In this issue of Anesthesia & Analgesia, Wu et al.1 compare anesthesia-controlled times between types of general anesthesia for ophthalmologic surgery. Wu et al.1 show decreases in extubation times with propofol-based total intravenous anesthesia compared to desflurane anesthesia.
Several other papers have previously studied operating room management of ophthalmologic surgery.2–9 Aspects of the prior work related to prolonged extubation times are nicely summarized by Wu et al., and so we mention that work just briefly. Prolonged extubation times (i.e., those ≥15 minutes) are associated with more time in the operating room and personnel waiting.10–12 When there are relatively long workdays (e.g., ≥8 hours of cases and turnovers per day per operating room), as at the authors’ hospital, increases in extubation times matter economically, slow down the operating room workflow, increase time to operating room exit, and increase the time from operating room exit until surgical incision of the next case.9–12
Wu et al.1 found that, despite differences in extubation times between drugs, there were not significant differences in operating room times. That finding is not inconsistent with prior work. Prolonged extubations are more frequent for procedures of long durations (e.g., ≥4 hours) and in the prone position.12 Since many ophthalmological surgical cases are of brief durations and are performed supine, such as those studied by Wu et al., likely there were not many cases with prolonged extubations. Consequently, larger sample sizes (to have more cases with prolonged extubations) would have been needed to detect differences in operating room times.
Wu et al.1 did find a glaring limitation of prior studies, including our13 own meta-analysis comparing differences in extubation times between propofol and desflurane. Their table 1 is a needs assessment showing that ophthalmologic surgery had not been studied.1 Our meta-analysis14 should have recognized this and described it as a limitation. Although we14 found that extubation times were briefer with desflurane than with propofol (e.g., for laparoscopic surgery and gynecology), Wu et al.1 found the opposite for ophthalmologic surgery. As they discuss, this may be due to how they used desflurane (e.g., absence of using high fresh gas flows after turning it off).1 Regardless, the manner in which drugs are administered reflects the surgical procedure. Another difference is that the meta-analysis14 was limited to randomized trials, and yet Wu et al.1 used an observational approach.
These unique findings by Wu et al. for ophthalmologic surgery1 complement differences between ophthalmologic surgery and other specialties that have previously been exploited to study operating room management. Consideration of very short duration procedures (e.g., cataract surgery) permitted large sample sizes to be obtained to study patient behaviors and preferences (e.g., number of surgeon visits before surgery).3,9 The anesthesia-controlled times were relatively long compared with surgical times, permitting critical evaluation of interventions to reduce anesthesia-controlled times.2 These studies took advantage of the unique aspects of ophthalmologic surgery.2,3,9
Luo et al. recently published that the anesthesia-controlled times for ophthalmology were dependent on the sequence of procedures in the operating room.8 Identical procedures were best scheduled consecutively.8 The anesthesia-controlled times were least when the procedure with general anesthesia, strabismus correction, was performed before other procedures.8 The brief durations of ophthalmologic surgery made it possible to perform this study, but potential mechanisms seem quite unlikely to apply to most other specialties. Yes, the management science of ophthalmologic surgery is different.
Shylo et al.6 studied the mathematics of scheduling multiple surgical cases simultaneously (i.e., waiting until there are multiple cases to be scheduled and then simultaneously assigning a date and start time to each case in the batch). Their objectives included constructing schedules that were robust to predictive error in case durations.6 These analyses are important because ophthalmologic cases have significantly greater proportional variability (e.g., mean absolute predictive error) in case durations than other specialties such as spine and general thoracic surgery.4,14 Shylo et al.’s analyses are based on the assumption that the time to complete a series of cases follows a normal distribution.6 The assumption is used for their best possible and heuristic scheduling rules to capture the underlying uncertainties in the sums of the durations of cases in ophthalmologic operating rooms.6 They show that for lists of several ophthalmic surgical cases, the assumption is quite accurate.6 Specifically, the estimated time to complete a list of cases is an unbiased estimator for the total, as holds in general.15 In addition, the percentiles of the underestimates and overestimates match those expected from a normal distribution.6 Our (consulting) experience has been that this empirical fit tends to be excellent for ophthalmologic lists of cases. Figures 1 and 2 show these findings graphically using data from an ambulatory surgery center. The assumption can be used for scheduling a gap between the list of cases of 2 ophthalmologists in the same operating room on the same day (i.e., morning and afternoon “sessions”).16 The assumption also holds for radiology procedures under anesthesia.17 However, and this is key, it is decidedly untrue for operating rooms with just a couple of cases per operating room per day (e.g., cardiac surgery).18,19
To summarize, Wu et al., Luo et al., and Shylo et al. in 2013 and 2014 help us understand that ophthalmologic surgery has some unique features pertaining to operating room management.1,6,7 Differences include not only the influence of drugs on extubation times, but also effects of procedure sequencing on anesthesia-controlled times and the probability distribution of lists of cases.
Dr. Franklin Dexter is the Statistical Editor and Section Editor for Economics, Education, and Policy for the Journal. This manuscript was handled by Dr. Steven L. Shafer, Editor-in-Chief, and Dr. Dexter was not involved in any way with the editorial process or decision.
Name: Franklin Dexter, MD, PhD.
Contribution: This author helped design the study and write the manuscript.
Attestation: Franklin Dexter has approved the final manuscript.
Name: Ruth E. Wachtel, PhD, MBA.
Contribution: This author helped write the manuscript.
Attestation: Ruth E. Wachtel has approved the final manuscript.
Jennifer Espy, B.F.A., Administrative Services Coordinator at the University of Iowa, helped edit the paper.
1. Wu ZF, Jian GS, Lee MS, Lin C, Chen YF, Chen YW, Huang YS, Cherng CH, Lu CH. An analysis of anesthesia-controlled operating room time after propofol-based total intravenous anesthesia compared with desflurane anesthesia in ophthalmic surgery: a retrospective study. Anesth Analg. 2014;119:1393–406
2. Vigoda MM, Gayer S, Tutiven J, Mueller A, Murtha M, Schefler AC, Murray TG. Targeting operating room inefficiencies in the complex management of vision-threatening diseases in children. Arch Ophthalmol. 2008;126:1241–3
3. Dexter F, Birchansky L, Bernstein JM, Wachtel RE. Case scheduling preferences of one surgeon’s cataract surgery patients. Anesth Analg. 2009;108:579–82
4. Dexter F, Dexter EU, Ledolter J. Influence of procedure classification on process variability and parameter uncertainty of surgical case durations. Anesth Analg. 2010;110:1155–63
5. Devi SP, Rao KS, Sangeetha SS. Prediction of surgery times and scheduling of operation theaters in ophthalmology department. J Med Syst. 2012;36:415–30
6. Shylo OV, Prokopyev OA, Schaefer AJ. Stochastic operating room scheduling for high-volume specialties under block booking. INFORMS J Comput. 2013;25:682–92
7. Luo L, Yao DD, Huang X, You Y, Cheng Y, Shi Y, Liu J, Gong R. Sequence-dependent anesthesia-controlled times: a retrospective study in an ophthalmology department of a single-site hospital. Anesth Analg. 2014;119:151–62
8. O’Neill L, Dexter F, Wachtel RE. Should anesthesia groups advocate funding of clinics and scheduling systems to increase operating room workload? Anesthesiology. 2009;111:1016–24
9. Dexter F, Bayman EO, Epstein RH. Statistical modeling of average and variability of time to extubation for meta-analysis comparing desflurane to sevoflurane. Anesth Analg. 2010;110:570–80
10. Masursky D, Dexter F, Kwakye MO, Smallman B. Measure to quantify the influence of time from end of surgery to tracheal extubation on operating room workflow. Anesth Analg. 2012;115:402–6
11. Dexter F, Epstein RH. Increased mean time from end of surgery to operating room exit in a historical cohort of cases with prolonged time to extubation. Anesth Analg. 2013;117:1453–9
12. Epstein RH, Dexter F, Brull SJ. Cohort study of cases with prolonged tracheal extubation times to examine the relationship with duration of workday. Can J Anaesth. 2013;60:1070–6
13. Wachtel RE, Dexter F, Epstein RH, Ledolter J. Meta-analysis of desflurane and propofol average times and variability in times to extubation and following commands. Can J Anesth. 2011;58:714–24
14. Dexter EU, Dexter F, Masursky D, Kasprowicz KA. Prospective trial of thoracic and spine surgeons’ updating of their estimated case durations at the start of cases. Anesth Analg. 2010;110:1164–8
15. Dexter F, Traub RD, Qian F. Comparison of statistical methods to predict the time to complete a series of surgical cases. J Clin Monit Comput. 1999;15:45–51
16. Wachtel RE, Dexter F. Reducing tardiness from scheduled start times by making adjustments to the operating room schedule. Anesth Analg. 2009;108:1902–9
17. Dexter F, Yue JC, Dow AJ. Predicting anesthesia times for diagnostic and interventional radiological procedures. Anesth Analg. 2006;102:1491–500
18. Alvarez R, Bowry R, Carter M. Prediction of the time to complete a series of surgical cases to avoid cardiac operating room overutilization. Can J Anesth. 2010;57:973–9
© 2014 International Anesthesia Research Society
19. Wang J, Yang K. Using type IV Pearson distribution to calculate the probabilities of underrun and overrun of lists of multiple cases. Eur J Anaesthesiol. 2014;31:363–70