The Use of Determinants of Length of Stay in the Post-anesthesia Care Unit (PACU) at the Philippine General Hospital among Postoperative Patients who Underwent Elective Surgeries to Create a Predictive Model for PACU Length of Stay
- Author:
Maria Teresita B. Aspi
1
;
Evangeline Ko-Villa
1
Author Information
- Publication Type:Journal Article
- Keywords: Anesthesia recovery; Post-anesthesia Care Unit; PACU
- MeSH: Length of Stay; Anesthesia; Anesthesiology; Regression Analysis
- From: Acta Medica Philippina 2020;54(5):490-497
- CountryPhilippines
- Language:English
-
Abstract:
Background:The aims of this study were to determine the average length of stay in the Post-anesthesia Care Unit (PACU LOS) in the Philippine General Hospital (PGH) and to create a model that will predict the PACU LOS based on the factors that significantly affect the LOS. Determination and prediction of PACU LOS is essential in resource utilization, and in cost containment and reduction. Addressing the modifiable variables that affect the PACU LOS may lead to an improvement in the LOS of patients in the PACU and, consequently, to better recovery room staffing and a reduced cost for the patients and the hospital.
Methods:A prospective chart review of 400 postoperative patients admitted in the PGH PACU was done. Summary statistics were presented. Using the set of variables found to be significant, a regression model was formulated to estimate the PACU LOS.
Results:The mean PACU LOS was 4.59 hours. There were significant differences in the mean PACU LOS based on the occurrence of complications. There were also significant differences in the median PACU LOS based on the type and duration of surgery, anesthetic technique, and duration of anesthesia. The multiple linear regression model that best predicted PACU LOS included ASA-PS classification, type of surgery, duration of surgery, anesthetic technique, and occurrence of intraoperative or postoperative complications.
Conclusions:The mean PACU LOS of the Philippine Genera Hospital is higher than that of published data. The factors included in the model that best predicts PACU LOS may be studied to improve the PACU LOS. - Full text:2227-Article Text-11689-1-10-20201028.pdf
