Abstract
Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT). Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2) required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006). Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001). Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.
Highlights
Monitoring procedure time is essential for operating room (OR) efficiency improvement and setting corresponding performance standards in time domain is beneficial to identify unusual events which may prolong procedure time and result in OR inefficiency.[1,2,3] With this information, the performance of individual specialists can be evaluated from the time perspectivePLOS ONE | DOI:10.1371/journal.pone.0134838 August 4, 2015Estimating Anesthetic Procedure Time Distribution and extents and reasons of prolonged procedure time can be unveiled
This is clearly unfavorable to the analysis of procedure time data since the upper tail of the procedure time distribution is of primary interest
A quantile regression approach allows for complete examination of influences of influential factors on the entire distribution of anesthetic induction time (AIT) and details about the prolonged AIT could be inspected more explicitly
Summary
Monitoring procedure time is essential for operating room (OR) efficiency improvement and setting corresponding performance standards in time domain is beneficial to identify unusual events which may prolong procedure time and result in OR inefficiency.[1,2,3] With this information, the performance of individual specialists can be evaluated from the time perspectivePLOS ONE | DOI:10.1371/journal.pone.0134838 August 4, 2015Estimating Anesthetic Procedure Time Distribution and extents and reasons of prolonged procedure time can be unveiled. Procedure time data may be readily accessible from OR information systems, how to extract useful information from these data without an appropriate analytic approach is not so intuitive. First of all, these data are not collected for study purpose and subject to miscellaneous confounding effects. Most of parametric statistical models which can be used to analyze procedure time typically focus on conditional mean but ignore both tails of a response distribution. This is clearly unfavorable to the analysis of procedure time data since the upper tail of the procedure time distribution is of primary interest. Common analytic approaches often impose strict assumptions on how the covariates are permitted to affect event time, like the covariates can only affect the location but not the shape of event time distribution, and fail to characterize the dynamic relationships between outcome and predictor variables.[4]
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