Evaluation of an Algorithm to Reduce Antibiotic Prescribing for Acute Bronchitis

NCT ID: NCT00981994

Last Updated: 2016-11-28

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 3300 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2009-10-31
• Study Completion Date: 2012-09-30

Brief Summary

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.

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.

Conditions

Acute Respiratory Tract Infection

Keywords

respiratory infection; antimicrobial drugs; decision support; Adult patients

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Electronic Decision Support

Use of electronic decision support to provide the treatment algorithm for providers managing patients with acute respiratory infections.

Group Type: EXPERIMENTAL

Decision Support for ARI Management

Intervention Type: BEHAVIORAL

Use of history and physical examination findings to estimate probability of pneumonia in patients with acute respiratory infections and thereby guide treatment decisions

Paper Decision Support

Use of paper based tools to provide the treatment algorithm for providers managing patients with acute respiratory infections.

Group Type: EXPERIMENTAL

Decision Support for ARI Management

Intervention Type: BEHAVIORAL

Use of history and physical examination findings to estimate probability of pneumonia in patients with acute respiratory infections and thereby guide treatment decisions

Usual Care

Usual Care

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

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

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Primary care practice sites within the Geisinger Health System

Exclusion Criteria

• Sites with < 1000 visits per year for acute respiratory infection

• Minimum Eligible Age: 16 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of California, San Francisco

OTHER

Sponsor Role: collaborator

Geisinger Clinic

OTHER

Sponsor Role: collaborator

University of Pennsylvania

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Joshua P Metlay, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

University of Pennsylvania

Ralph Gonzales, MD,MS

Role: PRINCIPAL_INVESTIGATOR

University of California, San Francisco

References

Gonzales R, Anderer T, McCulloch CE, Maselli JH, Bloom FJ Jr, Graf TR, Stahl M, Yefko M, Molecavage J, Metlay JP. A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis. JAMA Intern Med. 2013 Feb 25;173(4):267-73. doi: 10.1001/jamainternmed.2013.1589.

Reference Type: DERIVED

PMID: 23319069 (View on PubMed)

Other Identifiers

5R01CI000611

Identifier Type: NIH

Identifier Source: org_study_id

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