title: A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries.
abstract: A predictive model that can identify patients who are at an increased risk for prolonged postanesthesia care unit (PACU) stay could help optimize resource utilization and case sequencing. Although previous studies identified some predictors, there is not a model that only utilizes various patients demographic and comorbidities, that are already known preoperatively, and that may affect PACU length of stay for outpatient procedures requiring the care of an anesthesiologist.
  
  
  
  We collected data from 4151 patients at a single institution from 2014 to 2015. The data set was split into a training set (cases before 2015) and a test set (cases during 2015). Bootstrap samples were chosen (R = 1000 replicates) and a logistic regression model was built on the samples using a combined method of forward selection and backward elimination based on the Akaike Information Criterion. The trained model was applied to the test set. Model performance was evaluated with the area under the receiver operating characteristic (ROC) Curve (AUC) for discrimination and the Hosmer-Lemeshow (HL) test for goodness-of-fit.
  
  
  
  The final model had 5 predictor variables for prolonged PACU length of stay, which included the following: morbid obesity, hypertension, surgical specialty, primary anesthesia type, and scheduled case duration. The model had an AUC value of 0.754 (95% confidence interval 0.733-0.774) on the training set and 0.722 (95% confidence interval 0.698-0.747) on the test set, with no difference between the 2 ROC curves (P = .06). The model had good calibration for the data in both the training and test data set indicated by nonsignificant P values from the HL test (P = .211 and .719 for the training and test set, respectively).
  
  
  
  We developed a predictive model with excellent discrimination and goodness-of-fit that can help identify those at higher odds for extended PACU length of stay. This information may help optimize case-sequencing methodologies.
authors.affiliation: From the Departments of *Biomedical Informatics and †Anesthesiology, University of California, San Diego, San Diego, California.
authors.forename: Rodney A
authors.initials: RA
authors.lastname: Gabriel
authors.forename: Ruth S
authors.initials: RS
authors.lastname: Waterman
authors.forename: Jihoon
authors.initials: J
authors.lastname: Kim
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000556:Ambulatory Surgical Procedures / Q000706:statistics & numerical data*; D000758:Anesthesia / Q000706:statistics & numerical data*; D002648:Child; D002675:Child, Preschool; D003422:Critical Care / Q000706:statistics & numerical data*; D005260:Female; D005544:Forecasting; D006801:Humans; D006973:Hypertension / Q000150:complications / Q000453:epidemiology; D007223:Infant; D007231:Infant, Newborn; D007362:Intensive Care Units / Q000706:statistics & numerical data; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009767:Obesity, Morbid / Q000150:complications / Q000453:epidemiology; D011182:Postoperative Care / Q000706:statistics & numerical data*; D012372:ROC Curve; D012307:Risk Factors; D055815:Young Adult