A preoperative predictive model for prolonged post-anaesthesia care unit stay after outpatient surgeries.

Autor:	Elsharydah A; Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, TX, USA., Walters DR; Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, TX, USA., Somasundaram A; Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, TX, USA., Bryson TD; Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, TX, USA., Minhajuddin A; Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, TX, USA.; Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA., Gabriel RA; Department of Anesthesiology, University of California, San Diego, San Diego, CA, USA.; Department of Biomedical Informatics, University of California, San Diego, San Diego, CA, USA., Grewal GK; Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Jazyk:	angličtina
Zdroj:	Journal of perioperative practice [J Perioper Pract] 2020 Apr; Vol. 30 (4), pp. 91-96. Date of Electronic Publication: 2019 May 28.
DOI:	10.1177/1750458919850377
Abstrakt:	Study Objective: To create a preoperative predictive model for prolonged post-anaesthesia care unit (PACU) stay for outpatient surgery and compare with an existing (University of California-San Diego, UCSD) model. Design: Retrospective observational study. Setting: Post-anaesthesia care unit. Patients: Outpatient surgical patients discharged on the same day in a large academic institution. Preoperative data were collected. The study period was three months in 2016. Measurements: Prolonged PACU stay defined as a length of stay longer than the third quartile. We utilized multivariate regression analyses and bootstrapping statistical techniques to create a predictive model for prolonged PACU stay. Main results: Four strong predictors for prolonged PACU stay: general anaesthesia, obstructive sleep apnoea, surgical specialty and scheduled case duration. Our model had an excellent discrimination performance and a good calibration. Conclusion: We developed a predictive model for prolonged PACU stay in our institution. This model is different from the UCSD model probably secondary to local and regional differences in outpatient surgery practice. Therefore, individual practice study outcomes may not apply to other practices without careful consideration of these differences.
Databáze:	MEDLINE
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