BACKGROUND: Persistent mineralocorticoid receptor activation is a pathologic response in type 2 diabetes and chronic kidney disease. Whereas mineralocorticoid receptor antagonists are beneficial in reducing cardiovascular complications, direct mechanistic pathways for these effects in humans are lacking. METHODS: The MAGMA trial (Mineralocorticoid Receptor Antagonism Clinical Evaluation in Atherosclerosis) was a randomized, double-blind, placebo-controlled trial in patients with high-risk type 2 diabetes with chronic kidney disease (not receiving dialysis) on maximum tolerated renin-angiotensin system blockade. The primary end point was change in thoracic aortic wall volume, expressed as absolute or percent value (ΔTWV or ΔPWV), using 3T magnetic resonance imaging at 12 months. Secondary end points were changes in left ventricle (LV) mass; LV fibrosis, measured as a change in myocardial native T1; and 24-hour ambulatory and central aortic blood pressures. Tertiary end points included plasma proteomic changes in 7596 plasma proteins using an aptamer-based assay. RESULTS: A total of 79 patients were randomized to placebo (n=42) or 25 mg of spironolactone daily (n=37). After a modified intent-to-treat, including available baseline data of study end points, patients who completed the trial protocol were included in the final analyses. At the 12-month follow-up, the average change in PWV was 7.1±10.7% in the placebo group and 0.87±10.0% in the spironolactone group ( P =0.028), and ΔTWV was 1.2±1.7 cm ³ in the placebo group and 0.037±1.9 cm ³ in the spironolactone group ( P =0.022). Change in LV mass was 3.1±8.4 g in the placebo group and −5.8±8.4 g in the spironolactone group ( P =0.001). Changes in LV T1 values were significantly different between the placebo and spironolactone groups (26.0±41.9 ms in the placebo group versus a decrease of −10.1±36.3 ms in the spironolactone group; P =6.33×10 ⁻⁴ ). Mediation analysis revealed that the spironolactone effect on thoracic aortic wall volume and myocardial mass remained significant after adjustment for ambulatory and central blood pressures. Proteomic analysis revealed a dominant effect of spironolactone on pathways involving oxidative stress, inflammation, and leukocyte activation. CONCLUSIONS: Among patients with diabetes with moderate to severe chronic kidney disease at elevated cardiovascular risk, treatment with spironolactone prevented progression of aortic wall volume and resulted in regression of LV mass and favorable alterations in native T1, suggesting amelioration of left-ventricular fibrosis. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02169089.

BACKGROUND: Use of intracoronary imaging is associated with improved outcomes in patients undergoing percutaneous coronary intervention (PCI). Yet, the impact of intracoronary imaging on real-time physician decision-making during PCI is not fully known. METHODS: The LightLab Initiative is a multicenter, prospective, observational study designed to characterize the use of a standardized optical coherence tomography (OCT) workflow during PCI. Participating physicians performed pre-PCI and post-PCI OCT in accordance with this workflow and operator assessments of lesion characteristics and treatment plan were recorded for each lesion based on angiography alone and following OCT. Physicians were categorized as having low (n=15), intermediate (n=13), or high (n=14) OCT use in the year preceding participation. RESULTS: Among 925 patients with 1328 lesions undergoing PCI, the prescribed OCT workflow was followed in 773 (84%) of patients with 836 lesions. Operator lesion assessment and decision-making during PCI changed with OCT use in 86% (721/836) of lesions. Pre-PCI OCT use changed operator decision-making in 80% of lesions, including lesion assessment (45%), vessel preparation strategy (27%), stent diameter (37%), and stent length (36%). Post-PCI OCT changed stent optimization decision-making in 31% of lesions. These findings were consistent across strata of physician prior OCT experience. CONCLUSIONS: A standardized OCT workflow impacted PCI decision-making in 86% of lesions, with a predominant effect on pre-PCI lesion assessment and planning of treatment strategy. This finding was consistent regardless of operator experience level and provides insight into mechanisms by which intravascular imaging might improve PCI outcomes.

Despite ongoing progress in stent technology and deployment techniques, in-stent restenosis (ISR) still remains a major issue following percutaneous coronary intervention (PCI) and accounts for 10.6% of all interventions in the United States. With the continuous rise in ISR risk factors such as obesity and diabetes, along with an increase in the treatment of complex lesions with high-risk percutaneous coronary intervention (CHIP), a substantial growth in ISR burden is expected. This review aims to provide insight into the mechanisms, classification, and management of ISR, with a focus on exploring innovative approaches to tackle this complication comprehensively, along with a special section addressing the approach to complex calcified lesions.

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software, which provides highly automated, comprehensive analysis of coronary plaques and stents in IVOCT images. User specifications for OCTOPUS were obtained from detailed, iterative discussions with IVOCT analysts in the Cardiovascular Imaging Core Laboratory at University Hospitals Cleveland Medical Center, a leading laboratory for IVOCT image analysis. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks for sequential comparisons. Intuitive, interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent area and stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays (e.g., segmented calcifications). To compare images over time, linked visualizations were enabled to display up to four registered vessel segments at a time. OCTOPUS has been deployed for nearly 1 year and is currently being used in multiple IVOCT studies. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 s/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We demonstrated use cases for sequential analysis. To analyze plaque progression, we loaded multiple pullbacks acquired at different points (e.g., pre-stent, 3-month follow-up, and 18-month follow-up) and evaluated frame-level development of in-stent neo-atherosclerosis. In ex vivo cadaver experiments, the OCTOPUS software enabled visualization and quantitative evaluation of irregular stent deployment in the presence of calcifications identified in pre-stent images. We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning.