title: Derivation and out-of-sample validation of a modeling system to predict length of surgery.
abstract: BACKGROUND
  We performed a retrospective study to compare the precision of a regression model (RM) system with the precision of the standard method of surgical length prediction using historical means (HM).
  
  
  METHODS
  Data were collected on patients who underwent carotid endarterectomy and lower-extremity bypass. Multiple linear regression was used to model the operative time length (OTL). The precision of the RM versus HM in predicting case length then was compared in a validation dataset.
  
  
  RESULTS
  With respect to carotid endarterectomy, surgeon, surgical experience, and cardiac surgical risk were significant predictors of OTL. For lower-extremity bypass, surgeon, use of prosthetic conduit, and performance of a sequential bypass or hybrid procedure were significant predictors of OTL. The precision of out-of-sample prediction was greater for the RM system compared with HM for both procedures.
  
  
  CONCLUSIONS
  A regression methodology to predict case length appears promising in decreasing uncertainty about surgical case length.
authors.affiliation: Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston Veterans Affairs Medical Center, Houston, TX 77030, USA. pkougias@bcm.edu
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Kougias
authors.forename: Vikram
authors.initials: V
authors.lastname: Tiwari
authors.forename: Sonia
authors.initials: S
authors.lastname: Orcutt
authors.forename: Amber
authors.initials: A
authors.lastname: Chen
authors.forename: George
authors.initials: G
authors.lastname: Pisimisis
authors.forename: Neal R
authors.initials: NR
authors.lastname: Barshes
authors.forename: Carlos F
authors.initials: CF
authors.lastname: Bechara
authors.forename: David H
authors.initials: DH
authors.lastname: Berger
mesh_terms: D015331:Cohort Studies; D016894:Endarterectomy, Carotid / Q000706:statistics & numerical data*; D006801:Humans; D059032:Learning Curve; D016014:Linear Models; D015999:Multivariate Analysis; D061646:Operative Time; D012189:Retrospective Studies; D058017:Vascular Grafting / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data*