title: Predicting surgical risk: exclusion of laboratory data set maintains predictive accuracy.
abstract: Studies have demonstrated that predictive accuracy of acuity models flattens when more than 10 variables are used. The authors hypothesized that the exclusion of laboratory data would produce a reliable predictive model. The American College of Surgeons National Surgical Quality Improvement Program data set from 2005 to 2008 was reviewed. Logistic regression was used to develop models from 86 preoperative variables to predict 30-day morbidity and mortality. Models were compared by measuring area under the receiver operating characteristic (AUROC) values that then were analyzed with unpaired t test. As the number of variables decreased, the change in the AUROC for mortality and morbidity were not statistically significant for 10- and 5-variable models. Although the AUROC for acuity models decreased slightly for morbidity and mortality when laboratory values were excluded, these changes were not statistically significant. This study shows that models developed to predict surgical outcomes can achieve similar predictive accuracy without laboratory data.
authors: Department of Surgery, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202, USA. athtsiouris@hotmail.com
authors: Athanasios
authors: A
authors: Tsiouris
authors: Vic
authors: V
authors: Velanovich
authors: Sarah
authors: S
authors: Whitehouse
authors: Zeeshan
authors: Z
authors: Syed
authors: Ilan
authors: I
authors: Rubinfeld
mesh_terms: D003955:Diagnostic Tests, Routine; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D011183:Postoperative Complications / Q000453:epidemiology*; D011787:Quality of Health Care; D012372:ROC Curve; D018570:Risk Assessment; D013514:Surgical Procedures, Operative / Q000009:adverse effects* / Q000592:standards; D014481:United States / Q000453:epidemiology
keywords: laboratory data; risk models; surgical outcomes