Share this article on:

A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries

Gabriel, Rodney A. MD*†; Waterman, Ruth S. MD; Kim, Jihoon MS*; Ohno-Machado, Lucila MD, PhD*

doi: 10.1213/ANE.0000000000001827
Patient Safety: Original Clinical Research Report

BACKGROUND: A predictive model that can identify patients who are at an increased risk for prolonged postanesthesia care unit (PACU) stay could help optimize resource utilization and case sequencing. Although previous studies identified some predictors, there is not a model that only utilizes various patients demographic and comorbidities, that are already known preoperatively, and that may affect PACU length of stay for outpatient procedures requiring the care of an anesthesiologist.

METHODS: We collected data from 4151 patients at a single institution from 2014 to 2015. The data set was split into a training set (cases before 2015) and a test set (cases during 2015). Bootstrap samples were chosen (R = 1000 replicates) and a logistic regression model was built on the samples using a combined method of forward selection and backward elimination based on the Akaike Information Criterion. The trained model was applied to the test set. Model performance was evaluated with the area under the receiver operating characteristic (ROC) Curve (AUC) for discrimination and the Hosmer-Lemeshow (HL) test for goodness-of-fit.

RESULTS: The final model had 5 predictor variables for prolonged PACU length of stay, which included the following: morbid obesity, hypertension, surgical specialty, primary anesthesia type, and scheduled case duration. The model had an AUC value of 0.754 (95% confidence interval 0.733–0.774) on the training set and 0.722 (95% confidence interval 0.698–0.747) on the test set, with no difference between the 2 ROC curves (P = .06). The model had good calibration for the data in both the training and test data set indicated by nonsignificant P values from the HL test (P = .211 and .719 for the training and test set, respectively).

CONCLUSIONS: We developed a predictive model with excellent discrimination and goodness-of-fit that can help identify those at higher odds for extended PACU length of stay. This information may help optimize case-sequencing methodologies.

Supplemental Digital Content is available in the text.Published ahead of print January 11, 2017.

From the Departments of *Biomedical Informatics and Anesthesiology, University of California, San Diego, San Diego, California.

Published ahead of print January 11, 2017.

Accepted for publication November 15, 2016.

Funding: Support from National Library of Medicine (NLM) training grant number T15LM011271.

Conflicts of Interest: See Disclosures at the end of the article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website.

Reprints will not be available from the authors.

Address correspondence to Rodney A. Gabriel, MD, Department of Anesthesiology, University of California, San Diego, 200 West Arbor Dr, MC 8770, San Diego, CA 92103. Address e-mail to ragabriel@ucsd.edu.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

The postanesthesia care unit (PACU) provides immediate care to postsurgical patients and is an integral component of a safe perioperative workflow. For outpatient surgeries, this unit allows appropriate postsurgical care for patients before determining safety of patient discharge to their home or an external facility. The postoperative care system is very complex and dynamic because it involves multiple interrelations between the operating room, PACU, intensive care unit, and ward bed availability – it is a costly system that requires its various components to function efficiently.1

Several studies have identified clinical and administrative predictors for prolonged PACU length of stay in various surgical populations.2–9 These studies had focused on pediatric populations,6,7 adult patients receiving general anesthesia,2,4 orthopedic patients undergoing regional anesthesia,5 or all surgical patients at a single or multiple institutions.3,10 There have been only a few studies investigating delays and costs in outpatient surgery recovery rooms.9,11,12 In particular, 1 study performed multiple linear modeling utilizing pre, intra, and postoperative factors to predict prolonged length of stay in the ambulatory suite.9 The aforementioned studies oftentimes included predictive variables that are not known before surgery and occur either intraoperatively or even postoperatively in the PACU. Predictors in this category included intraoperative fluids, postoperative pain symptoms, intraoperative arrhythmias, intubated state, actual case duration, nausea/vomiting to name a few.2,4,9 These data are nonetheless valuable because they may guide anesthetic management intraoperatively in an effort to reduce postoperative complications and decrease PACU length of stay. However, it would also be prudent to have tools that help predict patients at risk for prolonged PACU length of stay before surgery. This would require creating a predictive model utilizing only variables that are known before the actual surgery and exclude variables first known either intraoperatively or postoperatively.

In the current study, we used data from a single institution and analyzed all outpatient surgeries to develop a predictive model for patients at risk for prolonged PACU length of stay. We defined the outcome as PACU length of stay greater than or equal to the 75th percentile of the time spent in the PACU. The predictive variables only include those that are known before surgery in an effort to create a predictive model utilizing only preoperative data. Therefore, intra and postoperative events were not included in the model. We sought to develop a model utilizing data from 2014 and validate it with data from a separate year.

Back to Top | Article Outline

METHODS

Study Sample

Data were collected retrospectively from the data warehouse of the University of California, San Diego (UCSD) Health care Systems. All data from surgical patients from January 1, 2014 to October 1, 2015 were extracted. The resulting data set remained deidentified and did not contain sensitive patient-health information as defined by the UCSD Human Research Protections Program and, therefore, was exempt from the informed consent requirement by our Institutional Review Board. This article adheres to the applicable Equator guidelines for quality improvement studies.

Only outpatient surgeries/procedures were included in the data set, defined as procedures that allow patients to be discharged directly home after adequate postanesthesia monitoring. At our hospitals, one of our outpatient PACUs consists of 10 recovery beds, to which postoperative patients who undergo nonoperating room procedures or ambulatory surgeries go. There are 4 operating rooms running Monday through Friday. There is a mix of providers consisting of attending anesthesiologists, certified registered nurse anesthetists (CRNAs), and resident anesthesiologists. Any combination of staffing ratio occurs here—solo attending coverage, 1:2 attending:CRNA/resident supervision, or 1:3 attending:CRNA supervision. Only cases that fit into surgical/procedural categories as Otolaryngology (ENT), Gastroenterology, Gynecology, General Surgery, Ophthalmology, Plastic Surgery, Urology, and “other” were included. “Other” consisted of multiple subspecialties that contained only a minor sample size and, therefore, were grouped together. They included Cardiothoracic, Neurosurgery, Pulmonary, Trauma, and Vascular. All cases that did not require an anesthesiologist were excluded. The following data were collected for each case; minutes in the PACU, sex, age, primary anesthesia type, body mass index (BMI), scheduled case duration (hours), surgical category (as defined above), non-English primary language, and presence of the following comorbidities: diabetes mellitus type 2, active smoker, obstructive sleep apnea, hypertension, coronary artery disease, atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease, liver cirrhosis, chronic kidney disease, renal dialysis utilization, history of postoperative nausea/vomiting (PONV), and presence of a pacemaker or implantable cardioverter defibrillator. (Table 1 lists the International Classification of Diseases, Ninth Revision codes utilized to extract the comorbidity data for each case.) Of note, scheduled case duration is defined as the expected duration of operating room time or the amount of time booked for the case (therefore, it will be known preoperatively). This is contrary to actual case duration, which is a value determined postoperatively. Primary anesthesia type was broken down to either general anesthesia, regional anesthesia (including neuraxial or peripheral nerve blockade for primary anesthetic), or monitored anesthesia care (MAC). The regional anesthesia group comprised both neuraxial and peripheral nerve blockade because, at our outpatient surgery site, only 11 spinals and no epidurals were included in the final data set during this time period. This is likely because of the higher rates of PACU delays occurring with spinal anesthetics in patients who had planned to go home after surgery based on our institutional practice. Therefore, it was our own institutional culture to offer neuraxial anesthesia only for special circumstances in outpatient surgeries. Only case duration was treated as continuous variables, whereas all other variables were categorical. Our goal was to develop a model that would be very easy to apply in practice. Age was divided into those less than or greater than or equal to 65 years of age to separate geriatric patients from their younger counterparts. BMI was divided into those less than 40 versus those greater than or equal to 40 kg/m2 to separate morbid obesity from others. Dummy variables were created for nonbinary values (ie, for surgical specialty and anesthesia type). For surgical category, we arbitrarily chose General Surgery as the reference group. For primary anesthesia type, MAC was chosen as the reference group. Finally, all cases that had missing data for any of these variables were removed from the final data set.

Table 1

Table 1

PACU delay was treated as a binary outcome, defined as greater than or equal to the 3rd quartile of PACU length of stay. PACU length of stay was not normally distributed and therefore we decided to use interquartile ranges and the median value versus standard deviations and mean. We assume that mean PACU length of stay will differ across institutions; however, by defining the outcome variable this way, we create a generalizable outcome across health care practices.

Back to Top | Article Outline

Statistical Analysis

R, a software environment for statistical computing (R version 3.3.0), was used to perform all statistical analyses. The original data set was examined for missing data and all patients with missing data were eliminated before the model-building process. Data were imported into R from a comma separated values file.

Before model building, the data were divided into a training set (all cases occurring before the year 2015) and a test set (cases performed in 2015). Variables were entered into the model if they met a significance level of P < .50 from the univariable logistic regression analysis. These variables were then inputted into model building. Bootstrap samples (R = 1000 replicates) were chosen, and a logistic regression model was built on the samples using a combination of forward selection and backward elimination based on the Akaike Information Criterion (AIC). It was determined a priori that only variables that were retained in ≥60% of all bootstrapped models would be retained in the final model. In addition, variables were allowed to stay in the model if they met a significance level of P < .10. Odds ratios (OR) and their associated 95% confidence interval (CI) were reported for each covariate of the final logistic regression model. The trained model was then applied to the test set. Model performance on both the training and test set were evaluated with area under the receiver operating characterisitic (ROC) curve (AUC) for discrimination and the Hosmer-Lemeshow (HL) test for goodness-of-fit. An AUC of 1.0 represents perfect discrimination, whereas an AUC of 0.5 represents no discrimination.13 DeLong’s U-statistics method was applied to assess the statistical differences between 2 ROC curves generated by the training and test data.14 Calibration curves were also developed to examine the fit of the model. For each data set, the predicted risk was plotted against the observed risk for each of the 10 risk percentiles created from the data set. Calibration was further evaluated with HL goodness-of-fit with χ2 test using deciles of predicted risks.

Back to Top | Article Outline

RESULTS

There were a total of 10,465 patients undergoing an outpatient procedure, in which 4151 cases remained after exclusion. The resulting data set only included outpatient surgeries/procedures that required care from an anesthesiologist. The majority of excluded patients were due to cases only requiring nursing sedation, and therefore no anesthesiologist was involved (n = 6228). The mean and the standard deviation of PACU length of stay were 78.52 and 93.31 minutes for the entire data set, respectively. The median PACU length of stay of the entire data set was 47 minutes. The data were divided into a training set (cases performed before 2015) and test set (cases performed in 2015). The median PACU length of stay in the training and test set were 55 and 44, respectively. Prolonged PACU length of stay was defined as greater than the third interquartile range, ie, greater than 100 minutes in the training set and 89 minutes in the test set. The distribution of patient, anesthetic, and surgical characteristics in each data set is shown in Table 2. The univariable analysis results performed on the training set are shown in Table 3.

Table 2

Table 2

Table 3

Table 3

The final logistic regression model contained 5 input variables, which included patient, surgical, and anesthetic factors. Patient factors included BMI ≥ 40 kg/m2 and the presence of hypertension. Surgical factors included the surgical subspecialty and scheduled case duration. The anesthetic factor included the utilization of general anesthesia. The final multivariable logistic regression model with each variables’ β-coefficient, OR (95% CI), and P value are listed in Table 4. The strongest predictors for extended PACU length of stay were scheduled case duration (for every increase of 1 hour, OR 1.60, 95% CI 1.41–1.83, P < .0001) and utilization of general anesthesia (OR 2.20, 95% 1.51–3.20, P < .0001) as the primary anesthetic when compared with MAC. The variables that are most protective for extended PACU length of stay included surgical/procedural specialties such as ophthalmology (OR 0.11, 95% CI 0.06–0.022, P < .0001) when compared with general surgery.

Table 4

Table 4

Figure 1

Figure 1

Figure 2

Figure 2

The model has an AUC of 0.754 (95% CI 0.733–0.774) on the training set and 0.722 (95% CI 0.698–0.747) on the test set, with no difference between the 2 ROC curves (P = .062) (Figure 1). Calibration, as indicated by goodness-of-fit by the HL test, is shown in Figure 2. The logistic regression model had good calibration for the data in both the training and test data set as indicated by a P value of >.10 (P = .211 and P = .719, respectively). Supplemental Digital Content 1 and 2 (Supplemental Appendix Tables 1 and 2, http://links.lww.com/AA/B607, http://links.lww.com/AA/B608) lists the results of the HL test for the training and test data, respectively.

Back to Top | Article Outline

Sample Calculation

To demonstrate utilization of the model, a simple example is provided here. The example patient is an 85-year-old woman with morbid obesity and hypertension, who is scheduled to undergo a laparoscopic cholecystectomy (General Surgery), which is booked for 3 hours. She will undergo general anesthesia as her primary anesthesia type. The median length of stay in the PACU at her facility is 45 minutes with a third interquartile range of 90 minutes. Her probability of having a PACU length of stay greater than or equal to 90 minutes from the logistic regression model will be calculated as:

Probability of extended PACU stay = 1/(1 + e−(−2.426 + 0.515 + 0.377 + 0.789 + 0.472 × 3)) = 0.662

Back to Top | Article Outline

DISCUSSION

We developed a predictive model for extended PACU length of stay for outpatient surgeries using multivariable logistic regression that demonstrated adequate discrimination and calibration. The predictors in the model included presence of hypertension, morbid obesity, primary anesthesia type (general anesthesia), scheduled case duration, as well as surgical specialty. The goal of this model was not to serve as a tool to decrease PACU length of stay, but rather to identify surgical patients at higher risk for this outcome metric before day of surgery during the operational decision-making period.

Several studies have identified clinical and administrative predictors for prolonged PACU length of stay in various surgical populations.2–10 When studying outcomes such as PACU length of stay, it is useful to focus predictive modeling on special subsets, rather than all surgical cases (ie, all adult patients undergoing general anesthesia). By focusing on a special group, in this case outpatient procedures, bias introduced from the wide range of surgical subspecialties may be reduced. Furthermore, past studies included variables that are not known preoperatively, such as events that either occurred intraoperatively or during postoperative care (ie, intubated state in the recovery room, intraoperative arrhythmias, and postoperative pulmonary symptoms).2,3,6,9 The covariates we chose to be included in our model included only those that are known preoperatively. Of note, we utilized scheduled case duration (time allotted for surgery before actual case) as opposed to the actual case duration. This is because actual case duration is not known until the postoperative period and therefore would not be useful in this type of predictive modeling.15

There is no uniform definition of what is considered “extended PACU length of stay” in current studies; therefore, results may not be transferrable across studies or institutions. For example, the following definitions have been used: duration of stay > 2 hours16 or duration greater than 60 minutes.2,17 National data, consisting of hundreds of institutions, were used in another study; however, the definition of prolonged PACU length of stay was heterogeneous across health care facilities.10 Finally, other studies treated PACU length of stay as a continuous variable and the predicted durations were compared.4,7,9 In the current predictive model, extended PACU length of stay was treated as a binary outcome defined as time greater than third interquartile range. Because of the heterogeneity of PACU workflows across institutions, we felt that defining the outcome this way would be generalizable. In this sense, one is comparing the outcome metric within an institution’s own constraints.

Patient-specific, anesthetic, and surgical covariates were found to be statistically significant in its association with this outcome metric. It is not surprising that among primary anesthesia type, general anesthesia, when compared with MAC, was associated with extended PACU length of stay. This is likely related to the nature and complexity of surgeries requiring general anesthesia than those that do not. Furthermore, general anesthesia requires much more anesthetic medication and usually longer periods of recovery postoperatively than MAC. Longer scheduled case duration was associated with extended PACU length of stay. Case duration has been noted in previous studies to be associated with this outcome.2,3,10 The assumption here is that longer anesthesia could be related to more complex surgeries and increased anesthetic utilization, thereby increasing recovery time. The presence of obstructive sleep apnea was significantly associated with the outcome in univariable analysis but was no longer significant in the multivariable model. Morbid obesity, however, remained significant, which is a subpopulation with increased risk for sleep apnea. These patients are at increased risk of postoperative airway obstruction, especially in the setting of opioid utilization.18 Upper airway problems, especially from airway obstruction due to pharyngeal laxity from pharyngeal muscle weakness, are one of the more common PACU complications.19 It is clinically prudent to observe these patients relatively longer than patients without obstructive sleep apnea. The presence of hypertension revealed increased odds for extended PACU length of stay likely related to the hemodynamic challenges faced in this population during recovery.

Many surgical subspecialties, including Otolaryngology, Gastroenterology, Ophthalmology, and Plastic Surgery had decreased odds for extended PACU length of stay when compared with General Surgery. This could be related to the nature of surgeries and the patient populations in specialties such as General Surgery and Gynecology, as well as the fact that these subspecialties afford higher risks to PONV in outpatient surgery. For example, Sarin et al20 demonstrated that, in the ambulatory surgery suite, laparoscopic cholecystectomy, tubal ligation, and pelviscopy had increased odds for PONV.

Previous studies have found that age was associated with extended PACU length of stay, which is not consistent with the current findings.4,10 Although the geriatric population may be more prone to a number of postoperative complications and may require longer periods of time for adequate recovery compared with their younger counterparts, age alone, as suggested by this study, does not necessarily predict extended PACU length of stay. Likely, it could be the associated comorbidities related to age that contribute to it. Patients appropriate for outpatient surgery usually have less comorbidity burden and undergo less complex surgeries; therefore, this sample population likely consists of healthier elderly patients.

Operating room management is a complex process that involves multiple planning steps at different time points: (1) strategic decision making, which requires years of planning before implementation and involves permanent changes to operating room functionality; (2) tactical decision making, which is performed once or twice a year and tends to change operating room capacity, for example, through changes in total block time; (3) operational decision making, which has a time course of weeks to minutes and involves allocation of operating room time, scheduling of cases, and staff assignment; and finally (4) decisions made the day of surgery based on maintaining patient safety, operating room efficiency, and patient waiting times.21 Predicting cases that may afford prolonged recovery room length of stays may be useful during the operational decision-making stage, in which scheduling of cases and staff assignment occurs (specifically, in this case, recovery room staffing).

Minimizing the peak number of patients in the PACU is important because this can reduce the number of PACU admission delays (which occurs when there is inadequate nurse staffing in the PACU to safely take care of the incoming patients). Furthermore, prolonged PACU length of stay at the end of the day may potentially overutilize nursing if needed staffing is not adequately estimated. If given historical information regarding inflow into the PACU from the operating rooms and a set timeframe at which nursing shifts are made, the staffing of the recovery room can be adjusted.22,23

However, what is the optimal sequence at which to schedule cases? The answer requires many interrelated variables and its usefulness is dependent on the type of operating room environment.23–25 The concept of resequencing cases does not afford much benefit, in terms of optimizing staffing assignment, in large PACUs24; however, it does have benefit in facilities with just a few operating rooms, such as that of outpatient surgery centers (as is performed in the current study).23,25 In facilities with a small number of operating rooms (such as ambulatory surgical suites), the goal here is to minimize the number of recovery room nurses needed in a given day by finding the optimal sequence in which cases should be scheduled that would minimize the peak number of patients in the PACU and minimize makespan (ie, the completion time of the last patient in the PACU).23,25 Although the current study does not attempt to create such an algorithm, which is beyond the scope of this article, it does create a model that helps identify patients that may stay in the recovery room longer. This potentially could be an important variable inputted into future algorithms that can calculate optimal case sequencing in outpatient surgery centers with a small number of operating rooms in an effort to minimize makespan. For example, would it be advantageous to resequence cases such that patients with predicted prolonged PACU length of stay are scheduled earlier in the day? Of note, this predictive model does not forecast a specific time that the last patient of the day exits the PACU because this will also depend on the actual duration of cases – information only known by the end of the surgical day. Still, it is unclear whether identifying at-risk patients in advance will minimize makespan, not until case- sequencing algorithms that put this variable into account are tested.

There are several limitations to this study. One is its retrospective design. In addition, there are some nonclinical factors that may contribute to extended PACU length of stay and were not included in this model. These include transportation issues, nursing availability for education/discharge, and physician availability for orders.26,27 These types of delays are difficult to predict and are the very issues that may be avoided with appropriate predictive modeling and subsequent surgical and resource planning/utilization. It is unclear how these administrative or logistic delays would bias the results, because there has been no study performed at our hospital identifying whether certain patient or surgical characteristics are associated with this particular type of delay. Certainly, these types of delays may be a reason why the discriminating ability is not closer to 100%. Another limitation is that there are a large number of possible comorbidities that may be included in model building – many of which are not included here. We chose the most common comorbidities for this population. Plus, certain comorbidities have varying degrees of severity (ie, degree of systolic function in congestive heart failure) that were not controlled for here. However, at our institution, patients are selected to be appropriate for outpatient surgery based on severity of disease, so this mitigates this problem. Finally, this is a single-institution experience. However, the patient population and surgical mix may be similar to those of other outpatient surgery practices. Our next steps are to validate this model externally.

In conclusion, we developed a predictive model with excellent discrimination and goodness-of-fit that can help identify patients at higher odds for extended PACU length of stay. These predictions may aid in the development of effective case-sequencing methodologies aimed at optimizing recovery room staff scheduling in outpatient facilities with a relatively small number of operating rooms.

Back to Top | Article Outline

DISCLOSURES

Name: Rodney A. Gabriel, MD.

Contribution: This author helped design the study, conduct the study, collect the data, analyze the data, and prepare the manuscript.

Name: Ruth S. Waterman, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.

Name: Jihoon Kim, MS.

Contribution: This author helped analyze the data and prepare the manuscript.

Name: Lucila Ohno-Machado, MD, PhD.

Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.

This manuscript was handled by: Richard C. Prielipp, MD.

Back to Top | Article Outline

REFERENCES

1. Weissman C. The enhanced postoperative care system. J Clin Anesth. 2005;17:314–322.
2. Kim WO, Kil HK, Kang JW, Park HR. Prediction on lengths of stay in the postanesthesia care unit following general anesthesia: preliminary study of the neural network and logistic regression modelling. J Korean Med Sci. 2000;15:25–30.
3. Waddle JP, Evers AS, Piccirillo JF. Postanesthesia care unit length of stay: quantifying and assessing dependent factors. Anesth Analg. 1998;87:628–633.
4. Seago JA, Weitz S, Walczak S. Factors influencing stay in the postanesthesia care unit: a prospective analysis. J Clin Anesth. 1998;10:579–587.
5. Corey JM, Bulka CM, Ehrenfeld JM. Is regional anesthesia associated with reduced PACU length of stay?: A retrospective analysis from a tertiary medical center. Clin Orthop Relat Res. 2014;472:1427–1433.
6. Edler AA, Mariano ER, Golianu B, Kuan C, Pentcheva K. An analysis of factors influencing postanesthesia recovery after pediatric ambulatory tonsillectomy and adenoidectomy. Anesth Analg. 2007;104:784–789.
7. Silins V, Julien F, Brasher C, Nivoche Y, Mantz J, Dahmani S. Predictive factors of PACU stay after herniorraphy in infant: a classification and regression tree analysis. Paediatr Anaesth. 2012;22:230–238.
8. Bothner U, Georgieff M, Schwilk B. The impact of minor perioperative anesthesia-related incidents, events, and complications on postanesthesia care unit utilization. Anesth Analg. 1999;89:506–513.
9. Chung F, Mezei G. Factors contributing to a prolonged stay after ambulatory surgery. Anesth Analg. 1999;89:1352–1359.
10. Gabriel RA, Wu A, Huang CC, Dutton RP, Urman RD. National incidences and predictors of inefficiencies in perioperative care. J Clin Anesth. 2016;31:238–246.
11. Chung F. Recovery pattern and home-readiness after ambulatory surgery. Anesth Analg. 1995;80:896–902.
12. Dexter F, Tinker JH. Analysis of strategies to decrease postanesthesia care unit costs. Anesthesiology 1995;82:94–101.
13. Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform. 2005;38:404–415.
14. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–845.
15. Dexter F, Dexter EU, Ledolter J. Statistical grand rounds: Importance of appropriately modeling procedure and duration in logistic regression studies of perioperative morbidity and mortality. Anesth Analg. 2011;113:1197–1201.
16. Lau LL, Hung CT, Chan CK, et al. Anaesthetic clinical indicators in public hospitals providing anaesthetic care in Hong Kong: prospective study. Hong Kong Med J. 2001;7:251–260.
17. Brown I, Jellish WS, Kleinman B, et al. Use of postanesthesia discharge criteria to reduce discharge delays for inpatients in the postanesthesia care unit. J Clin Anesth. 2008;20:175–179.
18. Mulier JP. Perioperative opioids aggravate obstructive breathing in sleep apnea syndrome: mechanisms and alternative anesthesia strategies. Curr Opin Anaesthesiol. 2016;29:129–133.
19. Hines R, Barash PG, Watrous G, O’Connor T. Complications occurring in the postanesthesia care unit: a survey. Anesth Analg. 1992;74:503–509.
20. Sarin P, Urman RD, Ohno-Machado L. An improved model for predicting postoperative nausea and vomiting in ambulatory surgery patients using physician-modifiable risk factors. J Am Med Inform Assoc. 2012;19:995–1002.
21. Dexter F, Epstein RH, Traub RD, Xiao Y. Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. Anesthesiology 2004;101:1444–1453.
22. Dexter F, Epstein RH, Penning DH. Statistical analysis of postanesthesia care unit staffing at a surgical suite with frequent delays in admission from the operating room–a case study. Anesth Analg. 2001;92:947–949.
23. Dexter F, Epstein RH, Marcon E, de Matta R. Strategies to reduce delays in admission into a postanesthesia care unit from operating rooms. J Perianesth Nurs. 2005;20:92–102.
24. Marcon E, Dexter F. An observational study of surgeons’ sequencing of cases and its impact on postanesthesia care unit and holding area staffing requirements at hospitals. Anesth Analg. 2007;105:119–126.
25. Hsu VN, de Matta R, Iee CY. Scheduling patients in an ambulatory surgical center. Nav Res Logist. 2003;50:218–238.
26. Cowie B, Corcoran P. Postanesthesia care unit discharge delay for nonclinical reasons. J Perianesth Nurs. 2012;27:393–398.
27. Tessler MJ, Mitmaker L, Wahba RM, Covert CR. Patient flow in the post anesthesia care unit: an observational study. Can J Anesth. 1999;46:348–351.

Supplemental Digital Content

Back to Top | Article Outline
© 2017 International Anesthesia Research Society