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Enregistrement W4385578586 · doi:10.1016/j.shj.2023.100215

Editorial: Flattening the Curve

2023· editorial· en· W4385578586 sur OpenAlex
Tsuyoshi Kaneko, Connor P. Callahan

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Notice bibliographique

RevueStructural Heart · 2023
Typeeditorial
Langueen
DomaineMedicine
ThématiqueAortic Disease and Treatment Approaches
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineRoss procedureSurgeryRadiologyStenosisAortic valve replacement

Résumé

récupéré en direct d'OpenAlex

The Ozaki procedure (aortic valve neocuspidization) was first reported by Dr. Shigeyuki Ozaki in 2011. This procedure involves the recreation of the aortic valve leaflets using autologous pericardium.1Ozaki S. Kawase I. Yamashita H. et al.A total of 404 cases of aortic valve reconstruction with glutaraldehyde-treated autologous pericardium.J Thorac Cardiovasc Surg. 2014; 147: 301-306Abstract Full Text Full Text PDF PubMed Scopus (153) Google Scholar The proposed benefit of this procedure was longer coaptation length and preserved annular mobility leading to a larger orifice area. Midterm results have shown durable outcomes. However, its adaptation has been slow.2Pirola S. Mastroiacovo G. Arlati F. et al.Single center 5-years’ experience of Ozaki procedure: mid-term follow-up.Ann Thorac Surg. 2020; (pii: S0003-4975(20)31730-6)Google Scholar One of the reasons is the complex nature of the procedure, which involves autologous pericardial harvesting, resecting and creating the shape of each leaflet, and suturing to the annulus. To overcome complexity, a dedicated tool has been developed, which includes the sizers and the cusp template with a detailed guide to leaflet suturing. In this edition of the journal, Patel and colleagues have presented a standardized approach adopted at the Cleveland Clinic to minimize the learning curve of the Ozaki procedure. Their strategy included limiting the operators to 2 surgeons, using the dedicated tools to standardize the procedure, and most importantly, visiting Dr. Ozaki to observe the surgery and installing wet lab practice. Notably, in their first 20 cases, there was no major perioperative morbidity or mortality, while the cardiopulmonary bypass and aortic cross-clamp times steadily decreased by 20 ​minutes. Both cardiopulmonary time and aortic cross-clamp time plateaued after 20 cases. These steps proved quite effective given the minimum perioperative morbidity in their early experience and relatively rapid decrease in bypass and cross-clamp times. The authors emphasize that the 2 keys to minimizing their learning curve were observation and coaching from an expert, Dr Ozaki, and simulation in the wet lab. The authors are to be commended, as there is a learning curve for every procedure that we do, especially complex procedures. For context, other examples of challenging cardiac operations that can have a significant learning curve in the field of cardiac surgery include valve-sparing aortic root replacement and minimally invasive mitral valve repair. Beckmann and colleagues3Beckmann E. Martens A. Krueger H. et al.Aortic valve-sparing root replacement (David): learning curve and impact on outcome.Interact Cardiovasc Thorac Surg. 2020; 30: 754-761Crossref Scopus (11) Google Scholar described their learning curve with valve-sparing aortic root replacement. They found that less surgeon experience was a significant risk factor for aortic valve-related reoperation-free survival. Interestingly, 20 cases were needed to start seeing a decrease in aortic cross-clamp time and aortic valve reoperation. The reduction in both was seen even after 40 cases. Other series have indicated that the learning curve continued for up to 7 years in terms of aortic valve-related reoperations.4Chirichilli I. Scaffa R. Irace F.G. et al.Twenty-year experience of aortic valve reimplantation using the Valsalva graft.Eur J Cardio Thorac Surg. 2023; 63ezac591Crossref Scopus (0) Google Scholar For minimally invasive mitral surgery, Vo et al.5Vo A.T. Nguyen D.H. Van Hoang S. et al.Learning curve in minimally invasive mitral valve surgery: a single-center experience.J Cardiothorac Surg. 2019; 14: 213Crossref Scopus (19) Google Scholar described valve repair requiring at least 90 cases to have an acceptable technical complication rate. By taking adequate strategies, the Cleveland Clinic group was able to diminish the learning curve significantly, considering the complexity of the procedure. As highlighted in this article, simulation in the wet lab was vital to minimize their learning curve. As we advance, the simulation will be the key to improving outcomes in our field, specifically by minimizing the learning curve for every procedure we do. For example, congenital cardiac surgery is another segment of our field with a significant learning curve for junior surgeons and trainees and an area where outcomes have come under increasing scrutiny. The group at the Hospital for Sick Children in Toronto has published its Hands-On Surgical Training program. Their trainees and others at outside institutions participate monthly in three-dimensional (3D)-printed models for specific congenital cardiac lesions. This program has the benefit of hands-on training on challenging anatomy and repairs and further facilitates coaching from expert surgeons.6Van Arsdell G.S. Hussein N. Yoo S.J. Three-dimensional printing in congenital cardiac surgery-Now and the future.J Thorac Cardiovasc Surg. 2020; 160: 515-519Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar All participants in their program have found it to help improve their surgical skills.7Yoo S.J. Spray T. Austin E.H. Yun T.J. van Arsdell G.S. Hands-on surgical training of congenital heart surgery using 3-dimensional print models.J Thorac Cardiovasc Surg. 2017; 153: 1530-1540Abstract Full Text Full Text PDF PubMed Scopus (129) Google Scholar As 3D printing becomes easier to streamline, using models both for congenital and adult cardiac procedures will make the learning curve less steep for trainees and junior surgeons when they step into the operating room. With the advancement in the field of simulation and 3D printing, it is critical to leverage these to decrease the learning curve in a complex procedure. This study highlights that all surgeons will inevitably experience a learning curve with a new operation, even with a master surgeon. There is some concern about the early aortic regurgitation with this procedure, and adaptation to the general population needs further investigation. However, this manuscript proves that adequate preparation and steps can flatten the learning curve. Most importantly, these dogmas are also applicable to nonsurgical procedures. There are numerous upcoming technologies in the field of structural heart disease, such as transcatheter mitral/tricuspid repair/replacement. These procedures are often done by the heart team, including interventional cardiologists and/or cardiac surgeons. As we embrace the new innovations in the field of structural heart, we as a specialty need to constantly think of how to flatten the learning curve while maintaining the quality of the procedure. The authors' experience provides a roadmap for all of us to try to emulate. The authors have no funding to report.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,802

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,318
Écart entre enseignants0,300 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle