Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates.

BACKGROUND: Limited studies have investigated the accuracy of therapeutic decision-making using machine learning-based coronary computed tomography angiography (ML-CCTA) compared with CCTA. PURPOSE: To investigate the performance of ML-CCTA for therapeutic decision compared with CCTA. MATERIAL AND METHODS: The study population consisted of 322 consecutive patients with stable coronary artery disease. The SYNTAX score was calculated with an online calculator based on ML-CCTA results. Therapeutic decision-making was determined by ML-CCTA results and the ML-CCTA-based SYNTAX score. The therapeutic strategy and the appropriate revascularization procedure were selected using ML-CCTA, CCTA, and invasive coronary angiography (ICA) independently. RESULTS: The sensitivity, specificity, positive predictive value, negative predictive value, accuracy of ML-CCTA and CCTA for selecting revascularization candidates were 87.01%, 96.43%, 95.71%, 89.01%, 91.93%, and 85.71%, 87.50%, 86.27%, 86.98%, 86.65%, respectively, using ICA as the standard reference. The area under the receiver operating characteristic curve (AUC) of ML-CCTA for selecting revascularization candidates was significantly higher than CCTA (0.917 vs. 0.866, P = 0.016). Subgroup analysis showed the AUC of ML-CCTA for selecting percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) candidates was significantly higher than CCTA (0.883 vs. 0.777, P < 0.001, 0.912 vs. 0.826, P = 0.003, respectively). CONCLUSION: ML-CCTA could distinguish between patients who need revascularization and those who do not. In addition, ML-CCTA showed a slightly superior to CCTA in making an appropriate decision for patients and selecting a suitable revascularization strategy.

Prognostic value of coronary CTA-based classifications for predicting major events without obstructive coronary artery disease.

We aim to explore the classifications based on coronary computed tomography angiography (CTA) for predicting the risk of major adverse cardiovascular events (MACE) in patients with suspected non-obstructive coronary artery disease (CAD) and compare with traditional non-obstructive CAD (NOCAD) classification, Duke prognostic NOCAD index, Non-obstructive coronary artery disease reporting and data system (NOCAD-RADS). 4378 consecutive non-obstructive CAD patients were assessed by coronary CTA for traditional NOCAD classification, Duke prognostic NOCAD index, NOCAD-RADS and a new classification (stenosis proximal involvement, SPI) from two medical centrals. We defined proximal involvement as any plaque was present in the main or proximal segments of coronary artery (left main, left anterior descending artery, left circumflex artery, or right coronary artery). The main outcome was MACE. During a median follow-up of 3.7 years, a total of 310 patients experienced MACE event. Kaplan-Meier survival curves showed the cumulative events increased significantly associated with traditional NOCAD, Duke NOCAD index, NOCAD-RADS and SPI classifications (all P < 0.001). In multivariate Cox regressions, the risk for the events increased from HR 1.20 (95% CI 0.78-1.83, P = 0.408) for SPI 1 to 1.35 (95% CI 1.05-1.73, P = 0.019) for SPI 2, using SPI 0 as the reference group. Coronary CTA based SPI classification provided important prognostic information for all cause-mortality risk and MACE prediction in patients with non-obstructive CAD, which was non-inferior than traditional NOCAD, Duke NOCAD Index and NOCAD-RADS classifications. The plaque location information by coronary CTA may provide additional risk prediction in patients with non-obstructive CAD.

Non-obstructive CAD and Risk of All-cause Mortality in Middle-aged and Older Patients: a Nine-Year Follow-up and Multicentre Study.

AIM: We aimed to examine all-cause mortality risk in relation to the extent of non-obstructive coronary artery disease (CAD) by coronary computed tomography angiography (CTA) in Chinese middle-aged and older patients in a multicenter study with nine-year follow-up. METHODS: This was a retrospective, observational, multicentre study. The study population consisted of 3,240 consecutive middle-aged and older patients (age ≥ 40 years) with suspected CAD who underwent coronary CTA between June 2011 and December 2013 at three hospitals in Wuhan, China. Patients were grouped according to CAD extent for the final analysis: no CAD, 1-vessel non-obstructive CAD, 2-vessels non-obstructive CAD, and 3-vessels non-obstructive CAD. The primary endpoint was all-cause mortality. Kaplan-Meier method and Cox proportional hazards regression models were used for analysis. RESULTS: A total of 2,522 patients were included in the present analysis. Of these, 188 (7.5%) deaths occurred during the median 9.0 years (interquartile range 8.6-9.4) of study follow-up. The annualized all-cause mortality rate was 0.54 (95% CI: 0.44-0.68), 0.91 (95% CI: 0.68-1.21), 1.44 (95% CI: 1.01-1.93), and 2.00 (95% CI: 1.46-2.69) for the no CAD, 1-vessel non-obstructive CAD, 2-vessels non-obstructive CAD, and 3-vessels non-obstructive CAD group, respectively. Kaplan-Meier survival curves showed a significant increase in the cumulative events associated with the extent of non-obstructive CAD (P < 0.001). In multivariate Cox regression, after adjustment for age and sex, the presence of 3-vessels non-obstructive CAD was a significant predictor of all-cause mortality (HR 1.60, 95% CI: 1.04-2.45, P = 0.032). CONCLUSION: In this cohort of Chinese middle-aged and older patients undergoing coronary CTA, the presence and extent of non-obstructive CAD, compared to no CAD, were associated with a significantly greater nine-year risk of all-cause mortality. The present findings suggest the clinical importance of the stage of non-obstructive CAD and warrant investigation of the optimal risk stratification to improve outcomes among these patients.

Comparison of prognostic value between CAD-RADS 1.0 and CAD-RADS 2.0 evaluated by convolutional neural networks based CCTA.

Objectives: The aim of the present study was to investigate the prognostic value of the novel coronary artery disease reporting and data system (CAD-RADS) 2.0 compared with CAD-RADS 1.0 in patients with suspectedcoronary artery disease (CAD) evaluated by convolutional neural networks (CNN) based coronary computed tomography angiography (CCTA). Methods: A total of 1796 consecutive inpatients with suspected CAD were evaluated by CCTA for CAD-RADS 1.0 and CAD-RADS 2.0 classifications. Kaplan-Meier and multivariate Cox models were used to estimate major adverse cardiovascular events (MACE) inclusive of all-cause mortality or myocardial infarction (MI). The C-statistic was used to assess the discriminatory ability of the two classifications. Results: In total, 94 (5.2%) MACE occurred over the median follow-up of 45.25 months (interquartile range 43.53-46.63 months). The annualized MACE rate was 0.014 (95% CI: 0.011-0.017). Kaplan-Meier survival curves indicated that the CAD-RADS classification, segment involvement score (SIS) grade, and Computed Tomography Fractional Flow Reserve (CT-FFR) classification were all significantly associated with the increase in the cumulative MACE (all P < 0.001). CAD-RADS classification, SIS grade, and CT-FFR classification were significantly associated with endpoint in univariate and multivariate Cox analysis. CAD-RADS 2.0 showed a further incremental increase in the prognostic value in predicting MACE (c-statistic 0.702, 95% CI: 0.641-0.763, P = 0.047), compared with CAD-RADS 1.0. Conclusions: The novel CAD-RADS 2.0 evaluated by CNN-based CCTA showed higher prognostic value of MACE than CAD-RADS 1.0 in patients with suspected CAD.

Clinical Evaluation of the Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS) in Coronary Computed Tomography Angiography Using Convolutional Neural Networks.

RATIONALE AND OBJECTIVES: The coronary artery disease reporting and data system (CAD-RADS™) was recently introduced to standardise reporting. We aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADS™ in suspected coronary artery disease (CAD) patients. MATERIALS AND METHODS: Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADS™ with commercially-available automated segmentation and manual postprocessing in a retrospective validation cohort. RESULTS: Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no significant difference between the DL-based and the reader-based CAD-RADS™ grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 ± 1.80 min to 5.02 ± 0.8 min (p < 0.001). CONCLUSION: The standardised reporting of DL-based CAD-RADS™ in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.

Graft Patency Assessment with CCTA Using a Comprehensive Singlebranch Bridging Score.

AIMS: This study aims to assess the prognostic value of graft patency with coronary computed tomography angiography (CCTA) using a comprehensive single-branch targeted atherosclerotic risk score (CSBS) in patients before coronary artery bypass grafting (CABG). METHODS: This retrospective study contains a total of 88 patients who underwent clinical CCTA before off-pump CABG surgery between 2015 and 2018. Graft failure was defined as patients with missing multi-slice CCTA or coronary angiography. The predictive value of CSBS (ranging from 0-70 and divided into 2 groups: < 20 and ≥20) was analyzed using Kaplan-Meier analysis and Cox regression models. RESULTS: Patients' mean age was 61.2 ± 10.5 years, with a mean follow-up of 20.4 ± 15.2 months. A total of 203 grafts (21.5% arterial grafts) were analyzed and 30 of the vessels were occluded (14.8%). There was no significant difference in graft occlusion among the three targeted vessel groups. The Cox proportional hazard analysis showed that CSBS < 20 was a significant predictor of graft failure. CONCLUSION: Lower comprehensive single-branch targeted atherosclerotic risk score evaluated by CCTA is an independent prognostic factor for graft failure in patients before CABG surgery.

Dual-energy Computed Tomography for the Diagnosis of Acute Gouty Arthritis.

AIMS: To investigate the diagnostic value of Dual-energy Computed Tomography (DECT) in Acute Gouty Arthritis (AGA) or patients presenting suspected gouty arthritis. METHODS: This retrospective study was performed in a single centre from May 2017 to August 2018. Two hundred and twenty-six patients with an initial diagnosis of AGA in the preceding 15 days were included. All patients were referred for a DECT scan of the affected joints. The diagnosis criteria of gout with the American College of Rheumatology Classification Standard were regarded as the reference standard. RESULTS: After filtration, two hundred patients were included in the present study. The sensitivity, specificity, positive predictive value, and negative predictive value of DECT in the diagnosis of all AGA were 83.83%, 60.61%, 91.5%, and 42.55%, respectively. When AGA was subdivided according to the joint site, the sensitivity, specificity, positive predictive value, and negative predictive value were 80.68%, 61.11%, 91.03%, and 39.29% in feet, 93.55%, 40%, 93.55%, and 40% in knees and 87.5%, 71.43%, 91.3%, and 62.5% in ankles, respectively. CONCLUSION: DECT had a high sensitivity for the diagnosis of AGA. However, the specificity was limited, particularly for the diagnosis of acute gouty knee arthritis. Prospective multicenter studies of large samples will enhance the application of DECT among AGA patients in the future.

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

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.

The correlation of deep learning-based CAD-RADS evaluated by coronary computed tomography angiography with breast arterial calcification on mammography.

This study sought to evaluate the association of breast arterial calcification (BAC) on breast screening mammography with the Coronary Artery Disease-Reporting and Data System (CAD-RADS) based on Deep Learning-coronary computed tomography angiography (CCTA). This prospective single institution study included asymptomatic women over 40 who underwent CCTA and breast cancer screening mammography between July 2018 and April 2019. CAD-RADS was scored based on Deep Learning (DL). Mammograms were assessed visually for the presence of BAC. A total of 213 patients were included in the analysis. In comparison to the low CAD-RADS (CAD-RADS < 3) group, the high CAD-RADS (CAD-RADS ≥ 3) group, more often had a history of hypertension (P = 0.036), diabetes (P = 0.017), and chronic kidney disease (P = 0.006). They also had a significantly higher level of LDL-C (P = 0.024), while HDL-C was lower than in the low CAD-RADS group (P = 0.003). BAC was also significantly higher in the high CAD-RADS group (P = 0.002). In multivariate analysis, the presence of BAC [odd ratio (OR) 10.22, 95% CI 2.86-36.49, P < 0.001] maintained a significant associations with CAD-RADS after adjustment by meaningful variable. The same tendency was also found after adjustment by all covariates. There was a significant correlation between the severities of CAD detected by DL based CCTA and BAC in women undergoing breast screening mammography. BAC may be used as an additional diagnostic tool to predict the severity of CAD in this population.

Evaluation of LVDD by CCTA with Dual-source CT in Type 2 Diabetes Mellitus Patients.

BACKGROUND: Left ventricular diastolic dysfunction (LVDD) is a common abnormality among patients in T2DM. AIMS: We aimed to evaluate the feasibility of coronary computed tomography angiography (CCTA) for the assessment of LVDD in type 2 diabetes mellitus (T2DM) patients. METHODS: 80 consecutive T2DM patients who were referred for a clinically dual-source CCTA examination to evaluate suspected coronary artery disease and also underwent 2D echocardiography within 7 days of CCTA inclusion and exclusion criteria, were performed. Correlation between CCTA and echocardiography was tested through linear regression and Bland-Altman analysis. RESULTS: In total, 60 T2DM patients were included for the analysis. Pearson correlation showed good correlation for E (r = 0.28; P = 0.028), E/A (r = 0.69; P < 0.01); E (r = -0.06; P = 0.776), E/A (r = 0.54; P = 0.003) and E (r = 0.64; P < 0.01), E/A (r = 0.83; P < 0.01) in three groups, respectively. Overall, diagnostic accuracy for assessment in CCTA of diastolic dysfunction was 79.76% (95% CI: 68%-91%), 71.43% (95% CI: 58%-85%) and 87.50 (95% CI: 79%-96%) in three groups. CONCLUSION: The presented study proved that CCTA showed good correlations in the estimation of LV filling pressures compared with echocardiography in T2DM patients. Accordingly, retrospectively ECG-gated CCTA may provide valuable information on the evaluation of LVDD in T2DM patients.

Icariin promotes osteogenic differentiation of BMSCs by upregulating BMAL1 expression via BMP signaling.

Increasing research has demonstrated that expression of brain and muscle ARNT­like 1 (BMAL1) and other circadian clock genes can be regulated by drugs and toxicants. We previously demonstrated that icariin, extracted from Herba Epimedii, sromotes osteogenic differentiation. However, the mechanism underlying the association between icariin and BMAL1 in osteogenic differentiation of bone marrow­derived mesenchymal stem cells (BMSCs) remains unclear. The present study was designed with an aim to clarify the association between icariin and BMAL1 in osteogenic differentiation of BMSCs. The Cell Counting Kit­8 assay was used to evaluate cell proliferation. The expression of bone morphogenetic protein 2 (BMP2), RUNX family transcription factor 2 (RUNX2), alkaline phosphatase (ALP), osteocalcin (OC) and BMAL1 in BMSCs was evaluated by reverse transcription­quantitative PCR and western blotting. ALP and Alizarin red S (ARS) staining were also performed. Icariin promoted BMSC proliferation, and upregulated expression of osteogenic genes and BMAL1. In addition, expression of the osteogenic genes BMP2, RUNX2, ALP and OC were upregulated by BMAL1 overexpression. Furthermore, we confirmed that BMAL1 deficiency suppressed osteogenic differentiation in BMSCs. Finally, ARS staining of BMAL1­/­ BMSCs revealed that BMAL1 was an essential intermediary in matrix mineralization during osteogenic differentiation. In conclusion, these results demonstrated that icariin promoted osteogenic differentiation through BMAL1­BMP2 signaling in BMSCs. The present study thus described a novel target of icariin that has potential applications in the treatment of osteogenic disorders.