Evaluation of an Algorithm to Reduce Antibiotic Prescribing for Acute Bronchitis

The recruitment status of this study is unknown because the information has not been verified recently.
Verified April 2011 by University of Pennsylvania.
Recruitment status was  Active, not recruiting
Sponsor:
Collaborators:
University of California, San Francisco
Geisinger Clinic
Information provided by:
University of Pennsylvania
ClinicalTrials.gov Identifier:
NCT00981994
First received: September 14, 2009
Last updated: April 29, 2011
Last verified: April 2011
  Purpose

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.


Condition Intervention
Acute Respiratory Tract Infection
Behavioral: Decision Support for ARI Management

Study Type: Interventional
Study Design: Allocation: Randomized
Endpoint Classification: Efficacy Study
Intervention Model: Single Group Assignment
Masking: Open Label
Primary Purpose: Treatment
Official Title: Development, Implementation, and Evaluation of Novel Strategies to Reduce Inappropriate Antimicrobial Use in Community and Healthcare Settings

Resource links provided by NLM:


Further study details as provided by University of Pennsylvania:

Primary Outcome Measures:
  • Proportion of visits to primary care clinic associated with antibiotic prescriptions [ Time Frame: 30 days ] [ Designated as safety issue: No ]

Estimated Enrollment: 3300
Study Start Date: October 2009
Estimated Study Completion Date: September 2011
Estimated Primary Completion Date: May 2011 (Final data collection date for primary outcome measure)
Arms Assigned Interventions
Experimental: Electronic Decision Support
Use of electronic decision support to provide the treatment algorithm for providers managing patients with acute respiratory infections.
Behavioral: Decision Support for ARI Management
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.
Behavioral: Decision Support for ARI Management
Use of history and physical examination findings to estimate probability of pneumonia in patients with acute respiratory infections and thereby guide treatment decisions
No Intervention: Usual Care
Usual Care

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.

  Eligibility

Ages Eligible for Study:   16 Years and older
Genders Eligible for Study:   Both
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Primary care practice sites within the Geisinger Health System

Exclusion Criteria:

  • Sites with < 1000 visits per year for acute respiratory infection
  Contacts and Locations
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Please refer to this study by its ClinicalTrials.gov identifier: NCT00981994

Sponsors and Collaborators
University of Pennsylvania
University of California, San Francisco
Geisinger Clinic
Investigators
Principal Investigator: Joshua P Metlay, MD, PhD University of Pennsylvania
Principal Investigator: Ralph Gonzales, MD,MS University of California, San Francisco
  More Information

No publications provided by University of Pennsylvania

Additional publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):
Responsible Party: Joshua P. Metlay, MD, PhD, Associate Professor, University of Pennsylvania
ClinicalTrials.gov Identifier: NCT00981994     History of Changes
Other Study ID Numbers: 5R01CI000611
Study First Received: September 14, 2009
Last Updated: April 29, 2011
Health Authority: United States: Institutional Review Board

Keywords provided by University of Pennsylvania:
respiratory infection
antimicrobial drugs
decision support
Adult patients

Additional relevant MeSH terms:
Respiratory Tract Infections
Infection
Respiratory Tract Diseases
Anti-Infective Agents
Therapeutic Uses
Pharmacologic Actions

ClinicalTrials.gov processed this record on August 28, 2014