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

Performance of the 2019 ESC pre-test probability model in predicting obstructive coronary artery disease in a Chinese population using coronary computed tomography angiography outcomes

BACKGROUND

The 2019 European Society of Cardiology (ESC) guidelines proposed a pre-test probability (PTP) model to determine the likelihood of coronary artery disease (CAD). However, the prediction accuracy of this model has not yet been evaluated in Chinese populations. This study aimed to validate the 2019 ESC-PTP model in predicting CAD using coronary computed tomography angiography (CCTA) outcomes in a Chinese population.

METHODS

A total of 26,346 consecutive patients with suspected CAD who underwent CCTA were included. The 2019 ESC-PTP model and 2013 ESC-PTP model were calculated for each patient, considering age, sex, and the symptom of chest pain, and the patients were categorized into low-, intermediate-, and high-risk groups. The predictive performance of the 2019 ESC-PTP model was evaluated by comparing it with the 2013 ESC-PTP model and the observed prevalence of CAD from CCTA.

RESULTS

Among the 11,234 patients analyzed in the study, 1896 (16.9%) patients were found to have obstructive CAD from CCTA. The 2019 ESC-PTP model had better calibration compared to the 2013 ESC-PTP model. After categorization, 80.9% of patients (67.9% in men and 94.4% in women) were in the same risk category as in the 2019 ESC-PTP model, but the risks of younger patients (7.5% versus 2.5%; P ​< ​0.001) and patients with non-anginal chest pain (13.7% versus 8.2%; P ​< ​0.001) were underestimated in the 2019 ESC-PTP model.

CONCLUSION

The 2019 ESC-PTP model demonstrated a good calibration in predicting CAD in a Chinese population who underwent CCTA, but it exhibited an underestimation of CAD probability in younger patients and patients with non-anginal chest pain.

ESC pre-test probability estimates for obstructive coronary artery disease: can they be used in Brazil?

Aims

Cardiovascular disease, primarily coronary artery disease (CAD), is the leading cause of mortality worldwide. Accurate diagnosis of CAD often requires pre-test probability (PTP) estimation, traditionally performed using scoring systems like the Diamond-Forrester (DF) and European Society of Cardiology (ESC) models. However, the applicability of such models in specific populations may vary. This study compares the performance of DF and PTP scores in the Brazilian context, using coronary computed tomography angiography (CCTA) as a reference standard.

Methods and results

PTP for obstructive CAD was calculated using DF and ESC scores in 409 symptomatic patients without known CAD who underwent CCTA between 2019 and 2022. Predicted PTP was compared with actual CAD prevalence. DF overestimated CAD prevalence across age and symptom categories, while ESC showed better alignment with actual prevalence.

Conclusion

Our study confirms that the ESC PTP model is more appropriate than the DF model for determining PTP in the Brazilian population.

Calcium Scoring Improves Clinical Management in Patients With Low Clinical Likelihood of Coronary Artery Disease

BACKGROUND

Coronary artery calcium scoring (CACS) improves management of chest pain patients. However, it is unknown whether the benefit of CACS is dependent on the clinical likelihood (CL).

OBJECTIVES

This study aims to investigate for which patients CACS has the greatest benefit when added to a CL model.

METHODS

Based on data from a clinical database, the CL of obstructive coronary artery disease (CAD) was calculated for 39,837 patients referred for cardiac imaging due to symptoms suggestive of obstructive CAD. Patients were categorized according to the risk factor-weighted (RF-CL) model (very low, ≤5%; low, >5 to ≤15%; moderate >15 to ≤50%; high, >50%). CL was then recalculated incorporating the CACS result (CACS-CL). Reclassification rates and the number needed to test with CACS to reclassify patients were calculated and validated in 3 independent cohorts (n = 9,635).

RESULTS

In total, 15,358 (39%) patients were down- or upclassified after including CACS. Reclassification rates were 8%, 75%, 53%, and 30% in the very low, low, moderate, and high RF-CL categories, respectively. Reclassification to very low CACS-CL occurred in 48% of reclassified patients. The number needed to test to reclassify 1 patient from low RF-CL to very low CACS-CL was 2.1 with consistency across age, sex, and cohorts. CACS-CL correlated better to obstructive CAD prevalence than RF-CL.

CONCLUSIONS

Added to an RF-CL model for obstructive CAD, CACS identifies more patients unlikely to benefit from further testing. The number needed to test with CACS to reclassify patients depends on the pretest RF-CL and is lowest in patients with low (>5% to ≤15%) likelihood of CAD.

Safe Deferral of Coronary Computed Tomography Angiography for Patients With a Low Pretest Probability of Coronary Artery Disease in 2019 European Society of Cardiology Guidelines

BACKGROUND

Coronary computed tomography angiography is increasingly used as the first-line test for suspected coronary artery disease. Its overuse in a low pretest probability (PTP) population may lead to low diagnostic yield without change in patient management. We evaluated the clinical consequences of the updated 2019 European Society of Cardiology (ESC) chronic coronary syndromes guidelines' PTP estimation and whether imaging could be safely deferred in patients with a low PTP.

METHODS AND RESULTS

This retrospective cohort included all 1753 consecutive patients who underwent coronary computed tomography angiography for suspected coronary artery disease at Helsinki University Hospital between 2009 and 2017. PTP was calculated according to the 2013 and 2019 ESC guidelines. The overall mortality, cardiac deaths, myocardial infarctions, and hospitalizations for unstable angina were acquired from national registry data for 1 to 10 years of follow-up (median, 4 years). Updated 2019 ESC guidelines classified 72% of the patients as having low PTP, whose imaging could have been deferred. The revascularization rate (4.7%) and annual cardiac mortality (0.4%) were low in the 857 patients reclassified from the recommendation to test, according to the 2013 ESC guideline, to deferral of coronary computed tomography angiography, according to the new 2019 guideline.

CONCLUSIONS

The updated 2019 ESC guideline PTP score aids clinicians in safely preventing the overuse of cardiac imaging in patients deemed at low PTP of coronary artery disease. Diagnostic yield, revascularization rate, and cardiac mortality are low in patients with low pretest risk.

Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery calcium and epicardial fat volume

We sought to establish an explainable machine learning (ML) model to screen for hemodynamically significant coronary artery disease (CAD) based on traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV) measured from non-contrast CT scans. 184 symptomatic inpatients who underwent Single Photon Emission Computed Tomography/Myocardial Perfusion Imaging (SPECT/MPI) and Invasive Coronary Angiography (ICA) were enrolled. Clinical and imaging features (CAC and EFV) were collected. Hemodynamically significant CAD was defined when coronary stenosis severity ≥ 50% with a matched reversible perfusion defect in SPECT/MPI. Data was randomly split into a training cohort (70%) on which five-fold cross-validation was done and a test cohort (30%). The normalized training phase was preceded by the selection of features using recursive feature elimination (RFE). Three ML classifiers (LR, SVM, and XGBoost) were used to construct and choose the best predictive model for hemodynamically significant CAD. An explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanation of the model's decision. In the training cohort, hemodynamically significant CAD patients had significantly higher age, BMI and EFV, higher proportions of hypertension and CAC comparing with controls (P all < .05). In the test cohorts, hemodynamically significant CAD had significantly higher EFV and higher proportion of CAC. EFV, CAC, diabetes mellitus (DM), hypertension, and hyperlipidemia were the highest ranking features by RFE. XGBoost produced better performance (AUC of 0.88) compared with traditional LR model (AUC of 0.82) and SVM (AUC of 0.82) in the training cohort. Decision Curve Analysis (DCA) demonstrated that XGBoost model had the highest Net Benefit index. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 0.89, 68.0%, 96.8%, 94.4%, 79.0% and 83.9% in the XGBoost model. A XGBoost model based on EFV, CAC, hypertension, DM and hyperlipidemia to assess hemodynamically significant CAD was constructed and validated, which showed favorable predictive value. ML combined with SHAP can offer a transparent explanation of personalized risk prediction, enabling physicians to gain an intuitive understanding of the impact of key features in the model.

Pre-test probability for coronary artery disease in patients with chest pain based on machine learning techniques

BACKGROUND

A correct and prompt diagnosis of coronary artery disease (CAD) is a crucial component of disease management to reduce the risk of death and improve the quality of life in patients with CAD. Currently, the American College of Cardiology (ACC)/American Heart Association (AHA) and the European Society of Cardiology (ESC) guidelines recommend selecting an appropriate pre-diagnosis test for an individual patient according to the CAD probability. The purpose of this study was to develop a practical pre-test probability (PTP) for obstructive CAD in patients with chest pain using machine learning (ML); also, the performance of ML-PTP for CAD is compared to the final result of coronary angiography (CAG).

METHODS

We used a database from a single-center, prospective, all-comer registry designed to reflect real-world practice since 2004. All subjects underwent invasive CAG at Korea University Guro Hospital in Seoul, South Korea. We used logistic regression algorithms, random forest (RF), supporting vector machine, and K-nearest neighbor classification for the ML models. The dataset was divided into two consecutive sets according to the registration period to validate the ML models. ML training for PTP and internal validation used the first dataset registered between 2004 and 2012 (8631 patients). The second dataset registered between 2013 and 2014 (1546 patients) was used for external validation. The primary endpoint was obstructive CAD. Obstructive CAD was defined as having a stenosis diameter of >70% on the quantitative CAG of the main epicardial coronary artery.

RESULTS

We derived an ML-based model consisting of three different models according to the subject used to obtain the information, such as the patient himself (dataset 1), the community's first medical center (dataset 2), and doctors (dataset 3). The performance range of the ML-PTP models as the non-invasive test had C-statistics of 0.795 to 0.984 compared to the result of invasive testing via CAG in patients with chest pain. The training ML-PTP models were adjusted to have 99% sensitivity for CAD so as not to miss actual CAD patients. In the testing dataset, the best accuracy of the ML-PTP model was 45.7% using dataset 1, 47.2% using dataset 2, and 92.8% using dataset 3 and the RF algorithm. The CAD prediction sensitivity was 99.0%, 99.0%, and 98.0%, respectively.

CONCLUSION

We successfully developed a high-performance model of ML-PTP for CAD which is expected to reduce the need for non-invasive tests in chest pain. However, since this PTP model is derived from data of a single medical center, multicenter verification is required to use it as a PTP recommended by the major American societies and the ESC.

The feasibility of early detecting coronary artery disease using deep learning-based algorithm based on electrocardiography

BACKGROUND

Coronary Artery Disease (CAD) is a major cause of morbidity and mortality, yet it is frequently asymptomatic in the early stages and hence goes undetected.

OBJECTIVE

We aimed to develop a novel artificial intelligence-based approach for early detection of CAD patients based solely on electrocardiogram (ECG).

METHODS

This study included patients with suspected CAD who had standard 10-s resting 12-lead ECGs and coronary computed tomography angiography (cCTA) results within 4 weeks or less. The ECG and cCTA data from the same patient were matched based on their hospitalization or outpatient ID. All matched data pairs were then randomly divided into training, validation dataset for model development based on convolutional neural network (CNN) and test dataset for model evaluation. The accuracy (Acc), specificity (Spec), sensitivity (Sen), positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC) of the model were calculated by using the test dataset.

RESULTS

In the test dataset, the model for detecting CAD achieved an AUC of 0.75 (95% CI, 0.73 to 0.78) with an accuracy of 70.0%. Using the optimal cut-off point, the CAD detection model had sensitivity of 68.7%, specificity of 70.9%, positive predictive value (PPV) of 61.2%, and negative predictive value (NPV) of 77.2%. Our study demonstrates that a well-trained CNN model based solely on ECG could be considered an efficient, low-cost, and noninvasive method of assisting in CAD detection.

Multi-center study of inter-rater reproducibility, image quality, and diagnostic accuracy of CZT versus conventional SPECT myocardial perfusion imaging

BACKGROUND

Cadmium-zinc-telluride (CZT)-based detectors exhibit higher diagnostic sensitivity in myocardial perfusion imaging (MPI) than conventional Anger-MPI for detection of coronary artery disease (CAD); however, reduced specificity and diagnostic accuracy of CZT-MPI were observed. This study aims to compare these different camera systems and to examine the degree of inter-rater reproducibility among readers with varying experience in MPI.

METHODS

83 patients who underwent double stress/rest examinations using both a CZT and conventional SPECT cameras within one visit were included. Anonymized and randomized MPI-images were distributed to 15 international readers using a standardized questionnaire. Subsequent coronary angiography findings of ten patients served as a reference for analysis of sensitivity and specificity.

RESULTS

Image quality was significantly better in CZT-MPI with significantly lower breast attenuation (P < 0.05). CZT-MPI exhibited higher sensitivity than Anger-MPI (87.5% vs. 62.5%) and significantly reduced specificity (40% vs. 100%). Readers experienced with both camera systems had the highest inter-rater agreement indicating higher reproducibility (CZT 0.54 vs. conv. 0.49, P < 0.05).

CONCLUSIONS

Higher diagnostic sensitivity of CZT-MPI offers advantages in detection of CAD yet potentially of at the cost of reduced specificity, therefore it requires special training and a differentiated evaluation approach, especially for non-experienced readers with such camera systems.

Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing: validation in a low-risk to intermediate-risk outpatient clinic cohort

OBJECTIVES

Predicting the presence or absence of coronary artery disease (CAD) is clinically important. Pretest probability (PTP) and CAD consortium clinical (CAD2) model and risk scores used in the guidelines are not sufficiently accurate as the only guidance for applying invasive testing or discharging a patient. Artificial intelligence without the need of additional non-invasive testing is not yet used in this context, as previous results of the model are promising, but available in high-risk population only. Still, validation in low-risk patients, which is clinically most relevant, is lacking.

DESIGN

Retrospective cohort study.

SETTING

Secondary outpatient clinic care in one Dutch academic hospital.

PARTICIPANTS

We included 696 patients referred from primary care for further testing regarding the presence or absence of CAD. The results were compared with PTP and CAD2 using receiver operating characteristic (ROC) curves (area under the curve (AUC)). CAD was defined by a coronary stenosis >50% in at least one coronary vessel in invasive coronary or CT angiography, or having a coronary event within 6 months.

OUTCOME MEASURES

The first cohort validating the memetic pattern-based algorithm (MPA) model developed in two high-risk populations in a low-risk to intermediate-risk cohort to improve risk stratification for non-invasive diagnosis of the presence or absence of CAD.

RESULTS

The population contained 49% male, average age was 65.6±12.6 years. 16.2% had CAD. The AUCs of the MPA model, the PTP and the CAD2 were 0.87, 0.80, and 0.82, respectively. Applying the MPA model resulted in possible discharge of 67.7% of the patients with an acceptable CAD rate of 4.2%.

CONCLUSIONS

In this low-risk to intermediate-risk population, the MPA model provides a good risk stratification of presence or absence of CAD with a better ROC compared with traditional risk scores. The results are promising but need prospective confirmation.

Harmonizing multimodality imaging results using Bayesian analysis: the case of CT coronary angiography and CT-derived fractional flow reserve

Coronary computed tomographic angiography (CCTA) may provide both anatomic and CT fractional flow reserve data (CTFFR). The objective is to use Bayesian analysis to develop a model wherein the probability of significant coronary artery disease (CAD) by CTFFR can be determined given the prior probability (P) of the combined clinical and CCTA result. 172 patients referred for CCTA and subsequently underwent coronary angiography were automatically referred to CTFFR analysis. A clinical P risk score (CRS) was calculated per patient. CCTA exams were scored using CAD-RADS classification. CTFFR results were generated. CAD was defined as ≥ 3 RAD class for CCTA and ≤ .80 by CTFFR. P was calculated using CCTA and CTFFR accuracy from a prior clinical trial: post-test P for the CCTA result used the CRS as the prior risk, and CTFFR P used the post-test CRS + CCTA P as the prior risk (tri-variable). Patients were classified for each model into low (< 5%), intermediate, (5-70%) and high (> 70%) risk groups. There were 100 patients (58%), who had significant CAD at angiography. 58 patients had discordant CCTA/CTFFR results. The inclusion of the CRS and CRS + CCTA in the prior progressively reduced the intermediate risk cohort from 83 to 41% (p < 0.0001). Correct classifications (low-risk, negative angiogram plus high-risk, positive angiogram) increased by model: CRS = 12%, CRS + CCTA = 25%, CRS + CTFFR = 33%, CRS + CCTA + CTFFR = 44% (p < 0.001). Incorrect classifications were reduced to 15%. The tri-variable model performed better than either CCTA or CTFFR alone for all patients and for the sub-group with discordant imaging results. Discrepant CCTA and CTFFR results are present in one third of patients. The use of both the CRS and CCTA as the prior risk synergistically maximized the accuracy of the accuracy of the CTFFR technique.

Development and Validation of Retinal Vasculature Nomogram in Suspected Angina Due to Coronary Artery Disease

AIMS

To develop and validate a nomogram using retinal vasculature features and clinical variables to predict coronary artery disease (CAD) in patients with suspected angina.

METHODS

The prediction model consisting of 795 participants was developed in a training set of 508 participants with suspected angina due to CAD, and data were collected from January 2018 to June 2019. The held-out validation was conducted with 287 consecutive patients from July 2019 to November 2019. All patients with suspected CAD received optical coherence tomography angiography (OCTA) examination before undergoing coronary CT angiography. LASSO regression model was used for data reduction and feature selection. Multivariable logistic regression analysis was used to develop the retinal vasculature model for predicting the probability of the presence of CAD.

RESULTS

Three potential OCTA parameters including vessel density of the nasal and temporal perifovea in the superficial capillary plexus and vessel density of the inferior parafovea in the deep capillary plexus were further selected as independent retinal vasculature predictors. Model clinical electrocardiogram (ECG) OCTA (clinical variables＋ECG＋OCTA) was presented as the individual prediction nomogram, with good discrimination (AUC of 0.942 [95% CI, 0.923-0.961] and 0.897 [95% CI, 0.861-0.933] in the training and held-out validation sets, respectively) and good calibration. Decision curve analysis indicated the clinical applicability of this retinal vasculature nomogram.

CONCLUSIONS

The presented retinal vasculature nomogram based on individual probability can accurately identify the presence of CAD, which could improve patient selection and diagnostic yield of aggressive testing before determining a diagnosis.

Evaluating the Coronary Artery Disease Consortium Model and the Coronary Artery Calcium Score in Predicting Obstructive Coronary Artery Disease in a Symptomatic Mixed Asian Cohort

Background

The utility of a given pretest probability score in predicting obstructive coronary artery disease (CAD) is population dependent. Previous studies investigating the additive value of coronary artery calcium (CAC) on pretest probability scores were predominantly limited to Western populations. This retrospective study seeks to evaluate the CAD Consortium (CAD2) model in a mixed Asian cohort within Singapore with stable chest pain and to evaluate the incremental value of CAC in predicting obstructive CAD.

Methods and Results

Patients who underwent cardiac computed tomography and had chest pain were included. The CAD2 clinical model comprised of age, sex, symptom typicality, diabetes, hypertension, hyperlipidemia, and smoking status and was compared with the CAD2 extended model that added CAC to assess the incremental value of CAC scoring, as well as to the corresponding locally calibrated local assessment of the heart models. A total of 522 patients were analyzed (mean age 54±11 years, 43.1% female). The CAD2 clinical model obtained an area under the curve of 0.718 (95% CI, 0.668–0.767). The inclusion of CAC score improved the area under the curve to 0.896 (95% CI, 0.867–0.925) in the CAD2 models and from 0.767 (95% CI, 0.721–0.814) to 0.926 (95% CI, 0.900–0.951) in the local assessment of the heart models. The locally calibrated local assessment of the heart models showed better discriminative performance than the corresponding CAD2 models (P<0.05 for all).

Conclusions

The CAD2 model was validated in a symptomatic mixed Asian cohort and local calibration further improved performance. CAC scoring provided significant incremental value in predicting obstructive CAD.

Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia

BACKGROUND

Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFRML) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFRML compared to stress perfusion cardiovascular magnetic resonance (CMR) and tested if there is an additional value of CT-FFRML over coronary computed tomography angiography (cCTA).

METHODS

Our retrospective analysis included 269 vessels in 141 patients (mean age 67 ± 9 years, 78% males) who underwent clinically indicated cCTA and subsequent stress perfusion CMR within a period of 2 months. CT-FFRML values were calculated from standard cCTA.

RESULTS

CT-FFRML revealed no hemodynamic significance in 79% of the patients having ≥ 50% stenosis in cCTA. Chi2 values for the statistical relationship between CT-FFRML and stress perfusion CMR was significant (p < 0.0001). CT-FFRML and cCTA (≥ 70% stenosis) provided a per patient sensitivity of 88% (95%CI 64-99%) and 59% (95%CI 33-82%); specificity of 90% (95%CI 84-95%) and 85% (95%CI 78-91%); positive predictive value of 56% (95%CI 42-69%) and 36% (95%CI 24-50%); negative predictive value of 98% (95%CI 94-100%) and 94% (95%CI 90-96%); accuracy of 90% (95%CI 84-94%) and 82% (95%CI 75-88%) when compared to stress perfusion CMR. The accuracy of cCTA (≥ 50% stenosis) was 19% (95%CI 13-27%). The AUCs were 0.89 for CT-FFRML and 0.74 for cCTA (≥ 70% stenosis) and therefore significantly different (p < 0.05).

CONCLUSION

CT-FFRML compared to stress perfusion CMR as the reference standard shows high diagnostic power in the identification of patients with hemodynamically significant coronary artery stenosis. This could support the role of cCTA as gatekeeper for further downstream testing and may reduce the number of patients undergoing unnecessary invasive workup.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

This clinical practice guideline for the evaluation and diagnosis of chest pain provides recommendations and algorithms for clinicians to assess and diagnose chest pain in adult patients.

METHODS

A comprehensive literature search was conducted from November 11, 2017, to May 1, 2020, encompassing randomized and nonrandomized trials, observational studies, registries, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Collaboration, Agency for Healthcare Research and Quality reports, and other relevant databases. Additional relevant studies, published through April 2021, were also considered.

STRUCTURE

Chest pain is a frequent cause for emergency department visits in the United States. The "2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain" provides recommendations based on contemporary evidence on the assessment and evaluation of chest pain. This guideline presents an evidence-based approach to risk stratification and the diagnostic workup for the evaluation of chest pain. Cost-value considerations in diagnostic testing have been incorporated, and shared decision-making with patients is recommended.

Clinical values of resting electrocardiography in patients with known or suspected chronic coronary artery disease: a stress perfusion cardiac MRI study

Background

Electrocardiography (ECG) is an essential investigation in patients with chronic coronary artery disease (CAD). However, evidence regarding the diagnostic and prognostic value of ECG in this population is limited. Therefore, we sought to determine whether baseline ECG abnormalities were associated with myocardial ischemia and cardiac events in patients with known or suspected chronic CAD.

Methods

Consecutive patients with known (n = 146) or suspected chronic CAD (n = 349) referred for adenosine stress cardiac magnetic resonance (CMR) between 2011 and 2014 were enrolled. Resting ECGs were classified as major, minor, and no abnormalities. Predictors of myocardial ischemia on CMR and major adverse cardiac events (MACE) including cardiac death, nonfatal myocardial infarction, hospitalization for heart failure and late revascularization (> 180 days after CMR) were evaluated.

Results

Average age was 69 ± 11 years (51% men). One hundred and eighty-five patients (37.4%) had major and 154 (31.1%) had minor ECG abnormalities. In patients with suspected CAD, myocardial ischemia was presented in 83 patients (23.8%). Multivariable analysis demonstrated major ECG abnormality as the strongest predictor of myocardial ischemia (HR 2.51; 95% CI 1.44–4.36; p = 0.001). Adding ECG to clinical pretest probability models improved the prediction of myocardial ischemia in ROC analyses (p = 0.04). In the whole cohort (n = 495), 91 MACE occurred during the median follow-up period of 4.8 years. Multivariable analysis showed that diabetes mellites, history of heart failure, prior revascularization, left ventricular ejection fraction, ischemia, and major ECG abnormality were independent predictors of MACE.

Conclusion

Abnormal resting ECG is common in patients with known or suspected chronic CAD. ECG had important diagnostic and prognostic values in this population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02440-5.

2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

This clinical practice guideline for the evaluation and diagnosis of chest pain provides recommendations and algorithms for clinicians to assess and diagnose chest pain in adult patients.

METHODS

A comprehensive literature search was conducted from November 11, 2017, to May 1, 2020, encompassing randomized and nonrandomized trials, observational studies, registries, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Collaboration, Agency for Healthcare Research and Quality reports, and other relevant databases. Additional relevant studies, published through April 2021, were also considered.

STRUCTURE

Chest pain is a frequent cause for emergency department visits in the United States. The "2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain" provides recommendations based on contemporary evidence on the assessment and evaluation of chest pain. This guideline presents an evidence-based approach to risk stratification and the diagnostic workup for the evaluation of chest pain. Cost-value considerations in diagnostic testing have been incorporated, and shared decision-making with patients is recommended.

2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

This clinical practice guideline for the evaluation and diagnosis of chest pain provides recommendations and algorithms for clinicians to assess and diagnose chest pain in adult patients.

METHODS

A comprehensive literature search was conducted from November 11, 2017, to May 1, 2020, encompassing randomized and nonrandomized trials, observational studies, registries, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Collaboration, Agency for Healthcare Research and Quality reports, and other relevant databases. Additional relevant studies, published through April 2021, were also considered. Structure: Chest pain is a frequent cause for emergency department visits in the United States. The "2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain" provides recommendations based on contemporary evidence on the assessment and evaluation of chest pain. This guideline presents an evidence-based approach to risk stratification and the diagnostic workup for the evaluation of chest pain. Cost-value considerations in diagnostic testing have been incorporated, and shared decision-making with patients is recommended.

Diagnostic Performance of Dynamic Myocardial Perfusion Imaging Using Dual-Source Computed Tomography

BACKGROUND

Single-center studies indicated a high diagnostic accuracy of dynamic computed tomography perfusion (CTP) imaging in the diagnosis of coronary artery disease (CAD).

OBJECTIVES

This prospective multicenter study determined the diagnostic performance of combined coronary computed tomography angiography (CTA) and CTP for detecting hemodynamically significant CAD defined by invasive coronary angiography (ICA) with fractional flow reserve (FFR).

METHODS

Seven centers enrolled 174 patients with suspected or known CAD who were clinically referred for ICA. CTA and dynamic CTP were performed using dual-source CT before ICA. FFR was done as part of ICA in the case of 26% to 90% coronary diameter stenosis. Hemodynamically significant stenosis was defined as FFR of <0.8 or >90% stenosis on ICA.

RESULTS

The study protocol was completed in 157 participants, and hemodynamically significant stenosis was detected in 76 of 157 patients (48%) and 112 of 442 vessels (25%). According to receiver-operating characteristic curve analysis, adding dynamic CTP to CTA significantly increased the area under the curve from 0.65 (95% CI: 0.57-0.72) to 0.74 (95% CI: 0.66-0.81; P = 0.011) on the patient level, with decreased sensitivity (93% vs 72%; P < 0.001), improved specificity (36% vs 75%; P < 0.001), and improved overall accuracy (64% vs 74%; P < 0.001).

CONCLUSIONS

In this prospective multicenter study on dynamic CTP, the combination of anatomic assessment with coronary CTA and functional evaluation with dynamic CTP allowed more accurate identification of hemodynamically significant CAD compared with CTA alone. However, the clinical significance of this approach needs to be further investigated, including its usefulness in improving prognosis. (Assessment of Myocardial Perfusion Linked to Infarction and Fibrosis Explored With Dual-Source CT [AMPLIFiED]; UMIN000016353).

Pretest models for predicting abnormal stress single-photon emission computed tomography myocardial perfusion imaging

BACKGROUND

The frequency of abnormal stress SPECT myocardial perfusion imaging (MPS) decreased over the past decades despite an increase in the prevalence of cardiovascular risk factors. These findings strengthen the need to develop more effective strategies for appropriately referring patients with suspected coronary artery disease (CAD) to cardiac imaging. The aim of this study was to develop pretest assessment models for predicting abnormal stress MPS.

METHODS

We included 5,601 consecutive patients with suspected CAD, who underwent stress MPS at our academic center. Two different models were considered: a basic model including age, gender, and anginal symptoms; and a clinical model including also diabetes, hypertension, hypercholesterolemia, smoking, and family history of CAD.

RESULTS

In patients with abnormal MPS, the basic model classified more than 75% of patients as intermediate risk, whereas only 23% were incorrectly classified as low risk. In patients with normal MPS, 45% were correctly classified as low risk and none as high risk. Basic and clinical models had a limited discriminating capacity (area under the receiver operating characteristic curve 0.644 for basic model and 0.647 for clinical model) between individuals with and without abnormal stress MPS. The decision curve analysis demonstrates a high net benefit across a range of threshold probabilities from ~ 15% to ~30% for both models.

CONCLUSIONS

A pretest risk stratification based on traditional cardiovascular risk factors has a limited value for predicting an abnormal stress MPS in patients with suspected CAD. However, selecting a proper threshold probability enhances the appropriateness of referral to stress MPS.

Analysis of clinical risk models vs. clinician's assessment for prediction of coronary artery disease among predominantly female population

INTRODUCTION

Multiple risk models are used to predict the presence of obstructive coronary artery disease (CAD) in patients with chest pain. We aimed to compare the performance of these models to an experienced cardiologist's assessment utilizing coronary angiography (CA) as a reference.

MATERIALS AND METHODS

We prospectively enrolled patients without known CAD referred for elective CA. We assessed pretest probability of CAD using the following risk models: Diamond-Forrester (original and updated), Duke Clinical score, ACC/AHA, CAD consortium (basic and clinical) and PROMISE minimal risk tool. All patients completed self-administrative Rose angina questionnaire. Independently, an experienced cardiologist assessed the patients to provide a binary prediction of obstructive CAD prior to CA. Obstructive CAD was defined as >80% stenosis in epicardial coronary arteries by visual assessment, or fractional flow reserve <0.80 in intermediate lesions (30-80%).

RESULTS

A total of 150 patients were recruited (100 women, 50 men). Mean age was 58 (32-78) years. Obstructive CAD was found in 31 patients (21%). The area under the curve (AUC) for all the clinical risk prediction models (except the Duke Clinical Score, AUC 0.73, P = 0.07) was significantly lower compared with the clinician's assessment (AUC 0.51-0.65 vs. 0.81, respectively, P < 0.01). The clinician's assessment had sensitivity comparable to the Duke Clinical score, which was higher than all other clinical models. There was no difference in prediction performance on the basis of sex in this predominantly female population.

DISCUSSION/CONCLUSION

In stable patients with chest pain and suspected CAD, current clinical risk models which are universally based upon the characteristics of the chest pain, show suboptimal performance in predicting obstructive CAD. These findings have important clinical implications, as current appropriateness criteria for recommending CA are on the basis of these risk models.

Predictive Added Value of Selected Plasma Lipids to a Re-estimated Minimal Risk Tool

Background: Lipidomics is emerging for biomarker discovery in cardiovascular disease, and circulating lipids are increasingly incorporated in risk models to predict cardiovascular events. Moreover, specific classes of lipids, such as sphingomyelins, ceramides, and triglycerides, have been related to coronary artery disease (CAD) severity and plaque characteristics. To avoid unnecessary testing, it is important to identify individuals at low CAD risk. The only pretest model available so far to rule out the presence of coronary atherosclerosis in patients with chest pain, but normal coronary arteries, is the minimal risk tool (MRT).

Aim: Using state-of-the-art statistical methods, we aim to verify the additive predictive value of a set of lipids, derived from targeted plasma lipidomics of suspected CAD patients, to a re-estimated version of the MRT for ruling out the presence of coronary atherosclerosis assessed by coronary CT angiography (CCTA).

Methods: Two hundred and fifty-six subjects with suspected stable CAD recruited from five European countries within H2020-SMARTool, undergoing CCTA and blood sampling for clinical biochemistry and lipidomics, were selected. The MRT was validated by regression methods and then re-estimated (reMRT). The reMRT was used as a baseline model in a likelihood ratio test approach to assess the added predictive value of each lipid from 13 among ceramides, triglycerides, and sphingomyelins. Except for one lipid, the analysis was carried out on more than 240 subjects for each lipid. A sensitivity analysis was carried out by considering two alternative models developed on the cohort as baseline models.

Results: In 205 subjects, coronary atherosclerosis ranged from minimal lesions to overt obstructive CAD, while in 51 subjects (19.9%) the coronary arteries were intact. Four triglycerides and seven sphingomyelins were significantly (p < 0.05) and differentially expressed in the two groups and, at a lesser extent, one ceramide (p = 0.067). The probability of being at minimal risk was significantly better estimated by adding either Cer(d18:1/16:0) (p = 0.01), SM(40:2) (p = 0.04), or SM(41:1) at a lesser extent (p = 0.052) to reMRT than by applying the reMRT alone. The sensitivity analysis confirmed the relevance of these lipids. Furthermore, the addition of SM(34:1), SM(38:2), SM(41:2), and SM(42:4) improved the predictive performance of at least one of the other baseline models. None of the selected triglycerides was found to provide an added value.

Conclusions: Plasma lipidomics can be a promising source of diagnostic and prognostic biomarkers in cardiovascular disease, exploitable not only to assess the risk of adverse events but also to identify subjects without coronary atherosclerosis, thus reducing unnecessary further testing in normal subjects.

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.

Predicting Coronary Artery Disease in Primary Care: Development and Validation of a Diagnostic Risk Score for Major Ethnic Groups in Southeast Asia

BACKGROUND

Coronary artery disease (CAD) risk prediction tools are useful decision supports. Their clinical impact has not been evaluated amongst Asians in primary care.

OBJECTIVE

We aimed to develop and validate a diagnostic prediction model for CAD in Southeast Asians by comparing it against three existing tools.

DESIGN

We prospectively recruited patients presenting to primary care for chest pain between July 2013 and December 2016. CAD was diagnosed at tertiary institution and adjudicated. A logistic regression model was built, with validation by resampling. We validated the Duke Clinical Score (DCS), CAD Consortium Score (CCS), and Marburg Heart Score (MHS).

MAIN MEASURES

Discrimination and calibration quantify model performance, while net reclassification improvement and net benefit provide clinical insights.

KEY RESULTS

CAD prevalence was 9.5% (158 of 1658 patients). Our model included age, gender, type 2 diabetes mellitus, hypertension, smoking, chest pain type, neck radiation, Q waves, and ST-T changes. The C-statistic was 0.808 (95% CI 0.776-0.840) and 0.815 (95% CI 0.782-0.847), for model without and with ECG respectively. C-statistics for DCS, CCS-basic, CCS-clinical, and MHS were 0.795 (95% CI 0.759-0.831), 0.756 (95% CI 0.717-0.794), 0.787 (95% CI 0.752-0.823), and 0.661 (95% CI 0.621-0.701). Our model (with ECG) correctly reclassified 100% of patients when compared with DCS and CCS-clinical respectively. At 5% threshold probability, the net benefit for our model (with ECG) was 0.063. The net benefit for DCS, CCS-basic, and CCS-clinical was 0.056, 0.060, and 0.065.

CONCLUSIONS

PRECISE (Predictive Risk scorE for CAD In Southeast Asians with chEst pain) performs well and demonstrates utility as a clinical decision support for diagnosing CAD among Southeast Asians.

Diagnosis and Management of Stable Angina: A Review

IMPORTANCE

Nearly 10 million US adults experience stable angina, which occurs when myocardial oxygen supply does not meet demand, resulting in myocardial ischemia. Stable angina is associated with an average annual risk of 3% to 4% for myocardial infarction or death. Diagnostic tests and medical therapies for stable angina have evolved over the last decade with a better understanding of the optimal use of coronary revascularization.

OBSERVATIONS

Coronary computed tomographic angiography is a first-line diagnostic test in the evaluation of patients with stable angina due to higher sensitivity and comparable specificity compared with imaging-based stress testing. Moreover, coronary computed tomographic angiography allows detection of nonobstructive atherosclerosis that would not be identified with other noninvasive imaging modalities, improving risk assessment and potentially triggering more appropriate allocation of preventive therapies. Novel therapies treating lipids (proprotein convertase subtilisin/kexin type 9 inhibitors, ezetimibe, and icosapent ethyl) and type 2 diabetes (sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists) have improved cardiovascular outcomes in patients with stable ischemic heart disease when added to usual care. Randomized clinical trials showed no improvement in the rates of mortality or myocardial infarction with revascularization (largely by percutaneous coronary intervention) compared with optimal medical therapy alone, even in the setting of moderate to severe ischemia. In contrast, revascularization provides a meaningful benefit on angina and quality of life compared with antianginal therapies. Measures of the effect of angina on a patient's quality of life should be integrated into the clinic encounter to assist with the decision to proceed with revascularization.

CONCLUSIONS AND RELEVANCE

For patients with stable angina, emphasis should be placed on optimizing lifestyle factors and preventive medications such as lipid-lowering and antiplatelet agents to reduce the risk for cardiovascular events and death. Antianginal medications, such as β-blockers, nitrates, or calcium channel blockers, should be initiated to improve angina symptoms. Revascularization with percutaneous coronary intervention should be reserved for patients in whom angina symptoms negatively influence quality of life, generally after a trial of antianginal medical therapy. Shared decision-making with an informed patient is important for effective treatment of stable angina.

Appropriate use of multimodality stress testing for chest pain in new patient referrals to cardiologists

OBJECTIVE

In response to the growing use of imaging-based cardiac stress tests in the evaluation of stable ischemic heart disease, professional societies have developed appropriate use criteria (AUC). AUC will soon be linked to reimbursement of advanced diagnostic imaging for Medicare beneficiaries via Clinical Decision Support Mechanisms (CDSMs). We sought to characterize the frequency and type of stress test utilization for chest pain referrals evaluated by cardiologists and determine appropriateness.

METHODS

We conducted a retrospective review of new patient referrals seen by general cardiologists at an academic medical center between 2016 and 2017 for a diagnosis of chest pain or angina. Type of stress test ordered, if any, and its appropriateness (Appropriate, May be appropriate, and Rarely appropriate) were ascertained based on the 2013 multimodality AUC guideline document.

RESULTS

There were 535 total outpatients. After applying exclusion criteria, there were 349 patients in the sample; the average age was 52 ± 15 years and 53% were female. Most chest pain was nonanginal (65%). Pretest probability of CAD was most commonly intermediate (54%). A total of 183 patients (52%) were referred for stress testing. The majority of stress tests were considered appropriate (82%) by AUC.

CONCLUSION

Most patients referred to cardiologists for evaluation of chest pain or angina had nonanginal chest pain and an intermediate pretest probability of CAD. Stress testing was ordered in about half of these patients and the majority were considered appropriate by AUC. These findings suggest that indiscriminate use of CDSMs may not be warranted.

Selfies in cardiovascular medicine: welcome to a new era of medical diagnostics

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CCTA in the diagnosis of coronary artery disease

The world of cardiac imaging is proposing to physicians an ever-increasing spectrum of options and tools with the disadvantages of patients presently submitted to multiple, sequential, time-consuming, and costly diagnostic procedures and tests, sometimes with contradicting results. In the last two decades, the CCTA has evolved into a valuable diagnostic test in today's patient care, changing the official existing guidelines and clinical practice with a pivotal role to exclude significant CAD, in the referral of patients to the Cath-Lab, in the follow-up after coronary revascularization, and finally in the cardiovascular risk stratification.

Feasibility of using deep learning to detect coronary artery disease based on facial photo

Aims

Facial features were associated with increased risk of coronary artery disease (CAD). We developed and validated a deep learning algorithm for detecting CAD based on facial photos.

Methods and results

We conducted a multicentre cross-sectional study of patients undergoing coronary angiography or computed tomography angiography at nine Chinese sites to train and validate a deep convolutional neural network for the detection of CAD (at least one ≥50% stenosis) from patient facial photos. Between July 2017 and March 2019, 5796 patients from eight sites were consecutively enrolled and randomly divided into training (90%, n = 5216) and validation (10%, n = 580) groups for algorithm development. Between April 2019 and July 2019, 1013 patients from nine sites were enrolled in test group for algorithm test. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using radiologist diagnosis as the reference standard. Using an operating cut point with high sensitivity, the CAD detection algorithm had sensitivity of 0.80 and specificity of 0.54 in the test group; the AUC was 0.730 (95% confidence interval, 0.699–0.761). The AUC for the algorithm was higher than that for the Diamond–Forrester model (0.730 vs. 0.623, P &lt; 0.001) and the CAD consortium clinical score (0.730 vs. 0.652, P &lt; 0.001).

Conclusion

Our results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort. This technique may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community. Further studies to develop a clinical available tool are warranted.

Identifying low-risk chest pain in the emergency department: Obstructive coronary artery disease and major adverse cardiac events

BACKGROUND

Accurate risk stratification for obstructive coronary artery disease (CAD) and major cardiac adverse events (MACE) is important in emergency departments. We compared six established chest pain risk scores (the HEART score, CAD basic model, CAD clinical model, TIMI, GRACE, uDF) for prediction of obstructive CAD and MACE.

METHODS

Patients who presented to the emergency department with chest pain or symptoms of suspected CAD and underwent coronary computed tomographic angiography were analyzed. The primary endpoint was adverse outcomes including the presence of obstructive CAD (≥50% stenosis) and the occurrence of MACE within 6 weeks. We compared the risk scores by the area under the receiver-operating characteristic curve (AUC) and calculated their respective net reclassification index (NRI).

RESULTS

Adverse outcomes occurred in 285 (28.4%) out of the 1002 patients included. For the prediction of adverse outcomes, the AUC of the HEART score (0.792) was superior to those of the CAD clinical model (0.760), CAD basic model (0.749), TIMI (0.749), uDF (0.703), and GRACE (0.653). In terms of the NRI, the HEART score significantly improved the reclassification abilities of the uDF (0.39), GRACE score (0.27), CAD basic model (0.11), TIMI (0.10), and CAD clinical model (0.08) (all P < 0.05). The HEART score also had the highest negative predictive value as well (0.893).

CONCLUSIONS

The HEART score was superior to other cardiac risk scores in predicting both obstructive CAD and MACE. However, due to the high false-negative rate (11%) of the HEART score, its use for identifying low-risk patients should be considered with caution.

Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study

BACKGROUND

Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization.

METHODS

For prediction of obstructive CAD, the entire ICA arm of the study, comprising 719 patients was used. For revascularization, 1,028 patients were randomized to invasive coronary angiography (ICA) or coronary computed tomographic angiography (CCTA). Data was randomly split into 80% training 20% test sets for building and validation. Models used extreme gradient boosting (XGBoost).

RESULTS

Mean age was 60.6 ± 11.5 years and 64.3% were female. For the prediction of obstructive CAD, the AUC was significantly higher for ML at 0.779 (95% CI: 0.672-0.886) than for CAD2 (0.696 [95% CI: 0.594-0.798]) (P = 0.01). BMI, age, and angina severity were the most important variables. For revascularization, the model obtained an overall area under the receiver-operation curve (AUC) of 0.958 (95% CI = 0.933-0.983). Performance did not differ whether the imaging parameters used were from ICA (AUC 0.947, 95% CI = 0.903-0.990) or CCTA (AUC 0.941, 95% CI = 0.895-0.988) (P = 0.90). The ML model obtained sensitivity and specificity of 89.2% and 92.9%, respectively. Number of vessels with ≥70% stenosis, maximum segment stenosis severity (SSS) and body mass index (BMI) were the most important variables. Exclusion of imaging variables resulted in performance deterioration, with an AUC of 0.705 (95% CI 0.614-0.795) (P <0.0001).

CONCLUSIONS

For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores. In this ML model, imaging variables were most associated with revascularization. Imaging modality did not influence model performance. Removal of imaging variables reduced model performance.

Impact of sex-specific differences in calculating the pretest probability of obstructive coronary artery disease in symptomatic patients: a coronary computed tomographic angiography study

Objectives

Little is known about the impact of sex-specific differences in calculating the pretest probability (PTP) of obstructive coronary artery disease. We sought to determine whether the calculation of PTP differ by sex in symptomatic patients referred to coronary computed tomographic angiography (CCTA).

Patients and methods

The characteristics of 5777 men and women who underwent CCTA were compared. For each patient, PTP was calculated according to the updated Diamond–Forrester method (UDFM) and the Duke clinical score (DCS), respectively. Follow-up clinical data were also recorded. Area under the receiver operating characteristic curve, integrated discrimination improvement, net reclassification improvement, and the Hosmer–Lemeshow goodness-of-fit statistic were used to assess the models’ performance.

Results

The area under the receiver operating characteristic curve of UDFM and DCS showed little difference in men (0.782 vs. 0.785, P=0.4708) and women (0.668 vs. 0.654, P=0.1255), and calibration of neither model was satisfactory. Compared with UDFM, DCS showed positive integrated discrimination improvement (10% in men, P<0.0001, and 8% in women, P<0.0001, respectively), net reclassification improvement (12.17% in men, P<0.0001, and 27.19% in women, P<0.0001, respectively), and obviously reduced unnecessary noninvasive testing for women with negative CCTA.

Conclusion

Although the performance of neither model was favorable, DCS offered a more accurate calculation of PTP than UDFM and application of DCS instead of UDFM would result in a significant decrease in inappropriate testing, especially in women.

Frequency and Significance of Coronary Artery Disease and Myocardial Bridging in Patients With Hypertrophic Cardiomyopathy

The etiology of chest pain in hypertrophic cardiomyopathy (HC) is diverse and includes coronary artery disease (CAD) as well as HC-specific causes. Myocardial bridging (MB) has been associated with HC, chest pain, and accelerated atherosclerosis. We compared HC patients with age-, gender- and CAD pre-test probability-matched outpatients presenting with chest pain to investigate differences in the presence of MB and CAD using coronary computed tomography angiography (CCTA). We studied 84 HC patients who underwent CCTA and compared these with 168 matched controls (age 54 ± 11 years, 70% men, pre-test probability 12% [5% to 32%]). MB, calcium score, plaque morphology and presence and extent of CAD were assessed for each patient. Linear mixed models were used to assess differences between cases and controls. MB was more often seen in HC patients (50% vs 25%, p <0.001). Calcium score and the presence of obstructive CAD were similar in both groups (9 [0 to 225] vs 4 [0 to 82] and 18% vs 19%; p = 0.22 and p = 0.82). In the HC group, MB was associated with pathogenic DNA variants (p = 0.04), but not with the presence of chest pain (74% vs 76%, p = 0.8), nor with worse outcome (log-rank p = 0.30). In conclusion, the prevalence and extent of CAD was equal among patients with and without HC, demonstrating that pre-test risk prediction using the CAD Consortium clinical risk score performs well in HC patients. MB was twice as prevalent in the HC group compared with matched controls, but was not associated with chest pain or decreased event-free survival in these patients.

Screening coronary artery disease with computed tomography angiogram should limit normal invasive coronary angiogram, regardless of pretest probability

BACKGOUND

Performing functional testing (FT) or a computed tomography angiogram (CCTA) before invasive coronary angiogram (ICA) is recommended for coronary artery disease (CAD). We aimed to evaluate, in a real life setting, the rate of strictly normal ICA following a positive noninvasive test result.

METHODS

We included all patients who underwent an ICA with a prior positive FT or CCTA. Patients were categorized in 5 subgroups, according to pretest probability (PTP) of having a CAD. Main results of ICA were defined as normal ICA, nonobstructive CAD (non-oCAD), and obstructive CAD (oCAD).

RESULTS

For 4,952 patients who underwent ICA following either a positive FT (3276, 66.2%) or CCTA (1676, 33.8%) result, the PTP was (1) low (<15%; n = 968, 19.5%), (2) lower intermediate (15%-35%; n = 1336, 27.0%), (3) higher intermediate (35%-50%; n = 806, 16.3%), (4) high (50%-65%; n = 806, 17.7%), and (5) very high (> 65%; n = 965, 19.5%). ICA showed no CAD (819 patients, 16.5%), non-oCAD (1,193 patients, 24.1%), or oCAD (2940 patients, 59.4%). Without considering the PTP values, CCTA compared to FT showed less frequently normal ICA (7% vs 16.5%), and more frequently CAD (non-oCAD 27.9% vs 22.2%; oCAD 65.1% vs 56.4%) (all P < .0001). When we considered the different PTP values, CCTA always showed lower rates of normal ICA than the FT. In low- and lower intermediate-risk patients, CCTA detected more frequently oCAD compared to FT (P < .001).

CONCLUSIONS

CCTA is a better alternative than FT to limit unnecessary ICA regardless of PTP value, without missing abnormal ICA.

Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio

BACKGROUND

The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFRML) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFRTM) as a novel invasive resting pressure-wire index for detecting hemodynamically significant coronary artery stenosis.

METHODS

In our single center study, patients with coronary artery disease (CAD) who had a clinically indicated coronary computed tomography angiography (cCTA) and subsequent invasive coronary angiography (ICA) with pressure wire-measurement were included. On-site prototype CT-FFRML software and on-site CT-plaque software were used to calculate the hemodynamic relevance of coronary stenosis.

RESULTS

We enrolled 33 patients (70% male, mean age 68 ± 12 years). On a per-lesion basis, the area under the receiver operating characteristic curve (AUC) of CT-FFRML (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter4 (LL/MLD4; 0.80), minimal luminal diameter (MLD; 0.77), remodeling index (RI; 0.76), degree of luminal diameter stenosis (0.75), and minimal luminal area (MLA; 0.75).

CONCLUSION

CT-FFRML and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFRML had the best discriminatory power, using RFRTM as the reference standard.

Additional Value of Machine-Learning Computed Tomographic Angiography-Based Fractional Flow Reserve Compared to Standard Computed Tomographic Angiography

Background: Machine-learning-based computed-tomography-derived fractional flow reserve (CT-FFR_ML) obtains a hemodynamic index in coronary arteries. We examined whether it could reduce the number of invasive coronary angiographies (ICA) showing no obstructive lesions. We further compared CT-FFR_ML-derived measurements to clinical and CT-derived scores. Methods: We retrospectively selected 88 patients (63 ± 11years, 74% male) with chronic coronary syndrome (CCS) who underwent clinically indicated coronary computed tomography angiography (cCTA) and ICA. cCTA image data were processed with an on-site prototype CT-FFR_ML software. Results: CT-FFR_ML revealed an index of >0.80 in coronary vessels of 48 (55%) patients. This finding was corroborated in 45 (94%) patients by ICA, yet three (6%) received revascularization. In patients with an index ≤ 0.80, three (8%) of 40 were identified as false positive. A total of 48 (55%) patients could have been retained from ICA. CT-FFR_ML (AUC = 0.96, p ≤ 0.0001) demonstrated a higher diagnostic accuracy compared to the pretest probability or CT-derived scores and showed an excellent sensitivity (93%), specificity (94%), positive predictive value (PPV; 93%) and negative predictive value (NPV; 94%). Conclusion: CT-FFR_ML could be beneficial for clinical practice, as it may identify patients with CAD without hemodynamical significant stenosis, and may thus reduce the rate of ICA without necessity for coronary intervention.

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.

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

AIMS

Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA).

METHODS AND RESULTS

The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features.

CONCLUSION

A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.