title: Predictors of operative time in multicenter port-access valve registry: institutional differences in learning.
abstract: BACKGROUND
  The predictors of operative time and the effects of learning in isolated valve operations using port-access techniques have not been defined.
  
  
  METHODS
  Analysis of covariance was used to examine the determinants of procedure time, pump time, and aortic clamp time. In the largest prospective, registry of patients undergoing isolated aortic valve replacement (AVR, N=199), mitral repair (MVP, N=307), or mitral replacement (MVR, N=232) using port-access techniques 1997-1999 at 27 institutions.
  
  
  RESULTS
  Institutional case volume ranged from one to 214 (median 6). Operative time was longer in redo procedures (5.3 +/- 1.6 vs. 4.4 +/- 1.3 hr, p = 0.0001), longer with MVP or MVR vs. AVR (4.8 +/- 1.2 vs. 5.0 +/- 1.5 vs. 3.8 +/- 1.2 hr, p = 0.0001), and decreased with case number (mean decrease 1.00 +/- 0.19 min/case, p = 0.04). Operative time also varied between institutions (p = 0.001). Rate of learning (decrease in time per case) varied significantly between institutions only for MVP (p = 0.03). Similar analysis showed that pump time and clamp times did not significantly change over time (p > 0.17) but varied significantly between institutions. Institutional volume did not affect operative, pump, or clamp times or rate of learning (decrease in operative time/case).
  
  
  CONCLUSIONS
  These prospective registry data demonstrate that, for port-access valve procedures, procedure times continue to improve (learning) even after 100 cases. Procedure time and learning are affected by institutional differences and by the type of procedure, but are little affected by institutional volume. This data provides a model to understand learning of new surgical procedures, and this data suggests that port-access valve procedures can be mastered by a variety of institutions.
authors.affiliation: Duke University Medical Center, Durham, NC 27710, USA.
authors.forename: D D
authors.initials: DD
authors.lastname: Glower
authors.forename: L C
authors.initials: LC
authors.lastname: Siegel
authors.forename: A C
authors.initials: AC
authors.lastname: Galloway
authors.forename: G
authors.initials: G
authors.lastname: Ribakove
authors.forename: E
authors.initials: E
authors.lastname: Grossi
authors.forename: N
authors.initials: N
authors.lastname: Robinson
authors.forename: W H
authors.initials: WH
authors.lastname: Ryan
authors.forename: S
authors.initials: S
authors.lastname: Colvin
authors.forename: R
authors.initials: R
authors.lastname: Shemin
mesh_terms: D000704:Analysis of Variance; D006351:Heart Valves / Q000601:surgery*; D006801:Humans; D007858:Learning; D016014:Linear Models; D019060:Minimally Invasive Surgical Procedures / Q000639:trends*; D011446:Prospective Studies; D012086:Reoperation; D013997:Time Factors