Comparing the pooled cohort equations and coronary artery calcium scores in a symptomatic mixed Asian cohort.

Background: The value of pooled cohort equations (PCE) as a predictor of major adverse cardiovascular events (MACE) is poorly established among symptomatic patients. Coronary artery calcium (CAC) assessment further improves risk prediction, but non-Western studies are lacking. This study aims to compare PCE and CAC scores within a symptomatic mixed Asian cohort, and to evaluate the incremental value of CAC in predicting MACE, as well as in subgroups based on statin use. Methods: Consecutive patients with stable chest pain who underwent cardiac computed tomography were recruited. Logistic regression was performed to determine the association between risk factors and MACE. Cohort and statin-use subgroup comparisons were done for PCE against Agatston score in predicting MACE. Results: Of 501 patients included, mean (SD) age was 53.7 (10.8) years, mean follow-up period was 4.64 (0.66) years, 43.5% were female, 48.3% used statins, and 50.0% had no CAC. MI occurred in 8 subjects while 9 subjects underwent revascularization. In the general cohort, age, presence of CAC, and ln(Volume) (OR = 1.05, 7.95, and 1.44, respectively) as well as age and PCE score for the CAC = 0 subgroup (OR = 1.16 and 2.24, respectively), were significantly associated with MACE. None of the risk factors were significantly associated with MACE in the CAC > 0 subgroup. Overall, the PCE, Agatston, and their combination obtained an area under the receiver operating characteristic curve (AUC) of 0.501, 0.662, and 0.661, respectively. Separately, the AUC of PCE, Agatston, and their combination for statin non-users were 0.679, 0.753, and 0.734, while that for statin-users were 0.585, 0.615, and 0.631, respectively. Only the performance of PCE alone was statistically significant (p = 0.025) when compared between statin-users (0.507) and non-users (0.783). Conclusion: In a symptomatic mixed Asian cohort, age, presence of CAC, and ln(Volume) were independently associated with MACE for the overall subgroup, age and PCE score for the CAC = 0 subgroup, and no risk factor for the CAC > 0 subgroup. Whilst the PCE performance deteriorated in statin versus non-statin users, the Agatston score performed consistently in both groups.

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.

End-to-End, Pixel-Wise Vessel-Specific Coronary and Aortic Calcium Detection and Scoring Using Deep Learning.

Conventional scoring and identification methods for coronary artery calcium (CAC) and aortic calcium (AC) result in information loss from the original image and can be time-consuming. In this study, we sought to demonstrate an end-to-end deep learning model as an alternative to the conventional methods. Scans of 377 patients with no history of coronary artery disease (CAD) were obtained and annotated. A deep learning model was trained, tested and validated in a 60:20:20 split. Within the cohort, mean age was 64.2 ± 9.8 years, and 33% were female. Left anterior descending, right coronary artery, left circumflex, triple vessel, and aortic calcifications were present in 74.87%, 55.82%, 57.41%, 46.03%, and 85.41% of patients respectively. An overall Dice score of 0.952 (interquartile range 0.921, 0.981) was achieved. Stratified by subgroups, there was no difference between male (0.948, interquartile range 0.920, 0.981) and female (0.965, interquartile range 0.933, 0.980) patients (p = 0.350), or, between age <65 (0.950, interquartile range 0.913, 0.981) and age ≥65 (0.957, interquartile range 0.930, 0.9778) (p = 0.742). There was good correlation and agreement for CAC prediction (rho = 0.876, p < 0.001), with a mean difference of 11.2% (p = 0.100). AC correlated well (rho = 0.947, p < 0.001), with a mean difference of 9% (p = 0.070). Automated segmentation took approximately 4 s per patient. Taken together, the deep-end learning model was able to robustly identify vessel-specific CAC and AC with high accuracy, and predict Agatston scores that correlated well with manual annotation, facilitating application into areas of research and clinical importance.

Antiviral activity of ST081006 against the dengue virus.

Dengue virus, the causative agent for the dengue fever, infects approximately 50-100 million people worldwide per year. The high incidence of dengue fever, along with its potential to develop into a severe, life-threatening form, resulted in great interest in the discovery of an antiviral against it. In this study, we constructed a DENV2-EGFP infectious clone, established a fluorescence-based, high-throughput screening platform, and conducted a screen for anti-DENV compounds on a flavonoid-derivative library, Amongst the hits identified, ST081006 was found to be a strong inhibitor of the DENV replication. Time-course studies suggest that the presence of ST081006 is necessary to inhibit successive rounds of virus replication. Further investigations demonstrated that ST081006 affects the synthesis of both viral protein and viral RNA, and one anti-DENV mechanism is the direct inhibition of viral protein synthesis. The replication of all dengue serotypes, along with that of the enterovirus EV-A71, was shown to be affected by ST081006. Attempts to generate ST081006-resistant DENV were unsuccessful, and thus hints at host factors as potential drug target. Together, these results suggest that ST081006 affect DENV replication, likely by acting on a target involved in the viral protein and/or RNA synthesis pathway.