Overall Study Design:This studywill usemixed methods sequential exploratory approachto address the study’s aims, which are to assess the sustainability and spread of appropriate pre- and post-operative antimicrobial use achieved by the SCIP program (Figure 4). The research will adhere to the STROBE reporting checklist. Guided by the DSF, the final product of this study will yield an implementation playbook that will comprehensively describe for future implementation sites theneed for adaptingSCIP metrics and implementation strategies tofit local practice settings and the local environment (ecological system) (Figure 5) as well as the different evidence-basedimplementation strategies (ERIC)20that can be used to support program sustainability.
Quantitative Data Collection and Analysis:
Study Population and Setting: VA surgical data from FY2005 – 2020from 70 complex VA inpatient facilities will be used to complete the quantitative analysis. This large, national dataset will include inpatient and outpatient clean or clean contaminated surgeries in cardiac, orthopedic, general, gynecology, and vascular specialties as defined by Current Procedural Terminology (CPT) codes.Surgical data will be combined with manually validated data about peri-operative antimicrobial use collected from the External Peer Reviewed Program (EPRP) dataset. Thus, the EPRP data can be used as the “gold standard” manual review for the purposes of developing, iteratively refining, and validating electronic measurement tools that do not require the time and resource intensive process the original SCIP program required.
Development Cohort: After obtaining EPRP data on SCIP compliance by facility and specialty, SCIP-eligible procedures will be identified in the VA electronic health record (EHR) from 2005-2015 to build a dataset for each specialty cohort with SCIP compliance information and structured/unstructured data relevant to our SCIP INF-1 and 3 algorithms.EPRP data contains manually reviewed surgeries targeted by SCIP, and based on previously published work, we anticipate that our sample will include many non-compliant cases, particularly in the early period following program implementation.
Algorithm Development: First, a list of appropriate antimicrobials for surgical care as specified in the original SCIP guidelines and in current multi-society SSI prevention guidelines will be reviewed and mapped for each of the included surgical specialties.5, 21, 22Then, this list will be used to develop electronic measurement tools. Based on our prior work,23 we will develop each algorithm to detect SCIP INF-1 and 3 antimicrobial prophylaxis iteratively over several stages, by varying 1) types of variables included in the tool (e.g., text note extraction only, orders only, administration only, or combinations of the three), 2) timing of the searches (e.g., including or excluding the procedure date), and 3) types of antimicrobials included in the tool. The list will be used to search clinical notes for documentation of antibiotic administration pre- and/or post-operatively to measure compliance with SCIP metrics. The list will also be mapped to structured data in the VA EHR and orders and administration of relevant medications will be extracted.
The two algorithms (one for pre-operative antimicrobial administration and one for post-operative administration) will be applied to half of all EPRP reviewed surgeries targeted by SCIP from 2005-2015. Algorithm performance, i.e., criterion validity,will be assessed as sensitivity (how many true positive cases were identified as positive by the algorithm) and specificity (how many true negative cases were identified as negative by the algorithm). Manually reviewed EPRP data will be used as the gold standard for algorithm development. To finalize the algorithm, all discordant cases (e.g., algorithm flagged positive but EPRP manual review was negative, or algorithm flagged negative but EPRP manual review was positive) will undergo a second round of manual review to identify reasons for the discordant flag and to qualitatively classify the reason for discordance.These findings will be used to adjust and adapt the algorithms for each SCIP metric and each specialtyprocedure and facility to improve accuracy. We will also conduct an analysis stratified by facility, to determine if facility-level effects, such as coding differences, impact algorithm performance and accuracy.
Algorithm Validation:We will validate the final SCIP INF-1 and 3 algorithms by applying the structured and unstructured data extracts to SCIP-eligible procedures in the validation half of the 2005-2015 EPRP data. We will measure and criterion validity: sensitivity, specificity and positive predictive validity for each algorithm per specialty (cardiac, orthopedics, general, gynecology and vascular surgeries).
Assessment of Voltage Drop: Analysis (Interrupted Time Series): After the algorithms for measuring pre-and post-operative antimicrobial use are validated, we will then apply them to the 2016-2020 period, e.g., after SCIP discontinuation and after discontinuation of the EPRP manual review. SCIP INF-1 and 3 compliance stratified by specialty will be assessed separately.
We will test the hypotheses that SCIP INF-1 and 3 compliance experienced “voltage drop,” defined as decreasing adherence to antimicrobial use guidelines over time,afterSCIP retirement in 2015 and an overall change in rate over time using an interrupted time series model. Observations will be at the procedure level with facility repeated measures; thus, we will control for facility-level random effects.The outcome measure will be a binary indicator of compliance for each procedure, and the models will include a binary indicator of pre- or post-SCIP retirement, a continuous variable for calendar time, and an interaction between these two variables. The beta coefficient for the interaction term will represent the difference in slopes between pre and post SCIP retirement, the coefficient for pre and post will indicate the “voltage drop,” or immediate drop after the SCIP retirement, and the coefficient for time will indicate the slope for the pre-period. For each outcome, SCIP INF-1 and 3, and for each surgical specialty, we will fit separate models. To adjust for multiple comparisons, we will perform false discovery rate (FDR) correction on the betas for the interaction terms for each of the 10 comparisons. For each of the 10 comparisons, setting alpha at 0.01 to be conservative (given that we will adjust for multiple comparisons), assuming there will be correlation among patients from the same facilities (0.02), we can detect at least a 5% difference in slopes between pre and post SCIP retirement for each surgical specialty with greater than 90% power. This assumes approximately 95% SCIP compliance between 2011-2015.9
Assessment of Diffusion of Practices to Surgeries not Covered by SCIP: SCIP active reporting demonstrably increased appropriate antimicrobial prophylaxis compliance in SCIP-targeted procedures, largely due to changes in provider behavior that may have been in response to local policies designed to facilitate adoption. With respect to diffusion, the provider behavior change for one type of surgery may have led to changes for all procedures, not just those specifically targeted by SCIP.
Diffusion of Practices Cohort: Allclean or clean/contaminated surgical procedures within the five specialties performed from 2016-2020will be identified and an expanded cohort will be created.Our analysis will be limited to surgeries where pre-operative antibiotics are recommended.
Statistical Analysis: We will use binomial generalized linear mixed models to estimate the association between SCIP-targeted versus excluded procedures and the two SCIP metrics, adjusting for correlation among observations within the same facility using facility random effects. The variance-covariance matrix will allow us to estimate the correlation coefficients for nesting so that we can make inferences on the strength of these sources of correlation. Separate models will be fit for each outcome (SCIP INF-1 and 3) and surgical specialty; a total of 10 models. We will again apply FDR correction on the p-values for the primary comparisons related to our hypothesis.
Power Calculation: For each of the 10 models, we will have greater than 90% power to detect at least a 10% difference in the two SCIP compliance outcome measures between the comparison groups for each hypothesis. This assumes correlation of 0.02 at the facility level and alpha set at 0.01. For example, if compliance is 85% for females and 75% for males, we can detect this difference with an effective sample size of 938 individuals.
Qualitative Data Collection and Analysis:
Overview of Qualitative Analysis: For the qualitative aspects of the study, based on current guidance,24 we will interview up to six key stakeholders in different VA hospitals for each of the five specialties, for a total of 60 interviews in ten VA facilities. Interview participants will have work duties related to SSI prevention and antimicrobial stewardship: surgical staff (including anesthesiologists), infectious diseases staff, pharmacists, and surgical nurses.We will analyze interview data to identify facilitators and barriers to implementation sustainability and map findings to DSF constructs. Practice spread will also be assessed and evaluated using the Diffusion of Innovations Theory as a guide. As a final step, we will map facilitators and barriers to the ERIC implementation strategies and will develop an implementation playbook that can be used by VA hospitals to support long-term sustainability of the quality improvements in peri-operative antimicrobial use achieved by SCIP.
Key Stakeholders: Key stakeholders for peri-operative antimicrobial use include members of the surgical staff (surgeons,anesthesiologists, nurses), infectious diseases staff (physicians and infection control/antimicrobial stewardship team members) and inpatient pharmacists. These are the providers who will be targeted at each of the participating VA facilities.
Selection of Sites and Recruitment: We will purposefullyrecruit two facilities for each specialty (N=10 facilities) from the 70 high complexity VA facilities, targeting a range of sites, including regional diversity and urban, suburban, and rural variation. Through this selection process, we will recruit 60 stakeholders from 10 facilities (6 interview participants at each site).Our goal is to reach saturation within each of the five SCIP-targeted specialties across the sites; typical sample sizes for achieving this in implementation research range from 5 to 10 individuals in key roles.25
Recruitment: Operational partners from the national patient safety office and the national antimicrobial stewardship office will co-sign a letter outlining support for the project; the PIs will then send out this letter to a purposefully sampled selection of sites and inform them of the study prior to sending recruitment emails to providers. Thereafter, the project manager will send out recruitment emails to providers. We will use an opt-in approach. If we do not get enough stakeholders who agree to participate, then we will identify additional stakeholders to recruit using the approach described above.
Data Collection:Two co-investigators will conduct semi-structured telephone or video interviews with key stakeholders; interviews are estimated to take approximately 30-minutes. All interviews will be digitally audio recordedfor transcription; informed consent will be obtained prior to starting the interview. Interviewers will follow a semi-structured interview guide, which will consist of both structured and open-ended questions. The interview guide will be revised with input from operational partners prior to pilot testing and data collection. Through this process, we will ensure that we develop a set of questions that will allow us to collect rich information about facilitators and barriers of sustainabilityand also about factors that lead to diffusion of the practice changes, or lack of spread.
Informed by DSF and the Theory of Diffusion of Innovations, interviews will elicit information about the intervention (SCIP), the practice setting/context (surgical specialty), and the ecological system (VA facility/VISN).The overall purpose of the interviews will be to understand, from the perspectives of surgical staff, infectious diseases staff, and inpatient pharmacists,what types of processes/practices have been implemented to help with peri-operative and post-operative antimicrobial use and compliance; whether and how those processes/practices were adapted and sustained after SCIP was retired in 2015; whether and how those antimicrobial use and compliance processes/practices spread to other settings; and the facilitators that helped with and the barriers that hindered implementation, maintenance, and spread of those processes/practices.Information will be collected about the contextual factors within the practice setting that affectimplementation and sustainment of antimicrobial use and compliance by asking about the culture of the practice settingas well as trainings and resources that are available to help with antimicrobial use and compliance.Additionally, interviews will probe to understand whether there are other metrics, policies, regulations or guidelines that are being used for peri-operative and post-operative antimicrobial compliance and how those have influenced processes/practices.
Qualitative Data Analysis:Coding of interview transcripts will be organized usingNVivo, a qualitative analytic software. Transcripts will be initially coded using a priori constructs consistent with the DSF and/or the Diffusion of Innovations theory. We will use a directed content analysis approach with allowance for new themes to emerge.26 As coding proceeds, new emergent themes will iteratively be identified, elaborated on, and expanded based on team discussions, a process known as the constant comparative method.Inter-rater reliability will be established using the “check-coding” process.Coders will independently code the same interview transcripts and will then meet to compare their coding, discuss areas of difficulty, and reach consensus on the definitions and examples in the codebook. A new interview will then be independently coded by all, and the process will be repeated until a mutual understanding of the code definitions and when to apply the codes is achieved across all coders. After coding is complete, we will summarize the data by producing site-specific descriptive summaries, which will include key information (quotes and themes) about our findings for each of the DSF constructs - the intervention (SCIP), the practice setting/context, and the ecological system. Within the site-specific summaries, we will note any differences in perspectives between key stakeholders at the facility as well as differences by specialty. The site summaries will result in a rich description of each DSF constructs and the factors (e.g., facilitators and barriers) that affect implementation sustainability of SCIP.
Triangulation of Qualitative and Quantitative Data:When site-specific summaries are complete, we will triangulate our quantitative and qualitative findings. Utilizing Miles and Huberman’s analytical approaches,27 we will triangulate the quantitative data elements from the facilities and specialties and the compliance rates with the qualitative findings to create a cross-site matrix. We will compare and contrast evidence to determine the key factors that may affect implementation sustainability of SCIP for sites with high or low SCIP complianceas well as for different specialties. We will then develop descriptive cross-site summaries based on our analysis of the integrated data.
Mapping of Findings to Expert Recommendations for Implementation Change (ERIC) Implementation Strategies: The data matrices will be used to map DSF-defined barriers and facilitators to the evidence-based list of implementation strategies developed by the Expert Recommendations for Implementation Change (ERIC) group.20, 28Table 1 provides examples of implementation barriers we may find related to specific DSF constructs, and the selection and specification of ERIC implementation strategies that may be identified as a result of our mapping process (Figure 5). This process will identify and specify implementation strategies to address relevant challenges particular to pre- and post-operative antimicrobial use and compliance, and will inform the creation of an implementation playbook.
Following completion of the triangulation of qualitative and quantitative data, and mapping of findings to ERIC implementation strategies, we will create an implementation playbook: a document that comprehensively describes how to sustain SCIP best practices in sites with low compliance on either of the SCIP antimicrobial use metrics; different implementation strategies may be required depending upon whether sites have low sustainability with SCIP INF 1, 3, or both. The playbook will reflect the DSF in describing to sites the need to adapt SCIP antimicrobial use metrics and the ERIC implementation strategies to fit local practice settings, the local environment and Veteran populations (ecological system).Examples of sections that may be included in the implementation playbook are:1) evidence base for the SCIP metrics; 2) advice on communicating with facility leadership and staff about SCIP to generate interest; 3) assessment tools for sites to gauge their readiness; 4) planning tools, such as timelines to ensure appropriate rollout of relevant interventions; 5) operational tools, such as example policies, checklists, and example electronic ordersets that can be locally adapted; 6) training curricula and educational materials for SCIP site champions to train other staff; 7) a plan for supporting ongoing measurement through local adaptation and calibration of the informatics tools developed in our quantitative analyses, and measurement of clinical outcomes, including SSIs, and other adverse events. All of these elements are necessary to support future uptake and sustainment of guideline concordant antimicrobial use.
To ensure the implementation playbook is operationally useful, we will seek feedback from local and national stakeholders using a member checking process.29Member checking, also known as participant or respondent validation, is a technique for exploring the credibility of research results. This willensure that the playbook has been developed as intended to increase the uptake and sustainability of SCIP antimicrobial use metrics at sites with low compliance with one or both of the INF metrics. We will interview a subset of our respondents fromthe qualitative interviews (n∼20) to assess the perceived acceptability and feasibility of elements of the draft implementation playbook, selected implementation strategies, and locations for targeting the sustainability, spread and diffusion of SCIP practices. Using data collection and analysis methods previously described, we will identify areas of the playbook that require further changes and updates.