Outcomes of Intravascular Ultrasound–Guided Percutaneous Coronary Intervention in the United States

CVIT 2025 clinical expert consensus document on intravascular ultrasound

Intravascular ultrasound (IVUS) provides precise anatomic information in coronary arteries including quantitative measurements and morphological assessment. To standardize the IVUS analysis in the current era, this updated expert consensus document summarizes the methods of measurements and assessment of IVUS images and the clinical evidence of IVUS use in percutaneous coronary intervention.

CVIT 2023 clinical expert consensus document on intravascular ultrasound

Intravascular ultrasound (IVUS) provides precise anatomic information in coronary arteries including quantitative measurements and morphological assessment. To standardize the IVUS analysis in the current era, this updated expert consensus document summarizes the methods of measurements and assessment of IVUS images and the clinical evidence of IVUS use in percutaneous coronary intervention.

Association between fasting stress hyperglycemia ratio and contrast-induced acute kidney injury in coronary angiography patients: a cross-sectional study

Aims

Stress hyperglycemia ratio (SHR), an emerging indicator of critical illness, exhibits a significant association with adverse cardiovascular outcomes. The primary aim of this research endeavor is to evaluate the association between fasting SHR and contrast-induced acute kidney injury (CI-AKI).

Methods

This cross-sectional study comprised 3,137 patients who underwent coronary angiography (CAG) or percutaneous coronary intervention (PCI). The calculation of fasting SHR involved dividing the admission fasting blood glucose by the estimated mean glucose obtained from glycosylated hemoglobin. CI-AKI was assessed based on elevated serum creatinine (Scr) levels. To investigate the relationship between fasting SHR and the proportion of SCr elevation, piecewise linear regression analysis was conducted. Modified Poisson's regression analysis was implemented to evaluate the correlation between fasting SHR and CI-AKI. Subgroup analysis and sensitivity analysis were conducted to explore result stability.

Results

Among the total population, 482 (15.4%) patients experienced CI-AKI. Piecewise linear regression analysis revealed significant associations between the proportion of SCr elevation and fasting SHR on both sides (≤ 0.8 and > 0.8) [β = -12.651, 95% CI (-23.281 to -2.022), P = 0.020; β = 8.274, 95% CI (4.176 to 12.372), P < 0.001]. The Modified Poisson's regression analysis demonstrated a statistically significant correlation between both the lowest and highest levels of fasting SHR and an increased incidence of CI-AKI [(SHR < 0.7 vs. 0.7 ≤ SHR < 0.9) β = 1.828, 95% CI (1.345 to 2.486), P < 0.001; (SHR ≥ 1.3 vs. 0.7 ≤ SHR < 0.9) β = 2.896, 95% CI (2.087 to 4.019), P < 0.001], which was further validated through subgroup and sensitivity analyses.

Conclusion

In populations undergoing CAG or PCI, both lowest and highest levels of fasting SHR were significantly associated with an increased occurrence of CI-AKI.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

Clinical expert consensus document on intravascular ultrasound from the Japanese Association of Cardiovascular Intervention and Therapeutics (2021)

Intravascular ultrasound (IVUS) provides precise anatomic information in coronary arteries including quantitative measurements and morphological assessment. To standardize the IVUS analysis in the current era, this updated expert consensus document summarizes the methods of measurements and assessment of IVUS images and the clinical evidence of IVUS use in percutaneous coronary intervention.

Hot topics in interventional cardiology: Proceedings from the society for cardiovascular angiography and interventions (SCAI) 2021 think tank

The Society for Cardiovascular Angiography and Interventions (SCAI) Think Tank is a collaborative venture that brings together interventional cardiologists, administrative partners, and select members of the cardiovascular industry community annually for high-level field-wide discussions. The 2021 Think Tank was organized into four parallel sessions reflective of the field of interventional cardiology: (a) coronary intervention, (b) endovascular medicine, (c) structural heart disease, and (d) congenital heart disease. Each session was moderated by a senior content expert and co-moderated by a member of SCAI's Emerging Leader Mentorship program. This document presents the proceedings to the wider cardiovascular community in order to enhance participation in this discussion, create additional dialog from a broader base, and thereby aid SCAI, the industry community and external stakeholders in developing specific action items to move these areas forward.

Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease

BACKGROUND

Accurate segmentation of the coronary arteries with intravascular ultrasound (IVUS) is important to optimize coronary stent implantation. Recently, deep learning (DL) methods have been proposed to develop automatic IVUS segmentation. However, most of those have been limited to segmenting the lumen and vessel (i.e. lumen-intima and media-adventitia borders), not applied to segmenting stent dimension. Hence, this study aimed to develop a DL method for automatic IVUS segmentation of stent area in addition to lumen and vessel area.

METHODS

This study included a total of 45,449 images from 1576 IVUS pullback runs. The datasets were randomly split into training, validation, and test datasets (0.7:0.15:0.15). After developing the DL-based system to segment IVUS images using the training and validation datasets, we evaluated the performance through the independent test dataset.

RESULTS

The DL-based segmentation correlated well with the expert-analyzed segmentation with a mean intersection over union (± standard deviation) of 0.80 ± 0.20, correlation coefficient of 0.98 (95% confidence intervals: 0.98 to 0.98), 0.96 (0.95 to 0.96), and 0.96 (0.96 to 0.96) for lumen, vessel, and stent area, and the mean difference (± standard deviation) of 0.02 ± 0.57, -0.44 ± 1.56 and - 0.17 ± 0.74 mm² for lumen, vessel and stent area, respectively.

CONCLUSION

This automated DL-based IVUS segmentation of lumen, vessel and stent area showed an excellent agreement with manual segmentation by experts, supporting the feasibility of artificial intelligence-assisted IVUS assessment in patients undergoing coronary stent implantation.