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Learning curve predictors for minimally invasive mitral valve surgery; how far should the rabbit hole go?
Author(s) -
Dokollari Aleksander,
Cameli Matteo,
Kalra DidarKaran S.,
Pervez Mohammad B.,
Demosthenous Michalis,
Pernoci Marjela,
Bonneau Daniel,
Latter David,
Gelsomino Sandro,
Lisi Gianfranco,
Yanagawa Bobby,
Verma Subodh,
Bisleri Gianluigi,
Bonacchi Massimo
Publication year - 2020
Publication title -
journal of cardiac surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 58
eISSN - 1540-8191
pISSN - 0886-0440
DOI - 10.1111/jocs.14939
Subject(s) - medicine , cusum , aortic cross clamp , cardiopulmonary bypass , mitral regurgitation , area under the curve , surgery , creatinine , chest tube , logistic regression , cardiology , pneumothorax , operations management , economics
Objective To analyze predictors that influence the learning curve of minimally invasive mitral valve surgery (MIMVS). Methods Patients who underwent MIMVS between March 2010 to March 2015 were retrospectively analyzed. Predictive factors that influence the learning curve were analyzed. Results One hundred and five patients were included in the analysis. Cardiopulmonary bypass (CPB) time in minutes was 158.72 ± 40.98 and the aortic cross‐clamp (ACC) time in minutes was 114.48 ± 27.29. There were three operative mortalities, one stroke and five >2+ mitral regurgitation. ACC time in minutes was higher in the low logistic Euroscore II (LES) group (LES < 5% = 118.42 ± 27.94) versus (LES ≥ 5 = 88.66 ± 22.26), P < .05 while creatinine clearance in μmol/L was higher in the LES < 5% group (LES < 5% = 84.32 ± 33.7) versus (LES ≥ 5% = 41.66 ± 17.14), ( P < .05). One patient from each group required chest tube insertion for pleural effusion P < .05. The cumulative sum analysis (CUSUM) for the first 25 patients had CPB and ACC times that reached the upper limits. Between 25 to 64 patients the curve remained stable while with the introduction of reoperations and complex surgical procedures the CUSUM reached the upper limits. Conclusions The 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.