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

Elsharydah, Ahmad; Walters, Daren R.; Somasundaram, Alwin; Bryson, Trenton D.; Minhajuddin, Abu; Gabriel, Rodney A.; Grewal, Gaganpreet

doi:10.1177/1750458919850377

Cited by 11 publications

(15 citation statements)

References 15 publications

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“…In addition, local and regional variations in outpatient surgery practice may also have influenced the results of the two models. 13 operating room complex, who will undergo emergency surgery, whose anesthesia converted from one technique to another, who will not be admitted in the third floor PACU, who will be admitted in a critical area after the surgery, or who will develop complications necessitating admission to a critical care area were excluded from this study.…”

Section: Exclusion Criteriamentioning

confidence: 99%

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

Aspi¹,

Ko-Villa²

2020

Acta Med Philipp

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Background. The aims of this study were to determine the average length of stay in the Post-anesthesia CareUnit (PACU LOS) in the Philippine General Hospital (PGH) and to create a model that will predict the PACU LOSbased on the factors that significantly affect the LOS. Determination and prediction of PACU LOS is essential inresource utilization, and in cost containment and reduction. Addressing the modifiable variables that affect thePACU LOS may lead to an improvement in the LOS of patients in the PACU and, consequently, to better recoveryroom 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. Summarystatistics were presented. Using the set of variables found to be significant, a regression model was formulatedto estimate the PACU LOS. Results. The mean PACU LOS was 4.59 hours. There were significant differences in the mean PACU LOS basedon the occurrence of complications. There were also significant differences in the median PACU LOS based onthe type and duration of surgery, anesthetic technique, and duration of anesthesia. The multiple linear regressionmodel that best predicted PACU LOS included ASA-PS classification, type of surgery, duration of surgery, anesthetictechnique, and occurrence of intraoperative or postoperative complications. Conclusions. The mean PACU LOS of the Philippine Genera Hospital is higher than that of published data. Thefactors included in the model that best predicts PACU LOS may be studied to improve the PACU LOS.

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Section: Exclusion Criteriamentioning

confidence: 99%

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

Aspi¹,

Ko-Villa²

2020

Acta Med Philipp

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“…Previously, several studies have revealed risk factors for prolonged PACU LOS in various surgical populations (16)(17)(18)(19)(20)(21)(22). These studies suggest the need to focus on a special subset of patients as a way of reducing research bias (21), rather than focusing on all patients scheduled for surgery with general anesthesia (20).…”

Section: Discussionmentioning

confidence: 99%

“…Predictors for prolonged PACU LOS such as specific anesthesia type, surgical specialty, scheduled surgery duration, postoperative opioid administration, morbid obesity, hypertension, obstructive sleep apnoea have been described in different populations (16)(17)(18)(19)(20)(21)(22). The studies in most cases assessed outpatients (16,21), pediatric populations (17)(18)(19), or all adult surgical patients undergoing general anesthesia or regional anesthesia (20,22). However, no existing literature has reported a nomogram for prolonged PACU LOS of LC patients.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a nomogram for determining patients requiring prolonged postanesthesia care unit length of stay after laparoscopic cholecystectomy

Cao¹,

Li²,

Su³

et al. 2021

Ann Palliat Med

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Background: Laparoscopic cholecystectomy (LC) is a common surgical procedure for managing gallbladder disease. Prolonged length of stay (LOS) in the postanesthesia care unit (PACU) may lead to overcrowding and a decline in medical resource utilization. In this work, we aimed to develop and validate a predictive nomogram for identifying patients who require prolonged PACU LOS.Methods: Data from 913 patients undergoing LC at a single institution in China between 2018 and 2019 were collected, and grouped into a training set (456, cases during 2018) and a test set (457, cases during 2019). The definition of PACU LOS is the duration between admission to discharge from PACU, which we can derive from the electronic medical record system. Using the least absolute shrinkage and selection operator regression model, the optimal feature was selected, and multivariable logistic regression analysis was used to build the prolonged PACU LOS risk model. The C-index, calibration plot, and decision curve analysis (DCA) were used in assessing the model calibration, discrimination, and clinical application value, respectively. For external validation, the test set data was evaluated. Results:The predictive nomogram had 8 predictor variables for prolonged PACU LOS, including age, American Society of Anesthesiologists (ASA) grade, active smoker, gastrointestinal disease, liver disease, and cardiovascular disease. This model displayed efficient calibration and moderate discrimination with a C-index of 0.662 (95% confidence interval, 0.603 to 0.721) for the training set, and 0.609 (95% confidence interval, 0.549 to 0.669) for the test set. DCA demonstrated that the prolonged PACU LOS nomogram was reliable for clinical application when an intervention was decided at the possible threshold of 7%. Conclusions:We developed and validated a predictive nomogram with efficient calibration and moderate discrimination, and can be applied to identify patients most likely to be subjected to prolonged PACU LOS. This novel tool may shun overcrowding in PACU and optimize medical resource utilization.

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“…Previously, several studies have revealed risk factors for prolonged PACU LOS in various surgical populations [16] [22]. These studies suggest the need to focus on a special subset of patients as a way of reducing research bias [21], rather than focusing on all patients scheduled for surgery with general anesthesia [20].…”

Section: Discussionmentioning

confidence: 99%

“…Predictors for prolonged PACU LOS such as speci c anesthesia type, surgical specialty, scheduled surgery duration, postoperative opioid administration, morbid obesity, hypertension, obstructive sleep apnoea have been described in different populations [16] [17] [18] [19] [20] [21] [22]. The studies in most cases assessed outpatients [16] [21], pediatric populations [17] [18] [19], or all adult surgical patients undergoing general anesthesia or regional anesthesia [20] [22]. However, no existing literature has reported a nomogram for prolonged PACU LOS of LC patients.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Nomogram for Determining Patients Requiring Prolonged Postanesthesia Care Unit Length of Stay After Laparoscopic Cholecystectomy

Cao

et al. 2020

Preprint

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Background: Laparoscopic Cholecystectomy (LC) is a common surgical procedure for managing gallbladder disease. Prolonged length of stay (LOS) in the postanesthesia care unit (PACU) may lead to overcrowding and a decline in medical resource utilization. In this work, we aimed to develop and validate a predictive nomogram for identifying patients who require prolonged PACU LOS.Methods: Data from 913 patients undergoing LC at a single institution in China between 2018 and 2019 were collected, and grouped into a training set (cases during 2018) and a test set (cases during 2019). Using the least absolute shrinkage and selection operator regression model, the optimal feature was selected, and multivariable logistic regression analysis was used to build the prolonged PACU LOS risk model. The C-index, calibration plot, and decision curve analysis were used in assessing the model calibration, discrimination, and clinical application value, respectively. For external validation, the test set data was evaluated.Results: The predictive nomogram had 8 predictor variables for prolonged PACU LOS, including age, ASA grade, active smoker, gastrointestinal disease, liver disease, and cardiovascular disease. This model displayed efficient calibration and moderate discrimination with a C-index of 0.662 (95% confidence interval, 0.603 to 0.721) for the training set, and 0.609 (95% confidence interval, 0.549 to 0.669) for the test set. Decision curve analysis demonstrated that the prolonged PACU LOS nomogram was reliable for clinical application when an intervention was decided at the possible threshold of 7%.Conclusions: We developed and validated a predictive nomogram with efficient calibration and moderate discrimination, and can be applied to identify patients most likely to be subjected to prolonged PACU LOS. This novel tool may shun overcrowding in PACU and optimize medical resource utilization.

show abstract

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

Cited by 11 publications

References 15 publications

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

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

Development and validation of a nomogram for determining patients requiring prolonged postanesthesia care unit length of stay after laparoscopic cholecystectomy

Development and Validation of a Nomogram for Determining Patients Requiring Prolonged Postanesthesia Care Unit Length of Stay After Laparoscopic Cholecystectomy

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