Evaluation of an Algorithm to Reduce Antibiotic Prescribing for Acute Bronchitis
November 23, 2016 updated by: University of Pennsylvania
Development, Implementation, and Evaluation of Novel Strategies to Reduce Inappropriate Antimicrobial Use in Community and Healthcare Settings
Inappropriate use of antibiotics to treat patients with acute bronchitis is a significant factor contributing to the selection of antimicrobial drug resistant pathogens, which threaten the effectiveness of available therapies to treat common community-acquired bacterial infections.
A key factor driving overuse of antibiotics is inaccurate estimation of pneumonia risk among patients with acute cough illnesses.
This study will use a cluster randomized trial design within the Geisinger Health System's integrated clinic network to measure the efficacy of an algorithm driven clinical decision support tool to safely reduce the frequency of unnecessary antibiotic prescriptions for adult patients with lower respiratory tract infections.
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Detailed Description
The rapid rise in antibiotic resistance among common bacteria are adversely affecting the clinical course and health care costs of community-acquired infections.
Because antibiotic resistance patterns are strongly correlated with antibiotic use patterns, multiple organizations have declared reductions in unnecessary antibiotic use to be critical components of efforts to combat antibiotic resistance.
Among humans, the vast majority of unnecessary antibiotic prescriptions are used to treat acute respiratory tract infections (ARIs) that have a viral etiology.
In particular, despite the fact that numerous controlled trials have demonstrated no benefit of antibiotic therapy for patients with acute bronchitis, the majority of patients diagnosed with acute bronchitis continue to receive antibiotic therapy across diverse treatment settings.
Recently, the National Committee on Quality Assurance adopted the proportion of adult visits diagnosed as acute bronchitis when an antibiotic was NOT prescribed as a quality measure within the HEDIS data set.
Recent results from the HEDIS dataset emphasize the continued high rates of antibiotic prescribing for patients with acute bronchitis.
One key factor driving overuse of antibiotics in the management of patients with lower respiratory tract infections-such as acute bronchitis-is diagnostic uncertainty and inaccurate risk estimation of underlying pneumonia in such patients.
Recently, our study team has observed substantial reductions in antibiotic prescribing following the incorporation of a diagnostic and treatment algorithm into an acute care setting.
This acute cough management algorithm incorporates data on vital signs and symptoms distinguishing patients with community-acquired pneumonia from other patients with acute cough illness, specifically those with acute bronchitis.
The acute cough management algorithm has become even more valuable in recent years due to the introduction of quality measures that emphasize the timely administration of antibiotics for patients with community-acquired pneumonia.
Thus, strong empirical evidence of the effectiveness of such an algorithm could lead to wide adoption of the algorithm and substantial improvements in antibiotic prescribing.
The investigative team is proposing a unique partnership with Geisinger Health System, a large integrated health network, to implement and evaluate the algorithm.
Utilizing a cluster-randomized trial design across 33 practice sites, we will address the following aims: 1) To measure the reduction in antibiotic prescribing resulting from incorporation of the algorithm compared to usual care sites utilizing two different implementation strategies, one poster-based and one electronic health record-based, 2) To measure revisits, delayed hospitalizations and net economic costs associated with algorithm implementation, and 3) To evaluate local practice characteristics influencing the level of implementation and ultimate performance success at intervention sites.
In a final component of the study, the investigators will partner with NCQA to disseminate study results through the national network of participating plans and stimulate wide spread adoption of the algorithm and quality improvement methods.
Study Type
Interventional
Enrollment (Actual)
3300
Phase
- Not Applicable
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
16 years and older (Child, Adult, Older Adult)
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Description
Inclusion Criteria:
- Primary care practice sites within the Geisinger Health System
Exclusion Criteria:
- Sites with < 1000 visits per year for acute respiratory infection
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
No Intervention: Usual Care
|
|
|
Experimental: Electronic Decision Support
Use of electronic decision support to provide the treatment algorithm for providers managing patients with acute respiratory infections.
|
Use of history and physical examination findings to estimate probability of pneumonia in patients with acute respiratory infections and thereby guide treatment decisions
|
|
Experimental: Paper Decision Support
Use of paper based tools to provide the treatment algorithm for providers managing patients with acute respiratory infections.
|
Use of history and physical examination findings to estimate probability of pneumonia in patients with acute respiratory infections and thereby guide treatment decisions
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
Proportion of visits to primary care clinic associated with antibiotic prescriptions
Time Frame: 30 days
|
30 days
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Investigators
- Principal Investigator: Joshua P Metlay, MD, PhD, University of Pennsylvania
- Principal Investigator: Ralph Gonzales, MD,MS, University of California, San Francisco
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start
October 1, 2009
Primary Completion (Actual)
May 1, 2011
Study Completion (Actual)
September 1, 2012
Study Registration Dates
First Submitted
September 14, 2009
First Submitted That Met QC Criteria
September 18, 2009
First Posted (Estimate)
September 22, 2009
Study Record Updates
Last Update Posted (Estimate)
November 28, 2016
Last Update Submitted That Met QC Criteria
November 23, 2016
Last Verified
November 1, 2016
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 5R01CI000611 (U.S. NIH Grant/Contract)
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.