Background And Objectives: The purpose of this study was to evaluate the usefulness of transesophageal echocardiography during the implantation of the CoreValve percutaneous aortic prosthesis and the influence of the learning curve in its indications.
Methods: From April 2008 to January 2010, 53 patients were treated for symptomatic severe aortic stenosis with a CoreValve percutaneous aortic prosthesis. Transesophageal echocardiography was performed during the procedure for two reasons: if a discrepancy between the measures of the aortic annulus arose before the implantation and to provide visualization if a complication occurred during the procedure. Patients were divided in to two groups based on the learning curve (group I:the first 25 patients, group II: all of the subsequent patients).
Results: Thirty-six transesophageal echocardiographies were performed on 28 patients (53%).In 15 cases the indication was to assist us in determining the proper sized prosthesis, and in the other 21 cases it was performed due to a complication during the procedure. The numbers of intraprocedure transesophageal echocardiographies were similar in both learning-curve groups, (64% in group I vs. 71% in group II, P = 0.56) but the proportion of the studies indicated by serious complications was significantly lower in the second period of the learning curve (67% in group I vs. 17% in group II, P < 0.05).
Conclusions: In our experience, transesophageal echocardiography was very useful in more than one-half of the patients during the implantation of the CoreValve percutaneous aortic prosthesis. After overcoming the learning curve, its indication due to serious procedure-related complications decreased significantly.
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http://dx.doi.org/10.1002/ccd.23370 | DOI Listing |
Updates Surg
January 2025
Department of Gastrointestinal Surgery, The First People's Hospital of Foshan, No. 81 Lingnan Avenue North, Foshan, China.
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Department of Cardiothoracic Surgery, Fifth Affiliated Hospital of Sun Yat-Sen University, No.52 East Meihua Road, Zhuhai, Guangdong Province, 519000, China.
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Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, India.
Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinical tests and biopsies that delay treatment initiation. To address this, we developed a machine learning-based approach utilizing resected lung-tissue microbiome of AC and SCC patients for subtype classification. Differentially enriched taxa were identified using LEfSe, revealing ten potential microbial markers.
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