

It appears that the best cut-point for defining significant angiographic disease when evaluating diagnostic tests of ischemia is 75% or greater coronary luminal stenosis.

Exercise testing scores as an example of better decisions through science

INTRODUCTION

The application of common statistical techniques to clinical and exercise test data has the potential to become a useful tool for assisting in the diagnosis of coronary artery disease, assessing prognosis, and reducing the cost of evaluating patients with suspected coronary disease. Since general practitioners function as gatekeepers and decide which patients must be referred to the cardiologist, they need to optimally use the basic tools they have available (i.e., history, physical exam, and the exercise test).

METHODS

Review of the literature with a focus on the scientific techniques for aiding the decision-making process.

RESULTS

Scores derived from multivariable statistical techniques considering clinical and exercise data have demonstrated superior discriminating power when compared using receiver-operating-characteristic curves with the ST segment response. In addition, by stratifying patients as to probability of disease and prognosis, they provide a management strategy. While computers as part of information management systems can calculate complicated equations to provide scores, physicians are reluctant to trust them. Thus, these scores have been represented as nomograms or simple additive tables so physicians are comfortable with their application. Scores have also been compared with physician judgment and been found to estimate the presence of coronary disease and prognosis as well as expert cardiologists, and often better than nonspecialists.

CONCLUSION

Multivariate scores can empower the clinician to assure the cardiac patient with access to appropriate and cost-effective cardiological care.

Use of a simple clinical score to predict prognosis of patients with normal or mildly abnormal resting electrocardiographic findings undergoing evaluation for coronary artery disease

OBJECTIVE

To determine whether a simple clinical score, which was shown previously to predict the likelihood of severe coronary artery disease (CAD) in patients referred for coronary angiography, could predict prognosis in a separate cohort of patients with normal or mildly abnormal findings on their resting electrocardiogram (ECG) who were undergoing noninvasive evaluation for possible CAD.

PATIENTS AND METHODS

The study group included 2255 symptomatic patients with normal (n=1466) or mildly abnormal (nonspecific ST-T-wave abnormalities; n=789) findings on their resting ECG who were referred for exercise thallium testing between 1989 and 1991. Follow-up was 94% complete at a mean +/- SD duration of 6.9+/-1.5 years. The clinical score, which ranged from 0 (lowest risk) to 10 (highest risk), was calculated by awarding 1 point each for male sex, history of myocardial infarction, typical angina, diabetes mellitus, insulin use, and each decade of age older than 40 years.

RESULTS

In each ECG group, the clinical score was a significant predictor of cardiac death, nonfatal myocardial infarction, or late revascularization, considered individually or combined, unadjusted or with adjustment for age. Most patients had a score lower than 5; these patients had an excellent 5-year cardiac survival rate (99.7% for the normal ECG findings group and 98.8% for the ST-T-wave abnormalities group). The small subset of patients with a score higher than 5 had a much lower 5-year survival rate (923% for the 8% of patients with normal ECG findings and 86.6% for the 14% of patients with ST-T-wave abnormalities). For patients with a score of 5, the 5-year survival rate was 97.7% for the normal ECG findings group and 95.9% for the ST-T-wave abnormalities group.

CONCLUSION

In symptomatic patients with known or suspected CAD and normal or mildly abnormal resting ECG findings, this simple, easily computed clinical score is a useful and valid tool to help determine prognosis.

Better decisions through science: exercise testing scores

Statistical tools can be used to create scores for assisting in the diagnosis of coronary artery disease and assessing prognosis. General practitioners and internists frequently function as gatekeepers, deciding which patients must be referred to the cardiologist. Therefore, they need to use the basic tools they have available (ie, history, physical examination and the exercise test) in an optimal fashion. Scores derived from multivariable statistical techniques considering clinical and exercise data have demonstrated superior discriminating power compared with diagnosis only using the ST segment response. In addition, by stratifying patients as to probability of disease and prognosis, they provide a more practical management strategy than a response of normal or abnormal. Although computers, as part of information management systems, can calculate complicated equations and derive these scores, physicians are reluctant to trust them. However, when represented as nomograms or simple additive discrete pieces of information, scores are more readily accepted. The scores have been compared with physician judgment and have been found to estimate the presence of coronary disease and prognosis as well as expert cardiologists and often better than nonspecialists. However, the discriminating power of specific variables from the medical history and exercise test remains unclear because of inadequate study design and differences in study populations. Should expired gases be substituted for estimated METs? Should ST/heart rate index be used instead of putting ST depression and heart rate separately into the models? Should right-sided chest leads and heart rate in recovery be considered? There is a need for further evaluation of these easily obtained variables to improve the accuracy of prediction algorithms, especially in women. The portability and reliability of scores must be ensured because access to specialized care must be safeguarded. Assessment of the clinical and exercise test data and application of the newer scores can empower the clinician to assure the cardiac patient access to appropriate and cost-effective cardiologic care.

Assessing patients with possible heart disease using scores

Multivariable analysis of clinical and exercise test data has the potential to become a useful tool for assisting in the diagnosis of coronary artery disease, assessing prognosis, and reducing the cost of evaluating patients with suspected coronary disease. Since general practitioners are functioning as gatekeepers and decide which patients must be referred to the cardiologist, they need to use the basic tools they have available (i.e. history, physical examination and the exercise test), in an optimal fashion. Scores derived from multivariable statistical techniques considering clinical and exercise data have demonstrated superior discriminating power compared with simple classification of the ST response. In addition, by stratifying patients as to probability of disease and prognosis, they provide a management strategy. While computers, as part of information management systems, can run complicated equations and derive these scores, physicians are reluctant to trust them. Thus, these scores have been represented as nomograms or simple additive tables so physicians are comfortable with their application. Their results have also been compared with physician judgment and found to estimate the presence of coronary disease and prognosis as well as expert cardiologists and often better than nonspecialists. However, the discriminating power of specific variables from the medical history and exercise test remains unclear because of inadequate study design and differences in study populations. Should expired gases be substituted for estimated metabolic equivalents (METs)? Should ST/heart rate (HR) index be used instead of putting these measurements separately into the models? Should right-sided chest leads and HR in recovery be considered? There is a need for further evaluation of these routinely obtained variables to improve the accuracy of prediction algorithms especially in women. The portability and reliability of these equations must be demonstrated since access to specialised care must be safe-guarded. Hopefully, sequential assessment of the clinical and exercise test data and application of the newer generation of multivariable equations can empower the clinician to assure the cardiac patient access to appropriate and cost-effective cardiological care.

An agreement approach to predict severe angiographic coronary artery disease with clinical and exercise test data

OBJECTIVE

To demonstrate that an agreement approach to applying equations on the basis of clinical and exercise test variables is an accurate, self-calibrating, and cost-efficient method for predicting severe coronary artery disease in clinical populations.

DESIGN

Retrospective analysis of consecutive patients with complete data from exercise testing and coronary angiography referred for evaluation of possible coronary artery disease. After developing an equation in a training set, this equation and two other equations developed by other investigators were validated in a test set. The study was performed at two university-affiliated Veteran's Affairs medical centers.

PATIENTS

1080 consecutive men studied between 1985 and 1995 who had coronary angiography within 3 months of the treadmill test. The population was randomly divided into a training set of 701 patients and a test set of 379 patients. Patients with previous coronary artery bypass surgery, valvular heart disease, marked degrees of resting ST depression, and left bundle branch block were excluded.

MEASUREMENTS

Recording of clinical and exercise test data along with visual interpretation of the electrocardiogram recordings on standardized forms and abstraction of visually interpreted angiographic data from clinical catheterization reports.

RESULTS

Simple clinical and exercise test variables improved the standard application of exercise-induced ST criteria for predicting severe coronary artery disease. By setting probability thresholds for severe disease of <20% and >40% for the three prediction equations, the agreement approach divided the test set into three groups: low risk (patients with all three equations predicting <21% probability of severe coronary disease), no agreement, and high risk (all three equations with >39% probability) for severe coronary artery disease. Because the patients in the no agreement group would be sent for further testing and would eventually be correctly classified, the sensitivity of the agreement approach was 89% and the specificity was 96%. The agreement approach appeared to be unaffected by disease prevalence, missing data, variable definitions, or even angiographic criteria.

CONCLUSIONS

Requiring diagnosis of severe coronary disease to be dependent on agreement between these three equations has made them likely to function in all clinical populations. The agreement approach should be an efficient method for the evaluation of populations with varying prevalence of coronary artery disease, limiting the use of more expensive noninvasive and invasive testing to patients with a higher probability of left main or triple-vessel coronary artery disease. This approach provides a strategy that can be applied by inputting the results of basic clinical assessment into a programmable calculator or a computer to assist the practitioner in deciding when further evaluation is appropriate, thus assuring patients access to subspecialty care.

A consensus approach to diagnosing coronary artery disease based on clinical and exercise test data

OBJECTIVE

To demonstrate that a consensus approach for combining prediction equations based on clinical and exercise test variables derived from different populations can stratify patients referred for possible coronary artery disease (CAD) into low-, intermediate-, and high-risk groups.

DESIGN

Retrospective analysis of consecutive patients with complete data from exercise testing and coronary angiography referred for evaluation of possible CAD. After derivation of a logistic equation in our own training set of patients, this equation, along with two other equations developed independently by other investigators, was validated in a test set. The validation strategy for the consensus approach included the following: (1) calculation of probability scores for each patient using each logistic equation independently; (2) determination of probability thresholds in the training set to divide the patients into three groups-low risk (prevalence CAD <5%), intermediate risk (5 to 70%), and high risk (>70% prevalence of CAD); (3) using agreement among at least two of three of the prediction equations to generate "consensus" for each patient; and (4) application of the consensus approach thresholds to the test set of patients.

SETTINGS

Two university-affiliated Veteran's Affairs medical centers.

PATIENTS

We studied 718 consecutive men between 1985 and 1995 who had coronary angiography within 3 months of an exercise treadmill test for suspected CAD. The population was randomly divided into a training set of 429 patients and a test set of 289 patients. Patients with previous myocardial infarction or coronary artery bypass surgery, valvular heart disease, left bundle branch block, or any Q waves present on their resting ECG were excluded from the study.

MEASUREMENTS

Recording of clinical and exercise test data along with visual interpretation of the ECG recordings on standardized forms and abstraction of visually interpreted angiographic data from clinical catheterization reports.

RESULTS

We demonstrated that by using simple clinical and exercise test variables, we could improve on the standard use of ECG criteria during exercise testing for diagnosing CAD. Using the consensus approach divided the test set into populations with low, intermediate, and high risk for CAD. Since the patients in the intermediate group would be sent for further testing and would eventually be correctly classified, the sensitivity of the consensus approach is 94% and the specificity is 92%. The consensus approach controls for varying disease prevalence, missing data, inconsistency in variable definition, and varying angiographic criterion for stenosis severity. The percent of correct diagnoses increased from the 67% for standard exercise ECG analysis and from the 80% for multivariable predictive equations alone to >90% correct diagnoses for the consensus approach.

CONCLUSIONS

The consensus approach has made population-specific logistic regression equations portable to other populations. Excellent diagnostic characteristics can be obtained using simple data and measurements. The consensus approach is best applied utilizing a programmable calculator or a computer program to simplify the process of calculating the probability of CAD using the three equations.