Incremental predictive value for obstructive coronary artery disease by combination of Duke Clinical Score and Agatston score

Deep learning-based coronary artery calcium score to predict coronary artery disease in type 2 diabetes mellitus

Background

Coronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) patients often presents diffuse lesions, with extensive calcification, and it is time-consuming to measure coronary artery calcium score (CACS).

Objectives

To explore the predictive ability of deep learning (DL)-based CACS for obstructive CAD and hemodynamically significant CAD in T2DM.

Methods

469 T2DM patients suspected of CAD who accepted CACS scan and coronary CT angiography between January 2013 and December 2020 were enrolled. Obstructive CAD was defined as diameter stenosis ≥50%. Hemodynamically significant CAD was defined as CT-derived fractional flow reserve ≤0.8. CACS was calculated with a fully automated method based on DL algorithm. Logistic regression was applied to determine the independent predictors. The predictive performance was evaluated with area under receiver operating characteristic curve (AUC).

Results

DL-CACS (adjusted odds ratio (OR): 1.005; 95% CI: 1.003-1.006; P ＜ 0.001) was significantly associated with obstructive CAD. DL-CACS (adjusted OR:1.003; 95% CI: 1.002-1.004; P ＜ 0.001) was also an independent predictor for hemodynamically significant CAD. The AUCs, sensitivities, specificities, positive predictive values and negative predictive values of DL-CACS for obstructive CAD and hemodynamically significant CAD were 0.753 (95% CI: 0.712-0.792), 75.9%, 66.5%, 74.8%, 67.8% and 0.769 (95% CI: 0.728-0.806), 80.7%, 62.1%, 59.6% and 82.3% respectively. It took 1.17 min to perform automated measurement of DL-CACS in total, which was significantly less than manual measurement of 1.73 min (P ＜ 0.001).

Conclusions

DL-CACS, with less time-consuming, can accurately and effectively predict obstructive CAD and hemodynamically significant CAD in T2DM.

Noninvasive Coronary Atherosclerotic Plaque Imaging

Coronary artery disease is the leading cause of morbidity and mortality worldwide. Despite remarkable advances in the management of coronary artery disease, the prediction of adverse coronary events remains challenging. Over the preceding decades, considerable effort has been made to improve risk stratification using noninvasive imaging. Recently, these efforts have increasingly focused on the direct imaging of coronary atherosclerotic plaque. Modern imaging now allows imaging of coronary plaque burden, plaque type, atherosclerotic plaque activity, and plaque thrombosis, which have major potential to refine patient risk stratification, aid decision making, and advance future clinical practice.

Intra-plaque calcium and its relation with the progression of carotid atheromatous disease

BACKGROUND

Calcification and progression of atheromatous disease (AD) both have been independently related with the risk of stroke. However, the link between the two phenomena is still unclear. The main objective of this study was to analyze the temporal evolution of Ca content of carotid atheromatous plaques and its relation with the progression of carotid AD using quantitative CT Angiography (CTA).

METHODS

Forty-three asymptomatic patients with stenosis of the internal carotid artery (ICA)>50% completed the study. Contrast mold volume and calcium (Ca) content by quantitative CTA and Modified Agatston Score (Ca volume x radiological density) were assessed at baseline and after 12±2 months. Biochemical parameters, including main markers of Ca/Phosphorus (P) metabolism, were determined.

RESULTS

CTA measurement showed an increase of volumetric stenosis (volume decrease of the contrast mold), compared to baseline (475.45 (155.6) mm3 x U.H vs 501.3 (171.9) mm3 x U.H; p=0.04) as well as an increase of intra-plaque Ca (64.58 (57.8) mm3x U.H. vs 56.8 (52.3) p=0.002). An inverse correlation between baseline Ca content and volumetric stenosis progression (r= - 0.481; p<0.001), as well as between the increase of carotid Ca and plasma levels of vitamin D (r= 0.4; p=0.025) were also found. Multiple regression analysis found a model with baseline intra-plaque Ca, adjusted by body mass index (BMI) as most predictive of carotid AD progression.

CONCLUSIONS

These results suggest that a higher content of Ca confers greater stability against the progression of carotid AD and, eventually, its ability to generate symptomatology.

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.

Noninvasive Quantitative Plaque Analysis Identifies Hemodynamically Significant Coronary Arteries Disease

OBJECTIVE

To evaluate the diagnostic performance of automated quantitative analysis by coronary computed tomography angiography (CCTA) in identifying lesion-specific hemodynamic abnormality.

METHODS

A total of 132 patients (mean age, 61 y; 86 men) with 169 vessels (with 30% to 90% diameter stenosis), who successively underwent invasive coronary angiography with evaluation of fractional flow reserve (values ≤0.8 were defined as lesion-specific hemodynamic abnormalities), were analyzed by CCTA. CCTA images were quantitatively analyzed using automated software to obtain the following index: maximum diameter stenosis (MDS%); maximum area stenosis (MAS%); lesion length (LL); volume and burden (plaque volume×100 per vessel volume) of total plaque (total plaque volume [TPV], total plaque burden [TPB]), calcified plaque (calcified plaque volume [CPV], calcified plaque volume burden [CPB]), noncalcified plaque (noncalcified plaque volume [NCPV], noncalcified plaque volume burden [NCPB]), lipid plaque (lipid plaque volume [LPV], lipid plaque burden [LPB]), and fibrous plaque (fibrotic plaque volume [FPV], fibrotic plaque burden [FPB]); napkin-ring sign (NRS); remodeling index (RI); and eccentric index (EI). Logistic regression and area under the receiver operating characteristics (AUC) were used for statistical analysis.

RESULTS

Fractional flow reserve ≤0.80 was found in 57 (33.73%) of the 169 vessels. Vessels with hemodynamic significance had greater MDS% (64.43%±8.69% vs. 57.33%±9.95%, P<0.001), MAS% (73.18%±8.56% vs. 64.66%±8.95%, P<0.001), and lipid plaque burden (12.75% [9.73%, 19.56%] vs. 9.41% [4.10%, 15.70%], P=0.01) compared with vessels with normal hemodynamics. In multivariable logistic regression analysis, MAS% >68% (odds ratio: 7.20, 95% confidence interval [CI]=2.89-17.91, P<0.001) and LPB >10.03% (odds ratio=4.32, 95% CI=1.36-13.66, P=0.01) were significant predictors of hemodynamic abnormalities. In predicting lesion-specific hemodynamic abnormalities, the AUC was 0.77 (95% CI=0.70-0.85) for MAS% versus 0.71 (95% CI=0.63-0.79) for MDS% (P<0.05), 0.66 (95% CI=0.58-0.74) for LPV (P<0.05), 0.66 (95% CI=0.58-0.74) for LPB (P<0.05), and 0.63 (95% CI=0.54-0.71) for TPB (P<0.05). The AUC of MAS%+LPB (0.83, 95% CI=0.76-0.89) was significantly improved compared with that of MAS% (0.77, 95% CI=0.70-0.85, P<0.05).

CONCLUSIONS

Compared with MDS% and the volume burdens of plaque compositions, MAS% has a higher diagnostic accuracy for coronary hemodynamic abnormalities in the precise quantitative analysis of coronary plaques on the basis of CT. Furthermore, MAS%+LPB might improve the diagnostic accuracy beyond MAS% alone.

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

AIMS

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

METHODS AND RESULTS

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

CONCLUSION

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

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.