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Minimally ‐invasive parasternal aortic valve replacement–A slow learning curve towards improved outcomes
ConclusionsParasternal minimally ‐invasive aortic valve replacement is a feasible technique associated with a slow learning curve but the potential to achieve improved outcomes. Considering the transcatheter alternatives, the relative risk reduction may be worth investigating in future trials.
Source: Journal of Cardiac Surgery - January 14, 2020 Category: Cardiovascular & Thoracic Surgery Authors: Sophio Tkebuchava, Gloria F ärber, Christoph Sponholz, Frank Fuchs, Petra Heinisch, Michael Bauer, Torsten Doenst Tags: ORIGINAL ARTICLE Source Type: research

The learning curve effect on outcomes with frozen elephant trunk technique for extensive thoracic aorta disease
ConclusionThe learning curve with the FET procedure had a significant impact on hospital mortality and midterm survival over the follow ‐up period, albeit did not influence the freedom from reintervention on the downstream aorta.
Source: Journal of Cardiac Surgery - July 2, 2019 Category: Cardiovascular & Thoracic Surgery Authors: Fabr ício José Dinato, Ricardo Ribeiro Dias, José Augusto Duncan, Fábio Fernandes, Felix José Alvares Ramires, Charles Mady, Fabio Biscegli Jatene Tags: ORIGINAL ARTICLE Source Type: research

Learning curve predictors for minimally invasive mitral valve surgery; how far should the rabbit hole go?
ConclusionsThe learning curve is affected by many factors but this should not desist surgeons from approaching this technique. The introduction of high ‐risk patients in clinical practice should be carefully measured based on surgeon experience.
Source: Journal of Cardiac Surgery - August 12, 2020 Category: Cardiovascular & Thoracic Surgery Authors: Aleksander Dokollari, Matteo Cameli, Didar ‐Karan S. Kalra, Mohammad B. Pervez, Michalis Demosthenous, Marjela Pernoci, Daniel Bonneau, David Latter, Sandro Gelsomino, Gianfranco Lisi, Bobby Yanagawa, Subodh Verma, Gianluigi Bisleri, Massim Tags: ORIGINAL ARTICLE Source Type: research

Machine learning models for mitral valve replacement: A comparative analysis with the Society of Thoracic Surgeons risk score
ConclusionsThe proposed risk models complement existing STS models in predicting mortality, prolonged ventilation, and renal failure, allowing healthcare providers to more accurately assess a patient's risk of morbidity and mortality when undergoing MVS.
Source: Journal of Cardiac Surgery - October 20, 2021 Category: Cardiovascular & Thoracic Surgery Authors: Agni Orfanoudaki, Aikaterini Giannoutsou, Sabet Hashim, Dimitris Bertsimas, Robert C. Hagberg Tags: ORIGINAL ARTICLE Source Type: research