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Your search keyword '"Lavalle, S."' showing total 2 results
Start Over Author "Lavalle, S." Topic coronary artery disease
2 results on '"Lavalle, S."'

1. Evaluation and management of coronary artery disease in transcatheter aortic valve implantation candidates with severe aortic stenosis and coronary artery disease: technology and techniques.

Author

Laterra G, Strazzieri O, Reddavid C, Scalia L, Agnello F, Lavalle S, and Barbanti M

Subjects
Humans, Percutaneous Coronary Intervention methods, Percutaneous Coronary Intervention instrumentation, Transcatheter Aortic Valve Replacement instrumentation, Transcatheter Aortic Valve Replacement adverse effects, Aortic Valve Stenosis surgery, Coronary Artery Disease therapy
Abstract

Introduction: Patients with severe aortic stenosis referred for transcatheter aortic valve implantation (TAVI) often present with concomitant coronary artery disease (CAD). The management of CAD in these patients remains a topic of debate, encompassing the evaluation and timing of percutaneous coronary intervention (PCI)., Areas Covered: This review article aims to offer an overview of the role of coronary revascularization in TAVI patients, highlighting the advantages and disadvantages of different strategies: PCI before, concomitant with, and after TAVI. Considering that TAVI indications are expanding and patients with low surgical risk are now being referred for TAVI, the rate of PCI among patients undergoing TAVI is expected to increase. Historically, PCI was performed before TAVI. However, there is now a growing trend to defer PCI until after TAVI., Expert Opinion: It is plausible that in the future, there will be an increase in PCI after TAVI due to several factors: first, multiple studies have shown the safety of TAVI even in patients with severe untreated CAD; second, improvements in TAVI device implantation techniques, such as commissural alignment and patient-specific device selection, have improved access to the coronary arteries post-TAVI.

Published
2024
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2. Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients

Author

Stephanie Steidler, Alberto Zangrillo, Fabio Ciceri, Moreno Tresoldi, Carlo Martinenghi, Diego Palumbo, Anna Colarieti, Francesco De Cobelli, Salvatore Lavalle, Junaid Mushtaq, Renato Pennella, Antonio Esposito, Patrizia Rovere-Querini, Giovanni Landoni, Mushtaq, J., Pennella, R., Lavalle, S., Colarieti, A., Steidler, S., Martinenghi, C. M. A., Palumbo, D., Esposito, A., Rovere-Querini, P., Tresoldi, M., Landoni, G., Ciceri, F., Zangrillo, A., and De Cobelli, F.

Subjects
Male, Artificial intelligence, Multivariate analysis, Radiography, Disease, Comorbidity, Coronary Artery Disease, Severity of Illness Index, 030218 nuclear medicine & medical imaging, law.invention, Coronary artery disease, Pulmonary Disease, Chronic Obstructive, 0302 clinical medicine, law, COPD, Age Factors, Neurodegenerative Diseases, General Medicine, Middle Aged, Prognosis, Intensive care unit, Hospitalization, Intensive Care Units, Imaging Informatics and Artificial Intelligence, Severe acute respiratory syndrome, Italy, Radiology Nuclear Medicine and imaging, 030220 oncology & carcinogenesis, Female, Radiography, Thoracic, Emergency Service, Hospital, Coronavirus disease 2019 (COVID-19), 03 medical and health sciences, Deep Learning, Sex Factors, Artificial Intelligence, medicine, Humans, Radiology, Nuclear Medicine and imaging, Mortality, Aged, Proportional Hazards Models, Retrospective Studies, business.industry, SARS-CoV-2, COVID-19, Emergency department, medicine.disease, business, Tomography, X-Ray Computed
Abstract

Objective To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19. Methods This retrospective single-center study included adult patients presenting to the emergency department (ED) between February 25 and April 9, 2020, with SARS-CoV-2 infection confirmed on real-time reverse transcriptase polymerase chain reaction (RT-PCR). Initial CXRs obtained on ED presentation were evaluated by a deep learning artificial intelligence (AI) system and compared with the Radiographic Assessment of Lung Edema (RALE) score, calculated by two experienced radiologists. Death and critical COVID-19 (admission to intensive care unit (ICU) or deaths occurring before ICU admission) were identified as clinical outcomes. Independent predictors of adverse outcomes were evaluated by multivariate analyses. Results Six hundred ninety-seven 697 patients were included in the study: 465 males (66.7%), median age of 62 years (IQR 52–75). Multivariate analyses adjusting for demographics and comorbidities showed that an AI system-based score ≥ 30 on the initial CXR was an independent predictor both for mortality (HR 2.60 (95% CI 1.69 − 3.99; p < 0.001)) and critical COVID-19 (HR 3.40 (95% CI 2.35–4.94; p < 0.001)). Other independent predictors were RALE score, older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. Conclusion AI- and radiologist-assessed disease severity scores on CXRs obtained on ED presentation were independent and comparable predictors of adverse outcomes in patients with COVID-19. Trial registration ClinicalTrials.gov NCT04318366 (https://clinicaltrials.gov/ct2/show/NCT04318366). Key Points • AI system–based score ≥ 30 and a RALE score ≥ 12 at CXRs performed at ED presentation are independent and comparable predictors of death and/or ICU admission in COVID-19 patients. • Other independent predictors are older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. • The comparable performance of the AI system in relation to a radiologist-assessed score in predicting adverse outcomes may represent a game-changer in resource-constrained settings.

Published
2020

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