The External Validity of Prediction Models for the Diagnosis of Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: Insights From the PROMISE Trial

Evaluating the American Heart Association/American College of Cardiology Guideline-Recommended and Contemporary Pretest Probability Models in a Mixed Asian Cohort: The Contribution of Coronary Artery Calcium

BACKGROUND

Most pretest probability (PTP) tools for obstructive coronary artery disease (CAD) were Western -developed. The most appropriate PTP models and the contribution of coronary artery calcium score (CACS) in Asian populations remain unknown. In a mixed Asian cohort, we compare 5 PTP models: local assessment of the heart (LAH), CAD Consortium (CAD2), risk factor-weighted clinical likelihood, the American Heart Association/American College of Cardiology and the European Society of Cardiology PTP and 3 extended versions of these models that incorporated CACS: LAH(CACS), CAD2(CACS), and the CACS-clinical likelihood.

METHODS AND RESULTS

The study cohort included 771 patients referred for stable chest pain. Obstructive CAD prevalence was 27.5%. Calibration, area under the receiver-operating characteristic curves (AUC) and net reclassification index were evaluated. LAH clinical had the best calibration (χ2 5.8; P=0.12). For CACS models, LAH(CACS) showed least deviation between observed and expected cases (χ2 37.5; P<0.001). There was no difference in AUCs between the LAH clinical (AUC, 0.73 [95% CI, 0.69-0.77]), CAD2 clinical (AUC, 0.72 [95% CI, 0.68-0.76]), risk factor-weighted clinical likelihood (AUC, 0.73 [95% CI: 0.69-0.76) and European Society of Cardiology PTP (AUC, 0.71 [95% CI, 0.67-0.75]). CACS improved discrimination and reclassification of the LAH(CACS) (AUC, 0.88; net reclassification index, 0.46), CAD2(CACS) (AUC, 0.87; net reclassification index, 0.29) and CACS-CL (AUC, 0.87; net reclassification index, 0.25).

CONCLUSIONS

In a mixed Asian cohort, Asian-derived LAH models had similar discriminatory performance but better calibration and risk categorization for clinically relevant PTP cutoffs. Incorporating CACS improved discrimination and reclassification. These results support the use of population-matched, CACS-inclusive PTP tools for the prediction of obstructive CAD.

Comparison of the RF-CL and CACS-CL models to estimate the pretest probability of obstructive coronary artery disease and predict prognosis in patients with stable chest pain and diabetes mellitus

Background

The most appropriate tool for estimating the pretest probability (PTP) of obstructive coronary artery disease (CAD) in patients with diabetes mellitus (DM) and stable chest pain (SCP) remains unknown. Therefore, we aimed to validate and compare two recent models, namely, the risk factor-weighted clinical likelihood (RF-CL) model and coronary artery calcium score (CACS)-weighted clinical likelihood (CACS-CL) model, in these patient populations.

Methods

A total of 1,245 symptomatic patients with DM, who underwent CACS and coronary computed tomographic angiography (CCTA) scan, were identified and followed up. PTP of obstructive CAD for each patient was estimated using the RF-CL model and CACS-CL model, respectively. Area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to assess the performance of models. The associations of major adverse cardiovascular events (MACE) with risk groups were evaluated using Cox proportional hazards regression.

Results

Compared with the RF-CL model, the CACS-CL model revealed a larger AUC (0.856 vs. 0.782, p = 0.0016), positive IDI (12%, p < 0.0001) and NRI (34%, p < 0.0001), stronger association to MACE (hazard ratio: 0.26 vs. 0.38) and less discrepancy between observed and predicted probabilities, resulting in a more effective risk assessment to optimize downstream clinical management.

Conclusion

Among patients with DM and SCP, the incorporation of CACS into the CACS-CL model resulted in a more accurate estimation for PTP and prediction of MACE. Utilizing the CACS-CL model, instead of the RF-CL model, might have greater potential to avoid unnecessary and omissive cardiovascular imaging testing with minimal cost.

Diagnostic performance of clinical likelihood models of obstructive coronary artery disease to predict myocardial perfusion defects

AIMS

Clinical likelihood (CL) models are designed based on a reference of coronary stenosis in patients with suspected obstructive coronary artery disease. However, a reference standard for myocardial perfusion defects (MPDs) could be more appropriate. We aimed to investigate the ability of the 2019 European Society of Cardiology pre-test probability (ESC-PTP), the risk-factor-weighted (RF-CL) model, and coronary artery calcium score-weighted (CACS-CL) model to diagnose MPDs.

METHODS AND RESULTS

Symptomatic stable de novo chest pain patients (n = 3374) underwent coronary computed tomography angiography and subsequent myocardial perfusion imaging by single-photon emission computed tomography, positron emission tomography, or cardiac magnetic resonance. For all modalities, MPD was defined as coronary computed tomography angiography with suspected stenosis and stress-perfusion abnormality in ≥2 segments. The ESC-PTP was calculated based on age, sex, and symptom typicality, and the RF-CL and CACS-CL additionally included a number of risk factors and CACS. In total, 219/3374 (6.5%) patients had an MPD. Both the RF-CL and the CACS-CL classified substantially more patients to low CL (<5%) of obstructive coronary artery disease compared with the ESC-PTP (32.5 and 54.1 vs. 12.0%, P < 0.001) with preserved low prevalences of MPD (<2% for all models). Compared with the ESC-PTP [area under the receiver-operating characteristic curve (AUC) 0.74 (0.71-0.78)], the discrimination of having an MPD was higher for the CACS-CL model [AUC 0.88 (0.86-0.91), P < 0.001], while it was similar for the RF-CL model [AUC 0.73 (0.70-0.76), P = 0.32].

CONCLUSION

Compared with basic CL models, the RF-CL and CACS-CL models improve down classification of patients to a very low-risk group with a low prevalence of MPD.

Increasing frequency of dyspnea among patients referred for cardiac stress testing

OBJECTIVE

To assess the frequency, change in prevalence, and prognostic significance of dyspnea among contemporary patients referred for cardiac stress testing.

PATIENTS AND METHODS

We evaluated the prevalence of dyspnea and its relationship to all-cause mortality among 33,564 patients undergoing stress/rest SPECT-MPI between January 1, 2002 and December 31, 2017. Dyspnea was assessed as a single-item question. Patients were divided into three temporal groups.

RESULTS

The overall prevalence of dyspnea in our cohort was 30.2%. However, there was a stepwise increase in the temporal prevalence of dyspnea, which was present in 25.6% of patients studied between 2002 and 2006, 30.5% of patients studied between 2007 and 2011, and 38.7% of patients studied between 2012 and 2017. There was a temporal increase in the prevalence of dyspnea in each age, symptom, and risk factor subgroup. The adjusted hazard ratio for mortality was higher among patients with dyspnea vs those without dyspnea both among all patients, and within each chest pain subgroup.

CONCLUSIONS

Dyspnea has become increasingly prevalent among patients referred for cardiac stress testing and is now present among nearly two-fifths of contemporary cohorts referred for stress-rest SPECT-MPI. Prospective study is needed to standardize the assessment of dyspnea and evaluate the reasons for its increasing prevalence.

Integrated myocardial flow reserve (iMFR) assessment: optimized PET blood flow quantification for diagnosis of coronary artery disease

PURPOSE

Distinguishing obstructive epicardial coronary artery disease (CAD) from microvascular dysfunction and diffuse atherosclerosis would be of immense benefit clinically. However, quantitative measures of absolute myocardial blood flow (MBF) integrate the effects of focal epicardial stenosis, diffuse atherosclerosis, and microvascular dysfunction. In this study, MFR and relative perfusion quantification were combined to create integrated MFR (iMFR) which was evaluated using data from a large clinical registry and an international multi-center trial and validated against invasive coronary angiography (ICA).

METHODS

This study included 1,044 clinical patients referred for 82Rb rest/stress positron emission tomography myocardial perfusion imaging and ICA, along with 231 patients from the Flurpiridaz 301 trial (clinicaltrials.gov NCT01347710). MFR and relative perfusion quantification were combined to create an iMFR map. The incremental value of iMFR was evaluated for diagnosis of obstructive stenosis, adjusted for patient demographics and pre-test probability of CAD. Models for high-risk anatomy (left main or three-vessel disease) were also constructed.

RESULTS

iMFR parameters of focally impaired perfusion resulted in best fitting diagnostic models. Receiver-operating characteristic analysis showed a slight improvement compared to standard quantitative perfusion approaches (AUC 0.824 vs. 0.809). Focally impaired perfusion was also associated with high-risk CAD anatomy (OR 1.40 for extent, and OR 2.40 for decreasing mean MFR). Diffusely impaired perfusion was associated with lower likelihood of obstructive CAD, and, in the absence of transient ischemic dilation (TID), with lower likelihood of high-risk CAD anatomy.

CONCLUSIONS

Focally impaired perfusion extent derived from iMFR assessment is a powerful incremental predictor of obstructive CAD while diffusely impaired perfusion extent can help rule out obstructive and high-risk CAD in the absence of TID.

A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease

Background

Although machine learning (ML)-based prediction of coronary artery disease (CAD) has gained increasing attention, assessment of the severity of suspected CAD in symptomatic patients remains challenging.

Methods

The training set for this study consisted of 284 retrospective participants, while the test set included 116 prospectively enrolled participants from whom we collected 53 baseline variables and coronary angiography results. The data was pre-processed with outlier processing and One-Hot coding. In the first stage, we constructed a ML model that used baseline information to predict the presence of CAD with a dichotomous model. In the second stage, baseline information was used to construct ML regression models for predicting the severity of CAD. The non-CAD population was included, and two different scores were used as output variables. Finally, statistical analysis and SHAP plot visualization methods were employed to explore the relationship between baseline information and CAD.

Results

The study included 269 CAD patients and 131 healthy controls. The eXtreme Gradient Boosting (XGBoost) model exhibited the best performance amongst the different models for predicting CAD, with an area under the receiver operating characteristic curve of 0.728 (95% CI 0.623-0.824). The main correlates were left ventricular ejection fraction, homocysteine, and hemoglobin (p < 0.001). The XGBoost model performed best for predicting the SYNTAX score, with the main correlates being brain natriuretic peptide (BNP), left ventricular ejection fraction, and glycated hemoglobin (p < 0.001). The main relevant features in the model predictive for the GENSINI score were BNP, high density lipoprotein, and homocysteine (p < 0.001).

Conclusions

This data-driven approach provides a foundation for the risk stratification and severity assessment of CAD.

Clinical Trial Registration

The study was registered in www.clinicaltrials.gov protocol registration system (number NCT05018715).

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.

Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry

BACKGROUND AND HYPOTHESIS

The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner.

METHODS

From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model.

RESULTS

The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis.

CONCLUSIONS

This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.

Clinical Application of Myocardial Perfusion SPECT in Patients with Suspected or Known Coronary Artery Disease. What Role in the Multimodality Imaging Era?

Myocardial perfusion single photon emission computed tomography (SPECT) is widely used in assessing coronary artery disease (CAD) owing to its proven efficacy in extensive clinical experience. Like other functional tests, myocardial SPECT is recommended for the diagnosis of obstructive CAD, risk stratification assessment, and treatment decision making. Besides quantifying left ventricular volume, global and regional function by electrocardiography (ECG)-gated acquisition, myocardial SPECT can identify myocardial ischemia, scars, stunning, and viable hibernating myocardium. It provides comprehensive functional data across the spectrum of CAD and a cost-effective strategy in patients with intermediate pre-test probability of CAD or with a history of ischemic cardiomyopathy. With ongoing advances in cardiovascular prevention and risk factor management many patients referred for testing now have a low-to-intermediate probability of CAD. Besides, CAD has become a chronic condition resulting from novel therapeutic strategies. Against this background, approaches combining anatomical and functional tests in sequence or simultaneously include coronary artery calcium score integrated with perfusion imaging or fusion SPECT/coronary computed tomography angiography (CCTA). In this review we summarize current indications for myocardial perfusion SPECT and integration of SPECT with other imaging techniques to improve diagnostic performance, patient management, and outcome prediction in CAD.

Comparison of risk assessment strategies incorporating coronary artery calcium score with estimation of pretest probability to defer cardiovascular testing in patients with stable chest pain

BACKGROUND

The risk assessment of patients with stable chest pain (SCP) to defer further cardiovascular testing is crucial, but the most appropriate risk assessment strategy remains unknown. We aimed to compare current strategies to identify low risk SCP patients.

METHODS

5289 symptomatic patients who had undergone coronary artery calcium score (CACS) and coronary computed tomographic angiography scan were identified and followed. Pretest probability (PTP) of obstructive coronary artery disease (CAD) for every patient was estimated according to European Society of Cardiology (ESC)-PTP model and CACS-weighted clinical likelihood (CACS-CL) model, respectively. Based on the 2019 ESC guideline-determined risk assessment strategy (ESC strategy) and CACS-CL model-based risk assessment strategy (CACS-CL strategy), all patients were divided into low and high risk group, respectively. Area under receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) was used.

RESULTS

CACS-CL model provided more robust estimation of PTP than ESC-PTP model did, with a larger AUC (0.838 versus 0.735, p < 0.0001), positive IDI (9%, p < 0.0001) and less discrepancy between observed and predicted probabilities. As a result, compared to ESC strategy which only applied CACS-CL model to patients with borderline ESC-PTP, CACS-CL strategy incorporating CACS with estimation of PTP to entire SCP patients indicated a positive NRI (19%, p < 0.0001) and a stronger association to major adverse cardiovascular events, with hazard ratios: 3.97 (95% confidence intervals: 2.75-5.72) versus 5.11 (95% confidence intervals: 3.40-7.69).

CONCLUSION

The additional use of CACS for all SCP patients in CACS-CL strategy improved the risk assessment of SCP patients to identify individuals at low risk.

Coronary calcium score in the initial evaluation of suspected coronary artery disease

OBJECTIVE

We evaluated coronary artery calcium (CAC) scoring as an initial diagnostic tool in outpatients and in patients presenting at the emergency department due to suspected coronary artery disease (CAD).

METHODS

10 857 patients underwent CAC scoring and coronary CT angiography (CCTA) at Haukeland University Hospital in Norway during 2013-2020. Based on CCTA, obstructive CAD was defined as at least one coronary stenosis ≥50%. High-risk CAD included obstructive stenoses of the left main stem, the proximal left ascending artery or affecting all three major vascular territories with at least one proximal segment involved.

RESULTS

Median age was 58 years and 49.5% were women. The overall prevalence of CAC=0 was 45.0%. Among those with CAC=0, 1.8% had obstructive CAD and 0.6% had high-risk CAD on CCTA. Overall, the sensitivity, specificity, positive predictive value and negative predictive value (NPV) of CAC=0 for obstructive CAD were 95.3%, 53.4%, 30.0% and 98.2%, respectively. However, among patients <45 years of age, although the NPV was high at 98.9%, the sensitivity of CAC=0 for obstructive CAD was only 82.3%.

CONCLUSIONS

In symptomatic patients, CAC=0 correctly ruled out obstructive CAD and high-risk CAD in 98.2% and 99.4% of cases. This large registry-based cross-sectional study supports the incorporation of CAC testing in the early triage of patients with chest pain and as a gatekeeper to further cardiac testing. However, a full CCTA may be needed for safely ruling out obstructive CAD in the youngest patients (<45 years of age).

Development and validation of a nomogram for predicting significant coronary artery stenosis in suspected non-ST-segment elevation acute coronary artery syndrome with low-to-intermediate risk stratification

Background

Patients with non-ST-segment coronary artery syndrome (NSTE-ACS) have significant heterogeneity in their coronary arteries. A better assessment of significant coronary artery stenosis (SCAS) in low-to-intermediate risk NSTE-ACS patients would help identify who might benefit from invasive coronary angiography (ICA). Our study aimed to develop a multivariable-based model for pretesting SCAS in suspected NSTE-ACS with low-to-intermediate risk.

Methods

This prediction nomogram was constructed retrospectively in 469 suspected NSTE-ACS patients with low-to-intermediate risk. Patients were divided into a development group (n = 331, patients admitted to hospital before 1 May 2021) and a temporal validation group (n = 138, patients admitted to hospital since 1 May 2021). The outcome was existing SCAS, including left main artery stenosis ≥50% or any subepicardial coronary artery stenosis ≥70%, all confirmed by invasive coronary angiography. Pretest predictors were selected using Least Absolute Shrinkage and Selection Operator (LASSO) and stepwise logistic regression.

Results

Derivation analyses from the development group (n = 331, admitted before 1 May 2021) generated the 7 strongest predictors out of 25 candidate variables comprising smoker, diabetes, heart rate, cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-density lipoprotein cholesterol, and left atrial diameter. This nomogram model showed excellent discrimination ability with an area under the receiver operating characteristic curve (AUC) of 0.83 in the development set and 0.79 in the validation dataset. Good calibration was generally displayed, although it slightly overestimated patients' SCAS risk in the validation group. Decision curve analysis demonstrated the clinical benefit of this model, indicating its value in clinical practice. Furthermore, an optimal cut-off of prediction probability was assigned as 0.61 according to the Youden index.

Conclusion

A prediction nomogram consisting of seven readily available clinical parameters was established to pretest the probability of SCAS in suspected NSTE-ACS patients with low-to-intermediate risk, which may serve as a cost-effective risk stratification tool and thus assist in initial decision making.

External validation of the CRAX2MACE model in an Italian cohort of patients with suspected coronary artery disease undergoing stress myocardial perfusion imaging

BACKGROUND

Prevention and development of diagnostic and therapeutic techniques reduced morbidity and mortality for coronary artery disease (CAD). In this context, the cardiovascular risk assessment for major adverse cardiac events (MACE) at 2-year (CRAX2MACE) model for prediction of 2-year major adverse cardiac events was developed. We performed an external validation of this model.

METHODS

We included 1003 patients with suspected CAD undergoing stress-rest single-photon emission computed tomography myocardial perfusion imaging at our academic center between March 2015 and April 2019.

RESULTS

Considering the occurrence of MACE (death from any cause, acute myocardial infarction, or late coronary revascularization), for the CRAX2MACE model the area under the receiver operating characteristic curve was 0.612 and the Brier score was 0.061. The Hosmer-Lemeshow test estimated a non-optimal fit (χ2 28, P < .001). Considering only hard events (cardiac death, acute myocardial infarction), the external validation of the CRAX2MACE model revealed a Brier score of 0.053 and an area under the receiver operating characteristic curve of 0.621. Hosmer-Lemeshow test was calculated by deciles and showed a poor fit (χ2 31, P < .001).

CONCLUSION

CRAX2MACE model had a limited value for predicting 2-year major adverse cardiovascular events in an external validation cohort of patients with suspected CAD.

Are risk factors necessary for pretest probability assessment of coronary artery disease? A patient similarity network analysis of the PROMISE trial

BACKGROUND

Pretest probability (PTP) calculators utilize epidemiological-level findings to provide patient-level risk assessment of obstructive coronary artery disease (CAD). However, their limited accuracies question whether dissimilarities in risk factors necessarily result in differences in CAD. Using patient similarity network (PSN) analyses, we wished to assess the accuracy of risk factors and imaging markers to identify ≥50% luminal narrowing on coronary CT angiography (CCTA) in stable chest-pain patients.

METHODS

We created four PSNs representing: patient characteristics, risk factors, non-coronary imaging markers and calcium score. We used spectral clustering to group individuals with similar risk profiles. We compared PSNs to a contemporary PTP score incorporating calcium score and risk factors to identify ≥50% luminal narrowing on CCTA in the CT-arm of the PROMISE trial. We also conducted subanalyses in different age and sex groups.

RESULTS

In 3556 individuals, the calcium score PSN significantly outperformed patient characteristic, risk factor, and non-coronary imaging marker PSNs (AUC: 0.81 vs. 0.57, 0.55, 0.54; respectively, p ​< ​0.001 for all). The calcium score PSN significantly outperformed the contemporary PTP score (AUC: 0.81 vs. 0.78, p ​< ​0.001), and using 0, 1-100 and ​> ​100 cut-offs provided comparable results (AUC: 0.81 vs. 0.81, p ​= ​0.06). Similar results were found in all subanalyses.

CONCLUSION

Calcium score on its own provides better individualized obstructive CAD prediction than contemporary PTP scores incorporating calcium score and risk factors. Risk factors may not be able to improve the diagnostic accuracy of calcium score to predict ≥50% luminal narrowing on CCTA.

Dyslipidemia and coronary artery calcium: From association to development of a risk-prediction nomogram

BACKGROUND AND AIMS

The associations between dyslipidemia and coronary artery calcium (CAC) are controversial. We investigated their cross-sectional relationships and developed a predictive scoring system for prognostically significant coronary calcification (PSCC).

METHODS AND RESULTS

This study evaluated the lipid profiles and the CAC score (CACS) measured through multidetector computed tomography (MDCT) among Taiwanese adult patients in a tertiary hospital between 2011 and 2016. Patients with CACS higher than 100 were classified as having PSCC. Dyslipidemia for each lipid component was defined based on the clinical cutoffs or the use of the lipid-lowering agents. Multivariable logistic regression was used to assess the association between dyslipidemia and PSCC and the model performance was assessed using calibration plot, discrimination, and a decision curve analysis. Of the 3586 eligible patients, 364 (10.2%) had PSCC. Increased age, male sex, higher body mass index (BMI), and higher level of triglyceride (TG) were associated with PSCC. The adjusted odds ratios (95% confidence intervals) of PSCC was 1.15 (0.90-1.47) for dyslipidemia defined by total cholesterol (TC) ≥200 mg/dL, 1.06 (0.83-1.35) for low-density-lipoprotein-cholesterol (LDL-C) ≥130 mg/dL, and 1.36 (1.06-1.75) for TG ≥ 200 mg/dL. The positive association between TG ≥ 200 mg/dL and PSCC was not modified by sex. Incorporating hypertriglyceridemia did not significantly improve the predictive performance of the base model comprising of age, sex, BMI, smoking, hypertension, diabetes, estimated glomerular filtration rate, and fasting glucose.

CONCLUSIONS

Hypertriglyceridemia was significantly associated with the prevalent odds of PSCC. Our proposed predictive model may be a useful screening tool for PSCC.

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.

Cardiac CT angiography in current practice: An American society for preventive cardiology clinical practice statement✰

In this clinical practice statement, we represent a summary of the current evidence and clinical applications of cardiac computed tomography (CT) in evaluation of coronary artery disease (CAD), from an expert panel organized by the American Society for Preventive Cardiology (ASPC), and appraises the current use and indications of cardiac CT in clinical practice. Cardiac CT is emerging as a front line non-invasive diagnostic test for CAD, with evidence supporting the clinical utility of cardiac CT in diagnosis and prevention. CCTA offers several advantages beyond other testing modalities, due to its ability to identify and characterize coronary stenosis severity and pathophysiological changes in coronary atherosclerosis and stenosis, aiding in early diagnosis, prognosis and management of CAD. This document further explores the emerging applications of CCTA based on functional assessment using CT derived fractional flow reserve, peri‑coronary inflammation and artificial intelligence (AI) that can provide personalized risk assessment and guide targeted treatment. We sought to provide an expert consensus based on the latest evidence and best available clinical practice guidelines regarding the role of CCTA as an essential tool in cardiovascular prevention - applicable to risk assessment and early diagnosis and management, noting potential areas for future investigation.

Comparison of Different Investigation Strategies to Defer Cardiac Testing in Patients With Stable Chest Pain

OBJECTIVES

This study aimed to compare the current 5 investigation strategies to defer cardiac testing in patients with stable chest pain.

BACKGROUND

For the clinical management of stable chest pain, the identification of patients unlikely to benefit from further cardiac testing is important, but the most appropriate investigation strategy is unknown.

METHODS

A total of 4,207 patients referred to coronary computed tomography angiography for stable chest pain were classified into low- and high-risk groups according to the 2016 National Institute of Health and Care Excellence (NICE) guideline-determined strategy; PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk tool-based strategy; 2019 European Society of Cardiology (ESC) guideline-determined strategy; and coronary artery calcium score (CACS), either in isolation (the CACS strategy) or as part of a weighted clinical likelihood model-based strategy (the CACS-CL strategy). The associations of obstructive coronary artery disease on coronary computed tomography angiography, major adverse cardiovascular events, and subsequent clinical management with risk groups according to different strategies were evaluated and compared.

RESULTS

The NICE, PROMISE, ESC, CACS, and CACS-CL strategies classified a proportion (22.63%, 29.21%, 41.84%, 46.76%, and 51.41%, respectively) of patients into low-risk groups. Compared with the NICE, PROMISE, ESC, and CACS strategies, the CACS-CL strategy had a stronger association between risk groups and obstructive coronary artery disease (odd ratios: 16.00 vs 2.93, 5.53, 7.94, and 10.39, respectively), major adverse cardiovascular events (HRs: 6.83 vs 1.90, 2.94, 4.23, and 5.13, respectively) and intensive subsequent clinical management as well as better metrics of diagnostic accuracy and positive net reclassification improvement.

CONCLUSIONS

Among contemporary strategies used to identify patients with stable chest pain at low risk, the use of CACS, especially when combined with clinical risk features, showed the strongest potential to effectively defer cardiac testing.

Coronary artery calcium score: pivotal role as a predictor for detecting coronary artery disease in symptomatic patients

Chest pain and dyspnea are common presentations for symptomatic individuals with suspected coronary artery disease (CAD) in the primary care office and cardiology clinics. However, it is imperative to properly diagnose who should undergo further evaluation for cardiac etiologies of chest pain, with either noninvasive or invasive imaging tests. The purpose of this review is to highlight the role of coronary artery calcium (CAC) score as a screening tool for symptomatic patients to detect CAD. The purpose of CAC scoring is to establish the presence and severity of coronary atherosclerosis that can play a vital role in symptomatic patients. The use of CAC testing in symptomatic patients has traditionally been limited due to fundamental concerns, including the occurrence of coronary calcification relatively late in the atherosclerotic process and high prevalence of CAC in the population. Further issue relates to its low specificity for obstructive CAD, as well as demonstration of significant ethnic variability in plaque composition and calcification patterns. CAC testing gained attention as an inexpensive, rapid, reproducible and a well-tolerated alternative to exclude CAD in symptomatic patients and defer further invasive imaging tests. This article will review the available literature in regard to the use of CAC in symptomatic populations.

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.

The Long-To-Short-Axis Ratio and Multifocality are Associated With TP53 Mutation Status in Surgically Resected Hepatocellular Carcinomas

RATIONALE AND OBJECTIVES

In hepatocellular carcinoma (HCC), the tumor protein 53 (TP53) gene is frequently mutated and the mutations have been associated with poor prognosis. We aim to retrospectively identify the relationship between TP53 mutation status, tumor size (long-axis diameter, short-axis diameter, and long-to-short-axis ratio [L/S ratio]), margin and multifocality in surgically resected HCC.

MATERIALS AND METHODS

The image features and TP53 mutation data from 78 patients generated with National Cancer Institute's multi-institutional The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive databases were assessed. Binary logistic regression analyses were performed to identify independent factors of harboring TP53 mutation status. The final model was selected by using the backward elimination method.

RESULTS

TP53 mutations were found in 19 (31.5%) of 78 patients. TP53 mutation rates were significantly higher (a) in L/S ratio ≤ 1.2 14 of 41 [34.1%]) lesions than in L/S ratio >1.2 lesions (five of 37 [13.5%]) (p = 0.034) and (b) in nonmultifocality (17 of 54[31.5%]) than in multifocality lesions (two of 24 [8.3%]) (p = 0.028). On univariate logistic regression analysis, L/S ratio (≤1.20 vs >1.20. odds ratio [OR]: 3.319; p = 0.040; 95% confidence interval [CI]: 1.059-10.401 Area Under Curve (AUC) = 0.634) and multifocality (no vs yes OR: 5.054; p = 0.041; 95% CI: 1.065-23.986 AUC = 0.640) were associated with TP53 mutations. On multivariate logistic regression analysis, L/S ratio (≤1.20 vs >1.20 OR: 3.430; p = 0.040; 95% CI: 1.058-11.118) and multifocality (no vs yes OR: 5.232; p = 0.041; 95% CI: 1.072-25.526) were associated with TP53 mutations. The area under the receiver operating characteristic curve for predicting TP53 mutation status was 0.714 (95% CI: 0.590-0.837).

CONCLUSION

Our study focusing on identifying imaging aspects related to TP53 positive HCC. L/S ratio of HCC in combination with multifocality might be used to prognosticate TP53 mutation status. And the discriminatory power for this prediction model was good.

Epidemiology and pathophysiologic insights of coronary atherosclerosis relevant for contemporary non-invasive imaging

In the past few years significant changes have taken place in the diagnostic and therapeutic approach to patients with coronary artery disease (CAD) and/or ischemic heart disease (IHD). New discoveries about the development and progression of coronary atherosclerosis have changed the clinical landscape. At the same time a marked decrease in cardiovascular (CV) mortality and CAD incidence have been observed in many Countries but particularly in the most industrialized ones. This fall has been also observed in the incidence of stroke, sudden death, myocardial ischemia, myocardial infarction (MI), and prevalence of CAD. As a consequence, an increasing number of patients with chest pain exhibits non-significant stenosis at both invasive and non-invasive coronary angiography and the rate of coronary vessels revascularizations has greatly reduced. Coronary atherosclerosis and its characteristics have shown to be both diagnostic and therapeutic targets beyond obstructive CAD. The decreased prevalence of CAD in the general population has modified the pre-test probability (PTP) of disease. In this landscape the conventional stress imaging tests appear to have limited accuracy making the diagnosis of obstructive CAD very challenging. These diagnostic tests have been introduced and tested in a population with a much higher probability of disease and therefore the contemporary accuracy of these old tests appear much lower than in the past. In addition, in the past few years the relevance of the traditional ischemia guided coronary intervention strategy has been questioned. Given the low CV events granted by an optimal medical therapy in CAD the major attention has been directed on detecting coronary atherosclerosis. The earlier the better. At the same time, a growing number of data from clinical studies have shown a significant prognostic role for non-obstructive CAD and coronary atherosclerosis. All these facts have shifted the clinicians' attention from the functional evaluation of the coronary circulation to the anatomic burden of disease.

Prediction of obstructive coronary artery disease and prognosis in patients with suspected stable angina

AIMS

We hypothesized that the modified Diamond-Forrester (D-F) prediction model overestimates probability of coronary artery disease (CAD). The aim of this study was to update the prediction model based on pre-test information and assess the model's performance in predicting prognosis in an unselected, contemporary population suspected of angina.

METHODS AND RESULTS

We included 3903 consecutive patients free of CAD and heart failure and suspected of angina, who were referred to a single centre for assessment in 2012-15. Obstructive CAD was defined from invasive angiography as lesion requiring revascularization, >70% stenosis or fractional flow reserve <0.8. Patients were followed (mean follow-up 33 months) for myocardial infarction, unstable angina, heart failure, stroke, and death. The updated D-F prediction model overestimated probability considerably: mean pre-test probability was 31.4%, while only 274 (7%) were diagnosed with obstructive CAD. A basic prediction model with age, gender, and symptoms demonstrated good discrimination with C-statistics of 0.86 (95% CI 0.84-0.88), while a clinical prediction model adding diabetes, family history, and dyslipidaemia slightly improved the C-statistic to 0.88 (0.86-0.90) (P for difference between models <0.0001). Quartiles of probability of CAD from the clinical prediction model provided good diagnostic and prognostic stratification: in the lowest quartiles there were no cases of obstructive CAD and cumulative risk of the composite endpoint was less than 3% at 2 years.

CONCLUSION

The pre-test probability model recommended in current ESC guidelines substantially overestimates likelihood of CAD when applied to a contemporary, unselected, all-comer population. We provide an updated prediction model that identifies subgroups with low likelihood of obstructive CAD and good prognosis in which non-invasive testing may safely be deferred.

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.

Epicardial Fat Volume Improves the Prediction of Obstructive Coronary Artery Disease Above Traditional Risk Factors and Coronary Calcium Score

BACKGROUND

Recent studies have demonstrated the tremendous potential of epicardial fat volume (EFV) to predict obstructive coronary artery disease. We aimed to develop a new model to estimate pretest probability of obstructive coronary artery disease using traditional risk factors with coronary calcium score and EFV and compare it with proposed models in Chinese patients who underwent coronary computed tomography angiography.

METHODS

The new models were derived from 5743 consecutive patients using multivariate logistic regression and validated in an internal cohort using invasive coronary angiography as the outcome and an external cohort with clinical outcome data. Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve, integrated discrimination improvement and net reclassification improvement were calculated to validate and compare the performance of models.

RESULTS

EFV improved prediction above conventional risk factors and coronary calcium score (area under the receiver operating characteristic curve increased from 0.856 to 0.874, integrated discrimination improvement 0.0487, net reclassification improvement 0.1181, P<0.0001 for all). The final model included 5 predictors: age, sex, symptom, coronary calcium score, and EFV. Good internal validation and external validation of the new model were achieved, with positive net reclassification improvement and integrated discrimination improvement, excellent area under the receiver operating characteristic curve and favorable calibration. Further, the new model demonstrated a better prediction of clinical outcome, resulting in a more cost-effective risk stratification to optimize decision-making of downstream diagnosis and treatment.

CONCLUSIONS

Addition of EFV to conventional risk factors and coronary calcium score offered a more accurate and effective estimation for pretest probability of obstructive coronary artery disease, which may help to improve initial management of stable chest pain.

Imaging functional stress test for stable chest pain symptoms in patients at low pretest probability of coronary artery disease: Current practice and long-term outcome

BACKGROUND

Stress testing in patients with low pretest probability (PTP) of coronary artery disease (CAD) has become an increasing practice, potentially leading to underestimation of its true clinical value. Our aim was to describe the current use of most employed imaging functional tests and their prognostic value.

METHODS AND RESULTS

We selected patients with low PTP of CAD (CAD consortium clinical score < 15%) who underwent exercise or dipyridamole stress echocardiography or single photon emission computed tomography for suspected angina. Main exclusions were age < 45, known CAD, and abnormal rest wall motion. Of the 2279 subjects undergoing stress test, 883 (39%) had low PTP, and 91 (10.3%) had a positive test for ischemia. After a median follow-up of 5.8 years, 36 patients had events (21 died, 14 had nonfatal myocardial infarction). The percentage of events in the abnormal and normal stress test groups were similar (5 [5.5%] vs 31 [3.9%], P = ns), as the annualized event rate (0.87% vs 0.62%, P = ns). Age was the only variable associated with outcome in the regression analysis (hazard ratio 1.072, 95% CI 1.034-1.113, P < 0.001). An abnormal result was not associated with worse outcome in each of the subgroups of functional tests.

CONCLUSIONS

In our geographical area, a considerable proportion of patients undergoing imaging functional tests for stable chest pain have a low estimated PTP of CAD. Of these, 1 in 10 resulted positive for inducible ischemia. However, none of the most common imaging functional tests, single photon emission computed tomography (SPECT), and stress echocardiography offer prognostic information in these patients.

Impact of a decreasing pre-test probability on the performance of diagnostic tests for coronary artery disease

Aims

To provide a pooled estimation of contemporary pre-test probabilities (PTPs) of significant coronary artery disease (CAD) across clinical patient categories, re-evaluate the utility of the application of diagnostic techniques according to such estimates, and propose a comprehensive diagnostic technique selection tool for suspected CAD.

Methods and results

Estimates of significant CAD prevalence across sex, age, and type of chest pain categories from three large-scale studies were pooled (n = 15 815). The updated PTPs and diagnostic performance profiles of exercise electrocardiogram, invasive coronary angiography, coronary computed tomography angiography (CCTA), positron emission tomography (PET), stress cardiac magnetic resonance (CMR), and SPECT were integrated to define the PTP ranges in which ruling-out CAD is possible with a post-test probability of &lt;10% and &lt;5%. These ranges were then integrated in a new colour-coded tabular diagnostic technique selection tool. The Bayesian relationship between PTP and the rate of diagnostic false positives was explored to complement the characterization of their utility. Pooled CAD prevalence was 14.9% (range = 1–52), clearly lower than that used in current clinical guidelines. Ruling-out capabilities of non-invasive imaging were good overall. The greatest ruling-out capacity (i.e. post-test probability &lt;5%) was documented by CCTA, PET, and stress CMR. With decreasing PTP, the fraction of false positive findings rapidly increased, although a lower CAD prevalence partially cancels out such effect.

Conclusion

The contemporary PTP of significant CAD across symptomatic patient categories is substantially lower than currently assumed. With a low prevalence of the disease, non-invasive testing can rarely rule-in the disease and focus should shift to ruling-out obstructive CAD. The large proportion of false positive findings must be taken into account when patients with low PTP are investigated.

Pre-test probability prediction in patients with a low to intermediate probability of coronary artery disease: a prospective study with a fractional flow reserve endpoint

Aims

European and North American guidelines currently recommend pre-test probability (PTP) stratification based on simple probability models in patients with suspected coronary artery disease (CAD). However, no unequivocal recommendation has yet been established. We aimed to compare the ability of risk factors and different PTP stratification models to predict haemodynamically obstructive CAD with fractional flow reserve (FFR) as reference in low to intermediate probability patients.

Methods and results

We prospectively included 1675 patients with low to intermediate risk who had been referred to coronary computed tomography angiography (CTA). Patients with coronary stenosis were subsequently investigated by invasive coronary angiography (ICA) with FFR measurement if indicated. Discrimination and calibration were assessed for four models: the updated Diamond–Forrester (UDF), the CAD Consortium Basic, the Clinical, and the Clinical + Coronary artery calcium score (CACS). At coronary CTA, 24% of patients were diagnosed with a suspected stenosis and 10% had haemodynamically obstructive CAD at the ICA. Calibration for all CAD Consortium models increased compared with the UDF score. However, all models overestimated the probability of haemodynamically obstructive CAD. Discrimination increased by area under the receiver operating curve from 67% to 86% for UDF vs. CAD Consortium Clinical + CACS. The proportion of low-probability patients (pre-test score < 15%) was for the UDF, CAD Consortium Basic, Clinical, and Clinical + CACS: 14%, 58%, 51%, and 66%, respectively. The corresponding negative predictive values were 97%, 94%, 95%, and 98%, respectively.

Conclusion

CAD Consortium models improve PTP stratification compared with the UDF score, mainly due to superior calibration in low to intermediate probability patients. Adding the coronary calcium score to the models substantially increases discrimination.

Clinical Trials. gov identifier

NCT02264717.

Validity of Coronary Artery Disease Consortium Models for Predicting Obstructive Coronary Artery Disease & Cardiovascular Events in Patients with Acute Chest Pain Considered for Coronary Computed Tomographic Angiography

Although the majority of acute chest pain patients are diagnosed with noncardiac chest pain after noninvasive testing, identifying these low-risk patients before testing is challenging. The objective of this study was to validate the coronary artery disease (CAD) consortium models for predicting obstructive CAD and 30-day major adverse cardiovascular events (MACE) in acute chest pain patients considered for coronary computed tomography angiogram, as well as to determine the pretest probability threshold that identifies low-risk patients with <1% MACE. We studied 1,981 patients with no known CAD and negative initial troponin and electrocardiogram. We evaluated CAD consortium models (basic: age, sex, and chest pain type; clinical: basic + diabetes, hypertension, dyslipidemia, and smoking; and clinical + coronary calcium score [CAC] models) for prediction of obstructive CAD (≥50% stenosis on coronary CT angiogram) and 30-day MACE (Acute Myocardial Infarction, revascularization, and mortality). The C-statistic for predicting obstructive CAD was 0.77 (95% confidence interval [CI] 0.73 to 0.77) for the basic, 0.80 (95% CI 0.77 to 0.80) for the clinical, and 0.88 (95% CI 0.85 to 0.88) for the clinical + CAC models. The C-statistic for predicting 30-day MACE was 0.82 (95% CI 0.77 to 0.87) for the basic, 0.84 (95% CI 0.79 to 0.88) for the clinical, and 0.87 (95% CI 0.83 to 0.91) for the clinical + CAC models. In 47.3% of patients for whom the clinical model predicted ≤5% probability for obstructive CAD, the observed 30-day MACE was 0.53% (95% CI 0.07% to 0.999%); in the 66.9% of patients for whom the clinical + CAC model predicted ≤5% probability, the 30-day MACE was 0.75% (95% CI 0.29% to 1.22%). We propose a chest pain evaluation algorithm based on these models that classify 63.3% of patients as low risk with 0.56% (95% CI 0.15% to 0.97%) 30-day MACE. In conclusion, CAD consortium models have excellent diagnostic and prognostic value for acute chest pain patients and can safely identify a significant proportion of low-risk patients by achieving <1% missed 30-day MACE.

Coronary calcium score improves the estimation for pretest probability of obstructive coronary artery disease and avoids unnecessary testing in individuals at low extreme of traditional risk factor burden: validation and comparison of CONFIRM score and genders extended model

BACKGROUND

Reliability of models for estimating pretest probability (PTP) of obstructive coronary artery disease (CAD) has not been investigated in individuals at low extreme of traditional risk factor (RF) burden. Thus, we sought to validate and compare CONFIRM score and Genders extended model (GEM) among these individuals.

METHODS

We identified symptomatic individuals with 0 or 1 RF who underwent coronary calcium scan and coronary computed tomographic angiography (CCTA). Follow-up clinical data were also recorded. PTP of obstructive CAD for every individual was estimated according to CONFIRM score and GEM, respectively. Area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and Hosmer-Lemeshow (H-L) test were used to assess the performance of models.

RESULTS

There were 1201 individuals with 0 RF and 2415 with 1 RF. The AUC for GEM was significantly larger than that for CONFIRM score, no matter in individuals with 0 (0.843 v.s. 0.762, p < 0.0001) or 1 (0.823 v.s. 0.752, p < 0.0001) RF. Compared to CONFIRM score, GEM demonstrated positive IDI (5% in individuals with 0 RF and 8% in individuals with 1 RF), positive NRI (41.50% in individuals with 0 RF and 40.19% in individuals with 1 RF), better prediction of clinical events and less discrepancy between observed and predicted probabilities, resulting in a significant decrease of unnecessary testing, especially in negative individuals.

CONCLUSION

In individuals at low extreme of traditional RF burden of CAD, the addition of coronary calcium score provided a more accurate estimation for PTP and application of GEM instead of CONFIRM score could avoid unnecessary testing.

Outcomes and Mortality Prediction Model of Critically Ill Adults With Acute Respiratory Failure and Interstitial Lung Disease

BACKGROUND

We aimed to examine short- and long-term mortality in a mixed population of patients with interstitial lung disease (ILD) with acute respiratory failure, and to identify those at lower vs higher risk of in-hospital death.

METHODS

We conducted a single-center retrospective cohort study of 126 consecutive adults with ILD admitted to an ICU for respiratory failure at a tertiary care hospital between 2010 and 2014 and who did not undergo lung transplantation during their hospitalization. We examined associations of ICU-day 1 characteristics with in-hospital and 1-year mortality, using Poisson regression, and examined survival using Kaplan-Meier curves. We created a risk score for in-hospital mortality, using a model developed with penalized regression.

RESULTS

In-hospital mortality was 66%, and 1-year mortality was 80%. Those with connective tissue disease-related ILD had better short-term and long-term mortality compared with unclassifiable ILD (adjusted relative risk, 0.6; 95% CI, 0.3-0.9; and relative risk, 0.6; 95% CI, 0.4-0.9, respectively). Our prediction model includes male sex, interstitial pulmonary fibrosis diagnosis, use of invasive mechanical ventilation and/or extracorporeal life support, no ambulation within 24 h of ICU admission, BMI, and Simplified Acute Physiology Score-II. The optimism-corrected C-statistic was 0.73, and model calibration was excellent (P = .99). In-hospital mortality rates for the low-, moderate-, and high-risk groups were 33%, 65%, and 96%, respectively.

CONCLUSIONS

We created a risk score that classifies patients with ILD with acute respiratory failure from low to high risk for in-hospital mortality. The score could aid providers in counseling these patients and their families.