Learning curve analysis of mitral valve repair using telemanipulative technology

Patrick J. Charland, Tom Robbins, Evelio Rodriguez, Wiley L. Nifong, Randolph W. Chitwood

    Research output: Contribution to journalArticle

    16 Citations (Scopus)

    Abstract

    Objective: To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. Methods: A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. Results: We found a statistically significant learning curve (P < .01). The institutional learning percentage derived from total robot times† for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95% (R2 = .40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P < .01). Conclusions: Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis.

    Original languageEnglish (US)
    Pages (from-to)404-410
    Number of pages7
    JournalJournal of Thoracic and Cardiovascular Surgery
    Volume142
    Issue number2
    DOIs
    StatePublished - Aug 1 2011

    Fingerprint

    Learning Curve
    Mitral Valve
    Technology
    Learning
    Benchmarking
    Budgets
    Surgical Instruments
    Economics

    All Science Journal Classification (ASJC) codes

    • Surgery
    • Pulmonary and Respiratory Medicine
    • Cardiology and Cardiovascular Medicine

    Cite this

    Learning curve analysis of mitral valve repair using telemanipulative technology. / Charland, Patrick J.; Robbins, Tom; Rodriguez, Evelio; Nifong, Wiley L.; Chitwood, Randolph W.

    In: Journal of Thoracic and Cardiovascular Surgery, Vol. 142, No. 2, 01.08.2011, p. 404-410.

    Research output: Contribution to journalArticle

    Charland, Patrick J. ; Robbins, Tom ; Rodriguez, Evelio ; Nifong, Wiley L. ; Chitwood, Randolph W. / Learning curve analysis of mitral valve repair using telemanipulative technology. In: Journal of Thoracic and Cardiovascular Surgery. 2011 ; Vol. 142, No. 2. pp. 404-410.
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    abstract = "Objective: To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. Methods: A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. Results: We found a statistically significant learning curve (P < .01). The institutional learning percentage derived from total robot times† for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95{\%} (R2 = .40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P < .01). Conclusions: Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95{\%}. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis.",
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