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Computerized Decision Support System for Antibiotic Treatment

This study has been completed.
Sponsor:
ClinicalTrials.gov Identifier:
NCT00233376
First Posted: October 5, 2005
Last Update Posted: July 18, 2006
The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
Collaborator:
Eu Fifth Framework IST
Information provided by:
Rabin Medical Center
  Purpose
We developed a computerized decision support system for prescription of antibiotics to inpatients. The purpose of the study is to assess the performance of the system in different wards, in three different hospitals, in three countries.

Condition Intervention Phase
Community-Acquired Infection Cross Infection Procedure: Access to an antibiotic decision support system Behavioral: Distribution of local antibiotic guidelines Phase 3

Study Type: Interventional
Study Design: Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Treatment
Official Title: Improving Empirical Antibiotic Treatment Using TREAT,a Computerized Decision Support System. Cluster Randomized Trial

Resource links provided by NLM:


Further study details as provided by Rabin Medical Center:

Primary Outcome Measures:
  • Appropriate antibiotic treatment

Secondary Outcome Measures:
  • Overall 30-day mortality
  • Durations of fever
  • Duration of hospital stay
  • Antibiotic use
  • Antibiotic costs
  • Adverse events

Estimated Enrollment: 1500
Study Start Date: May 2004
Estimated Study Completion Date: November 2004
Detailed Description:

Antibiotic treatment for suspected moderate to severe bacterial infections is usually initiated empirically, prior to identification of the causative pathogen. Appropriate treatment, that is matching in-vitro susceptibilities of subsequently isolated pathogens, reduces the overall fatality rate of severe infections with adjusted odds ratios varying between 1.6 and 6.9. In the same studies, 20-50% of patients were given inappropriate empirical antibiotic treatment.

We developed a computerized decision support system (TREAT) based on a causal probabilistic network to improve antibiotic treatment of inpatients. The aims of the system were to improve the rate of appropriate antibiotic treatment, thereby reducing mortality, and to route antibiotic use towards ecologically economical antibiotics as determined by local resistance profiles. The system can be calibrated to different locations.

The TREAT system was tested in a multi-center observational cohort study. The study proved the system safe and effective. TREAT prescribed appropriate antibiotic treatment to 70% of patients, 58% of whom were treated appropriately by physicians. TREAT used a narrow antibiotic formulary and at lower costs, mainly lowering costs assigned by the model to future resistance. The system performed well in three different countries (Israel, Italy and Germany).

We then proceeded to assess the effect of TREAT on the management of inpatients in these sites in a cluster randomized controlled trial. We used wards as the unit of randomization to avoid contamination through education of users by the system, and to benefit from the interaction of TREAT with the ward as a whole.

Comparison: the TREAT system was installed in intervention wards and its use was offered to physicians at the time of empirical antibiotic treatment. Physicians were asked to inspect TREAT’s result interface. The final choice of antibiotic treatment was theirs. Control wards had no access to the system. We assessed outcomes in intervention vs. control wards with regard to patient outcomes, appropriateness of antibiotic treatment and antibiotic costs.

  Eligibility

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Ages Eligible for Study:   16 Years and older   (Child, Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Patients from whom blood cultures were drawn.
  • Patients prescribed antibiotics (not for prophylaxis).
  • Patients fulfilling sepsis diagnostic criteria.
  • Patients with a focus of infection.
  • Patients with shock compatible with septic shock.
  • Patients with febrile neutropenia

Exclusion Criteria:

  • HIV positive patients with a current (suspected or identified) opportunistic disease and/or AIDS defining illness currently or within the past six months
  • Organ or bone marrow transplant recipients
  • Children <18 years; suspected travel infections or tuberculosis
  • Pregnant women
  • Re-entries
  Contacts and Locations
Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT00233376


Locations
Germany
Department of Clinical Microbiology and Hospital Hygeine, Freiburg University Hospital
Freiburg, Germany
Israel
Rabin Medical Center; Beilinson Campus
Petah Tikva, Israel, 49100
Italy
Department of Infectious Diseases, Gemelli Hospital in Rome
Rome, Italy
Sponsors and Collaborators
Rabin Medical Center
Eu Fifth Framework IST
Investigators
Principal Investigator: Leonard Leibovici, M.D. Rabin Medical Center, Beilinson Campus
Study Chair: Steen Andreassen, PhD Center for Model-based Medical Decision Support, Aalborg University
  More Information

Additional Information:
ClinicalTrials.gov Identifier: NCT00233376     History of Changes
Other Study ID Numbers: IST-1999-11459
Fifth framework IST-1999-11459
First Submitted: October 4, 2005
First Posted: October 5, 2005
Last Update Posted: July 18, 2006
Last Verified: October 2005

Keywords provided by Rabin Medical Center:
Decision support system
Causal probabilistic network
Antibiotics
Antibiotic resistance
Community-acquired infections
Cross-infections

Additional relevant MeSH terms:
Infection
Communicable Diseases
Community-Acquired Infections
Cross Infection
Iatrogenic Disease
Disease Attributes
Pathologic Processes
Anti-Bacterial Agents
Antibiotics, Antitubercular
Anti-Infective Agents
Antitubercular Agents