Derivation and out-of-sample validation of a modeling system to predict length of surgery

Panagiotis Kougias, Vikram Tiwari, Sonia Orcutt, Amber Chen, George Pisimisis, Neal R. Barshes, Carlos F. Bechara, David H. Berger

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)563-568
Number of pages6
JournalAmerican Journal of Surgery
Volume204
Issue number5
DOIs
StatePublished - Nov 2012

Keywords

  • Modeling
  • Operative length
  • Precision
  • Regression

ASJC Scopus subject areas

  • Surgery

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