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The objective of this study is to evaluate implementation of diagnostic stewardship programs as a strategy to safely reduce antibiotic use, and to generate evidence and tools to support dissemination of diagnostic stewardship programs to a large and diverse group of hospitals.
Full description
The Bright STAR Collaborative, or Testing STewardship to reduce Antibiotic Resistance Collaborative, is a prospective multicenter quality improvement (QI) program with the goal of implementing diagnostic stewardship interventions to reduce bacterial culture use as a strategy to reduce antibiotic overuse. Investigators will use data collected by participating sites to determine whether reliable implementation of clinical practice guidelines for evaluation of patients can decrease antibiotic use in pediatric intensive care units. Investigators will perform a quasi-experimental study to compare outcome data in pre- and post- periods.
Greater than or equal to 10 institutions will participate in this collaborative. Participating institutions will develop and implement an evidenced-based clinical decision-making tool as part of their quality improvement (QI) program in their pediatric intensive care unit (PICU).
Specific Aim 1: Evaluate whether locally devised quality improvement programs focused on diagnostic stewardship of respiratory cultures lead to a reduction in respiratory cultures and antibiotic use.
Specific Aim 2: To determine whether these quality improvement initiatives are associated with unintended consequence of patient harm such as mortality, length of stay, readmissions, ventilator associated infections, sepsis and septic shock.
Variables: total respiratory culture rates, culture results, ICU length of stay, mortality rates, hospital and ICU readmission, cause of death, ventilator-associated infection/ventilator-associated condition rate, sepsis, septic shock.
Analysis: The analytic approach equates to estimating and comparing the respiratory culture incidence during the "baseline/pre-implementation" and "post-implementation" periods, using a generalized linear mixed model (GLMM) assuming a Poisson distribution for the monthly number of respiratory cultures with the monthly number of ventilator days as an offset. Similar analyses will be performed for secondary outcomes.
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Data sourced from clinicaltrials.gov
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