Secondary Logo

Journal Logo

Economics, Education, and Policy: Research Reports

The Effect of Hospital Size and Surgical Service on Case Cancellation in Elective Surgery

Results from a Prospective Multicenter Study

Schuster, Martin MD, MA*; Neumann, Christian PhD, MSc*; Neumann, Konrad PhD; Braun, Jan MD*; Geldner, Goetz MD; Martin, Joerg MD§; Spies, Claudia MD*; Bauer, Martin MD, PhD# for the CASCAES Study Group

Author Information
doi: 10.1213/ANE.0b013e318222be4d

Cancellation of scheduled surgical procedures is a problem in perioperative medicine and has negative effects on operating room (OR) economics and patient satisfaction. From the hospital perspective, “no show” of patients in cases of outpatient procedures or cancellation for medical or administrative reasons might lead to suboptimal use of OR capacity, because the fixed costs of the OR remain the same but no revenues can be generated if the case is cancelled.1 Additionally for inpatients, case cancellation might lead to prolonged hospital stay, additional costs for the hospital,2,3 and organizational problems for surgeons and anesthesiologists. For patients, short-term cancellation or postponing of a planned procedure can result in significant emotional distress, repeated preoperative fasting with negative physiological effects, and extra expenses.4,5

The incidence of case cancellation varies widely in the literature13,614 and a large variety of reasons have been reported to explain why elective cases are cancelled at the day of surgery: medical reasons (e.g., incomplete preoperative evaluation or treatment), OR-related organizational issues (e.g., insufficient OR capacity, missing material, or lacking personnel), and administrative reasons (e.g., insurance-related issues). Table 1 summarizes the major studies published on case cancellation. Mainly single-center studies have been published on this issue with case cancellation rates for the total study populations ranging from 4.6% and 18.3%. On the basis of the published data, it is unclear what effect hospital size and service type have on case cancellation rates. This information is of importance if case cancellation rates are compared among hospitals and services. We therefore analyzed case cancellation rates in 3 types of hospitals (university hospitals, large community hospitals, and small- to mid-size community hospitals) and 4 different surgical services (general surgery, urology, trauma and orthopedics, gynecology) in a large multicenter trial. The aim of the study was to understand more clearly in which way hospital type and service type has to be considered if cancellation rates are benchmarked.

Table 1:
Studies on Case Cancellation


The study was approved by the Ethic Committee of the Charité—Universitätsmedizin Berlin. Because only routine OR management data were recorded and patient-related data were recorded strictly anonymously in the participating centers, patient consent was waived. The study was endorsed by the German Society of Anesthesiology and Intensive Care as a project of the quality improvement initiative “Forum Quality Management and Economics.”

Independent OR managers participated in the study of 25 anesthesia departments with responsibility for OR management in their institution or department. University hospitals and community hospitals were included. Because there is no official categorization of nonuniversity hospitals, we split the group of community hospitals according to the number of beds into 2 groups: small- to mid-size and large community hospitals. We used the term community hospital for all nonuniversity hospitals in our study even though some community hospitals provide full-scale medical care except very specialized services (e.g., transplantation surgery, pediatric cardiac surgery) and have close to or >1000 beds. The average number of hospital beds can be seen in Tables 2 and 3.

Table 2:
Case and Service Specifics According to Hospital Size
Table 3:
Case and Service Specifics According to Subspecialty

The following anesthesia and surgical subspecialties were included in the study: general surgery (including vascular, hepatic, and thoracic surgical cases), urology, gynecology (without obstetric cases), and orthopedic/trauma surgery. For 2 weeks, all elective cases performed Monday through Friday were recorded in the participating institutions. Surgical cases performed without anesthesia involvement were excluded.

OR planning varied according to institution; however, in the study, last changes to OR schedules by the surgical services were accepted in all participating hospitals until the afternoon of the previous day. Therefore we used the final (4:00 PM version) OR plan for the next day as the basis to evaluate case cancellations of the next day. The rationale for this time point was that capacity and personnel planning for the next day have to be finalized before the end of the previous regular work day. Included were all cases planned to start in the regular block time until 3:00 PM. Some institutions used for some services extended OR block times. To have comparable data in all study centers, cases planned to start after 3:00 PM were excluded from the analysis. Because we studied several different hospitals, there was not one single institutional guideline regarding OR end time or the movement of cases to other ORs. The hospitals were free to reserve extra OR time for emergencies or reserve OR time in the afternoon staffed with regular or on-call personnel to accommodate the list of cases running late or complete cases in overtime.

In the hospitals studied, there was a similar approach to preoperative evaluation, because for legal reasons, in Germany all patients have to undergo a face-to-face evaluation by an anesthesiologist, which cannot be delegated to other staff. Additionally, the patient has to sign an informed consent regarding the anesthesia procedure. Both have to be completed at least 1 day before the operation. The patients are usually seen in the anesthesia clinic; however, bedridden inpatients are seen by the anesthesiologist at the ward. If patients have unresolved medical issues that are identified early (e.g., missing cardiology consultation in a patient who needs a cardiology consultation according to the guideline), the patients are removed from the OR plan before the finalization of the OR plan and therefore would not have been counted as case cancellation in our study. If medical problems are identified only after the finalization of the OR plan or pending tests, which are expected to be normal, require further evaluation, the patient might be cancelled after finalization of the OR plan.

For all cases we recorded the following information from the OR plan: sex and age of the patient and planned procedure time (skin-suture time). At the end of the day we retrieved from the OR information system whether the procedure had started before 3:00 PM OR AFTER 3:00 PM or whether the procedure was cancelled. On the basis of previous studies1,3,68 and our own experience, we provided a list with administrative, medical, and organizational cancellation reasons. The OR manager was asked to document the best-fitting reason. If the reason was not known by the OR manager, he or she was asked to contact the responsible surgeon to determine the correct reason.

The data were analyzed in Microsoft Excel 2002 (Microsoft, Redmond, WA), SPSS 18.0 (SPSS, Chicago, IL), and R (a language and environment for statistical computing; version 2.10.0; R Foundation for Statistical Computing, Vienna, Austria). If not otherwise stated, mean and the 95% confidence interval (CI) are displayed. The main 2 influencing factors examined in this study were hospital type and service type. For the multiple regression analysis we used a Poisson regression model with the number of cancellations as the dependent variable and the logarithm of the number of scheduled operations as offset. To account for multilevel structure of the data (up to 4 services belong to the same center and 10 days for each service were considered), we included a random intercept in the model. Regression analysis was performed by the glmmPQL procedure of the R package MASS (version 7.3.7). In addition we considered contrasts between the levels of the variables hospital type and service type. P values of the contrasts <0.05/9 = 0.0056 are considered significant (Bonferroni adjustment of factor 9). The multiple level of significance was 0.05. We showed the validity of the model by Monte Carlo simulations (see Appendix A, Supplemental Digital Content 1, No relevant α level inflation occurs if the assumptions for our Poisson regression are violated.


A total of 6028 cases were recorded in 82 anesthesia and surgical subspecialties. Nineteen cases had to be excluded because of incomplete data; therefore, 6009 cases were included in the final analysis.

Information regarding the hospitals and services in the study can be seen in Tables 2 and 3. The university hospitals in general had larger services with more OR capacity per service and longer-case durations than did the community hospitals. The general surgery and trauma/orthopedics services had more cases and more OR capacity per day than did the gynecology and urology services. The longest-case durations took place in the general surgical services.

Services in university hospitals had cancellation rates 2.23 (95% CI = 1.49 to 3.34) times higher than mid- to small-size community hospitals (12.4% [95% CI = 11.0% to 13.8%] versus 5.0% [95% CI = 4.0% to 6.2%]) (Fig. 1a). General surgical services had a significant 1.78 (95% CI = 1.25 to 2.53) times higher cancellation rate than did gynecology services (11.0% [95% CI = 9.7% to 12.5%] versus 6.6% [95% CI = 5.1% to 8.4%]) (Fig. 1b).

Figure 1:
Cancellation rate according to hospital type (a) and service type (b) in percentage of all cases. Displayed are mean and 95% confidence interval. Brackets and stars denote significant contrasts in the multiple Poisson regression model (P = 0.0001 for university hospitals versus mid- to small-size community hospitals; P = 0.0020 for general surgical services versus gynecology services). For numbers of cases in each group, see Table 1.

The reasons for the case cancellation recorded by the study teams in the hospitals according to service and hospital types can be seen in Tables 4 and 5.

Table 4:
Cancellation Reasons According to Hospital Type
Table 5:
Cancellation Reasons According to Service Type

Both hospital types and service types were included in the mixed-effects Poisson regression model. Nine contrasts were calculated for pairwise comparisons of hospital types and service types. The results of the multiple regression model are displayed in Tables 6 and 7.

Table 6:
Results of the Poisson Regression Models for the Dependent Variable “Cancellation Rate”
Table 7:
Contrasts Between Hospital Type and Service Types


Case cancellation is a problem for OR economics and physicians workflow. Case cancellation rates exceeding 10% of all scheduled cases have been reported for private and public hospitals in the United States,6,7,10,11 in Europe,2,9,11 and in Australia.8

Mainly single-center trials or data from homogenous hospital groups, such as the Veterans Affairs hospitals,10 have been published on this subject. The main disadvantage of single-center trials or homogenous hospital groups with respect to case cancellation is that local factors and specific administrative procedures or specific OR-planning processes might profoundly influence the incidence of and the reasons for case cancellation. Our hypothesis was that hospital size and type and surgical discipline might have an independent effect on cancellation rates. This is important when cancellation rates within 1 group of hospitals or different surgical specialties within the hospitals are compared.15

Overall, in our study, the cancellation rate varied among hospitals from 0.8% to 17.9% and among services from 0% to 22.5%. These cancellation rates are in general in the range of previously published studies (Table 1). University hospitals had significantly higher cancellation rates than did smaller hospitals, and among the services, general surgery services had the highest cancellation rates (Tables 6 and 7).

The higher rates of cancellation in academic institutions are of special interest. Often it is claimed that the unpredictability of maximum care leads to case cancellation; however, only in 0.3% of all cases in university hospitals were emergency operations reported to be the reason for the case cancellation.

The economic consequences of case cancellation depend on the assumptions and the framework used to evaluate costs16 for the patient, the physician, the hospital, the health insurances or the society. For the patient, the cancellation leads to direct or indirect costs, depending on the type of health insurance and whether opportunity costs, such as missing income for the extended hospital stay, is paid by a third party such as the employer or the health insurance. For the hospital, case cancellation increases costs from additional overnight stay for inpatients, or additional outpatient visits. However, from the hospital's perspective these extra costs for postponing a case must be weighed against the extra cost of performing the case partly or completely in overtime.16,17

Expected OR time overrun was reported to be the single most important reason in all hospital types and service types (Tables 4 and 5). This leads to the complex question of how rational the planning processes were in the different hospitals, on both strategic and tactical levels. If the allocated time was planned on the basis of minimizing the inefficiency of use of OR time, the amount of overutilized OR time would be nearly invariant among hospitals (e.g., 1 out of 3 ORs daily on the basis of a relative cost ratio of 2.0). Only in highly underutilized ORs will no OR overutilization occur. Excessive case time overrun, which leads to cancellation of the last case of the day as we saw in our study, can occur by chance but also because of suboptimal planning processes, e.g., systematic underestimation of case durations.18 Psychological bias of the OR manager and organizational behavior regarding OR overutilization also have major impact on case cancellation.18,19

In the largest study published, Argo et al. retrospectively analyzed data from the Veterans Health Administration and found an overall case cancellation rate of 12.4%. The patient-based reasons were much more prominent in this study, in comparison with our study, accounting for one third of all cancellations, probably because of the specific population that Veterans Affairs hospitals serve.10 In general there is no penalty for no-show in the German health system. The risk for no-show lies only with the hospital. If the patient has to be rescheduled because of case cancellation, the additional cost is carried by the hospital.

In an older study by Hand et al., about 10% of the cancellations were caused by insurance problems.6 Presently, these kinds of cancellations should have decreased, because OR managers strive to clear insurance status before scheduling patients in the OR. In publicly funded health care systems as in Germany, with mandatory and full-coverage health insurance for all citizens, the problem of no insurance coverage in elective cases is almost nonexistent, and only 1 case of insufficient insurance was noted.

To improve perioperative medical patient care, but also to decrease case cancellation, almost all anesthesia departments have established preoperative assessment clinics and most, if not all, patients are scheduled to see the anesthesiologist before the planned operation date. In several studies these preoperative anesthesia assessment clinics have reduced the incidence of case cancellation for medical reasons.3,12,13,20 However, the implementation of preoperative anesthesia clinics still faces considerable resistance.21

Some methodological limitations should be noted. Participation in the study was on a voluntary basis; therefore, the departments who participated might not be representative of all hospitals in Germany. However, the hospitals were well distributed over Germany, making regional influence less likely. As requested in the survey, the reasons for cancellation were collected by the physician in charge of OR management; in most cases it was an anesthesiologist. In most German hospitals, OR management is an integral part of the work of the anesthesia department, and the daily management of OR issues (case planning and sequencing, resolving conflicts regarding block time for emergency cases or add-on cases) is done by anesthesiologists who devote part or all of their work time to this process. We tried to reduce incorrect coding by providing a standardized list and, if the reason for cancellation was not completely clear or there were contradictory statements, we coded “reason unclear.” However, we did not validate the recording of the cancellation reasons in the different centers. In a recent study, Tung et al. found that in 28% of all cases a discrepancy was present between the cancellation rate reported by the responsible physician and the administrative database.14 This supports our approach of collecting the cancellation reason prospectively and contacting the responsible physician, because retrospective data analysis based on administrative databases does not seem to be a reliable source of reason for cancellation. In the hospitals we surveyed, OR managers are not administrative personnel but anesthesiologists who devote part or all of their time to managing the daily process flow in the OR and to organizing the necessary equipment and available team members. If changes in the OR list occur or emergency cases have to be performed, this is communicated to the OR manager, who organizes the necessary resources and also manages resource planning for late running of OR program. However, we cannot exclude that some cancellations would be classified differently by OR managers, and in some cases different reasons can be reasonably classified in more than 1 category. However, this will always be true in this type of study and some subjective bias must be accepted.

Some cancellation reasons and cancellation rates might vary among seasons. In our observation, snow and ice on the street increased not only no-shows but also increased the work load of trauma surgeons, drastically leading to short-term late running of the OR program because of emergency cases. However, seasonal effects were not the focus of our study, and all data from all hospitals were acquired during the same 2 weeks.

For practical reasons we included only 4 types of surgical services in our study. To study the effect of both different services and hospital size, we had to focus on those services that were present in the majority of hospitals, independent of the size. Services such as otolaryngology, pediatric surgery, or plastic surgery are often only present in larger hospitals, leading to a hospital size-based effect comparing these service-specific cancellation rates.

The pediatric patient population might have a different cancellation rate than that of adults. We did not exclude pediatric patients in our study, because pediatric patients are included in the services work flow of all services and hospitals to some extent. However, we did not study pediatric surgery as a specialty, as described above, and only 2.5% of all cases in the study were performed in patients younger than 12 years, and only 4.2% in patients younger than 18 years. Because of this small number we did not perform subgroup analyses.

A prospective trial might be susceptible to an investigation bias, also called the Hawthorne effect.22 However, it seems very unlikely that whole institutions changed their way of working just because of our study. Indeed, in many institutions it already was common practice to identify and record reasons for case cancellation before our study.

The cancellation rate would have been higher in our study had cancellation before the finalization of the OR plan been included. However, OR planning will always be a moving target ,and we decided to focus on the changes on the day of surgery, because at this late time point the negative effect of case cancellation on OR workflow, OR economics, and patient satisfaction is obvious.

One major disadvantage of our study is that, for practical reasons, we had to limit the study period to 2 weeks. Case cancellations for some of the documented reasons might have a tendency to be correlated (e.g., full intensive care unit). Consequently, it has been suggested that cancellation rates should be analyzed in batches of 4 weeks ' duration.15 However, to show that our model is robust, we changed the simulation assumption of constant probability of cancellation among the 10 days. In the simulation we assumed that the null hypothesis is true (no influence of hospital type and service type on the probability of cancellation). Hence we assumed an overall probability for cancellation of P = 0.09. We then sampled the probabilities for cancellation pijk for each single day, hospital type, and service type from a β distribution with expectation P = 0.09. The parameters of this β distribution were altered so that we gradually departed from the Poisson assumption. In one extreme the Poisson assumption holds, and in the other extreme, pijk is indeed sampled from a Bernoulli distribution. Therefore, we even included the extreme unlikely situation that either all or no cases are cancelled. In all instances the actual error of first kind lies well under 5%. Thus, our model is conservative even if Poisson assumption does not hold. The random intercepts account for individual influences affecting the single services.

In conclusion we found an increased rate of case cancellations in general surgical services and university hospitals. Focusing on the best practice, a lower rate of case cancellation would probably be achievable for many services.


The following persons and institutions participated in the Case Cancellation in Elective Surgery Study (CASCAES) study:

Executive and Writing committee: M. Schuster, C. Neumann, K. Neumann, J. Braun, G. Geldner, J. Martin, C. Spies, M. Bauer


Augsburg:Klinikum Augsburg - H. Forst, J. Pressl;

Bochum: Universitätsklinikum Knappschaftskrankenhaus Bochum-Langendreer - A. Gottschalk, A. Piontek;

Bremerhaven: Klinikum Bremerhaven - H.G. Henrich, E. Kramer;

Berlin: Charité Campus Benjamin Franklin - M. Haack; Charité Campus Mitte - H. Köth; Charité Campus Virchow Klinikum - R. Müller;

Dessau: Klinikum Dessau - S. Breuer, J. Priezel;

Flensburg: Ev.-Luth.-Diakonissenanstalt Flensburg - B. Andresen, N. Stegmann;

Göppingen: Klinik am Eichert Göppingen - D. Schlürmann, C. Undeutsch;

Göttingen: Universitätsmedizin Göttingen - A. Klockgether, J. Hinz;

Hamburg: Universitätsklinikum Hamburg-Eppendorf - C. Salzwedel, D. Reuter; ASKLEPIOS Klinik Harburg - D. Lewers, T. Kerner;

Hannover: Annastift Hannover - M. Przemeck, S. Ostermeier;

Itzehoe: Klinikum Itzehoe - J. Buck-Gramcko, M. Fiege;

Kiel: Universitätsklinikum Schleswig-Holstein Campus Kiel - G. Neumann, R. Hanβ;

Leverkusen: Klinikum Leverkusen - M. Keilen, A. Mitrenga-Theusinger;

Leipzig: Klinikum St. Georg Leipzig - J. Bräuning, G.J. Bartz;

Ludwigshafen: St. Marien- und St. Annastiftkrankenhaus Ludwigshafen - T. Weichel, A. Goertz;

Ludwigsburg: Klinikum Ludwigsburg - K. Pachl;

Löwenstein: Klinik Löwenstein – M. Kugler;

Mechernich: Kreiskrankenhaus Mechernich - R. Hering, T. Mücke;

Mönchengladbach: Städtische Kliniken Mönchengladbach - H. Röpcke, M. Bartz;

Ostercappeln: St. Raphael Krankenhaus Ostercappeln - M. Thien;

Saarlois: St. Elisabeth Klinik Saarlouis - S. Otto;

Tübingen: Universitätsklinikum Tübingen - H. Guggenberger, C. Fromme.


1. Basson MD, Butler TW, Verma H. Predicting patient nonappearance for surgery as a scheduling strategy to optimize operating room utilization in a Veterans' Administration hospital. Anesthesiology 2006;104:826–34
2. Mangan JL, Walsh C, Kernohan WG, Murphy JS, Mollan RA, McMillen R, Beverland DE. Total joint replacement: implication of cancelled operations for hospital costs and waiting list management. Quality Health Care 1992;1:34–7
3. van Klei WA, Moons KGM, Rutten CLG, Schuurhuis A, Knape JTA, Kalkman CJ, Grobbee DE. The effect of outpatient preoperative evaluation of hospital inpatients on cancellation of surgery and length of hospital stay. Anesth Analg 2002;94: 644–9
4. Tait AR, Voepel-Lewis T, Munro HM, Gutstein HB, Reynolds PI. Cancellation of pediatric outpatient surgery: economic and emotional implications for patients and their families. J Clin Anesth 1997;9:213–9
5. Ljungqvist O, Soreide E. Preoperative fasting. Br J Surg 2003; 90:400–6
6. Hand R, Levin P, Stanziola A. The causes of cancelled elective surgery. Qual Assur Util Rev 1990;5:2–6
7. Lacqua MJ, Evans JT. Cancelled elective surgery: an evaluation. Am Surgeon 1994;60:809–11
8. Schofield WN, Rubin GL, Piza M, Lai YY, Sindhusake D, Fearnside MR, Klineberg PL. Cancellation of operations on the day of intended surgery at a major Australian referral hospital. Med J Aust 2005;182:612–5
9. Sanjay P, Dodds A, Miller E, Arumugam PJ, Woodward A. Cancelled elective operations: an observational study from a district general hospital. J Health Org Manage 2007;21:54–58
10. Argo JL, Vick CC, Graham LA, Itani KM, Bishop MJ, Hawn MT. Elective surgical case cancellation in the Veterans Health Administration system: identifying areas for improvement. Am J Surg 2009;198:600–6
11. Seim AR, Fagerhaug T, Ryen SM, Curran P, Saether OD, Myhre HO, Sandberg WS. Causes of cancellations on the day of surgery at two major university hospitals. Surg Innov 2009;16:173–80
12. Ferschl MB, Tung A, Sweitzer B, Huo D, Glick DB. Preoperative clinic visits reduce operating room cancellations and delays. Anesthesiology 2005;103:855–9
13. Pollard JB, Olson L. Early outpatient preoperative anesthesia assessment: does it help to reduce operating room cancellations? Anesth Analg 1999;89:502–5
14. Tung A, Dexter F, Jakubczyk S, Glick DB. The limited value of sequencing cases based on their probability of cancellation. Anesth Analg 2010;111:749–56
15. Dexter F, Marcon E, Epstein RH, Ledolter J. Validation of statistical methods to compare cancellation rates on the day of surgery. Anesth Analg 2005;101:465–73
16. Tessler MJ, Kleiman SJ, Huberman MM. A “zero tolerance for overtime” increases surgical per case costs. Can J Anaesth 1997;44:1036–41
17. Stepaniak PS, Mannaerts GH, de Quelerij M, de Vries G. The effect of the operating room coordinator's risk appreciation on operating room efficiency. Anesth Analg 2009;108:1249–56
18. Dexter F, Macario A, Ledolter J. Identification of systematic underestimation (bias) of case durations during case scheduling would not markedly reduce overutilized operating room time. J Clin Anesth 2007;19:198–203
19. Wachtel RE, Dexter F. Review of behavioral operations experimental studies of newsvendor problems for operating room management. Anesth Analg 2010;110:1698–1710
20. Correll DJ, Bader AM, Hull MW, Hsu C, Tsen LC, Hepner DL. Value of preoperative clinic visits in identifying issues with potential impact on operating room efficiency. Anesthesiology 2006;105:1254–9
21. Lemmens LC, Kerkkamp HE, van Klei WA, Klazinga NS, Rutten CL, van Linge RH, Moons KG. Implementation of outpatient preoperative evaluation clinics: facilitating and limiting factors. Br J Anaesth 2008;100:645–51
22. Parsons HM. What happened at Hawthorne? Science 1974; 183:922–32


Name: Martin Schuster.

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

Attestation: Martin Schuster has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Name: Christian Neumann.

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

Attestation: Christian Neumann has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Konrad Neumann.

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

Attestation: Konrad Neumann has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Jan Braun.

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

Attestation: Jan Braun has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Goetz Geldner.

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

Attestation: Goetz Geldner has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Joerg Martin.

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

Attestation: Joerg Martin has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Claudia Spies.

Contribution: This author helped design the study, conduct the study, and write the manuscript.

Attestation: Claudia Spies has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Martin Bauer.

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

Attestation: Martin Bauer has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Supplemental Digital Content

© 2011 International Anesthesia Research Society