Robotic Mitral Valve Repair: The Learning Curve. Academic Article uri icon

Overview

abstract

  • OBJECTIVE: Adoption of robotic mitral valve surgery has been slow, likely in part because of its perceived technical complexity and a poorly understood learning curve. We sought to correlate changes in technical performance and outcome with surgeon experience in the "learning curve" part of our series. METHODS: From 2006 to 2011, two surgeons undertook robotically assisted mitral valve repair in 458 patients (intent-to-treat); 404 procedures were completed entirely robotically (as-treated). Learning curves were constructed by modeling surgical sequence number semiparametrically with flexible penalized spline smoothing best-fit curves. RESULTS: Operative efficiency, reflecting technical performance, improved for (1) operating room time for case 1 to cases 200 (early experience) and 400 (later experience), from 414 to 364 to 321 minutes (12% and 22% decrease, respectively), (2) cardiopulmonary bypass time, from 148 to 102 to 91 minutes (31% and 39% decrease), and (3) myocardial ischemic time, from 119 to 75 to 68 minutes (37% and 43% decrease). Composite postoperative complications, reflecting safety, decreased from 17% to 6% to 2% (63% and 85% decrease). Intensive care unit stay decreased from 32 to 28 to 24 hours (13% and 25% decrease). Postoperative stay fell from 5.2 to 4.5 to 3.8 days (13% and 27% decrease). There were no in-hospital deaths. Predischarge mitral regurgitation of less than 2+, reflecting effectiveness, was achieved in 395 (97.8%), without correlation to experience; return-to-work times did not change substantially with experience. CONCLUSIONS: Technical efficiency of robotic mitral valve repair improves with experience and permits its safe and effective conduct.

publication date

  • November 1, 2017

Research

keywords

  • Learning Curve
  • Mitral Valve Annuloplasty
  • Mitral Valve Insufficiency
  • Postoperative Complications
  • Robotic Surgical Procedures

Identity

Scopus Document Identifier

  • 85052319838

Digital Object Identifier (DOI)

  • 10.1097/IMI.0000000000000438

PubMed ID

  • 29232301

Additional Document Info

volume

  • 12

issue

  • 6