• Kamal Maheshwari, Jing You, Kenneth C Cummings, Maged Argalious, Daniel I Sessler, Andrea Kurz, and Jacek Cywinski.
• From the Departments of *General Anesthesiology, Anesthesiology Institute; †Outcomes Research, Anesthesiology Institute; and ‡Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio.
• Anesth. Analg. 2017 Aug 1; 125 (2): 580-592.

BackgroundOperating room (OR) utilization generally ranges from 50% to 75%. Inefficiencies can arise from various factors, including prolonged anesthesia preparation time, defined as the period from induction of anesthesia until patients are considered ready for surgery. Our goal was to use patient-related and procedure-related factors to develop a model predicting anesthesia preparation time.MethodsFrom the electronic medical records of adults who had noncardiac surgery at the Cleveland Clinic Main Campus, we developed a model that used a dozen preoperative factors to predict anesthesia preparation time. The model was based on multivariable regression with "Least Absolute Shrinkage and Selection Operator" and 10-fold cross-validation. The overall performance of the final model was measured by R, which describes the proportion of the variance in anesthesia preparation time that is explained by the model.ResultsA total of 43,941 cases met inclusion and exclusion criteria. Our final model had only moderate discriminative ability. The estimated adjusted R for prediction model was 0.34 for the training data set and 0.27 for the testing data set.ConclusionsUsing preoperative factors, we could explain only about a quarter of the variance in anesthesia preparation time-an amount that is probably of limited clinical value.

20 keywords...

hide…