Effects of lowest-dose vs. highest-dose pitavastatin on coronary neointimal hyperplasia at 12-month follow-up in type 2 diabetic patients with non-ST elevation acute coronary syndrome: an optical coherence tomography analysis.

Current ACC/AHA guidelines recommend high-dose statin therapy after coronary stenting, especially in diabetic patients; however, pitavastatin 4 mg or pitavastatin 1 mg are frequently used after coronary stenting in Asia, even in patients with acute coronary syndrome. We compared the effects of highest-dose and lowest-dose pitavastatin therapy on coronary neointimal hyperplasia at 12-month follow-up in diabetic patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) using optical coherence tomography. A total of 72 diabetic patients with NSTE-ACS were randomized to lowest-dose pitavastatin [1 mg (n = 36)] or highest-dose pitavastatin [4 mg (n = 36)] after everolimus-eluting stent implantation. The primary endpoint was to compare the normalized neointimal volume at 12-month follow-up. Normalized neointimal volume was significantly lower in the pitavastatin 4 mg group (4.00 ± 2.80 vs. 8.24 ± 2.83 mm3/mm, p < 0.01) at 12-month follow-up. There was also significant difference in neointimal area between the pitavastatin 4 mg group and pitavastatin 1 mg group (0.41 ± 0.28 vs. 0.74 ± 0.23 mm2, p < 0.01). Improvement of brachial artery flow-mediated dilation (baFMD) was significantly higher in the pitavastatin 4 mg group than in pitavastatin 1 mg group (0.15 ± 0.15 vs. - 0.03 ± 0.19 mm, p < 0.001). In addition, the improvement of adiponectin levels was significantly greater in the pitavastatin 4 mg group than in the pitavastatin 1 mg group (2.97 ± 3.98 vs. 0.59 ± 2.80 µg/mL, p < 0.05). Pitavastatin 4 mg significantly improved inflammatory cytokines and lipid profiles compared to pitavastatin 1 mg during the 12-month follow-up, contributing to the reduction of neointimal hyperplasia and to the improvement of baFMD in diabetic patients with NSTE-ACS requiring coronary stenting. Thus, the administration of pitavastatin 4 mg can be safely and effectively used in high-risk patients requiring coronary stenting. Trial registration NCT02545231 (Clinical Trial registration information: https://clinicaltrials.gov/ct2/show/NCT02545231 ).

Deep learning-based prediction for significant coronary artery stenosis on coronary computed tomography angiography in asymptomatic populations.

Background: Although coronary computed tomography angiography (CCTA) is currently utilized as the frontline test to accurately diagnose coronary artery disease (CAD) in clinical practice, there are still debates regarding its use as a screening tool for the asymptomatic population. Using deep learning (DL), we sought to develop a prediction model for significant coronary artery stenosis on CCTA and identify the individuals who would benefit from undergoing CCTA among apparently healthy asymptomatic adults. Methods: We retrospectively reviewed 11,180 individuals who underwent CCTA as part of routine health check-ups between 2012 and 2019. The main outcome was the presence of coronary artery stenosis of ≥70% on CCTA. We developed a prediction model using machine learning (ML), including DL. Its performance was compared with pretest probabilities, including the pooled cohort equation (PCE), CAD consortium, and updated Diamond-Forrester (UDF) scores. Results: In the cohort of 11,180 apparently healthy asymptomatic individuals (mean age 56.1 years; men 69.8%), 516 (4.6%) presented with significant coronary artery stenosis on CCTA. Among the ML methods employed, a neural network with multi-task learning (19 selected features), one of the DL methods, was selected due to its superior performance, with an area under the curve (AUC) of 0.782 and a high diagnostic accuracy of 71.6%. Our DL-based model demonstrated a better prediction than the PCE (AUC, 0.719), CAD consortium score (AUC, 0.696), and UDF score (AUC, 0.705). Age, sex, HbA1c, and HDL cholesterol were highly ranked features. Personal education and monthly income levels were also included as important features of the model. Conclusion: We successfully developed the neural network with multi-task learning for the detection of CCTA-derived stenosis of ≥70% in asymptomatic populations. Our findings suggest that this model may provide more precise indications for the use of CCTA as a screening tool to identify individuals at a higher risk, even in asymptomatic populations, in clinical practice.