Validation of a Simulation-Based Resuscitation Curriculum for Maternal Cardiac Arrest

Abstract

OBJECTIVE:

To assess the knowledge, skills, and self-efficacy of health care participants completing a simulation-based blended learning training curriculum on managing maternal medical emergencies and maternal cardiac arrest (Obstetric Life Support).

METHODS:

A formative assessment of the Obstetric Life Support curriculum was performed with a prehospital cohort comprising emergency medical services professionals and a hospital-based cohort comprising health care professionals who work primarily in hospital or urgent care settings and respond to maternal medical emergencies. The training consisted of self-guided precourse work and an instructor-led simulation course using a customized low-fidelity simulator. Baseline and postcourse assessments included multiple-choice cognitive test, self-efficacy questionnaire, and graded Megacode assessment of the team leader. Megacode scores and pass rates were analyzed descriptively. Pre– and post–self-confidence assessments were compared with an exact binomial test, and cognitive scores were compared with generalized linear mixed models.

RESULTS:

The training was offered to 88 participants between December 2019 and November 2021. Eighty-five participants consented to participation; 77 participants completed the training over eight sessions. At baseline, fewer than half of participants were able to achieve a passing score on the cognitive assessment as determined by the expert panel. After the course, mean cognitive assessment scores improved by 13 points, from 69.4% at baseline to 82.4% after the course (95% CI 10.9–15.1, P<.001). Megacode scores averaged 90.7±6.4%. The Megacode pass rate was 96.1%. There were significant improvements in participant self-efficacy, and the majority of participants (92.6%) agreed or strongly agreed that the course met its educational objectives.

CONCLUSION:

After completing a simulation-based blended learning program focused on managing maternal cardiac arrest using a customized low-fidelity simulator, most participants achieved a defensible passing Megacode score and significantly improved their knowledge, skills, and self-efficacy.

The maternal mortality rate in the United States is rising and ranks highest among developed countries,¹ with cardiovascular disease being one of the leading causes.² At the same time, more than 80% of pregnancy-related deaths are preventable with timely, appropriate care.^3-6 Despite rising pregnancy-related mortality rates and widely accepted evidence-based practices for maternal cardiac arrest response,^7,8 knowledge of resuscitation techniques among credentialed professionals, including obstetricians, is “variable and often inadequate.”^8-12

Medical professionals in the United States lack national credentialing standards for managing maternal cardiac arrest. In comparison, cognitive and technical skill mastery for pediatric cardiac arrest is reviewed and tested as a part of basic and advanced life support courses, despite a lower pediatric-related mortality ratio (13.7 deaths/100,000 [ages 5–14 years] for 2020) compared with the maternal mortality ratio (23.8 deaths/100,000 for 2020).^1,13

Subject matter experts in obstetrics, education, and resuscitation (Appendix 1, available online at http://links.lww.com/AOG/D370) convened to develop Obstetric Life Support, an evidence-based simulation training curriculum focused on preventing, recognizing, and treating maternal medical emergencies and maternal cardiac arrest (https://obls.org/why-obls). The study aimed to assess the knowledge, skills, and self-efficacy of prehospital and hospital-based participants who completed the Obstetric Life Support training curriculum.

METHODS

We conducted a formative assessment of the Obstetric Life Support course with interdisciplinary health care professionals with varying experience levels. The Obstetric Life Support learning objectives, format, and content were developed over 15 months through a rigorous process using subject matter experts (n=23).^14,15 Subject matter experts represented diverse disciplines, including anesthesiology, cardiology, critical care medicine, emergency medicine, emergency medical services (EMS), neonatology, maternal–fetal medicine, midwifery, neurology, nursing, obstetrics, resuscitation, and simulation; a patient representative who survived a maternal cardiac arrest; a biostatistician; and an expert in communication in health care. The experts practiced in the five geographic regions in the United States and in Canada. In addition, the expert panel had representatives who identified as female (n=9) or an underrepresented minority (n=4).

Obstetric Life Support includes two distinct interdisciplinary simulation training courses¹: prehospital Obstetric Life Support for community-based EMS health care professionals working in a field environment (eg, emergency medical technicians [EMTs], advanced EMTs, paramedics, flight paramedics, and firefighters) and² hospital-based Obstetric Life Support for health care professionals who work in hospital or urgent care settings and who respond to maternal medical emergencies (eg, physicians, midlevel practitioners, and nurses and technicians working in emergency medicine, labor and delivery, and the intensive care unit). Participants complete a blended learning curriculum consisting of 4 hours of self-guided precourse work and a 6-hour in-person instructor-led simulation course. Course materials are customized to prehospital and hospital contexts, and simulated maternal cardiac arrest scenarios, or Megacodes, were selected on the basis of their prevalence as a proximate cause of maternal collapse or maternal cardiac arrest as determined by our expert panel (Box 1).

Box 1. Maternal Medical Emergencies Covered During Obstetric Life Support Training.

Postpartum hemorrhage

Cardiomyopathy

Myocardial infarction

Sepsis

Amniotic fluid embolism

Venous thromboembolism

Acute respiratory distress syndrome

Magnesium sulfate toxicity

Opioid overdose

Neurologic emergencies (eg, seizures, stroke)

Hypertensive emergencies

Electrolyte abnormalities

Maternal cardiac arrest

Trauma (eg, motor vehicle collision, intimate partner violence, suicide)

Anesthetic complications

Precourse work consists of reading a printed manual reviewing normal pregnancy physiology; causes of severe maternal morbidity and mortality; recognition of abnormal pregnancy physiology and maternal deterioration; basic and advanced cardiac life support resuscitation modifications for maternal cardiac arrest; postresuscitation care; and optimizing teamwork, communication, and debriefing skills.¹⁶ Each chapter contains key points and review questions to assess knowledge retention, as well as several innovative illustrations and cognitive aids (eg, checklists, mnemonics, and algorithms).

The instructor-led, hospital-based training team includes up to six participants from varied disciplines; prehospital teams include up to four participants (Box 2). Participants take turns practicing different roles during simulated scenarios, including team leader, air-way management, chest compressor, and left uterine displacement. Hospital-based participants also practice code cart management and cardiopulmonary monitor roles. Participants stay in real-world roles (eg, nurses or advanced EMTs do not perform a resuscitative cesarean delivery).

Box 2. Team Composition of Obstetric Life Support Pilot Participants for Hospital-Based and Prehospital Courses.

Hospital-Based Teams

Required*

Emergency medical responder: physician or midlevel practitioner from LD, EM, FP, or ICU, and

Emergency medical responder: nurse from LD, EM, FP, or ICU, and

Surgeon (obstetrician or general, vascular, or trauma surgery)

Optional

Anesthesiologist or certified registered nurse anesthetist, or

GME learner or a nurse or nursing student (EM, FP, ICU, LD)

Prehospital Teams

Required†

EMTs, AEMTs, paramedics, or flight paramedics

Optional

Firefighters

LD, labor and delivery; EM, emergency medicine; FP, family practice; ICU, intensive care unit; GME, graduate medical education; EMT, emergency medical technician; AEMT, advanced emergency medical technician.

^*Hospital-based teams were required to have at least one emergency medical responder physician or midlevel practitioner, one nurse, and a surgeon on the interdisciplinary team.

^†Prehospital teams were required to have at least one EMT, AEMT, paramedic, or flight paramedic on the team.

Team sizes mimic real-world scenarios. For example, most ambulance teams comprise two participants who begin assessing and managing the pregnant patient while calling in additional teams to assist. Each participant trains as a team leader using the Obstetric Life Support cognitive aids to lead an EMS or rapid response for a maternal medical emergency.

The hospital-based training uses a ratio of two instructors to six participants, and the prehospital-based training uses a ratio of two instructors to four participants. The instructor-to-student ratios were selected to maximize the ability of instructors to detect performance errors with increased group size¹⁷ and to reduce bias on Megacode assessments by averaging the scores from two instructors.

Obstetric Life Support instructors lead participants through different training scenarios using Rapid Cycle Deliberate Practice,¹⁸ an instructional methodology to teach resuscitation and team skills. Each participant practices hands-on techniques with a customized, low-fidelity simulator with real-time feedback on critical maternal cardiac arrest skills.¹⁹ In addition, Obstetric Life Support instructors guide team leaders through postarrest management and a critical event debriefing of the team. Finally, all participants receive formal, structured, and empathetic communication training and practice sharing difficult news with a simulated family member.

At the end of the instructor-led training session, two instructors independently assess each participant as a team leader during a simulated Megacode. The team leader remains in that role throughout the Megacode scenario and can take suggestions from other team members. Participants who fail the course are invited to repeat the course after completion of the pilot study.

The Baylor College of Medicine IRB approved this study (H-45625). For the pilot course, participants were recruited for interdisciplinary teams on the basis of their specialty (Box 2). One team member had to have at least 5 years on the job to reflect minimum standards for transporting critically ill patients in the field²⁰ and the composition of a real-world hospital-based rapid response team. We planned to test the curriculum until the majority of participants achieved a passing score on their Megacode assessment. Eight pilot sessions were initially planned, four hospital-based and four prehospital, with a midway evaluation of the data by the subject matter experts. Two teams were trained at each session. If necessary, additional sessions would be added to achieve mastery of resuscitation skills.

All pilot-training sessions were videotaped to assess changes in participant competencies and to gain additional insight into the training methods and tools. In addition, all participants and instructors provided written and verbal feedback on their experience with the course. The feedback was consolidated and reviewed by the expert panel and led to curriculum and simulator modifications. Examples of course modifications included grammatical and content corrections to the Obstetric Life Support manual, the addition of scene survey data for prehospital scenarios, the format of debriefing role play from “reflective” to “reflective checklist” using the Megacode evaluation checklist, improvement in instructor-led case flow, and changes of the size of the green light indicator on the simulator to reflect successful left uterine displacement. The course format remained the same throughout the pilot sessions.

Instructors of the first pilot session were study investigators and subject matter experts who helped to develop the course. Instructors were subsequently identified from pilot participants who passed the course and achieved a score of 85% or higher on the cognitive assessment and a recommendation from at least one instructor. Eligible instructors were required to complete a virtual 4-hour Train-the-Trainer course concurrently piloted during this period. In addition, they were required to complete the Debriefing Assessment for Simulation in Healthcare^© (Center for Medical Simulation, Boston, MA) course or to provide proof of equivalent training. All instructors were proctored by an experienced Obstetric Life Support instructor during their first time instructing the course.

Baseline and postcourse assessments were developed and validated. Multiple-choice cognitive tests were created for three participant categories: hospital-based, prehospital advanced (eg, advanced EMTs or paramedics), and prehospital basic (eg, EMTs). The hospital-based assessment was validated through a two-round Angoff process by subject matter experts. The prehospital-based assessments were validated through a similar process. The unmodified Angoff cut scores were 79.5% for hospital-based and prehospital–advanced participants and 72.9% for prehospital–basic participants. The 54-item self-efficacy questionnaire was modified from an existing survey tool, the World Health Organization’s Personnel, Infrastructure, Procedures, Equipment, and Supplies survey. Self-efficacy was assessed in four skill categories (Appendix 2, available online at http://links.lww.com/AOG/D370)-clinical, procedural, knowledge, and communication–with a five-point Likert scale. Anchors range from “1: not at all confident” to “5: very confident.” Finally, a validated 40-item detailed scoring checklist, or Megacode checklist, was developed by the expert panel and included items on resuscitation, post–return of spontaneous resuscitation care, team leader performance, and communication and teamwork. A reference standard for the Megacode checklists was developed by experts who reviewed and rated the checklist while watching three different training videos. A passing score for the Megacode assessment was derived by a modified Angoff method with the expert panel. The provisional panel-recommended cut score for the Megacode assessment (both prehospital and hospital-based) was an average score higher than 74% with no critical failures, defined as two or more critical elements scored 0. Critical elements were derived by the expert panel according to the potential for harm if these steps were not performed in a real-life scenario and included the following: recognized cardiac arrest in a timely manner; initiated high-quality chest compressions; established and maintained effective ventilation; performed and maintained left uterine displacement throughout resuscitation; and defibrillated if indicated. For hospital-based assessment, the critical elements also included the following: completed resuscitative cesarean delivery by 5 minutes at the site of the arrest.

After improvements were made in the course that were based on participant feedback and pilot data from the early session (sessions 1–4), the expert panel finalized the provisional Megacode passing score at higher than 74%; however, they modified the critical failure definition as missing no critical elements during a maternal resuscitation, recognizing the vital importance of these steps in effective maternal resuscitation. In addition, a Beuk adjustment of the cut scores for the cognitive assessment based on normative pilot data resulted in a modified cut score of 70% or higher for the hospital-based participants and 67% or higher for prehospital participants. The overall pass rate was defined as passing the cognitive and Megacode assessments after the training. Participants provided written and verbal feedback on the course and simulator.

Self-efficacy scores were compared with the exact binomial test. Precognitive and postcognitive session scores were compared with general linear mixed models and reported as a mean change (95% CI) with P values. Megacode scores were reported as mean±SD percent score and the number of critical fails. Pass rates were reported as percentages. A general linear mixed model and comparison of proportions tested the research hypothesis that the effect of the educational intervention on test scores and passing rates depended on the session, early (sessions 1–4) compared with late (sessions 5–8).

RESULTS

The Obstetric Life Support curriculum was evaluated over eight sessions between December 2019 and November 2021. Four sessions were conducted for hospital-based and four for prehospital participants. Pilot testing took place at the CHRISTUS Simulation Center at CHRISTUS Children’s, Texas; the University of Connecticut Health Simulation Center in Farmington, Connecticut; and the Healthcare Innovation and Sciences Centre in Spring Branch, Texas.

We approached 88 prehospital and hospital-based participants from eligible disciplines to participate in pilot training (Fig. 1). Eighty-five participants consented for participation; 84 completed the online baseline cognitive assessments, and 71 completed the online baseline confidence questionnaires. Seventy-seven participants completed Obstetric Life Support training and the postcognitive assessment, including one participant who did not complete the baseline cognitive assessment. The early session included 33 participants (ie, 17 prehospital and 16 hospital-based), and the late session included 44 participants (ie, 17 prehospital, and 27 hospital-based). Table 1 contains the baseline demographics of the participants who completed Obstetric Life Support training. The average age of the participants was 43±13 years. Most participants practiced in Texas and Connecticut, where the pilot sessions took place. More than half of those who completed the course were hospital-based participants (eg, obstetricians, nurses, anesthesiologists, intensivists, emergency medicine physicians, midwives, and technicians). Most prehospital participants had advanced certification (eg, advanced EMT, paramedic, flight paramedic). Eighteen participants were from underrepresented minority groups. Of the 77 participants who completed the course, 60 participants had complete data on the pre–confidence and post–confidence assessments. Missing post–confidence assessments came only from participants in the first and fifth sessions.

Fig. 1.

Obstetric Life Support (OBLS) pilot study flow diagram. *For example, illnesses, called in to cover shifts.

Table 1.

Maternal Medical Emergencies Covered during OBLS training.

Postpartum hemorrhage

Cardiomyopathy

Myocardial infarction

Sepsis

Amniotic fluid embolism

Venous thromboembolism

Acute respiratory distress syndrome

Magnesium sulfate toxicity

Opioid overdose

Neurologic emergencies (e.g. seizures, stroke)

Hypertensive emergencies

Electrolyte abnormalities

Maternal cardiac arrest

Trauma (e.g. motor vehicle collision, intimate partner violence, suicide)

Anesthetic complications

At baseline, fewer than half of the participants passed the cognitive assessment, with more hospital-based participants achieving a passing score than prehospital participants (63.0% vs 33.3%, respectively). After Obstetric Life Support training, cognitive assessment scores increased by 13% (95% CI 10.9–15.1, P<.001), from 69.4% before taking the course to 82.4% after the course (Fig. 2). We found no difference in cognitive assessment scores between the early session (mean score increased from 68.5% to 81.4%, d=12.9, P<.001) and the late session (mean score increased from 70.1% to 83.2%, d=13.0, P<.001) (Appendix 3, available online at http://links.lww.com/AOG/D370).

Figure 2.

Pre and post-intervention cognitive assessment scores using 85 pre- and 77 post-data, which includes 76 pairs (i.e., both pre and post), 8 with pre only and 1 with post only.

Obstetric Life Support Megacode scores averaged 90.766.4% with a range of 70.3% to 100%. There were three Megacode failures; two in-hospital participants, both labor and delivery nurses, did not achieve an average Megacode score higher than 74%, and one prehospital–advanced participant received a score of 0 on two critical elements. This resulted in a very high Megacode pass rate of 96.1% for the entire cohort. The overall course pass rate (defined as passing both the cognitive and Megacode assessments) was 90.1%. Of the seven overall course failures, four failures resulted from participants not passing the cognitive assessment, and three failures resulted from participants not passing the Megacode assessment. Summary statistics for the cognitive and Megacode scores and overall pass rates for the prehospital and hospital-based participants are provided in Table 2.

Table 2.

Team composition of OBLS pilot participants for hospital-based and prehospital courses.

Hospital-based teams	Prehospital teams
Required*: Emergency medical responder - physician or advanced practice provider from LD, EM, FP, or ICU, and Emergency medical responder - nurse from LD, EM, FP, or ICU, and Surgeon (obstetrician or general/vascular/trauma surgery) Optional: Anesthesiologist or certified registered nurse anesthetist, or GME learner or a nurse/nursing student (EM, FP, ICU, LD)	Required†: EMT, AEMT, paramedics or fight paramedics Optional: Firefighters

EM, emergency medicine; FP, family practice; ICU, intensive care unit; GME, graduate medical education; LD, labor and delivery; EMTs, emergency medical technicians; AEMTs, advanced emergency medical technicians.

^*Hospital-based teams were required to have at least one emergency medical responder physician or advance practice provider, one nurse and a surgeon on the interdisciplinary team.

^†Prehospital teams were required to have at least one EMT, AEMT, paramedic or flight paramedic on the team.

The higher number of overall passing scores during the late compared with early session was attributable mainly to significant improvements in the Megacode passing scores, despite the use of stricter cutoff criteria during the late session (Table 3). Cognitive assessment pass rates remained unchanged. Therefore, course improvements resulted in a higher overall course pass rate attributable to improved participant performance on the Megacode assessment and were not affected by the Beuk adjustment.

Table 3.

Demographics of OBLS participants who completed training.

		Hospital- based	Prehospital	Total
Number		43 (55.8)	34 (44.2)	77 (100)
Average Age		45.4±11.2	42.3±13.0	43±13
Underrepresented minority		10 (23.3)	8 (23.5)	18 (23.4)
Gender
	Female	35 (81.4)	8 (23.5)	43 (55.8)
	Male	5 (11.6)	29 (85.3)	34 (44.2)
Category	Prehospital-advanced		30 (88.2)
	Prehospital-basic		7 (20.6)
	Physician*	15 (34.9)
	Advanced Practice Provider†	4 (9.3)
	Nurse‡	13 (30.2)
	GME learner§	6 (14.0)
	Other∥	2 (4.7)
Experience > 5 years		24 (55.8)	24 (70.6)	48 (62.3)
Geographic area of practice
	Northeast	25 (58.1)	1 (2.9)	26 (33.8)
	Southwest	12 (28.9)	33 (97.0)	45 (58.4)
	West	3 (6.9)	1 (2.9)	4 (5.2)
	Southeast	0	0	0
	Midwest	0	2 (5.9)	2 (2.6)
Qualified as instructor		30 (69.8)	14 (41.2)	44 (57.1)

GME, graduate medical education

Data are mean±SD or n (%)

^*Physicians consist of obstetrician gynecologists (n=4), maternal fetal medicine specialists (n=4), critical care physician (n=3), obstetric hospitalist (n=1), trauma surgeon (n=1), emergency medicine physician (n=1), and anesthesiologist (n=1)

^†Advanced practice providers include certified registered nurse anesthetist (n=2), nurse practitioner (n=1) and midwife (n=1)

^‡Nurses consist of Labor and delivery nurses (n=8), critical care nurse (n=2), emergency medicine nurse (n=2), and women’s health ambulatory clinic nurse (n=1)

^§GME learners consist of obstetrics and gynecology residents (n=3) and maternal fetal medicine fellows (n=3)

^∥“Other” consists of emergency medicine and intensive care unit medical technicians (n=2)

There were significant improvements in participants’ self-efficacy in all areas except confidence in “managing maternal sepsis” and “when to perform cardiac pacing” (Appendix 4, available online at http://links.lww.com/AOG/D370). Items with the highest postcourse self-efficacy included critical items in resuscitation (eg, when and how to call a code, how to perform high-quality chest compressions, adequate ventilation and left uterine displacement); items with the lowest postcourse self-efficacy included knowledge regarding advanced resuscitation skills (eg, initiation of extracorporeal membrane oxygenation, confidence in ability to request organ donation) (Appendix 5, available online at http://links.lww.com/AOG/D370). Most participants (92.6%) agreed or strongly agreed that the course met its educational objectives (Fig. 3). Forty-four participants (57%) met the eligibility criteria to become instructors. Twenty participants completed the Obstetric Life Support Train-the-Trainer course, including Debriefing Assessment for Simulation in Healthcare^© training, and taught at least one pilot course (Fig. 1).

Fig. 3.

Obstetric Life Support participant satisfaction scores.

DISCUSSION

This pilot study describes the validation of a simulation-based blended learning curriculum to teach the recognition, evaluation, and treatment of maternal medical emergencies and cardiac arrest. This study highlights that gap in knowledge for first responders and hospital-based professionals, with just less than 50% of the entire cohort being able to pass the cognitive assessment at baseline. The improvements in participants’ knowledge, skills, and self-efficacy, as well as the high satisfaction with the training, demonstrate that Obstetric Life Support is an effective course for promoting the participant-level achievement of its expert-derived learning objectives.

A rigorous process was used to develop the curriculum with the guidance of a diverse team of researchers, clinicians, simulation specialists, and a survivor of maternal cardiac arrest.^14,15 The learning objectives incorporate findings from a formal needs assessment of the target audience,²¹ a systematic review of the current evidence, and existing guidelines.^7,8 The in-person training uses best practices in resuscitation education delivery, including Rapid Cycle Deliberate Practice, effective debriefing, contextualization of content, and facilitation of the development of teamwork skills.²² Rapid Cycle Deliberate Practice is a simulation-based instructional strategy that effectively teaches resuscitation skills^23,24 by allowing the participants to receive expert-derived solutions to errors during a simulated event to encourage rapid acquisition of necessary skills.²⁵ The significant improvements in Megacode pass rates and the high satisfaction with the training reinforce Rapid Cycle Deliberate Practice as an effective tool to teach resuscitation skills. In addition, the iterative development and improvement of a custom-designed, mobile, low-cost simulator was valuable for teaching skills in maternal resuscitation and received positive feedback from participants. The low-fidelity simulator is essential for scalable dissemination of the training curriculum, especially in low-resource settings.

The Obstetric Life Support curriculum was designed for an interprofessional team most likely involved in responding to a maternal medical emergency. Interdisciplinary simulation-based education has many advantages over traditional discipline-specific training.^26,27 Interdisciplinary simulation-based education improves interdisciplinary cooperation, allowing participants to learn about clinical management while learning about, with, and from each other.²⁶ In addition, interdisciplinary simulation-based education increases realism and participant self-efficacy in managing complex situations compared with discipline-specific training by mimicking the composition of the interprofessional team responding to a real-life emergency.²⁷

The strengths of this study include the diversity of training, experience, and medical specialties represented among the experts who developed the course and among the pilot participants who provided valuable feedback over multiple iterative sessions. Another study strength was the number of participants who qualified for and completed the Obstetric Life Support Train-the-Trainer course and served as instructors for the subsequent pilot sessions. The ability to train effective instructors demonstrates that the teachings of Obstetric Life Support are reproducible, making future scalability of the training more realistic.

There are important limitations of this study. We did not assess translational outcomes, such as improvements in response to real-life maternal medical emergencies in Obstetric Life Support–trained teams. In addition, we did not assess long-term retention of knowledge, skills, and self-efficacy to establish the appropriate time interval for retraining. Although a future goal is to demonstrate validity in real-life contexts, long-term retention is being assessed as part of a randomized trial of Obstetric Life Support currently underway. Lastly, because the pilot study did not have equal representation from participants in all contexts and from every geographic area of the United States, the Obstetric Life Support training may require modification to be relevant to these contexts. Future work in different contexts or with different learners will help inform the changes to the curriculum for the most effective implementation and dissemination of Obstetric Life Support.

In conclusion, the improvements in participants’ knowledge, skills, and self-efficacy and mastery of resuscitation response to maternal cardiac arrest after training demonstrate the effectiveness of Obstetric Life Support. In addition, these results highlight the importance of using a rigorous development process and best practices in simulation-based learning programs. Further studies are needed to develop and evaluate implementation strategies for Obstetric Life Support to promote a national scale-up of this important health care innovation.

Figure 4.

Self-efficacy scores.

Table 4.

OBLS assessment results and pass rates for the entire cohort and by group.

		Cognitive assessment results*			Cognitive Pass Rate†	Megacode assessment results‡		Megacode Pass Rate§	Overall Course Pass Rate∥
		N	Score	#Fails	%	Score	#Fails	%	%
Overall	Pre	85 (100)	69.4 ± 12	43	49.4
	Post	77 (100)	82.4 ± 12	4	94.8	90.7±6.4	3	96.1	90.1
Prehospital Advanced	Pre	35 (41.2)	66 ± 11	22	37.1
	Post	31 (40.3)	78 ± 11	3	90.3	89±4.3	1	96.8	96.8
Prehospital-Basic	Pre	4 (4.7)	54 ± 7.8	4	0
	Post	3 (3.9)	68 ± 2.0	0	100	89±3.7	0	100	100
Hospital-based	Pre	46 (54.1)	74 ± 12	17	63.0
	Post	43 (55.8)	87 ± 11	1	97.7	91.9±7.5	2	95.3	93.0

^*Cognitive mean scores available for pre- and post-training

^†Cognitive pass rates available for pre- and post-training

^‡Megacode mean sores available for post-training only

^§Megacode pass rate available or post-training only

^∥Overall course pass rate available for post-training only

Table 5.

Comparison of pass rates using final expert-derived passing scores for participants who completed the OBLS course during early and late sessions.

	Early Session (Sessions 1-4)						Late Session (Sessions 5-8)
	N	Cognitive assessment results	Cognitive Pass Rate	Megacode assessment results	Megacode Pass Rate	Overall Course Pass Rate	N	Cognitive assessment results	Cognitive Pass Rate	Megacode assessment results	Megacode Pass Rate	Overall Course Pass Rate
Prehospital Basic	2	69.5±2.1	100	88.5±5.0	100	100	1	67.0	100	88.1	100	100
Prehospital Advanced	15	74±9.4	73.3	87.6±5.7	33.3	35.7	16	79.0±11.9	87.5	91.0±4.0	100	87.5
Hospital-based	16	89.0±9.5	93.8	89.1±9.1	87.5	81.3	27	87.0±11.3	96.3	94.3±5.9	100	96.3
All cohorts	33	81.4±11.7	84.8	82.0±11.7	78.8	66.7	44	83.2±12.0*	93.2*	93.5±5.1†	100†	93.2

Data are presented as number (N), mean±SD and pass rate (%)

^*p-values were not different for cognitive assessment results (p = 0.5123) and cognitive pass rates (p = 0.2353) between early and late sessions

^†p-values were significant for Megacode assessment results (p < 0.0001) and Megacode pass rates (p=0.0012) between early and late sessions