KEY POINTS

Question: What are the obstetric anesthesia staffing procedure-based workload ratios and facility utilization at the facilities providing high levels of obstetric anesthesia care? Findings: We determined staffing procedure-based workload ratios and facility utilization at both academic and nonacademic Society of Obstetric Anesthesia and Perinatology (SOAP) Centers of Excellence institutions in the United States. Meaning: Our study provides anesthesia staffing procedure-based workload ratios from SOAP Centers of Excellence institutions that can be applied to other institutions to help them plan their optimal staffing and facility utilization.

See Article, page 1138

The Society of Obstetric Anesthesia and Perinatology (SOAP) Centers of Excellence (COE) for Obstetric Anesthesia Care is a designation that recognizes institutions that demonstrate excellence in obstetric anesthesia care.1,2 Adequate and appropriate staffing is an essential component to provide optimal obstetric anesthesia care, ensure timely response, and have additional staffing for emergencies. Staffing is a key domain of the SOAP COE designation, with recommendations that include obstetric anesthesia-trained leadership, dedicated in-house physician anesthesiologist obstetric anesthesia coverage, and an ability to mobilize additional anesthesia providers for unanticipated obstetric emergencies or high clinical volume beyond staffing capacity.1,2 The American College of Obstetrician and Gynecologist (ACOG) Levels of Maternal Care also provide guidance for staffing with dedicated obstetric anesthesia services and adequate level of training to fulfill a requirement for subspecialty care and regional perinatal health care centers (levels III and IV).3

Despite SOAP and ACOG emphasis on specialty staffing, specific recommendation for ideal staffing ratios or specific criteria sets for staffing numbers are not made due to limited data to guide optimal workload and staffing models for obstetric anesthesiology services. The rapidly changing obstetric clinical setting can make identifying appropriate staffing models difficult. Too few staff threatens maternal safety and neonatal outcomes, and timely access to equitable care, while too many staff results in suboptimal resources allocation and fiscal wastage. Staffing levels need to account for more scheduled procedures during weekdays4,5 and to provide didactic and clinical education for the trainees in academic centers. Staffing levels are often inadequate after-hours, especially with unscheduled activities creating surges in clinical volume.6 Institutional volume and timeliness of service expectations may impact staffing needs. Additionally, staffing quantity and level of provider need to be adjusted based on case acuity and for pregnant patients at high risk for maternal mortality and morbidity. Therefore, identifying optimal models in clinical obstetric anesthesia staffing is important to guide facilities on how to provide safe, efficient, and cost-effective care.

The aim of this study was to describe procedure (epidural analgesia procedures and cesarean delivery anesthesia provision) time-based staffing workloads among the 53 SOAP COEs and to provide procedure-based workload ratios from these best practices; that other institutions can use to compare with and help guide their obstetric anesthesia staffing. We surveyed current SOAP COEs to estimate average obstetric anesthesia procedure-based workload, staffing ratios, and utilization of operating and labor rooms (OR and LR). Staffing procedure-based workload ratios were compared between weekdays, weeknights, and weekend shifts, and academic and nonacademic centers.

METHODS

This article was written in accordance with Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines. Stanford University institutional review board (IRB) exemption was obtained before conducting this survey of SOAP COE leads. We designed a survey on Research Electronic Data Capture (REDCap) after obtaining a waiver for consent from the IRB of Stanford University. The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by the Stanford Medicine Research Information Technology team. The REDCap platform services at Stanford are subsidized by (a) Stanford School of Medicine Research Office and (b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085. The SOAP COE for Obstetric Anesthesia Care is a designation in obstetric anesthesia care set up in 2018, and all centers surveyed had received this designation at the time of this survey.1,2 An email invitation to complete the survey was sent to all the 53 SOAP COE obstetric anesthesia leads in September 2020, which included both a brief cover letter and a unique REDCap survey web link. The contact person designated on the initial COE application or alternate contact (if subsequently changed COE leadership) was emailed. The investigators were automatically notified of any invalid email addresses, and updated contact information was obtained by contacting other known anesthesiologist personnel at that COE. Nonresponders were sent 2 reminder email invitations, each 1 week apart.

The survey was developed by the investigators and included 18 questions over 2 sections (Supplemental Digital Content 1, Appendix 1, https://links.lww.com/AA/D972). The survey was developed by 3 obstetric anesthesiologists and trialed on 5 obstetric anesthesiologists to ensure understandability. The first survey section included hospital practice, total deliveries, cesarean delivery rate, labor neuraxial rate, and the number of LRs and ORs. The second survey section included the number of in-house providers assigned to the obstetric anesthesia service during the weekday, on weeknights, and over the weekend. We also asked SOAP COE responders to report assessment of staffing as adequate or inadequate for each of the shift periods. The staffing breakdown (attending, resident, fellow, and Certified Registered Nurse Anesthetists [CRNAs]) and the number of backup and home call staffing available to cover the obstetric anesthesia service were determined. All survey items except the last comment boxes were required for survey submission. We only assessed epidural procedure and cesarean delivery numbers and did not ask questions to determine workload with nonprocedure tasks such as managing critically ill parturients, doing pre- and postprocedural evaluations, attending multidisciplinary discussions to coordinate high-risk patients, and being on standby for emergencies.

The obstetric anesthesia procedure-based clinical activity was estimated using a time-based workload ratio (TWR) during the weekday, weeknight, and weekend shifts.7,8 Obstetric anesthesia clinical activity was calculated using the formula of Obstetric Anesthesia Activity Index (OAAI).7 OAAI was defined as the number of cesarean deliveries and labor neuraxial analgesia procedures multiplied by the time (hours) previously reported to be associated with the procedure,9 calculated as a daily workload.

OAAI = [ + ]/365 (ie, procedure workload in hours per day).

The OAAI equation was adapted to consider the difference of the workload in each shift (weekdays, weeknights, and weekends). For a TWR per provider, we used the index developed at Stanford (Stanford Workload Index [SWI]). Based on the previous single-center study, the hourly workload for weekday shifts was 89% greater than the ones for weeknights and weekends.8

TWR = []/hours per shift

TWR for weekday shift (D), total 10 hours (7 am–5 pm)

TWR for weeknight shift (N), total 14 hours (5 pm–7 am, next day)

TWR for weekend shift (E), total 24 hours (7 am–7 am, next day)

D = 1.89#* N = 1.89* E; N = E

#Shift time-based procedure workload ratios: (89% greater workload for weekdays versus weeknights/weekends).8

1 week = 7 days = 5 weekday shifts + 5 weeknight shifts + 2 weekend shifts

Calculation of D, N, and E from OAAI

OAAI = (D * 10 * 5 + N * 14 * 5 + E * 24 * 2)/7

= (50D + 70N + 48E)/7

= (50 * 1.89 * N + 70N + 48N)/7

= 30.4 N

Therefore, weeknight (N) or weekend (E) shift = OAAI/30.4 = 0.033 * OAAI, and weekday (D) shift = 1.89 * N = 0.062 * OAAI

D = 0.062 * OAAI, N = E = 0.033 * OAAI

SWI = D, N, or E/ number of providers for each shift.

We calculated the following 6 SWI variables:

SWI for weekday per attending and for total staff available

SWI for weeknight per attending and for total staff available

SWI for weekend per attending and for total staff available

The SWI was compared between weekday, weeknight, and weekend shifts, academic and nonacademic centers, and centers reporting adequate compared to inadequate staffing. OR and LR utilization rates were also calculated. OR utilization rate = number of cesarean deliveries/number of ORs, and LR utilization rate = number of vaginal deliveries/number of LRs.

Statistical Analysis

The primary aim of the analysis was to estimate the procedure-based workload per provider for different shifts. The secondary aims were to evaluate the extent of facility utilization per annual deliveries, staffing ratios per annual deliveries, and TWR per each shift; compare academic and nonacademic centers; and determine centers reporting adequate versus inadequate staffing. We used descriptive statistics to summarize the information. We a priori decided to survey but exclude the 4 non-US COEs from the analysis to remove potential variations in practice.

Data are presented as median and interquartile ranges, numbers, and ratios. Signed-rank tests (adjusted for multiple comparisons when appropriate) were used to test for differences in continuous data between groups. Regression analysis was used to evaluate the relationships between staffing and room numbers per annual number of deliveries. Quadratic models were calculated for the relationship between the SWIs and annual deliveries.

Fisher exact test was used to test the distribution of hospital volume strata and hospital type. The statistical significance threshold was set at 0.05. Data were analyzed using Microsoft excel 2016 (Microsoft Corp) and JMP statistical software (SAS Institute). Graphs were created using Microsoft Excel 2016 and JMP for visual comparison.

RESULTS

A total of 51 replies from 53 surveys were received (96% response rate). We excluded 4 non-US centers, leaving 47 replies from the US institutions for the analysis. Delivery volumes varied widely from 300 to 12,000 deliveries a year (Supplemental Digital Content 2, Table 1, https://links.lww.com/AA/D973). Cesarean delivery rates and neuraxial labor analgesia rates were relatively uniform (Table 1). There was no significant difference between academic and nonacademic practices in terms of delivery volumes and rates of neuraxial labor analgesia or cesarean delivery rates.

Table 1. - Delivery, Cesarean, and Labor Neuraxial Rates Among the SOAP COE for Obstetric Anesthesia Care Institutions in the United States Annual delivery volume Academic (n = 33) Private (n = 14) ≤2500 7 2 2501–3500 6 2 3501–4500 7 3 4501–6000 7 3 >6000 6 4 Cesarean delivery rate (%) 30 (25–35) 33 (29–35) Labor neuraxial rate (%) 85 (81–90) 85 (80–91)

A total of 47 SOAP COE institutions contributed data. Data reported as median (IQR).

No significant difference in cesarean delivery rates, labor neuraxial analgesia rates, and distribution of the sizes of hospital between academic and private hospitals. Fisher exact test used to test distribution of hospital volume strata and hospital type (P = .98). Mann-Whitney U test used to test for differences between cesarean delivery rates and labor neuraxial analgesia rates (P = .22 and .66, respectively). Wilcoxon/Kruskal-Wallis test used for strata differences in cesarean delivery and neuraxial rates (P = .50 and .28, respectively).

Abbreviations: COE, Center of Excellence; IQR, interquartile range; SOAP, Society of Obstetric Anesthesia and Perinatology.

Staffing increased with delivery volume (Figure 1A). Higher delivery volumes were associated with increased SWI (Figure 1B). There is approximately a 3-fold difference between the lowest delivery volume strata (<2500 deliveries per year) and the highest delivery volume strata (>6000 deliveries per year) across all shifts. The best fit when modeling the relationship between the number of deliveries and the SWI was with a quadratic equation (Figure 1C). The SWIs were similar across all types of shifts and all increased at similar rates as delivery volume increased (Figure 1C).

Figure 1.:

Staffing and SWI analysis. A, Anesthesia provider staffing at each obstetric suite was correlated with the number of deliveries per year across all shifts (R 2 values = 0.34 [weekday], 0.29 [weeknight], and 0.27 [weekend]; P < .001 for all shifts). To calculate total anesthesia staffing units per shift, we weighted support staff (residents, fellows, and CRNAs) to be equivalent to 0.5 attending, and counted attending anesthesiologists as 1.0 staffing units. B, The SWI calculates hours worked per shift hour per obstetric anesthesia staff. To calculate total obstetric anesthesia provider staff per shift, we weighted support staff (residents, fellows, CRNAs) to be equivalent of 0.5 attending, and counted attending anesthesiologists as 1.0 staffing units. The Kruskal-Wallis test demonstrated significant differences among the strata, and the Wilcoxon test was used for individual comparisons; P values = .001 (weekday), <.001 (weeknight), and <.001 (weekend shifts). Pairwise comparisons found that weekday ≤2500 and >6000 delivery volume strata are significantly different from other strata on all shifts (P < .05) except for the weekday shifts; >6000 was not different from strata 4501–6000. Weeknight shift: 2501–3500 strata were statistically different than 4501–6000 (P < .05), and weekend shift: 2501–3500 strata were different compared to 4501–6000 and 3501–4500 delivery volume strata (P < .05). C, The SWI calculates hours worked per shift hour per obstetric anesthesia staff unit. The quadratic equation was the line of best fit. R 2 values for SWI per shifts were: 0.42 (weekday), 0.57 (weeknight), and 0.60 (weekend). To calculate total obstetric anesthesia provider staff per shift, we weighted support staff (residents, fellows, and CRNAs) to be equivalent of 0.5 attending, and counted attending anesthesiologists as 1.0 staffing units. CRNA indicates Certified Registered Nurse Anesthetist; SWI, Stanford Work Index.

Table 2. - SWI per Hour of Attending and Total Staff Providing Obstetric Anesthesia Care at SOAP COE Institutions Shift SWI Academic Nonacademic P value Mean SWI per attending on duty Weekday 0.43 (0.33–0.53) 0.48 (0.41–0.65) .13 Weeknight 0.30 (0.23–0.39) 0.34 (0.24–0.53) .32 Weekend 0.30 (0.24–0.40) 0.34 (0.24–0.53) .32 Mean SWI for all staffa Weekday 0.20 (0.15–0.26) 0.33 (0.29–0.42) <.001 Weeknight 0.19 (0.13–0.21) 0.23 (0.20–0.30) .009 Weekend 0.19 (0.13–0.21) 0.23 (0.19–0.30) .026

A total of 47 SOAP COE institutions contributed data. Data reported as median (IQR).

Mann-Whitney U test used to test for differences between academic to nonacademic SOAP COE practices.

The mean workload was measured using the SWI. See text for the calculation for this procedure and time-based calculation.

Abbreviations: COE, Center of Excellence; CRNA, Certified Registered Nurse Anesthetist; IQR, interquartile range; SOAP, Society of Obstetric Anesthesia and Perinatology; SWI, Stanford Work Index.

aTo calculate total obstetric anesthesia provider staff per shift, we weighted support staff (residents, fellows, and CRNAs) to be equivalent of 0.5 attending and added this to the number of attending anesthesiologists on duty.

Table 3. - Workload Comparisons Between SOAP COE Institutions Reporting Adequate Compared to Inadequate Staffing During Shifts SWI Adequate coverage Inadequate coverage P value Weekday SWI N = 41 N = 6 0.22 (0.16–0.32) 0.24 (0.18–0.29) 1.00 After Hours N = 33 N = 14  Weeknight SWI 0.19 (0.13–0.23) 0.20 (0.19–0.23) .49  Weekend SWI 0.19 (0.13–0.28) 0.20 (0.19–0.23) .58

A total of 47 SOAP COE institutions contributed data. Data reported as median (IQR).

The mean workload was measured by the SWI for total staff. See text for how this procedure and time-based SWI was calculated. To calculate total obstetric anesthesia provider staff per shift, we weighted support staff (residents, fellows, and CRNAs) to be equivalent of 0.5 attending and added this to the number of attending anesthesiologists on duty.

Abbreviations: COE, Center of Excellence; CRNA, Certified Registered Nurse Anesthetist; IQR, interquartile range; SOAP, Society of Obstetric Anesthesia and Perinatology; SWI, Stanford Work Index.

Table 4. - SOAP COE Institutions Room Utilization Presented by Delivery Volume Strata of the Hospital Annual delivery volume Utilization OR utilization LR utilization (delivery/OR/d) (delivery/LR/d) ≤2500 0.73 (0.57–0.88) 0.35 (0.29–0.38) 2501–3500 0.96 (0.73–1.19) 0.48 (0.39–0.52) 3501–4500 1.22 (1.08–1.24) 0.61 (0.54–0.64) 4501–6000 1.42 (1.14–1.53) 0.68 (0.62–0.84) >6000 1.52 (1.35–2.22) 0.69 (0.56–0.93) P value <.001 <.001

A total of 47 SOAP COE institutions contributed data. Data reported as median (IQR).

Differences between strata are statistically significant (P < .001 and .001 for the operating and labor rooms, respectively). Individual comparisons of the operating room strata show that the >6000 strata are significantly different than every other group (P < .05), and 4501–6000 strata were significantly different from the ≤2500 strata (P < .05). For the labor rooms, the >6000 and 4501–6000 strata were significantly different from ≤2500 and 2501–3500 (P < .05), and the ≤2500 group was significantly different from 3501 to 4500 (P < .05). Wilcoxon signed rank tests with corrections for multiple comparisons.

Abbreviations: COE, Center of Excellence; LR, labor room; OR, operating room; IQR, interquartile range; SOAP, Society of Obstetric Anesthesia and Perinatology; SWI, Stanford Work Index.

Figure 2.:

Labor and delivery room analysis. A, The number of ORs versus annual cesarean deliveries. B, The number of LRs versus annual vaginal deliveries. C, Total number of obstetric suite rooms (LRs, ORs, and triage rooms) versus total annual deliveries. R 2 of 0.240 (ORs), 0.479 (LRs), and 0.554 (total rooms); all relationships are significant (P < .001). The shaded area is the 95% confidence interval. LR indicates labor room; OR, operating room.

SWI of the attending anesthesiologists was not different between academic and nonacademic anesthesia groups (Table 2). Attending anesthesiologists spend 43% (academic) and 48% (nonacademic practices) during weekday shifts, and 30% (academic) and 34% (nonacademic practices) during night or weekend of their shift time on obstetric anesthesia interventions/clinical care. When total staff (attendings, residents, fellows, and CRNAs; with nonattending providers counting as 0.5 of a staff member) were factored into the SWI calculation, the SWI ratios were reduced more in academic versus nonacademic practices due to more total staff contributing to the procedure-based workload ratios in academic practices (Table 2). There was no statistical difference whether centers reported being adequately or inadequately staffed (Table 3). OR and LR utilization by delivery volume strata of the hospital is outlined in Table 4. Higher volume centers demonstrated greater LR and OR utilization rates. Hospitals with more cesarean deliveries had more ORs (Figure 2A), and centers with more vaginal deliveries had more LRs (Figure 2B). The tightest correlation was with the total number of rooms on the obstetric suite (ORs, LRs, or triage rooms) and the hospital volume (R2 = 0.55; Figure 2C).

DISCUSSION

The study provides obstetric anesthesia procedure-based staffing workloads among the SOAP COEs and compares the workload associated with different shifts and centers. Previous studies suggested using the number of obstetric anesthesia interventions and time for the related procedures instead of using a crude number of deliveries.7,10 We compared the workload using an index (SWI) that factors in the estimated time for the direct clinical procedures and the numbers of anesthesia providers,8 and standardized the index by workload per provider in each shift. The SWI appears to provide a better model of how COEs staff their services, for example, a model of the number of staff (Figure 1A) shows markedly higher staffing of the weekday shifts; however, Figure 1B, C shows that the COEs staff all 3 shifts in a similar manner when the SWI is used in the model.

Our analysis shows that the attending anesthesiologist’s procedure-based workload is similar in academic compared to nonacademic practices. When we apply the SWI for all providers (attending anesthesiologists plus support staff), we observe that the procedure-based workload is lower in academic compared to nonacademic SOAP COE practices. However, we caution comparisons between these practices, as we did not account for time for nonclinical work including teaching nor differences in high-risk/case mix volumes or other workload factors. Differences in clinical operation strategies, surgical durations, and other factors that influence operational efficiencies may account for less support staff working during after-hours shifts in nonacademic practices.

Providers’ procedure-based workload increased as annual delivery volume increased across the institutions. This suggests that hospitals with higher volumes of deliveries can streamline staff needs better due to a more consistent demand for anesthesia services. The SWI does flatten when the annual volume reaches 6000 deliveries. Our results find that for individual private practice anesthesiologists or faculty anesthesiologists, approximately one-third of their time was spent doing obstetric anesthesia procedures during the shift. SWI values have been shown to be greater for weekdays than after hours (weeknights and weekends).8 This is most likely due to scheduling biases as elective labor inductions, elective cesarean deliveries, and high-risk procedures are generally scheduled during daytime hours during the week. Scheduling impact on the workload during the weekday is acknowledged in other studies.4,5,11,12

Some institutions reported inadequate staffing support either consistently or only during the shift with high volume surges. There was no difference in procedure-based workload index between the institutions reporting adequate and inadequate staffing coverage. Possible reasons for similar workloads despite certain centers reporting that they feel their staffing is inadequate may relate to various staffing composition and backup systems among the institutions. Furthermore, self-reporting of inadequacy is inherently subjective. The estimation of the workload index using the numbers of providers is based on the ordinary situation and does not include the extra personnel for clinical volume surges.

There is significant variability across the centers in facility utilization rates. Our data suggest that hospitals with higher numbers of deliveries use ORs and LRs more efficiently than hospitals with lower numbers of deliveries. The correlation between the total number of obstetric suite rooms (including ORs, LRs, and triage rooms) and the annual delivery numbers was the strongest, suggesting this metric should be used to guide facility planning. Interestingly, OR versus cesarean delivery volume poorly correlated suggests OR redundancies required for unexpected emergencies, which is common clinical practice.

Our study has limitations. We relied on survey reporting, which has limitations including estimations and perception-based assessments, rather than using raw data from each institution. However, those surveyed were obstetric anesthesia leads who would be expected to have correct staffing numbers and other workload data including facility utilization rates. We did not determine scheduling differences and operational differences among institutions that can impact different shift procedure-based workload ratios and staffing. We did not include the time for maintenance of labor analgesia (medication adjustments/administrations), additional procedures (such as removal of retained placenta, repair of perineal laceration, or postpartum tubal ligation), postpartum follow-up visits, patient consultations unrelated to labor analgesia/cesarean anesthesia, multidisciplinary/patient safety labor and delivery rounds, and nonclinical responsibilities (including teaching, multidisciplinary meetings, and quality assurance reviews). These nonclinical components are a significant time expense at SOAP COE institutions that offer the highest levels of maternal care. The results show that on average, approximately a third of an obstetric anesthesiologist’s time on the obstetric unit is spent directly performing procedures (eg, placing epidurals for labor or anesthesia for cesarean deliveries). We did not measure time spent managing medically complicated patients, documentation, teaching, managing labor analgesia and postoperative patients, and so on. We did not measure the actual time taken to perform procedures at each institution, and the time spent for procedures was drawn from previous studies,7,9 reporting 90 minutes for cesarean deliveries and 45 minutes for labor analgesia procedures. We acknowledge that these times can be different between teaching hospitals and private practices.13 We also used the procedure-based workload distribution among weekday, weeknight, and weekend shifts, which was drawn from a previous single-center study.8 The relative number of scheduled and unscheduled cases may differ between institutions.

To calculate total obstetric anesthesia provider staff per shift, we weighted 1 attending anesthesiologist to be equivalent to 2 of the support staff (residents, fellows, and CRNAs). There is no validated staff conversion model, and we acknowledge that the use of a different weighting will impact workload ratios. We used a conversion ratio because the attending anesthesiologist and a trainee/CRNA cannot cover the same number of patients as 2 attendings. The ACGME requires a qualified MD/DO anesthesia provider supervise trainees, and SOAP COEs stipulate physician-led CRNA supervision. The conversion ratio was estimated 0.5 since 1 teaching anesthesiologist can supervise 2 concurrent cases with residents to be paid under the Medicare physician fee schedule.14 Obstetric anesthesia service needs to provide staff not only for the average daily demands but also promptly for critical and urgent needs. Providing staffing resources by a proactive11 instead of a reactive approach assures the safety and quality of obstetric anesthesia service. Having redundancies in staffing workload is essential to be available to handle emergencies and unexpected procedures. Staffing should not be determined solely based on the mean workload reported in this analysis; optimal staffing levels need to consider the availability of backup staffing options and other institution-specific resources. Finally, we expect SOAP COEs to provide a staffing model that emphasizes appropriate safety. However, we did not assess patient outcomes; therefore, these procedure-based workload ratios should not be considered necessary to optimize maternal outcomes.

In conclusion, the study provides procedure-based (ie, labor neuraxial analgesia and cesarean delivery anesthesia provision) staffing workloads among the SOAP COE institutions that can be used by other institutions with obstetric anesthesia services to guide discussions on obstetric anesthesia staffing planning. Our results highlight the importance of considering the procedure-based workload associated with different shifts and between academic and nonacademic centers. The results show that approximately one-third of an obstetric anesthesiologist’s workload is spent on performing procedures. We did not measure the other task anesthesiologists practice as peripartum physicians (eg, managing critically ill parturients, doing pre- and postprocedural evaluations, or performing emergent and unexpected procedures). Future studies should focus on measuring the time spent on these other tasks, and the effect different staffing models have on maternal and neonatal outcomes. Studies to determine the optimal staffing models to handle workload fluctuations are also required.

DISCLOSURES

Name: Mary Im, MD.

Contribution: This author helped with study conception and design, data analysis, interpretation, and drafting of the manuscript.

Conflicts of Interest: None.

Name: Edward T. Riley, MD.

Contribution: This author helped with study conception and design, data analysis, interpretation, and drafting of the manuscript.

Conflicts of Interest: None.

Name: Dan Hoang, MD.

Contribution: This author helped with study conduct and editing of the manuscript.

Conflicts of Interest: None.

Name: Grace Lim, MD, MS.

Conflicts of Interest: None.

Contribution: This author helped with data interpretation and editing of the manuscript.

Conflicts of Interest: G. Lim is a member of the Society of Obstetric Anesthesia and Perinatology Centers of Excellence Subcommittee.

Name: Mark Zakowski, MD.

Contribution: This author helped with data interpretation and editing of the manuscript.

Conflicts of Interest: M. Zakowski is a member of the Society of Obstetric Anesthesia and Perinatology Centers of Excellence Subcommittee.

Name: Brendan Carvalho, MBBCh, FRCA.

Contribution: This author helped with study conception and design, data interpretation, and drafting/editing of the manuscript.

Conflicts of Interest: B. Carvalho is Chair of the Society of Obstetric Anesthesia and Perinatology Centers of Excellence Subcommittee.

This manuscript was handled by: Jill M. Mhyre, MD.