Accuracy of traditional age, gender and symptom based pre-test estimation of angiographically significant coronary artery disease in patients referred for coronary computed tomographic angiography

Discrimination capability of pretest probability of stable coronary artery disease: a systematic review and meta-analysis suggesting how to improve validation procedures

OBJECTIVE

Externally validated pretest probability models for risk stratification of subjects with chest pain and suspected stable coronary artery disease (CAD), determined through invasive coronary angiography or coronary CT angiography, are analysed to characterise the best validation procedures in terms of discriminatory ability, predictive variables and method completeness.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Global Health (Ovid), Healthstar (Ovid) and MEDLINE (Ovid) searched on 22 April 2020.

ELIGIBILITY CRITERIA

We included studies validating pretest models for the first-line assessment of patients with chest pain and suspected stable CAD. Reasons for exclusion: acute coronary syndrome, unstable chest pain, a history of myocardial infarction or previous revascularisation; models referring to diagnostic procedures different from the usual practices of the first-line assessment; univariable models; lack of quantitative discrimination capability.

METHODS

Eligibility screening and review were performed independently by all the authors. Disagreements were resolved by consensus among all the authors. The quality assessment of studies conforms to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A random effects meta-analysis of area under the receiver operating characteristic curve (AUC) values for each validated model was performed.

RESULTS

27 studies were included for a total of 15 models. Besides age, sex and symptom typicality, other risk factors are smoking, hypertension, diabetes mellitus and dyslipidaemia. Only one model considers genetic profile. AUC values range from 0.51 to 0.81. Significant heterogeneity (p<0.003) was found in all but two cases (p>0.12). Values of I² >90% for most analyses and not significant meta-regression results undermined relevant interpretations. A detailed discussion of individual results was then carried out.

CONCLUSIONS

We recommend a clearer statement of endpoints, their consistent measurement both in the derivation and validation phases, more comprehensive validation analyses and the enhancement of threshold validations to assess the effects of pretest models on clinical management.

PROSPERO REGISTRATION NUMBER

CRD42019139388.

Comparison of the CAD consortium and updated Diamond-Forrester scores for predicting obstructive coronary artery disease

OBJECTIVE

Current guidelines recommend the use of the updated Diamond-Forrester (DF) method and Coronary Artery Disease (CAD) Consortium models to assess the pretest probability of obstructive CAD. The present study aimed to compare the performance of these models among patients with chest pain evaluated in an emergency department (ED).

METHODS

We compared three scores (DF, CAD consortium basic, and clinical) among 1247 consecutive patients with chest pain who underwent coronary computed tomographic angiography (CTA). Invasive angiography was performed to confirm the stenosis for those who showed obstructive CAD on CTA, if clinically indicated. Primary outcome was the presence of obstructive CAD (≧50% stenosis).

RESULTS

Overall, 426 (34.2%) patients were diagnosed with obstructive CAD. The expected prevalence of CAD was underestimated by the CAD consortium clinical model (23.4%) and overestimated by the DF model (53.1%). For the prediction of obstructive CAD, the CAD consortium clinical model had superior area under the receiver-operating curve (0.754), followed by the CAD consortium basic (0.736), and finally, the DF model (0.718). Whereas the CAD consortium models more accurately classified patients without any CAD or nonobstructive CAD as low-risk patients, the DF model more accurately classified high-risk patients with obstructive CAD. The net reclassification improvement of CAD consortium basic and clinical models were 24.7% and 27.9%, respectively.

CONCLUSIONS

Compared with the DF model, the CAD consortium clinical model appears to improve the prediction of low-risk patients with <15% probability of having obstructive CAD. However, this model needs caution when using in high-risk population.

Comparison of International Guidelines for Assessment of Suspected Stable Angina: Insights From the PROMISE and SCOT-HEART

OBJECTIVES

This study sought to compare the performance of major guidelines for the assessment of stable chest pain including risk-based (American College of Cardiology/American Heart Association and European Society of Cardiology) and symptom-focused (National Institute for Health and Care Excellence) strategies.

BACKGROUND

Although noninvasive testing is not recommended in low-risk individuals with stable chest pain, guidelines recommend differing approaches to defining low-risk patients.

METHODS

Patient-level data were obtained from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) and SCOT-HEART (Scottish Computed Tomography of the Heart) trials. Pre-test probability was determined and patients dichotomized into low-risk and intermediate-high-risk groups according to each guideline's definitions. The primary endpoint was obstructive coronary artery disease on coronary computed tomography angiography. Secondary endpoints were coronary revascularization at 90 days and cardiovascular death or nonfatal myocardial infarction up to 3 years.

RESULTS

In total, 13,773 patients were included of whom 6,160 had coronary computed tomography angiography. The proportions of patients identified as low risk by the American College of Cardiology/American Heart Association, European Society of Cardiology, and National Institute for Health and Care Excellence guidelines, respectively, were 2.5%, 2.5%, and 10.0% within PROMISE, and 14.0%, 19.8%, and 38.4% within SCOT-HEART. All guidelines identified lower rates of obstructive coronary artery disease in low- versus intermediate-high-risk patients with a negative predictive value of ≥0.90. Compared with low-risk groups, all intermediate-high-risk groups had greater risks of coronary revascularization (odds ratio [OR]: 2.2 to 24.1) and clinical outcomes (OR: 1.84 to 5.8).

CONCLUSIONS

Compared with risk-based guidelines, symptom-focused assessment identifies a larger group of low-risk chest pain patients potentially deriving limited benefit from noninvasive testing. (Scottish Computed Tomography of the Heart Trial [SCOT-HEART]; NCT01149590; Prospective Multicenter Imaging Study for Evaluation of Chest Pain [PROMISE]; NCT01174550).

The importance of characteristics of angina symptoms for the prediction of coronary artery disease in a cohort of stable patients in the modern era

OBJECTIVE

Angina is an important clinical symptom indicating underlying coronary artery disease (CAD). Its characteristics are important for the diagnosis and risk stratification of patients with CAD. Currently, we aimed to investigate the association of chest pain characteristics with the presence of obstructive CAD in a contemporary cohort of patients undergoing coronary angiography for suspected stable CAD.

METHODS

Consecutive patients undergoing coronary angiography for suspected stable CAD (n = 686) in a single university hospital cardiology department were enrolled. Chest pain was classified as typical angina, atypical angina, nonangina chest pain, and lack of symptoms. The presence of significant angiographic CAD was diagnosed by standard coronary angiography.

RESULTS

Typical angina symptoms were associated with a higher prevalence of CAD (odds ratio [OR], 3.47, p < 0.001), whereas atypical angina symptoms were associated with a lower prevalence of CAD (OR, 0.49, p = 0.003) than the nonangina symptoms/or asymptomatic status. In multivariate analysis, typical angina symptoms remained an independent predictor of CAD (OR, 2.54, p < 0.001), with a greater predictive accuracy than other clinical risk factors (area under the curve [AUC], 0.715, p < 0.001) and similar to the accuracy of the high-sensitivity C-reactive protein (AUC, 0.712, p < 0.001). In a multivariate model, the combination of all studied factors further improved the predictive accuracy (AUC, 0.81, p < 0.001).

CONCLUSION

In a contemporary cohort of patients referred for coronary angiography for stable CAD, the presence of typical angina symptoms was the most important independent predictor of obstructive CAD. The association of atypical angina symptoms with low CAD prevalence compared to nonangina chest pain or absence of significant symptoms probably reflects different management and referral strategies in these groups of patients.

Clinical risk factors alone are inadequate for predicting significant coronary artery disease

OBJECTIVE

We sought to derive and validate a model for identifying suspected ACS patients harboring undiagnosed significant coronary artery disease (CAD).

METHODS

This was a secondary analysis of data from a randomized control trial (RCT). Patients randomized to the CTA arm of an RCT examining a CTA-based strategy for ruling-out acute coronary syndrome (ACS) constitute the derivation cohort, which was randomly divided into a training dataset (2/3, used for model derivation) and a test dataset (1/3, used for internal validation (IV)). ED patients from a different center receiving CTA to evaluate for suspected ACS constitute the external validation (EV) cohort. Primary outcome was CTA-assessed significant CAD (stenosis of ≥50% in a major coronary artery).

RESULTS

In the derivation cohort, 11.2% (76/679) of subjects had CTA-assessed significant CAD, and in the EV cohort, 8.2% of subjects (87/1056) had CTA-assessed significant CAD. Age was the strongest predictor of significant CAD among the clinical risk factors examined. Predictor variables included in the derived logistic regression model were: age, sex, tobacco use, diabetes, and race. This model exhibited an area under the receiver operating characteristic curve (ROC AUC) of 0.72 (95% CI: 0.61-0.83) based on IV, and 0.76 (95% CI: 0.70, 0.82) based on EV. The derived random forest model based on clinical risk factors yielded improved but not sufficient discrimination of significant CAD (ROC AUC = 0.76 [95% CI: 0.67-0.85] based on IV). Coronary artery calcium score was a more accurate predictor of significant CAD than any combination of clinical risk factors (ROC AUC = 0.85 [95% CI: 0.76-0.94] based on IV; ROC AUC = 0.92 [95% CI: 0.88-0.95] based on EV).

CONCLUSIONS

Clinical risk factors, either individually or in combination, are insufficient for accurately identifying suspected ACS patients harboring undiagnosed significant coronary artery disease.

Impact of Clinical Guideline Recommendations on the Application of Coronary Computed Tomographic Angiography in Patients with Suspected Stable Coronary Artery Disease

Background:

Coronary computed tomographic angiography (CCTA) has been widely used in patients who are at intermediate risk for having stable coronary artery disease (SCAD), and 2013 European Society of Cardiology Guidelines on the Management of SCAD (2013G) recommended the appropriate application of CCTA. However, 2013G has not been subjected to systematic analyses for subsequent impact on clinical practice.

Methods:

A total of 5320 patients suspected with SCAD were enrolled and scheduled for CCTA from March 2013 to September 2014. For each patient, pretest probability of SCAD was calculated according to updated Diamond-Forrester model (UDFM). Appropriate CCTA or appropriate stress test was determined as described in the 2013G. A generalized estimating equation model was used to determine the trends in the half-monthly rate of appropriate CCTA.

Results:

Overall, only 61.37% of patients received appropriate CCTA, and there was insignificant change over time (P = 0.8701). The application of CCTA in patients who should have had a stress test accounted for most of the inappropriate CCTA before (22.29%) or after (19.98%) the publication of the 2013G. In all patients or any subgroup, no significant change in the adjusted half-monthly rate of appropriate CCTA was found after the publication of the 2013G (odds ratio, 1.002; 95% confidence interval, 0.982–1.021; P = 0.8678).

Conclusions:

These findings suggest that the 2013G have not, to date, been fully incorporated into clinical practice, and the clinical utilization of CCTA remains unreasonable to some extent.

Testing in Patients With Stable Coronary Artery Disease - The Debate Continues

The major guidelines on stable coronary artery disease recommend revascularizing patients with large areas of myocardium at risk. The algorithms on how to prove that such high risk is present differ considerably. The opinions on the use of coronary CT (calcium scoring and angiography) vary widely. This review aims to summarize the recommendations of the major guidelines, commenting on differences between the guidelines and discussing whether extending the role of coronary CT angiography should be considered in the light of new CT data.

Pre-test probability of obstructive coronary stenosis in patients undergoing coronary CT angiography: Comparative performance of the modified diamond-Forrester algorithm versus methods incorporating cardiovascular risk factors

BACKGROUND

Current guidelines recommend the use of the Modified Diamond-Forrester (MDF) method to assess the pre-test likelihood of obstructive coronary artery disease (CAD). We aimed to compare the performance of the MDF method with two contemporary algorithms derived from multicenter trials that additionally incorporate cardiovascular risk factors: the calculator-based 'CAD Consortium 2' method, and the integer-based CONFIRM score.

METHODS

We assessed 1069 consecutive patients without known CAD undergoing coronary CT angiography (CCTA) for stable chest pain. Obstructive CAD was defined as the presence of coronary stenosis ≥50% on 64-slice dual-source CT. The three methods were assessed for calibration, discrimination, net reclassification, and changes in proposed downstream testing based upon calculated pre-test likelihoods.

RESULTS

The observed prevalence of obstructive CAD was 13.8% (n=147). Overestimations of the likelihood of obstructive CAD were 140.1%, 9.8%, and 18.8%, respectively, for the MDF, CAD Consortium 2 and CONFIRM methods. The CAD Consortium 2 showed greater discriminative power than the MDF method, with a C-statistic of 0.73 vs. 0.70 (p<0.001), while the CONFIRM score did not (C-statistic 0.71, p=0.492). Reclassification of pre-test likelihood using the 'CAD Consortium 2' or CONFIRM scores resulted in a net reclassification improvement of 0.19 and 0.18, respectively, which would change the diagnostic strategy in approximately half of the patients.

CONCLUSIONS

Newer risk factor-encompassing models allow for a more precise estimation of pre-test probabilities of obstructive CAD than the guideline-recommended MDF method. Adoption of these scores may improve disease prediction and change the diagnostic pathway in a significant proportion of patients.

European Society of Cardiology-Recommended Coronary Artery Disease Consortium Pretest Probability Scores More Accurately Predict Obstructive Coronary Disease and Cardiovascular Events Than the Diamond and Forrester Score: The Partners Registry

BACKGROUND

The most appropriate score for evaluating the pretest probability of obstructive coronary artery disease (CAD) is unknown. We sought to compare the Diamond-Forrester (DF) score with the 2 CAD consortium scores recently recommended by the European Society of Cardiology.

METHODS

We included 2274 consecutive patients (age, 56±13 years; 57% male) without prior CAD referred for coronary computed tomographic angiography. Computed tomographic angiography findings were used to determine the presence or absence of obstructive CAD (≥50% stenosis). We compared the DF score with the 2 CAD consortium scores with respect to their ability to predict obstructive CAD and the potential implications of these scores on the downstream use of testing for CAD, as recommended by current guidelines.

RESULTS

The DF score did not satisfactorily fit the data and resulted in a significant overestimation of the prevalence of obstructive CAD (P<0.001); the CAD consortium basic score had no significant lack of fitness; and the CAD consortium clinical provided adequate goodness of fit (P=0.39). The DF score had a lower discrimination for obstructive CAD, with an area under the receiver-operating characteristics curve of 0.713 versus 0.752 and 0.791 for the CAD consortium models (P<0.001 for both). Consequently, the use of the DF score was associated with fewer individuals being categorized as requiring no additional testing (8.3%) compared with the CAD consortium models (24.6% and 30.0%; P<0.001). The proportion of individuals with a high pretest probability was 18% with the DF and only 1.1% with the CAD consortium scores (P<0.001) CONCLUSIONS: Among contemporary patients referred for noninvasive testing, the DF risk score overestimates the risk of obstructive CAD. On the other hand, the CAD consortium scores offered improved goodness of fit and discrimination; thus, their use could decrease the need for noninvasive or invasive testing while increasing the yield of such tests.

[Diamond-Forrester and cardiac CT : Is there a need to redefine the pretest probability of coronary artery disease?]

Apart from the Diamond-Forrester classification, which is widely used particularly in the USA for the pretest probability of coronary artery disease, other scores also exist, such as an updated version of the classification table by Genders et al., the Morise score and the Duke clinical risk score. These scores estimate the probability of coronary artery disease, defined as the presence of at least one high-grade stenosis, based on symptom characteristics, age, gender and other parameters. All of the scores were derived from patient cohorts in which invasive coronary angiography had been performed for clinical reasons. It has subsequently been shown that these scores, especially those developed several decades ago, substantially overestimate the pretest probability of coronary artery disease. When these risk scores are applied to patients for whom a non-invasive work-up of suspected coronary artery disease is planned, for example by coronary computed tomography (CT) angiography, the expected prevalence of significant coronary stenosis will be overestimated. This, in turn, influences the test characteristics and the significance of the non-invasive examination (positive and negative predictive values) and needs to be taken into account when interpreting test results.

Comparison of Coronary Artery Disease Consortium 1 and 2 Scores and Duke Clinical Score to Predict Obstructive Coronary Disease by Invasive Coronary Angiography

BACKGROUND

The first step in evaluating a patient with suspected stable coronary artery disease (CAD) is the determination of the pretest probability. The European Society of Cardiology guidelines recommend the use of the CAD Consortium 1 score (CAD1), which contrary to CAD Consortium 2 (CAD2) score and Duke Clinical Score (DCS), does not include modifiable cardiovascular risk factors.

HYPOTHESIS

Using scores that include modifiable risk factors (DCS and CAD2) enhances prediction of CAD.

METHODS

We retrospectively included all patients referred to invasive coronary angiography for suspected CAD from January/2008-December/2012 (N = 2234). Pretest probability was calculated using 3 models (CAD1, DCS, and CAD2), and they were compared using the net reclassification improvement.

RESULTS

Mean patient age was 63.7 years, 67.5% were male, and the majority (66.9%) had typical angina. Coronary artery disease was diagnosed in 58.5%, and the area under the curve was 0.685 for DCS, 0.664 for CAD1, and 0.683 for CAD2, with a statistically significant difference between CAD1 and the others (P < 0.001). The net reclassification improvement was 20% for DCS, related to adequate reclassification of 32% of patients with CAD to a higher risk category, and 5% for CAD2, at the cost of adequate reclassification of 34% of patients without CAD to a lower risk category.

CONCLUSIONS

Prediction of CAD using scores that include modifiable cardiovascular risk factors seems to improve accuracy. Our results suggest that, in high-prevalence populations, DCS may better identify patients at higher risk and CAD2 those at lower risk for CAD.

Is it Time to Update How Suspected Angina Is Evaluated prior to the Use of Specialized Tests Implications Based on a Systematic Review

OBJECTIVES

Appropriate use of specialized tests to assess chest pain is based classically on minimal information such as age, gender and the patient's description of pain. This approach has not been reevaluated in decades. We examined the relationship between history, examination and routine laboratory tests to identify factors warranting prospective validation as predictors of underlying coronary artery disease (CAD).

METHODS

Studies linking obstructive CAD (≥50% diameter stenosis of at least one vessel by invasive angiography or cardiac computed tomographic angiography) and elements of history, examination and laboratory tests were identified.

RESULTS

Forty-one prospectively identified papers were analyzed. Advanced age, gender and chest pain descriptors were extremely important, although the last was less so in women, in whom the presence of risk factors may be more important. Physical examination and chest X-ray were largely noncontributory. Laboratory tests were of variable utility other than to identify risk factors not already known from the history. However, biomarkers such as troponin, brain natriuretic factor and inflammatory markers were promising. The electrocardiogram was mainly important for the identification of ST-T abnormalities.

CONCLUSIONS

This review identifies the most promising factors warranting prospective validation for improving the pretest probability estimation of CAD, so appropriate use criteria for the utilization of specialized diagnostic tests can be updated and improved.

Limitations of Chest Pain Categorization Models to Predict Coronary Artery Disease

We aimed to evaluate how chest pain categorization, currently used to assess the pretest probability of coronary artery disease (CAD), predicts the actual presence of CAD in a population of patients with stable symptoms. We studied 475 consecutive patients enrolled in the Evaluation of Integrated Cardiac Imaging for the Detection and Characterization of Ischemic Heart Disease study based on possible symptoms of CAD. Chest pain or discomfort was categorized as typical angina, atypical angina, or as nonanginal according to the guidelines. Exertional dyspnea and fatigue suspected to be angina equivalents were classified as atypical angina. Patients with a probability of CAD <20 or >90% based on age, gender, and symptoms were excluded. The end points of this substudy were significant CAD (defined by invasive coronary angiography as >50% reduction in lumen diameter in the left main stem or >70% stenosis in a major coronary vessel or 30% to 70% stenosis with fractional flow reserve ≤0.8), inducible myocardial ischemia at noninvasive stress imaging, and their association. Patients' symptoms had limited ability to predict the presence of significant CAD, global chi-square being 5.0. The inclusion of age increased global chi-square to 18.7 and gender increased it further to 51.1. Using inducible myocardial ischemia or the association of CAD with inducible ischemia as end points, the ability to predict these end points was again better for patient demographics than for patient symptoms. Thus, the ability of current models based on symptoms, age, and gender to predict the presence of CAD is mainly based on patient demographics as opposed to symptoms.

Overestimation of pretest probability of coronary artery disease by Duke clinical score in patients undergoing coronary CT angiography in a Japanese population

BACKGROUND

The Duke clinical score (DCS) is commonly used to estimate the pretest probability of coronary artery disease (CAD). However, the criterion was developed in a population undergoing catheter angiography.

OBJECTIVE

To test the hypothesis that DCS overestimates the CAD probability when applied to patients evaluated with coronary CT angiography (CCTA). A second objective is to compute an adjustment of the calculated DCS to apply to this population.

METHODS

The DCS was calculated for the 3996 consecutive CCTA studies (February 2009 to April 2013) performed for symptomatic patients with no known CAD. Performance of the DCS for the detection of CAD was evaluated by the area under the receiver operating characteristic curve. Using the training cohort (n = 2789), a linear regression line between the calculated probability and the observed prevalence of CAD identified a modified DCS cutoff for a better risk categorization; this was internally validated by a separate cohort (n = 1207).

RESULTS

The DCS showed a good discrimination (area under the receiver operating characteristic curve = 0.71) for the detection of CAD (prevalence = 23.3%). The calibration analysis showed an overall 2.4-fold overestimation by DCS with a DCS < 23% corresponding to the low-risk category (ie, observed prevalence of CAD < 10%). There was no appropriate DCS cutoff to define high-risk category (ie, prevalence > 90%). The validation cohort showed a prevalence of 9.4% when DCS < 23% was used to define low risk.

CONCLUSION

Among patients who underwent CCTA, DCS overestimated the pretest probability by at least 2-fold; the DCS < 23% should define the lower risk probability. The DCS poorly identifies high-risk population and thus development of new CCTA-based criteria is warranted.