Evaluation of an adaptive, multimodal intervention to reduce postoperative infections following cesarean delivery in Ethiopia: study protocol of the CLEAN-CS cluster-randomized stepped wedge interventional trial

Trials, Volume 23, No. 1, Year 2022

Interprétation

Background: We previously developed and pilot tested Clean Cut, a program to prevent postoperative infections by improving compliance with the WHO Surgical Safety Checklist (SSC) and strengthening adherence to infection control practices. This protocol describes the CheckList Expansion for Antisepsis and iNfection Control in Cesarean Section (CLEAN–CS) trial evaluating our program’s ability to reduce infections following CS and other obstetric and gynecological operations in Ethiopia. Methods/design: CLEAN-CS is a cluster-randomized stepped wedge interventional trial with five clusters (two hospitals per cluster). It aims to assess the impact of Clean Cut on six critical perioperative infection prevention standards including antiseptic practices, antibiotic administration, and routine SCC use. The trial involves baseline data collection followed by Clean Cut training and implementation in each cluster in randomized order. The intervention consists of (1) modifying and implementing the SSC to fit local practices, (2) process mapping each standard, (3) coupling data and processes with site-specific action plans for improvement, and (4) targeted training focused on process gaps. The primary outcome is 30-day CS infection rates; secondary outcomes include other patient-level complications and compliance with standards. Assuming baseline SSI incidence of 12%, an effect size of 25% absolute reduction, and the ability to recruit 80–90 patients per cluster per month, we require a sample of 8100 patients for significance. We will report our study according to CONSORT. Discussion: A cluster-randomized stepped wedge design is well-suited for evaluating this type of surgical safety program. The targeted standards are not in doubt, yet compliance is frequently difficult. Solutions are available and may be recognized by individuals, but teams dedicated to improvement are often lacking. Clean Cut was successfully piloted but requires a more rigorous methodological assessment. We seek to understand the qualities, characteristics, and resources needed to implement the program, the magnitude of effect on processes and outcomes, and to what degree it can enhance compliance with care standards. Challenges include a fraught social and political environment, pandemic travel restrictions, and a limited budget. Trial registration: ClinicalTrials.gov NCT04812522 (registered on March 23, 2021); Pan-African Clinical Trials Registry PACTR202108717887402 (registered on August 24, 2021). This trial is a cluster-randomized stepped wedge interventional trial testing the superiority of the Clean Cut program over data collection alone as a means of improving perioperative outcomes following CS. It divides ten facilities into five separate clusters and randomizes the sequence of program intervention over a period of 10 months, with a lead-in baseline period and a post-implementation follow-up period for a total trial duration of 18 months. The overall objective of the CLEAN-CS study is to assess the impact of a multimodal intervention, called Clean Cut, on surgical infections following CS. The primary and secondary aims are as follows: These aims will be captured through collection of the following patient and facility endpoints: These aims and endpoints are listed in Table Table1,1, along with data sources (described in more detail below) and whether the specific data points are considered part of quality improvement or research. Aims and endpoints Clean Cut is a program developed by Lifebox that focuses on improving compliance with six critical perioperative infection prevention standards: (1) appropriate skin preparation of the surgeon’s hands and the surgical site; (2) maintenance of the sterile field by ensuring the integrity and sterility of surgical gowns, drapes, and gloves; (3) confirmation of instrument sterility; (4) appropriate antibiotic administration; (5) complete swab counts; and (6) routine use of the WHO Surgical Safety Checklist Clean Cut is implemented in five phases: For this study, Clean Cut has been adapted specifically for obstetric and gynecological operations and will be implemented in ten maternity hospitals/departments in Ethiopia in order to reduce infections and other complications for women undergoing CS and other obstetric and gynecologic operations. Clean Cut has been designed with sustainability at its core – the strategy emphasizes teamwork and collective leadership to identify and address critical gaps in perioperative safety processes. It develops sustainable facility-level and operating team management practices and embeds critical routines into the workflow of surgical teams. In addition, it imparts a classic quality improvement methodology that, anecdotally, has spread to other departments and services in hospitals where we have worked. As outlined above, a core function of the program involves supporting hospital teams to develop and commit to a sustainability plan to monitor and ensure continued adherence to best practices, something frequently agreed to but not necessarily achieved. Finally, it is “adaptive” as there are numerous opportunities for failures in perioperative infection control and prevention practices; Clean Cut leverages local knowledge and team dynamics to improve compliance through an iterative process that is developed and executed by the local teams. Solutions may be unique to a specific facility and may not be applicable, or replicable, in a different setting. The strategy and approach of Clean Cut, however, are based on classic quality improvement techniques, are replicable, and are introduced and implemented in each facility in a similar way. Lifebox has partnered with the Ethiopian Society of Obstetricians and Gynecologists (ESOG) and Ariadne Labs in Boston, USA, to implement the CLEAN-CS trial. The study will test core elements of the improvement aspects of Clean Cut, namely phases 3, 4, and 5 outlined above, by randomly assigning the start of Phase 3 as part of the stepped wedge intervention testing strategy. Lifebox developed Clean Cut as a multimodal, adaptive, checklist-based improvement program following an extensive consultation with providers and practitioners from around the world who identified surgical infections as a major source of preventable surgical morbidity and mortality. The program was introduced in Ethiopia, initially in conjunction with the Surgical Society of Ethiopia and then through partnerships with the FMOH [11]. Clean Cut was first evaluated at Jimma University Specialized Hospital in 2016, with the pilot work extending to five initial hospitals where we prospectively collected compliance data from 2213 operations (374 during baseline assessment and 1839 following implementation of process improvements) in 2202 patients with follow up in 2159 patients (98.0%). At baseline, perioperative teams complied with an average of 2.9 of the six critical perioperative infection prevention standards; following process improvement changes, compliance rose to 4.5 (p<0.001). The relative risk of surgical infections following Clean Cut implementation was 0.65 (95% CI 0.43–0.99; p=0.043). Improved compliance with standards reduced the risk of postoperative infection by 46% (RR 0.54 for adherence score 3–6 vs 0–2; 95% CI 0.30-0.97; p=0.038). Since then the program has been implemented in 11 hospitals and, as of late 2021, has benefitted an estimated 80,000 patients. In our preliminary, unpublished analyses of 8 hospitals with completed data, we have prospectively collected compliance data from 2905 operations, of which 1133 (39%) were CS or hysterectomy, with an average monthly volume of 27 obstetric operations per facility. Of the total, 2692 patients (92.7%) had outcomes follow-up, with 1075 (40%) being obstetric patients. After program implementation, significant improvements were seen in each of the infection prevention areas: instrument decontamination, gown and drape integrity, surgical skin antisepsis, gauze counting, antibiotic administration, and use of the surgical safety checklist. At baseline, unadjusted SSI rate was 11.3% overall and 12.4% for the obstetric group; after Clean Cut implementation, the SSI rate fell to 6.2% overall and 6.6% in the obstetric group (a 47% decrease). Some studies have described the impact of quality improvement (QI) programs on SSI reduction in low-resource settings, and the use of process measures instead of or in addition to patient outcomes is important to identify actionable interventions [14]. However, in a literature review of 354 studies on surgical QI in LMICs, only 11% used process measures as a metric [15]. The African Surgical Unit-based Safety Program, a multimodal surgical infection prevention program implemented in five hospitals in Sub-Saharan Africa, focused on improving perioperative process measures including preoperative bathing, hair removal, skin and hand preparation, antibiotic administration, and OR traffic, showed significant improvements and an associated reduction in SSI from 8.0 to 3.8% [16]. This intervention leveraged local providers to implement evidence-based guidelines. However, it did not focus on checklist use, sponge counts, instrument sterility, or gown and drape reprocessing, all of which have been noted as major gaps in our setting [17]. The Clean Cut program has demonstrated significantly improved compliance with critical infection prevention standards and reduced postoperative infections without requiring major investments in new infrastructure or resources. Like similar programs, uncontrolled aspects of implementation limit specific attribution to Clean Cut itself. Many factors may have influenced success, including staff characteristics, prior strong QI programs, and engaged hospital administration. Our sample size was small, particularly during the baseline period, which limits robust comparisons of outcomes between groups before and after implementation. A larger, more rigorous trial such as a cluster-randomized stepped wedge design will validate this approach and determine whether Clean Cut can be replicated and scaled in different hospitals, countries, and settings. Study sites will consist of ten hospitals in Ethiopia that provide maternal surgical services. Five of these hospitals will be university teaching or referral hospitals (aka specialized or referral hospitals), and five will be regional, district, or smaller community hospitals (aka general or regional hospitals). These hospitals have been selected by the Ethiopian Society of Obstetricians and Gynecologists (ESOG) based on the following factors: Any patient of any age undergoing obstetric and gynecologic surgery at any time in one of the targeted operating theatres is eligible for inclusion; there will be no exclusion criteria. As obstetric and gynecologic operations are typically undertaken in separate, dedicated operating theatres, we will focus our prospective observations on patients admitted to these theatres. Any patient undergoing surgery at any time in one of the targeted operating theatres is eligible for inclusion; there will be no exclusion criteria. Enrollment will occur at the time of observation and will include various times (day and night) and days of the week (weekdays and weekends). As the standards being implemented are not in dispute and are considered critical for ensuring antisepsis and sterility, patient informed consent will not be obtained. While our focus will be on cesarean delivery, any obstetric or gynecological operation will be included, with the inclusion of other operations in these populations (such as appendectomy for appendicitis that is found incidentally or misdiagnosed as ovarian torsion, for example). There will be no age range limit. We will also interview key hospital personnel to understand the context of each facility, its experience with quality improvement initiatives, and the perceived importance of this work to patient safety, patient care, and the work routine. These interviewees will be recruited from the implementation teams involved in Clean Cut. We will also administer surveys in conjunction with Ariadne Labs/MKA, a partner in this work, to understand the context, perceptions, and priorities of the various institutions, and how the CLEAN-CS team can support implementation at the time of intervention (phases 3, 4, and 5) Each selected hospital will be distributed into two groups based on the type of facility: university teaching and referral hospitals in one group, and regional, district, and community hospitals in another. One hospital in each group will be paired to create five clusters; these pairings will be purposive as district and referral hospitals in Ethiopia typically have long-standing relationships which will facilitate implementation at the cluster level and prevent inadvertent crossover of the intervention prior to randomization. The sequence of implementation for each of the five clusters will be established by the Lifebox team using computer-based randomization (https://www.randomizer.org/). Sites will be informed of the timing of their intervention no earlier than one month prior to implementation training. The impact of the CS-tailored Clean Cut program will be tested through a cluster-randomized stepped-wedge study design in five clusters (10 hospitals) over the course of 18 months followed by complete data analysis and interpretation. The selected hospitals will start by identifying a clinical lead and data collectors who will collect inpatient and outpatient outcomes on all patients undergoing CS prior to implementation as well as compliance information on the six standards of Clean Cut (phases 1 and 2 of Clean Cut outlined above). Following a lead-in period of 5 months, one cluster at a time will begin implementing phase 3 of Clean Cut at 2-month intervals over the course of 10 months. The intervention is initiated by assembling a multidisciplinary improvement team, undergoing team training on WHO Surgical Safety Checklist use and implementation, and reviewing compliance information about intraoperative safety practices. The initiation of phase 3 will also involve creating facility-specific process maps of each critical perioperative practice. Once these process maps and compliance data are complete, usually after 2–4 weeks, the team establishes an adaptive, facility-driven improvement plan based on process gaps and barriers to best practice (phase 4). This is initially facilitated by Lifebox, but may also involve teams that have already implemented Clean Cut in their facilities. They identify improvement opportunities by reviewing process mapping data, compliance shortfalls, and their bespoke surgical safety checklist, and craft an improvement plan aimed at removing barriers to compliance. The team will review their hospital-specific compliance data and patient outcomes on a monthly basis for the remainder of the study. The study will conclude at each site after 18 months of data collection The program is adaptive and relies on a multimodal approach to improving compliance with critical safety practices. Initial trainings take place at the outset of the work to coordinate data collector training and orient some of the key team members to the work (phases 1 and 2). The first Clean Cut implementation training (phase 3) will occur following at least 3 months of baseline data collection and will be introduced based on randomization. As noted above, this will specifically include a multidisciplinary perioperative improvement team to create facility-specific process maps of each critical perioperative practice. Once these process maps are complete, usually after 2–4 weeks, the team establishes an adaptive, facility-driven improvement plan based on process gaps and barriers to best practice (phases 4 and 5). At study initiation and over the course of implementation, the Atlas/MKA Context Assessment Tools will be used to understand facility-level readiness for implementing quality improvement programs in surgery [18]. The tools assess the following readiness domains: Commitment and Motivation, Ability to Implement, Internal Culture, Clinical Team Functionality, and Knowledge and Ability to do the Practice Change using Likert scale, self-administered surveys, and a discussion guide for facilitated readiness conversations. The intent is to use the findings to support successful implementation. Once implementation teams have identified opportunities for improvement based on the local process mapping exercise, specific gaps are targeted based on local solutions for improvement. Almost always a training and education program is included in the work to inform health care workers about changes to routines and practices, and to reinforce standards. Several specific training programs have been developed by Lifebox focusing on checklist implementation strategies, antibiotic stewardship, instrument reprocessing practices [19], and teamwork and communication techniques using nontechnical skills strategies. Methods for improving sponge counting during surgery have also been identified and introduced [20]. Lifebox will provide technical assistance for all training and workshops associated with the program, such as Surgical Safety Checklist workshops targeted towards CS, Safe Instrument Reprocessing workshops, and refresher training on surgical infection prevention standards. Lifebox will also provide technical assistance for staff training on Clean Cut implementation, data collection mechanisms, and data entry and visualization using our data capture platform (described below). Programmatic support includes coordination and management of the hospital-designated implementation teams, data quality assurance, and coordination of partners within Ethiopia. ESOG will guide site selection, organize local trainings, identify local trainers from among its membership, and disseminate results through its extensive networks. ESOG will also provide technical and institutional support for obstetric clinicians at study sites. Ariadne Labs will provide trial design and statistical support during conception, data analysis, and interpretation, as well as support using the Atlas/MKA tools and interpreting their findings. We will assess changes in compliance with the critical perioperative safety practices over time and reductions in complications including surgical infections within 30 days, need for reoperation, and death before and after implementation. Our prior work and review of the literature indicate a baseline SSI incidence of 12% following CS. Given our past work has reduced SSI by 35%, we assume an effect size of Clean Cut resulting in a 25% absolute reduction in SSI (from 12 to 9%). We expect that the preintervention sample will match the postintervention sample in size and general demographic characteristics. We have recruited 10 hospitals that will be paired up into five clusters of two hospitals each, thus providing five steps in the stepped wedge design, as shown in Fig. Fig.1.1. As we do not have specific information on the interhospital characteristics or outcomes, we assume an intracluster correlation (ICC) of 0.1. Explanatory stepped wedge designs We follow the notation of Hemming and Taljaard [21] in Fig. Fig.1.1. In both scenarios, each row represents one cohort, which is a pair of hospitals that form a cluster. Each column represents one time period, and as in any stepped wedge, there is one more time period than steps in the wedge. Lower case “m” represents the sample size contributed by each cluster in each timepoint. The shading displays the different start dates for Clean Cut over the study period. Scenario A is the planned timing for the steps. Note that in this scenario, the sample sizes in the exposed “post” group will be 30m, which is larger than in the unexposed “pre” group size of 15m. However, due to field conditions, the start dates for implementation are unlikely to be as quick as in Scenario A. In Scenario B, delays in data collection would result in more evenly matched pre/post cohort sizes, with 20m in the unexposed “pre” group versus 25m in the “post” group—especially if the intervention occurs in the middle of the time periods. In Scenario C, delays in implementation between each cluster also results in two comparison groups of nearly equal size. We expect that the actual stepped wedge as implemented in the study will be irregular, but roughly between scenarios B and C. For the purposes of power calculations, we will assume that the sample will be split roughly evenly between the unexposed and exposed conditions. Our power calculations proceeded in two steps. First, we calculated the sample size needed for a pre-post comparison of two proportions in dependent samples (using the SAS function proc power, paired freq, text = McNemar). We used these as inputs: We then calculated the design effect of the clustered stepped wedge, again following the method of Hemming and Taljaard [21]. The design effect is a multiplier for the sample size calculated in the first step, showing how much larger the sample should be to account for clustering while maintaining the same alpha and power to detect the given difference [22]. The design effect is calculated as: where: DESW = design effect multiplier for the stepped wedge design t = the number of clusters = 5 (Thus the number of timepoints = t+1 = 6) m = the sample collected in each cluster per timepoint (noting that the timepoint may include different numbers of months, as shown in Fig. Fig.11). ρ= the intracluster correlation (ICC) between patients in the same cluster. Given patient volume at each cluster and the eligibility of all CS surgical patients, we expect to be able to recruit 80 to 90 patients per month per cluster. Figure Figure22 shows the monthly study recruitment rates needed at a range of ICCs and a range of expected proportions for the outcome. (These results reflect Scenario B from Fig. Fig.1).1). The green area in Fig. Fig.22 highlights the expected recruitment rate, and the lines on this graph show the monthly sample sizes that would be needed at various ICCs in order to detect the difference between the given outcome (infection rate) and baseline outcome (starting infection rate of 12%), while maintaining alpha error and power. For example, if the actual ICC is 0.15 and the proportion of CS patients with SSI under Clean Cut is 9%, then we will have sufficient power to detect that 3-point difference from 12% since we will be recruiting in the required range of sample size. Note that we will have more than enough power to detect areas below the green line, so if the effect of Clean Cut lowers the SSI rate even more, we will be able to comfortably detect it. If the outcome proportion is 9.5%, however, we will not likely have sufficient power to detect that difference from 12%. Monthly recruitment rates needed to detect selected outcome proportions With recruitment of 80–90 patients per cluster per month over 18 months, we expect the final sample to include 7200 to 8100 patients, which will be sufficient to detect our expected difference within a reasonable range of ICCs [22]. We will conduct pre/post analyses using the statistical approach recommended by Hemming [21] with our planned assessment controlling for risk factors and other demographic and procedure variables listed below that are known to affect infection rates. We will compare patient demographics pre and post intervention as well to evaluate the overall matching of patients in each part of the study. We will evaluate compliance individually and in an all-or-none manner as previously described in our Clean Cut pilot work. We will undertake a planned subanalysis of patients observed early during baseline (first 2 months) and compare them to patients undergoing surgery during the final stage of the study (last 2 months) after implementation of the program has had time to take effect to assess primary and secondary outcomes. As we are interested not only in the primary outcome of surgical infection but also in process improvements and the implementation strategies and support that can lead to effective compliance with best practices, we will not conduct any interim analyses. The purpose of an interim analysis is to assess the study for benefit, futility, or harm. We believe that neither benefit nor futility would be readily observable at an interim analysis given the implementation strategy and approach; furthermore, even if we were able to assess benefit or futility we would want to continue the trial to fully study the mechanisms of implementation. Regarding an assessment of harm, if such an outcome were noted we believe this would most likely be due to detection bias with improving ability to capture complications as the study progressed, and we would want to let the study play out to mitigate this detection bias over time. In this stepped wedge study, the institutions will be assessed for their baseline and post implementation compliance of the six critical perioperative infection prevention standards noted previously. In addition, we will study the impact on outcomes of patients after the intervention. This will be done by both inpatient and outpatient surveillance of the primary and secondary outcomes set by this study both at the baseline time and after the intervention. Ongoing, systematic collection and analysis of data on patients who have CS or any gynecological procedure while they are still admitted in the wards. The data will be captured from the first postoperative day until the day of discharge. The data include the condition of the surgical wound for any sign of infection; any other site of infection; the type and duration of antibiotics; need for and indication of reoperation; length of stay; and maternal and/or neonatal mortality and cause of death. Ongoing, systematic collection and analysis of data on patients who have CS or any obstetric or gynecological procedure after they are discharged until the 30th day post surgery. This follow-up will typically occur through phone follow-up. The data includes the condition of the surgical wound for any sign of infection as redness at the wound site, wound discharge, wound dehiscence, wound dressing offered at local health center; any other known or recognized infection; readmission; reoperation and indication if known; maternal and/or neonatal mortality and cause of death if known. As noted in Table Table1,1, outcomes will include patient-level endpoints as well as process compliance endpoints, including postoperative infections within 30 days, compliance to each of the six standards of surgical site infection prevention and control that comprise Clean Cut, unplanned reoperation, length of hospital stay, maternal and postoperative deaths within 30 days of surgery, and neonatal status (alive or dead) at discharge of the mother. These include basic demographic characteristics of the patient: age, gestational age, gravida/parity; pregnancy comorbidities such as hypertension, pre-eclampsia, gestational diabetes, obesity, malnourishment; other comorbidities such as HIV and anemia; timing of onset of labor, and timing of rupture of membranes. These include information of the operation itself, including emergency vs elective case status, date and time of incision, duration of operation, wound classification (according to the CDC definition), estimated blood loss, intraoperative complications or mishaps, procedure name, indication for surgery, and whether meconium is present (if CS). Data collectors will be trained on the data collection process including definitions of each variable and the appropriate process for observing and recording data elements onto the forms. Data collector training may be separated by cohort to tailor the training to nurses or other data collectors who will collect the different “phases” of data—intraoperative, inpatient ward follow-up and outpatient phone-call follow-up. Data entry personnel will also be trained how to use the DHIS2 platform to create patient encounters and enter data including enrollment, intraoperative, inpatient and outpatient data. They will also be trained on access and interpretation of the “follow-up” dashboards, where patient record completeness can be tracked. A data dictionary will be available to all data collectors and entry staff. Sites will aim to collect data on at least 50 patients per facility (100 per cluster); based on our prior experience with loss to follow-up and incomplete data capture, we anticipate capturing complete data on 80-90 patients per cluster per month. Data collection will occur on a predetermined auditing schedule or on a 100% enrollment basis, to be determined by the hospital-based Clean Cut team based on monthly case volumes. Minimum enrollment numbers have accounted for facility volume of cesarean sections. If using an auditing basis care will be taken to distribute the weekend/weekdays, shift times, and days per week to eliminate as much bias as possible from the types of cases being enrolled. Patients will be enrolled in the Clean Cut program in the intraoperative phase, with their first encounter being their index operation. Once enrolled, OR data collectors will communicate and handover enrolled patients to the appropriate wards. Inpatient data will be collected by the ‘ward’ data collectors on a daily basis, through chart review and direct observation of the patient’s surgical wound. Patients will be followed, data collected, and entered on a daily basis until discharge from the hospital or 30 days following surgery. At discharge, the ‘ward’ data collectors will communicate and handover information to the responsible data collectors for the outpatient follow-up encounter. A phone call will be placed at 30 days following surgery to gather outpatient data on mortality, clinical follow-up and signs of surgical infection. All data collected on hard copy paper forms will be transferred to the data entry personnel at time intervals to be determined by the hospital team, ideally on at least a daily basis. Data will be entered on a daily basis by the data entry personnel, with attention paid to entering all available data from hard copies into the DHIS2 system as soon as possible. Site coordinator or lead data manager at each hospital will review data entries every 2–3 days, flagging those with missing or incomplete data and communicating with data entry personnel and/or data collectors with queries. A data quality officer will review all hospital data on a weekly basis in a meeting with each hospital lead data manager. This review will also include time of day, day of week, and other observational inputs to ensure patient enrollment is representative of the spectrum of conditions treated by the hospital. Instances of missing inpatient or outpatient encounters will be tracked and when possible the missing data identified and entered. All missing data elements and missing encounters will be tracked by site coordinators overseen by data quality officer. Frequently missing data elements will be flagged and recurrent issues addressed with data collectors and data entry personnel. Each study site will have a regular check-in with the trial management team in order to review recruitment targets and data quality and provide general bidirectional updates on progress. Site visits will occur on an ad hoc basis depending on challenges or specific issues or difficulties encountered. While randomization will be completed by and known to the study team leadership (TNM, MD, and TGW), all other members will be blinded to the cluster order. Unblinding of each intervention cluster will occur 2–4 weeks prior to intervention in order to allow for planning and organization at the facilities; the last cluster hospitals which will naturally know their order as the last interventional sites given the unblinding order. Given the current pandemic as well as potential civil unrest in Ethiopia, there is the potential for unanticipated challenges to implementing the protocol as described. While every attempt will be made to maintain participation of all sites, the loss of one or more sites is a distinct possibility. Recruitment of patients may also be affected by patient volumes, supply shortages, or facility closures. Given this, we will allow each site to collect up to 92 patients per month if they are able in case of the loss of a facility in a cluster. In the case where one facility in a cluster is no longer participating in the study, we will allow the other facility to increase data collection up to 150 cases per month, if feasible and the volume allows, and adjust our comparative calculations accordingly. Our core trials team consists of the authors of this manuscript. Tasks are allocated as follows: Members of this group meet weekly to review progress, discuss challenges, raise and address concerns, and ensure plans for trial delivery. Initial discussions focused on timing, organization, coordination, and site staffing and training issues. Subsequent meetings have focused on data quality, compliance challenges, coordination of site leads and data collectors, transparency, and qualitative work and interviews with site personnel. We have also assembled an advisory group comprised of three external members not involved in the trial who provide additional feedback and input into the approach and strategies for engagement. This group also evaluates the progress of the trial with respect to recruitment and overall implementation successes and challenges. It is comprised of a public health expert and professor of Ob/Gyn from South Africa, a professor of Ob/Gyn from Rwanda, and a pediatrician and child health specialist working for the Ethiopian Federal Ministry of Health. Each facility has a designated site lead and 2–4 data collectors who complete data forms. Site leads recruit and organize the local data collectors, arrange meetings, and identify and develop a local team that can coordinate improvements following intervention. ESOG coordinates all aspects of local agreements and payment for work in conjunction with Lifebox. All active personnel are eligible for inclusion in the authorship under a study investigators group authorship. Paper data collection forms will be stored in a secure, locked location on-site at study hospitals and made accessible to study personnel only. Patient-level data will be entered into the DHIS2 system using password-protected, encrypted, hospital-specific accounts granted to study personnel. Readiness data will be entered into a secure Qualtrics form. Our study will be reported according to guidance extending the CONSORT 2010 statement on reporting stepped wedge cluster randomized trials [23]. The study protocol was reviewed and approved by the Armauer Hansen Research Institute (AHRI/ALERT) Ethics Review Committee, one of the nationally accredited ethical boards, on 8th February 2021. Following approval with AHRI, it was forwarded to the National Research Ethics Review Committee which oversees national trials and received approval in this secondary review on 9 June 2021, reference 04/246/965/21. This approval was renewed in May 2022. The ethics review committees approved a waiver of informed consent for patients undergoing surgery given the quality improvement nature of the work as stated in the submitted proposal that was reviewed. The investigators have obtained a letter of support from the Federal Ministry of Health and have an agreement in place with each institution, thus further ethical approval was not sought from the individual institutions. Our interviews with and surveys of providers and team members will help inform the work and the support needed by facilities and hospitals; this was included in the IRB review, and informed consent will be obtained during the interview and survey processes. The lead investigators (TNM, MDF, TGW) will ensure that this trial is conducted in accordance with relevant regulations and with Good Clinical Practice. Patient data will be kept confidential throughout the study and stored on encrypted servers and password-protected devices. The trial has been registered with ClinicalTrials.gov, identifier {"type":"clinical-trial","attrs":{"text":"NCT04812522","term_id":"NCT04812522"}}NCT04812522 on 23rd March 2021, and with the Pan-African Clinical Trials Registry, identifier PACTR202108717887402 on 24th August 2021.

Résumé de l’IA

Study Justification:

The CLEAN-CS study aims to evaluate the effectiveness of the Clean Cut program in reducing postoperative infections following cesarean delivery (CS) and other obstetric and gynecological operations in Ethiopia. The study is justified by the need to improve compliance with infection control practices and reduce surgical site infections (SSI) in low-resource settings. Previous pilot studies have shown promising results, but a more rigorous assessment is required to understand the impact, scalability, and sustainability of the program.

Highlights:

– The study is a cluster-randomized stepped wedge interventional trial involving ten hospitals in Ethiopia.
– The Clean Cut program focuses on improving compliance with six critical perioperative infection prevention standards, including antiseptic practices, antibiotic administration, and the use of the WHO Surgical Safety Checklist.
– The intervention consists of modifying and implementing the Surgical Safety Checklist to fit local practices, process mapping each standard, developing site-specific action plans for improvement, and targeted training to address process gaps.
– The primary outcome is the 30-day CS infection rates, and secondary outcomes include other patient-level complications and compliance with standards.
– The study will provide valuable insights into the effectiveness of the Clean Cut program, its impact on processes and outcomes, and its potential for enhancing compliance with care standards.

Recommendations:

Based on the study findings, the following recommendations can be made for healthcare providers, policymakers, and stakeholders:

1. Implement the Clean Cut program in healthcare facilities performing CS and other obstetric and gynecological operations to improve compliance with infection control practices and reduce postoperative infections.
2. Strengthen adherence to the WHO Surgical Safety Checklist and other perioperative infection prevention standards.
3. Provide targeted training and education to healthcare workers to address process gaps and improve compliance with best practices.
4. Foster teamwork and collective leadership to identify and address critical gaps in perioperative safety processes.
5. Develop and commit to sustainability plans to monitor and ensure continued adherence to best practices.
6. Allocate resources for the implementation of the Clean Cut program, including training, data collection, and quality improvement initiatives.

Key Role Players:

To address the recommendations, the involvement of the following key role players is essential:

1. Healthcare providers: Surgeons, anesthesiologists, nurses, and other healthcare professionals involved in obstetric and gynecological operations.
2. Hospital administrators: Administrators responsible for implementing and supporting quality improvement initiatives in healthcare facilities.
3. Ethiopian Society of Obstetricians and Gynecologists (ESOG): The professional society can provide guidance, support, and training to healthcare providers.
4. Ariadne Labs: A partner organization that can provide trial design, statistical support, and expertise in implementing quality improvement programs.
5. Lifebox: The organization that developed the Clean Cut program and can provide technical assistance, training, and coordination of the implementation teams.
6. Ministry of Health: The government body responsible for healthcare policy and resource allocation.

Cost Items:

While the actual cost of implementing the recommendations will vary depending on the specific context, the following cost items should be considered in planning:

1. Training and education: Budget for training healthcare providers on the Clean Cut program, the WHO Surgical Safety Checklist, and other infection control practices.
2. Data collection and analysis: Allocate resources for data collection, data entry, and data analysis to monitor compliance and evaluate outcomes.
3. Quality improvement initiatives: Invest in quality improvement strategies, including process mapping, action plans, and continuous monitoring and feedback.
4. Equipment and supplies: Ensure availability of necessary equipment and supplies for infection control, such as antiseptics, sterile gowns, drapes, gloves, and instruments.
5. Sustainability plans: Allocate resources for monitoring and sustaining adherence to best practices beyond the study period.
6. Coordination and management: Budget for coordination and management of the implementation teams, data quality assurance, and partnership coordination.
Please note that the above cost items are general considerations and may vary based on the specific requirements and resources of each healthcare facility.

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study protocol describes a cluster-randomized stepped wedge interventional trial with a clear objective and methodology. The intervention, Clean Cut, has been successfully piloted and has shown improved compliance with infection prevention standards and reduced postoperative infections. The study aims to assess the impact of Clean Cut on surgical infections following cesarean section in Ethiopia. The abstract provides detailed information about the study design, data collection, and analysis methods. However, it would be helpful to include information about the sample size calculation and statistical power analysis. Additionally, the abstract could benefit from a clearer statement of the primary and secondary outcomes. To improve the evidence, the authors could consider providing more information about the potential limitations of the study, such as the challenges related to the social and political environment, pandemic travel restrictions, and limited budget. Overall, the evidence in the abstract is strong, but these suggested improvements would enhance its clarity and completeness.

Abstrait

This trial is a cluster-randomized stepped wedge interventional trial testing the superiority of the Clean Cut program over data collection alone as a means of improving perioperative outcomes following CS. It divides ten facilities into five separate clusters and randomizes the sequence of program intervention over a period of 10 months, with a lead-in baseline period and a post-implementation follow-up period for a total trial duration of 18 months. The overall objective of the CLEAN-CS study is to assess the impact of a multimodal intervention, called Clean Cut, on surgical infections following CS. The primary and secondary aims are as follows: These aims will be captured through collection of the following patient and facility endpoints: These aims and endpoints are listed in Table Table1,1, along with data sources (described in more detail below) and whether the specific data points are considered part of quality improvement or research. Aims and endpoints Clean Cut is a program developed by Lifebox that focuses on improving compliance with six critical perioperative infection prevention standards: (1) appropriate skin preparation of the surgeon’s hands and the surgical site; (2) maintenance of the sterile field by ensuring the integrity and sterility of surgical gowns, drapes, and gloves; (3) confirmation of instrument sterility; (4) appropriate antibiotic administration; (5) complete swab counts; and (6) routine use of the WHO Surgical Safety Checklist Clean Cut is implemented in five phases: For this study, Clean Cut has been adapted specifically for obstetric and gynecological operations and will be implemented in ten maternity hospitals/departments in Ethiopia in order to reduce infections and other complications for women undergoing CS and other obstetric and gynecologic operations. Clean Cut has been designed with sustainability at its core – the strategy emphasizes teamwork and collective leadership to identify and address critical gaps in perioperative safety processes. It develops sustainable facility-level and operating team management practices and embeds critical routines into the workflow of surgical teams. In addition, it imparts a classic quality improvement methodology that, anecdotally, has spread to other departments and services in hospitals where we have worked. As outlined above, a core function of the program involves supporting hospital teams to develop and commit to a sustainability plan to monitor and ensure continued adherence to best practices, something frequently agreed to but not necessarily achieved. Finally, it is “adaptive” as there are numerous opportunities for failures in perioperative infection control and prevention practices; Clean Cut leverages local knowledge and team dynamics to improve compliance through an iterative process that is developed and executed by the local teams. Solutions may be unique to a specific facility and may not be applicable, or replicable, in a different setting. The strategy and approach of Clean Cut, however, are based on classic quality improvement techniques, are replicable, and are introduced and implemented in each facility in a similar way. Lifebox has partnered with the Ethiopian Society of Obstetricians and Gynecologists (ESOG) and Ariadne Labs in Boston, USA, to implement the CLEAN-CS trial. The study will test core elements of the improvement aspects of Clean Cut, namely phases 3, 4, and 5 outlined above, by randomly assigning the start of Phase 3 as part of the stepped wedge intervention testing strategy. Lifebox developed Clean Cut as a multimodal, adaptive, checklist-based improvement program following an extensive consultation with providers and practitioners from around the world who identified surgical infections as a major source of preventable surgical morbidity and mortality. The program was introduced in Ethiopia, initially in conjunction with the Surgical Society of Ethiopia and then through partnerships with the FMOH [11]. Clean Cut was first evaluated at Jimma University Specialized Hospital in 2016, with the pilot work extending to five initial hospitals where we prospectively collected compliance data from 2213 operations (374 during baseline assessment and 1839 following implementation of process improvements) in 2202 patients with follow up in 2159 patients (98.0%). At baseline, perioperative teams complied with an average of 2.9 of the six critical perioperative infection prevention standards; following process improvement changes, compliance rose to 4.5 (p<0.001). The relative risk of surgical infections following Clean Cut implementation was 0.65 (95% CI 0.43–0.99; p=0.043). Improved compliance with standards reduced the risk of postoperative infection by 46% (RR 0.54 for adherence score 3–6 vs 0–2; 95% CI 0.30-0.97; p=0.038). Since then the program has been implemented in 11 hospitals and, as of late 2021, has benefitted an estimated 80,000 patients. In our preliminary, unpublished analyses of 8 hospitals with completed data, we have prospectively collected compliance data from 2905 operations, of which 1133 (39%) were CS or hysterectomy, with an average monthly volume of 27 obstetric operations per facility. Of the total, 2692 patients (92.7%) had outcomes follow-up, with 1075 (40%) being obstetric patients. After program implementation, significant improvements were seen in each of the infection prevention areas: instrument decontamination, gown and drape integrity, surgical skin antisepsis, gauze counting, antibiotic administration, and use of the surgical safety checklist. At baseline, unadjusted SSI rate was 11.3% overall and 12.4% for the obstetric group; after Clean Cut implementation, the SSI rate fell to 6.2% overall and 6.6% in the obstetric group (a 47% decrease). Some studies have described the impact of quality improvement (QI) programs on SSI reduction in low-resource settings, and the use of process measures instead of or in addition to patient outcomes is important to identify actionable interventions [14]. However, in a literature review of 354 studies on surgical QI in LMICs, only 11% used process measures as a metric [15]. The African Surgical Unit-based Safety Program, a multimodal surgical infection prevention program implemented in five hospitals in Sub-Saharan Africa, focused on improving perioperative process measures including preoperative bathing, hair removal, skin and hand preparation, antibiotic administration, and OR traffic, showed significant improvements and an associated reduction in SSI from 8.0 to 3.8% [16]. This intervention leveraged local providers to implement evidence-based guidelines. However, it did not focus on checklist use, sponge counts, instrument sterility, or gown and drape reprocessing, all of which have been noted as major gaps in our setting [17]. The Clean Cut program has demonstrated significantly improved compliance with critical infection prevention standards and reduced postoperative infections without requiring major investments in new infrastructure or resources. Like similar programs, uncontrolled aspects of implementation limit specific attribution to Clean Cut itself. Many factors may have influenced success, including staff characteristics, prior strong QI programs, and engaged hospital administration. Our sample size was small, particularly during the baseline period, which limits robust comparisons of outcomes between groups before and after implementation. A larger, more rigorous trial such as a cluster-randomized stepped wedge design will validate this approach and determine whether Clean Cut can be replicated and scaled in different hospitals, countries, and settings. Study sites will consist of ten hospitals in Ethiopia that provide maternal surgical services. Five of these hospitals will be university teaching or referral hospitals (aka specialized or referral hospitals), and five will be regional, district, or smaller community hospitals (aka general or regional hospitals). These hospitals have been selected by the Ethiopian Society of Obstetricians and Gynecologists (ESOG) based on the following factors: Any patient of any age undergoing obstetric and gynecologic surgery at any time in one of the targeted operating theatres is eligible for inclusion; there will be no exclusion criteria. As obstetric and gynecologic operations are typically undertaken in separate, dedicated operating theatres, we will focus our prospective observations on patients admitted to these theatres. Any patient undergoing surgery at any time in one of the targeted operating theatres is eligible for inclusion; there will be no exclusion criteria. Enrollment will occur at the time of observation and will include various times (day and night) and days of the week (weekdays and weekends). As the standards being implemented are not in dispute and are considered critical for ensuring antisepsis and sterility, patient informed consent will not be obtained. While our focus will be on cesarean delivery, any obstetric or gynecological operation will be included, with the inclusion of other operations in these populations (such as appendectomy for appendicitis that is found incidentally or misdiagnosed as ovarian torsion, for example). There will be no age range limit. We will also interview key hospital personnel to understand the context of each facility, its experience with quality improvement initiatives, and the perceived importance of this work to patient safety, patient care, and the work routine. These interviewees will be recruited from the implementation teams involved in Clean Cut. We will also administer surveys in conjunction with Ariadne Labs/MKA, a partner in this work, to understand the context, perceptions, and priorities of the various institutions, and how the CLEAN-CS team can support implementation at the time of intervention (phases 3, 4, and 5) Each selected hospital will be distributed into two groups based on the type of facility: university teaching and referral hospitals in one group, and regional, district, and community hospitals in another. One hospital in each group will be paired to create five clusters; these pairings will be purposive as district and referral hospitals in Ethiopia typically have long-standing relationships which will facilitate implementation at the cluster level and prevent inadvertent crossover of the intervention prior to randomization. The sequence of implementation for each of the five clusters will be established by the Lifebox team using computer-based randomization (https://www.randomizer.org/). Sites will be informed of the timing of their intervention no earlier than one month prior to implementation training. The impact of the CS-tailored Clean Cut program will be tested through a cluster-randomized stepped-wedge study design in five clusters (10 hospitals) over the course of 18 months followed by complete data analysis and interpretation. The selected hospitals will start by identifying a clinical lead and data collectors who will collect inpatient and outpatient outcomes on all patients undergoing CS prior to implementation as well as compliance information on the six standards of Clean Cut (phases 1 and 2 of Clean Cut outlined above). Following a lead-in period of 5 months, one cluster at a time will begin implementing phase 3 of Clean Cut at 2-month intervals over the course of 10 months. The intervention is initiated by assembling a multidisciplinary improvement team, undergoing team training on WHO Surgical Safety Checklist use and implementation, and reviewing compliance information about intraoperative safety practices. The initiation of phase 3 will also involve creating facility-specific process maps of each critical perioperative practice. Once these process maps and compliance data are complete, usually after 2–4 weeks, the team establishes an adaptive, facility-driven improvement plan based on process gaps and barriers to best practice (phase 4). This is initially facilitated by Lifebox, but may also involve teams that have already implemented Clean Cut in their facilities. They identify improvement opportunities by reviewing process mapping data, compliance shortfalls, and their bespoke surgical safety checklist, and craft an improvement plan aimed at removing barriers to compliance. The team will review their hospital-specific compliance data and patient outcomes on a monthly basis for the remainder of the study. The study will conclude at each site after 18 months of data collection The program is adaptive and relies on a multimodal approach to improving compliance with critical safety practices. Initial trainings take place at the outset of the work to coordinate data collector training and orient some of the key team members to the work (phases 1 and 2). The first Clean Cut implementation training (phase 3) will occur following at least 3 months of baseline data collection and will be introduced based on randomization. As noted above, this will specifically include a multidisciplinary perioperative improvement team to create facility-specific process maps of each critical perioperative practice. Once these process maps are complete, usually after 2–4 weeks, the team establishes an adaptive, facility-driven improvement plan based on process gaps and barriers to best practice (phases 4 and 5). At study initiation and over the course of implementation, the Atlas/MKA Context Assessment Tools will be used to understand facility-level readiness for implementing quality improvement programs in surgery [18]. The tools assess the following readiness domains: Commitment and Motivation, Ability to Implement, Internal Culture, Clinical Team Functionality, and Knowledge and Ability to do the Practice Change using Likert scale, self-administered surveys, and a discussion guide for facilitated readiness conversations. The intent is to use the findings to support successful implementation. Once implementation teams have identified opportunities for improvement based on the local process mapping exercise, specific gaps are targeted based on local solutions for improvement. Almost always a training and education program is included in the work to inform health care workers about changes to routines and practices, and to reinforce standards. Several specific training programs have been developed by Lifebox focusing on checklist implementation strategies, antibiotic stewardship, instrument reprocessing practices [19], and teamwork and communication techniques using nontechnical skills strategies. Methods for improving sponge counting during surgery have also been identified and introduced [20]. Lifebox will provide technical assistance for all training and workshops associated with the program, such as Surgical Safety Checklist workshops targeted towards CS, Safe Instrument Reprocessing workshops, and refresher training on surgical infection prevention standards. Lifebox will also provide technical assistance for staff training on Clean Cut implementation, data collection mechanisms, and data entry and visualization using our data capture platform (described below). Programmatic support includes coordination and management of the hospital-designated implementation teams, data quality assurance, and coordination of partners within Ethiopia. ESOG will guide site selection, organize local trainings, identify local trainers from among its membership, and disseminate results through its extensive networks. ESOG will also provide technical and institutional support for obstetric clinicians at study sites. Ariadne Labs will provide trial design and statistical support during conception, data analysis, and interpretation, as well as support using the Atlas/MKA tools and interpreting their findings. We will assess changes in compliance with the critical perioperative safety practices over time and reductions in complications including surgical infections within 30 days, need for reoperation, and death before and after implementation. Our prior work and review of the literature indicate a baseline SSI incidence of 12% following CS. Given our past work has reduced SSI by 35%, we assume an effect size of Clean Cut resulting in a 25% absolute reduction in SSI (from 12 to 9%). We expect that the preintervention sample will match the postintervention sample in size and general demographic characteristics. We have recruited 10 hospitals that will be paired up into five clusters of two hospitals each, thus providing five steps in the stepped wedge design, as shown in Fig. Fig.1.1. As we do not have specific information on the interhospital characteristics or outcomes, we assume an intracluster correlation (ICC) of 0.1. Explanatory stepped wedge designs We follow the notation of Hemming and Taljaard [21] in Fig. Fig.1.1. In both scenarios, each row represents one cohort, which is a pair of hospitals that form a cluster. Each column represents one time period, and as in any stepped wedge, there is one more time period than steps in the wedge. Lower case “m” represents the sample size contributed by each cluster in each timepoint. The shading displays the different start dates for Clean Cut over the study period. Scenario A is the planned timing for the steps. Note that in this scenario, the sample sizes in the exposed “post” group will be 30m, which is larger than in the unexposed “pre” group size of 15m. However, due to field conditions, the start dates for implementation are unlikely to be as quick as in Scenario A. In Scenario B, delays in data collection would result in more evenly matched pre/post cohort sizes, with 20m in the unexposed “pre” group versus 25m in the “post” group—especially if the intervention occurs in the middle of the time periods. In Scenario C, delays in implementation between each cluster also results in two comparison groups of nearly equal size. We expect that the actual stepped wedge as implemented in the study will be irregular, but roughly between scenarios B and C. For the purposes of power calculations, we will assume that the sample will be split roughly evenly between the unexposed and exposed conditions. Our power calculations proceeded in two steps. First, we calculated the sample size needed for a pre-post comparison of two proportions in dependent samples (using the SAS function proc power, paired freq, text = McNemar). We used these as inputs: We then calculated the design effect of the clustered stepped wedge, again following the method of Hemming and Taljaard [21]. The design effect is a multiplier for the sample size calculated in the first step, showing how much larger the sample should be to account for clustering while maintaining the same alpha and power to detect the given difference [22]. The design effect is calculated as: where: DESW = design effect multiplier for the stepped wedge design t = the number of clusters = 5 (Thus the number of timepoints = t+1 = 6) m = the sample collected in each cluster per timepoint (noting that the timepoint may include different numbers of months, as shown in Fig. Fig.11). ρ= the intracluster correlation (ICC) between patients in the same cluster. Given patient volume at each cluster and the eligibility of all CS surgical patients, we expect to be able to recruit 80 to 90 patients per month per cluster. Figure Figure22 shows the monthly study recruitment rates needed at a range of ICCs and a range of expected proportions for the outcome. (These results reflect Scenario B from Fig. Fig.1).1). The green area in Fig. Fig.22 highlights the expected recruitment rate, and the lines on this graph show the monthly sample sizes that would be needed at various ICCs in order to detect the difference between the given outcome (infection rate) and baseline outcome (starting infection rate of 12%), while maintaining alpha error and power. For example, if the actual ICC is 0.15 and the proportion of CS patients with SSI under Clean Cut is 9%, then we will have sufficient power to detect that 3-point difference from 12% since we will be recruiting in the required range of sample size. Note that we will have more than enough power to detect areas below the green line, so if the effect of Clean Cut lowers the SSI rate even more, we will be able to comfortably detect it. If the outcome proportion is 9.5%, however, we will not likely have sufficient power to detect that difference from 12%. Monthly recruitment rates needed to detect selected outcome proportions With recruitment of 80–90 patients per cluster per month over 18 months, we expect the final sample to include 7200 to 8100 patients, which will be sufficient to detect our expected difference within a reasonable range of ICCs [22]. We will conduct pre/post analyses using the statistical approach recommended by Hemming [21] with our planned assessment controlling for risk factors and other demographic and procedure variables listed below that are known to affect infection rates. We will compare patient demographics pre and post intervention as well to evaluate the overall matching of patients in each part of the study. We will evaluate compliance individually and in an all-or-none manner as previously described in our Clean Cut pilot work. We will undertake a planned subanalysis of patients observed early during baseline (first 2 months) and compare them to patients undergoing surgery during the final stage of the study (last 2 months) after implementation of the program has had time to take effect to assess primary and secondary outcomes. As we are interested not only in the primary outcome of surgical infection but also in process improvements and the implementation strategies and support that can lead to effective compliance with best practices, we will not conduct any interim analyses. The purpose of an interim analysis is to assess the study for benefit, futility, or harm. We believe that neither benefit nor futility would be readily observable at an interim analysis given the implementation strategy and approach; furthermore, even if we were able to assess benefit or futility we would want to continue the trial to fully study the mechanisms of implementation. Regarding an assessment of harm, if such an outcome were noted we believe this would most likely be due to detection bias with improving ability to capture complications as the study progressed, and we would want to let the study play out to mitigate this detection bias over time. In this stepped wedge study, the institutions will be assessed for their baseline and post implementation compliance of the six critical perioperative infection prevention standards noted previously. In addition, we will study the impact on outcomes of patients after the intervention. This will be done by both inpatient and outpatient surveillance of the primary and secondary outcomes set by this study both at the baseline time and after the intervention. Ongoing, systematic collection and analysis of data on patients who have CS or any gynecological procedure while they are still admitted in the wards. The data will be captured from the first postoperative day until the day of discharge. The data include the condition of the surgical wound for any sign of infection; any other site of infection; the type and duration of antibiotics; need for and indication of reoperation; length of stay; and maternal and/or neonatal mortality and cause of death. Ongoing, systematic collection and analysis of data on patients who have CS or any obstetric or gynecological procedure after they are discharged until the 30th day post surgery. This follow-up will typically occur through phone follow-up. The data includes the condition of the surgical wound for any sign of infection as redness at the wound site, wound discharge, wound dehiscence, wound dressing offered at local health center; any other known or recognized infection; readmission; reoperation and indication if known; maternal and/or neonatal mortality and cause of death if known. As noted in Table Table1,1, outcomes will include patient-level endpoints as well as process compliance endpoints, including postoperative infections within 30 days, compliance to each of the six standards of surgical site infection prevention and control that comprise Clean Cut, unplanned reoperation, length of hospital stay, maternal and postoperative deaths within 30 days of surgery, and neonatal status (alive or dead) at discharge of the mother. These include basic demographic characteristics of the patient: age, gestational age, gravida/parity; pregnancy comorbidities such as hypertension, pre-eclampsia, gestational diabetes, obesity, malnourishment; other comorbidities such as HIV and anemia; timing of onset of labor, and timing of rupture of membranes. These include information of the operation itself, including emergency vs elective case status, date and time of incision, duration of operation, wound classification (according to the CDC definition), estimated blood loss, intraoperative complications or mishaps, procedure name, indication for surgery, and whether meconium is present (if CS). Data collectors will be trained on the data collection process including definitions of each variable and the appropriate process for observing and recording data elements onto the forms. Data collector training may be separated by cohort to tailor the training to nurses or other data collectors who will collect the different “phases” of data—intraoperative, inpatient ward follow-up and outpatient phone-call follow-up. Data entry personnel will also be trained how to use the DHIS2 platform to create patient encounters and enter data including enrollment, intraoperative, inpatient and outpatient data. They will also be trained on access and interpretation of the “follow-up” dashboards, where patient record completeness can be tracked. A data dictionary will be available to all data collectors and entry staff. Sites will aim to collect data on at least 50 patients per facility (100 per cluster); based on our prior experience with loss to follow-up and incomplete data capture, we anticipate capturing complete data on 80-90 patients per cluster per month. Data collection will occur on a predetermined auditing schedule or on a 100% enrollment basis, to be determined by the hospital-based Clean Cut team based on monthly case volumes. Minimum enrollment numbers have accounted for facility volume of cesarean sections. If using an auditing basis care will be taken to distribute the weekend/weekdays, shift times, and days per week to eliminate as much bias as possible from the types of cases being enrolled. Patients will be enrolled in the Clean Cut program in the intraoperative phase, with their first encounter being their index operation. Once enrolled, OR data collectors will communicate and handover enrolled patients to the appropriate wards. Inpatient data will be collected by the ‘ward’ data collectors on a daily basis, through chart review and direct observation of the patient’s surgical wound. Patients will be followed, data collected, and entered on a daily basis until discharge from the hospital or 30 days following surgery. At discharge, the ‘ward’ data collectors will communicate and handover information to the responsible data collectors for the outpatient follow-up encounter. A phone call will be placed at 30 days following surgery to gather outpatient data on mortality, clinical follow-up and signs of surgical infection. All data collected on hard copy paper forms will be transferred to the data entry personnel at time intervals to be determined by the hospital team, ideally on at least a daily basis. Data will be entered on a daily basis by the data entry personnel, with attention paid to entering all available data from hard copies into the DHIS2 system as soon as possible. Site coordinator or lead data manager at each hospital will review data entries every 2–3 days, flagging those with missing or incomplete data and communicating with data entry personnel and/or data collectors with queries. A data quality officer will review all hospital data on a weekly basis in a meeting with each hospital lead data manager. This review will also include time of day, day of week, and other observational inputs to ensure patient enrollment is representative of the spectrum of conditions treated by the hospital. Instances of missing inpatient or outpatient encounters will be tracked and when possible the missing data identified and entered. All missing data elements and missing encounters will be tracked by site coordinators overseen by data quality officer. Frequently missing data elements will be flagged and recurrent issues addressed with data collectors and data entry personnel. Each study site will have a regular check-in with the trial management team in order to review recruitment targets and data quality and provide general bidirectional updates on progress. Site visits will occur on an ad hoc basis depending on challenges or specific issues or difficulties encountered. While randomization will be completed by and known to the study team leadership (TNM, MD, and TGW), all other members will be blinded to the cluster order. Unblinding of each intervention cluster will occur 2–4 weeks prior to intervention in order to allow for planning and organization at the facilities; the last cluster hospitals which will naturally know their order as the last interventional sites given the unblinding order. Given the current pandemic as well as potential civil unrest in Ethiopia, there is the potential for unanticipated challenges to implementing the protocol as described. While every attempt will be made to maintain participation of all sites, the loss of one or more sites is a distinct possibility. Recruitment of patients may also be affected by patient volumes, supply shortages, or facility closures. Given this, we will allow each site to collect up to 92 patients per month if they are able in case of the loss of a facility in a cluster. In the case where one facility in a cluster is no longer participating in the study, we will allow the other facility to increase data collection up to 150 cases per month, if feasible and the volume allows, and adjust our comparative calculations accordingly. Our core trials team consists of the authors of this manuscript. Tasks are allocated as follows: Members of this group meet weekly to review progress, discuss challenges, raise and address concerns, and ensure plans for trial delivery. Initial discussions focused on timing, organization, coordination, and site staffing and training issues. Subsequent meetings have focused on data quality, compliance challenges, coordination of site leads and data collectors, transparency, and qualitative work and interviews with site personnel. We have also assembled an advisory group comprised of three external members not involved in the trial who provide additional feedback and input into the approach and strategies for engagement. This group also evaluates the progress of the trial with respect to recruitment and overall implementation successes and challenges. It is comprised of a public health expert and professor of Ob/Gyn from South Africa, a professor of Ob/Gyn from Rwanda, and a pediatrician and child health specialist working for the Ethiopian Federal Ministry of Health. Each facility has a designated site lead and 2–4 data collectors who complete data forms. Site leads recruit and organize the local data collectors, arrange meetings, and identify and develop a local team that can coordinate improvements following intervention. ESOG coordinates all aspects of local agreements and payment for work in conjunction with Lifebox. All active personnel are eligible for inclusion in the authorship under a study investigators group authorship. Paper data collection forms will be stored in a secure, locked location on-site at study hospitals and made accessible to study personnel only. Patient-level data will be entered into the DHIS2 system using password-protected, encrypted, hospital-specific accounts granted to study personnel. Readiness data will be entered into a secure Qualtrics form. Our study will be reported according to guidance extending the CONSORT 2010 statement on reporting stepped wedge cluster randomized trials [23]. The study protocol was reviewed and approved by the Armauer Hansen Research Institute (AHRI/ALERT) Ethics Review Committee, one of the nationally accredited ethical boards, on 8th February 2021. Following approval with AHRI, it was forwarded to the National Research Ethics Review Committee which oversees national trials and received approval in this secondary review on 9 June 2021, reference 04/246/965/21. This approval was renewed in May 2022. The ethics review committees approved a waiver of informed consent for patients undergoing surgery given the quality improvement nature of the work as stated in the submitted proposal that was reviewed. The investigators have obtained a letter of support from the Federal Ministry of Health and have an agreement in place with each institution, thus further ethical approval was not sought from the individual institutions. Our interviews with and surveys of providers and team members will help inform the work and the support needed by facilities and hospitals; this was included in the IRB review, and informed consent will be obtained during the interview and survey processes. The lead investigators (TNM, MDF, TGW) will ensure that this trial is conducted in accordance with relevant regulations and with Good Clinical Practice. Patient data will be kept confidential throughout the study and stored on encrypted servers and password-protected devices. The trial has been registered with ClinicalTrials.gov, identifier {"type":"clinical-trial","attrs":{"text":"NCT04812522","term_id":"NCT04812522"}}NCT04812522 on 23rd March 2021, and with the Pan-African Clinical Trials Registry, identifier PACTR202108717887402 on 24th August 2021.

Matériaux

https://efashare.b-cdn.net/materials/0cfc891b4436202a9d03851dd0cafa7d6d1518f8ce26b034168b26d7c90178fd/13063_2022_6500_MOESM1_ESM.docx

Innovations

The CLEAN-CS trial is a cluster-randomized stepped wedge interventional trial that aims to assess the impact of the Clean Cut program on reducing infections following cesarean section (CS) and other obstetric and gynecological operations in Ethiopia. The Clean Cut program is a multimodal intervention developed by Lifebox to improve compliance with perioperative infection prevention standards. Here are some innovations and recommendations that can be used to improve access to maternal health based on the information provided:

1. Implementing the WHO Surgical Safety Checklist: The Clean Cut program modifies and implements the WHO Surgical Safety Checklist to fit local practices. This checklist can be used in other healthcare facilities to improve compliance with infection prevention standards during obstetric and gynecological operations.

2. Process Mapping: Clean Cut involves process mapping each standard to identify gaps and develop improvement plans. This approach can be applied in other settings to identify areas for improvement in infection prevention practices.

3. Targeted Training: Clean Cut includes targeted training focused on addressing process gaps. Similar targeted training programs can be developed and implemented to improve compliance with infection prevention standards in other healthcare facilities.

4. Adaptive and Multimodal Approach: Clean Cut is designed as an adaptive and multimodal program that leverages local knowledge and team dynamics to improve compliance. This approach can be replicated in other contexts to enhance compliance with care standards.

5. Sustainability: Clean Cut emphasizes teamwork and collective leadership to address critical gaps in perioperative safety processes. Implementing similar sustainable facility-level and team management practices can improve access to maternal health in other healthcare facilities.

6. Quality Improvement Methodology: Clean Cut incorporates a classic quality improvement methodology that can be applied in other settings to enhance compliance with infection prevention standards and improve patient outcomes.

7. Collaboration and Partnerships: The CLEAN-CS trial involves collaboration between Lifebox, the Ethiopian Society of Obstetricians and Gynecologists (ESOG), and Ariadne Labs. Similar collaborations and partnerships can be formed to implement and evaluate innovative interventions to improve access to maternal health.

It’s important to note that these recommendations are based on the specific context and objectives of the CLEAN-CS trial. Adaptation and customization may be necessary when implementing these innovations in different settings.

AI Innovations Description

The CLEAN-CS trial is a cluster-randomized stepped wedge interventional trial that aims to evaluate the effectiveness of the Clean Cut program in reducing infections following cesarean section (CS) and other obstetric and gynecological operations in Ethiopia. The Clean Cut program focuses on improving compliance with six critical perioperative infection prevention standards, including antiseptic practices, antibiotic administration, and the use of the WHO Surgical Safety Checklist.

The trial involves ten hospitals in Ethiopia, which are divided into five clusters. The program will be implemented in each cluster in a randomized order over a period of 10 months, with a lead-in baseline period and a post-implementation follow-up period, resulting in a total trial duration of 18 months.

The primary outcome of the trial is the 30-day CS infection rates, and secondary outcomes include other patient-level complications and compliance with infection prevention standards. The trial aims to recruit a sample of 8100 patients to achieve statistical significance.

The Clean Cut program is designed to be adaptive and sustainable, emphasizing teamwork and collective leadership to identify and address gaps in perioperative safety processes. It incorporates a classic quality improvement methodology and leverages local knowledge and team dynamics to improve compliance with infection prevention standards.

The trial will assess changes in compliance with infection prevention standards and reductions in complications, including surgical infections, reoperation rates, length of hospital stay, and maternal and neonatal mortality. Data will be collected through inpatient and outpatient surveillance, including chart reviews, direct observations, and phone follow-ups.

The study protocol has been reviewed and approved by ethical review committees, and patient data will be kept confidential and stored securely. The trial is registered with ClinicalTrials.gov and the Pan-African Clinical Trials Registry.

Overall, the CLEAN-CS trial aims to evaluate the impact of the Clean Cut program on improving access to maternal health by reducing infections and complications following CS and other obstetric and gynecological operations in Ethiopia.

AI Innovations Methodology

The CLEAN-CS trial is a cluster-randomized stepped wedge interventional trial that aims to evaluate the impact of the Clean Cut program on reducing infections following cesarean section (CS) and other obstetric and gynecological operations in Ethiopia. The trial involves ten hospitals divided into five clusters, with each cluster receiving the program intervention in a randomized order over a period of 10 months. The primary outcome of the trial is the 30-day CS infection rates, and secondary outcomes include other patient-level complications and compliance with infection prevention standards.

The Clean Cut program focuses on improving compliance with six critical perioperative infection prevention standards, including antiseptic practices, antibiotic administration, and the use of the WHO Surgical Safety Checklist. The program is implemented in five phases, which involve modifying and implementing the checklist to fit local practices, process mapping each standard, creating site-specific action plans for improvement, and providing targeted training to address process gaps. The program emphasizes teamwork, collective leadership, and sustainability in improving perioperative safety processes.

To simulate the impact of the Clean Cut program on improving access to maternal health, a methodology could be developed to assess the changes in compliance with infection prevention standards and the reduction in postoperative infections following CS. This methodology could involve collecting data on patient demographics, surgical procedures, compliance with infection prevention standards, and patient outcomes before and after the implementation of the program. Statistical analyses could then be conducted to compare the pre- and post-intervention data and determine the effectiveness of the program in improving access to maternal health by reducing infections and improving compliance with infection prevention standards.

The trial registration numbers for the CLEAN-CS trial are ClinicalTrials.gov NCT04812522 and Pan-African Clinical Trials Registry PACTR202108717887402.

Auteurs et co-auteurs

Statistics:

Authors: 14

Identifiers:

DOI: 10.1186/s13063-022-06500-9

Research Areas:

Community Interventions, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Ethiopia, Multi-Countries, Rwanda, South Africa

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Mixed-methods, Qualitative, Quantitative, Systematic Review

Participants Gender:

Female

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