BACKGROUND: Coronary atherectomy is used to treat severely calcified coronary artery lesions which are more frequent with increasing age, but its impact in older adults has not been sufficiently examined. METHODS: We compared adults ≥ 18 years old who underwent coronary atherectomy during inpatient PCI in 2016-2023 from the Vizient Clinical Data Base and compared outcomes in younger (< 65 years), youngest-old (65-74 years), middle-old (75-84 years), and oldest-old (≥ 85 years) adults. Primary outcome was in-hospital mortality, and secondary outcomes included postprocedural complications. RESULTS: Among 47,337 patients who underwent coronary atherectomy, 19,862 (42.0%) were younger adults and 27,475 (58.0%) were older adults, including 13,583 youngest-old, 10,206 middle-old, and 3,686 oldest-old adults. Compared with younger adults, youngest-old adults had higher mortality (adjusted odds ratio [aOR] = 1.37, P < 0.001), ischemic stroke (aOR = 1.35, P = 0.005), gastrointestinal hemorrhage (GIH) (aOR = 1.44, P < 0.001), acute kidney injury (AKI) (aOR = 1.43, P < 0.001), tamponade (aOR = 1.86, P < 0.001), and pericardiocentesis (aOR = 2.32, P < 0.001). Middle-old adults had higher mortality (aOR = 1.80, P < 0.001), GIH (aOR = 1.42, P = 0.002), AKI (aOR = 1.63, P < 0.001), tamponade (aOR = 2.52, P < 0.001), and pericardiocentesis (aOR = 3.13, P < 0.001). Oldest-old adults had the highest odds for mortality (aOR = 2.03, P < 0.001), GIH (aOR = 1.48, P = 0.016), AKI (aOR = 2.26, P < 0.001), tamponade (aOR = 3.86, P < 0.001), and pericardiocentesis (aOR = 4.21, P < 0.001). There was a significant interaction (P-interaction=0.035) between atherectomy and age groups with regard to the odds of in-hospital mortality. CONCLUSIONS In this large claims-based study, in-hospital mortality, GIH, AKI, tamponade, and pericardiocentesis were higher in older adults compared with younger adults, in a stepwise manner by age group.

Artificial intelligence (AI) has revolutionized multiple domains of cardiovascular patient care and is being increasingly applied at different stages of the echocardiography workflow. Contemporary applications include automated view classification, real-time image quality guidance, chamber and function assessments (ejection fraction, chamber volumes, strain), and assisted diagnosis of cardiac conditions, such as heart failure, valvular disease, and cardiomyopathies. In some domains of echocardiography, AI algorithms can match or surpass the performance of human experts. Recent advances in large language models and multi-modal vision-language models have paved the way for complete end-to-end echocardiographic report generation. In this narrative review, we provide an introductory survey of the landscape of currently available AI tools for enhancing each step of the echocardiography workflow. We aim to equip practising community physicians with frameworks to understand and apply AI-based ultrasound technologies in an informed manner. By understanding current applications and limitations, and by selecting tools judiciously, clinicians may safely integrate AI into echocardiography practice to improve patient care.