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A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction

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  • Hsin-Lun Wu
  • Wen-Kuei Chang
  • Ken-Hua Hu
  • Richard M Langford
  • Mei-Yung Tsou
  • Kuang-Yi Chang

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

Suggested Citation

  • Hsin-Lun Wu & Wen-Kuei Chang & Ken-Hua Hu & Richard M Langford & Mei-Yung Tsou & Kuang-Yi Chang, 2015. "A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-10, August.
    • Handle: RePEc:plo:pone00:0134838
    DOI: 10.1371/journal.pone.0134838
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