Dataset Information

Prognostic value of CAD-RADS classification by coronary CTA in patients with suspected CAD.

ABSTRACT:

Background

The study sought to compare Coronary Artery Disease Reporting and Data System (CAD-RADS) classification with traditional coronary artery disease (CAD) classifications and Duke Prognostic CAD Index for predicting the risk of all-cause mortality in patients with suspected CAD.

Methods

9625 consecutive suspected CAD patients were assessed by coronary CTA for CAD-RADS classification, traditional CAD classifications and Duke Prognostic CAD Index. Kaplan-Meier and multivariable Cox models were used to estimate all-cause mortality. Discriminatory ability of classifications was assessed using time dependent receiver-operating characteristic (ROC) curves and The Hosmer-Lemeshow goodness-of-fit test was employed to evaluate calibration.

Results

A total of 540 patients died from all causes with a median follow-up of 4.3 ± 2.1 years. Kaplan-Meier survival curves showed the cumulative events increased significantly associated with CAD-RADS, three traditional CAD classifications and Duke Prognostic CAD Index. In multivariate Cox regressions, the risk for the all-cause death increased from HR 0.861 (95% CI 0.420-1.764) for CAD-RADS 1 to HR 2.761 (95% CI 1.961-3.887) for CAD-RADS 4B&5, using CAD-RADS 0 as the reference group. The relative HRs for all-cause death increased proportionally with the grades of the three traditional CAD classifications and Duke Prognostic CAD Index. The area under the time dependent ROC curve for prediction of all-cause death was 0.7917, 0.7805, 0.7991for CAD-RADS in 1 year, 3 year, 5 year, respectively, which was non-inferior to the traditional CAD classifications and Duke Prognostic CAD Index.

Conclusions

The CAD-RADS classification provided important prognostic information for patients with suspected CAD with noninvasive evaluation, which was non-inferior than Duke Prognostic CAD Index and traditional stenosis-based grading schemes in prognostic value of all-cause mortality. Traditional and simplest CAD classification should be preferable, given the more number of groups and complexity of CAD-RADS and Duke prognostic index, without using more time consuming classification.

SUBMITTER: Huang Z

PROVIDER: S-EPMC8487531 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveThe aim of this work was to evaluate the predictive value of FAR combined with CACS for MACCEs.BackgroundThe fibrinogen-albumin-ratio (FAR), a novel biomarker of inflammation, is associated with the severity of coronary artery disease (CAD). Coronary calcification score (CACS) is associated with the severity of coronary stenosis and is closely related to the prognosis of CAD patients. What is the prognostic value of FAR in patients with chest pain, which has not been reported. This study aims to evaluate the relationship between CACS and FAR and their impact on prognosis in patients with suspected CAD.MethodsWe used information from 12,904 individuals who had coronary computed tomography angiography (CTA) for chest pain and tracked down any significant adverse cardiac and cerebrovascular events (MACCEs). The following formula was used to calculate FAR: fibrinogen (g/L)/albumin (g/L). Patients were separated into groups with greater levels of FAR (FAR-H) and lower levels of FAR (FAR-L) in accordance with the ideal cut-off value of FAR for MACCEs prediction. In addition, patients were divided into three groups based on their CACS scores (CACS ≤ 100, 100 < CACS ≤ 400, and CACS > 400).Results4946 patients [62(55-71) years, 64.4% male] were ultimately enrolled in the present study. During follow-up, a total of 234 cases (4.7%) of MACCEs were documented. Linear regression analysis results showed that CACS (R2 = 0.004, Standard β = 0.066, P < 0.001) was positively associated with FAR in patients with chest pain.Compared to ones with FAR-L, FAR-H had an increased risk for MACCEs (adjusted HR 1.371(1.053-1.786) P = 0.019). Multivariate Cox regression showed that age (adjusted HR 1.015 95% CI 1.001-1.028;p = 0.03), FAR (adjusted HR 1.355 95% CI 1.042-1.763;p = 0.023),FBG (adjusted HR 1.043 95% CI 1.006-1.083;p = 0.024) and CACS (adjusted HR 1.470 95% CI 1.250-1.727;p < 0.001) were the independent risk factors for MACCEs. The FAR and CACS significantly improved MACCEs risk stratification, contributing to substantial net reclassification improvement ( NRI 0.122, 95% CI 0.054-0.198, P < 0.001) and integrated discrimination improvement(IDI 0.011, 95% CI 0.006-0.017, P < 0.001).ConclusionFAR was an independent risk factor for MACCEs. The results showed that CACS was positively associated with FAR in patients with suspected CAD. A higher level of FAR and heavier coronary calcification burden was associated with worse outcomes among patients with suspected CAD. FAR and CACS improved the risk identification of patients with suspected CAD, leading to a significant reclassification of MACCEs.

Project description:IntroductionThe present study aimed to investigate the association of the paraoxonase 1 (PON1) Q192R polymorphism with coronary artery disease (CAD) and cardiometabolic risk factors in Iranian patients suspected of CAD.MethodsThis cross-sectional study was conducted on 428 patients undergoing angiography. The data related to demographic information and physical activity were collected by valid and reliable questionnaires. The PON-1 genotypes were detected by the polymerase chain reaction-restriction fragment length polymorphism (RFLP-PCR) technique. The Gensini and SYNTAX score, anthropometric measurements, and biochemical and clinical parameters were measured by standard protocols.Results and discussionFindings indicated that the odds of obesity was significantly higher in people with the RR genotype compared to the QQ genotype carriers (OR: 2.95 CI: 1.25-6.93, P = 0.014) and also odds of low high-density lipoprotein cholesterol (HDL-C) was marginally higher (OR: 2.31 CI: 0.97-5.49, P = 0.056). There was no significant association between other CAD risk factors with PON1 Q192R polymorphism (P > 0.05). Further analysis showed a significant interaction between sex and 192QR (P = 0.019) and 192 RR (P = 0.007) genotypes on body mass index (BMI). More specifically, the risk of obesity in men carrying the RR genotype was 3.38 times (OR: 3.38 CI: 1.08-10.58, P = 0.036). Also, a significant joint effect of the RR genotype and sex on HDL-C was seen (P = 0.003). The stratification based on sex showed that the risk of low HDL-C is significantly higher in women carrying the RR genotype (OR: 6.18 CI: 1.21-31.46, P = 0.028). A marginal sex-genotype interaction was also found in the risk of elevated alanine aminotransferase (ALT) (P = 0.057). In summary, the findings showed that the risk of obesity and low HDL-C was higher in people carrying the RR genotype. On the other hand, a Q192R polymorphism-sex interaction was observed on the risk of obesity, elevated ALT, and low HDL-C.

Project description:BackgroundRisk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA).MethodsPatients with suspected CAD (n = 203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers.FindingsA total of 196 patients with analyzable protein levels (n = 332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79 ± 0·01, outperforming prediction with generally available clinical characteristics (AUC = 0·65 ± 0·04, p < 0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC = 0·85 ± 0·05), again outperforming prediction with generally available characteristics (AUC = 0·70 ± 0·04, p < 0·05).InterpretationUsing machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. FUND: This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.