predictors of asp recommendation disagreement 807
and provider-level factorsmight also predict PAFR disagreement at our institution.
methods Study Setting
Lucile Packard Children’s Hospital Stanford (LPCHS) is a 302-bed freestanding children’s hospital in Palo Alto, California. The level IV neonatal intensive care unit (NICU) has 40 beds; the level II intermediate care nursery (ICN) has 20 beds; the cardiovascular intensive care unit (CVICU) has 32 beds; and the pediatric intensive care unit (PICU) has 36 beds. In addition to stem cell transplant, the hospital has high-volume heart, kidney, liver, multivisceral, and lung transplant programs. Also, 2 community hospital settings with pediatric services are licensed and staffed by our institution: a 30-bed general pediatric unit and a 6-bed level II NICU.
Prospective Audit and Feedback Program
The PAF program at LPCHS began on March 30, 2015, with review of injectable antimicrobial orders active for ≥48 hours in the PICU. The program subsequently expanded to the NICU and ICN on July 20, 2015, the CVICU on December 7, 2015, the cardiology service on February 8, 2016, and the hematology-oncology service on May 31, 2016. These units were selected due to their high rates of antimicrobial utilization and presumed opportunities for improvement. Prospective audit and feedback were initially performed by the ASP medical director and ASP pharmacist 3 days per week before the addition of a second ASP pharmacist on September 13, 2016, allowed for the expansion of PAF to 5 days per week. The PAF program subsequently expanded to all inpatient pediatric units at LPCHS on January 17, 2017. Recommenda- tions were generally discussed with the unit-based pharmacist (UBP), who subsequently communicated any recommendations directly with the patient care team(Figure 1). WhentheUBPwas not available, recommendations were communicated directly to the patient care team by an ASP representative. When the infectious disease (ID) service was consulting, PAFR were com- municatedwith the IDteamrather than the primary service. The ASP team also tracked and documented whether the care team adhered to the recommendations within 48 hours of audit by noting whether the recommendations were followed, whether they were not followed (with a reason provided), or whether an alternative approach was agreed upon between the ASP and care team. In addition to the verbal communication, all PAFR, including whether the team did or did not follow the recom- mendation, were documented in the electronic medical record beginning June 2, 2016. In addition to PAF, our hospital has a limited restricted formulary of antimicrobials (ie, cidofovir, colistin, daptomycin, linezolid, micafungin, posaconazole, and tigecycline) that require approval from the ID team prior to being dispensed from the pharmacy.
Study Design
All patients admitted to LPCHS with an audit between March 30, 2015, and April 17, 2017, were included in the study. Because patients could be on multiple antimicrobial regimens during their hospital stay and because multiple audits and recommendations can occur for a given antimicrobial, the unit of measure was each recommendation. The PAFRs not followed due to reason other than disagree (eg, patient was discharged or patient was transferred to a unit without active audit and feedback) were excluded. The Stanford University School of Medicine Institutional Review Board approved the study protocol. Data were obtained from the LPCHS ASP team’s internal
PAF tracking system and the LPCHS enterprise data warehouse. Potential predictors for PAFR disagreement were categorized as patient level, antimicrobial, programmatic, and provider level. Patient-level data included patient demo- graphics, markers of infection (eg, procalcitonin, C-reactive protein, or fever), presence of mechanical ventilation, presence of ventricular assist device (VAD) or extracorporeal membrane oxygenation (ECMO) cannulation, and receipt of solid organ or stem-cell transplant. The documented infec- tious problem for antimicrobial therapy (online Appendix A), total hospital length of stay (LOS), LOS in an intensive care unit, and time from admission to audit were also captured. Antimicrobials were categorized based on their spectrum of activity (online Appendix B). Programmatic factors included PAFR type (online Appendix C), communicator of PAFR to patient care team (online Appendix D), and the time between PAF program commencement in a given unit and the date of audit. Provider-level characteristics included the medical service, attending gender, and number of years the attending physician had been in practice since completing medical training. The PAFR outcome data included whether the recommendation was followed, and if not, the reason. Recommendations that resulted in an alternative approach agreed upon between the patient care team and the ASP were considered to have been followed. The primary outcome of interest for this study was disagreement with PAFR. Potential predictors of disagreement were assessed, including patient-level, antimicrobial, pro- grammatic, and provider-level factors. Categorical predictors were compared with the outcome variable using the Pearson χ2 test of independence. A logistic regression model was used to estimate the probability that a recommendation was not fol- lowed based on the additive effects of a collection of categorical and numeric variables that were deemed potentially relevant either clinically or operationally. The relationship between numeric variables and the outcome variable was explored for nonlinearity. In cases in which a nonlinear relationship was discovered, a categorical variable was created from the numerical variable that would allow the model to fit the non- linear aspect of the relationship. Initially, a backward stepwise selection was used where variables were dropped if the P value
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