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

• Published in: The American journal of surgery Vol. 204; no. 5; pp. 563 - 568
• Main Authors: Kougias, Panagiotis, M.D, Tiwari, Vikram, Ph.D, Orcutt, Sonia, M.D, Chen, Amber, Pisimisis, George, M.D, Barshes, Neal R., M.D., M.P.H, Bechara, Carlos F., M.D., M.S, Berger, David H., M.D., M.H.C.M
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.11.2012; Elsevier; Elsevier Limited
• Subjects: Accuracy; Biological and medical sciences; Cohort Studies; Confidence intervals; Efficiency; Endarterectomy, Carotid - statistics & numerical data; General aspects; Hospitals; Humans; Learning Curve; Linear Models; Medical sciences; Modeling; Multivariate Analysis; Operative length; Operative Time; Precision; Regression; Regression analysis; Retrospective Studies; Studies; Surgery; Variables; Vascular Grafting - instrumentation; Vascular Grafting - methods; Vascular Grafting - statistics & numerical data; Regression; Precision; Modeling; Operative length; Medicine; Test validation; Treatment; Length; Surgery; Prediction; Predictive factor
• ISSN: 0002-9610; 1879-1883
• DOI: 10.1016/j.amjsurg.2012.07.013

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.