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Efficacy Study of a Computer Decision Support System to Treat Depression

A Pilot Efficacy Trial of a Computer Decision Support System Compared to Usual Care for Depression Treatment in Primary Care

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT00551083
Acronym
CDSS-D
Enrollment
60
Registered
2007-10-30
Start date
2005-03-31
Completion date
2006-06-30
Last updated
2007-10-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Depressive Disorder

Brief summary

The purpose of this study was designed to test the feasibility and effectiveness of implementing a Computerized Decision Support System for depression (CDSS-D) during acute care in a primary care setting.

Detailed description

The research project was designed to test the feasibility and effectiveness of implementing a Computerized Decision Support System for depression (CDSS-D) during acute care in a primary care setting. The basic experimental design was a two-group, two-site study design. Three different clinics with a total of 4 primary care physicians agreed and provided informed consent and participated in the study. Half of the physicians used the CDSS-D to treat patients with MDD and the other half provided Usual Care (UC) treatment. The intervention, CDSS-D, incorporated a pre-existing depression treatment algorithm (Texas Medication Algorithm Project for Depression, Trivedi et al) with computer decision support programming, providing the treatment group physicians with a computerized algorithm.

Interventions

OTHERComputerized Decision Support System for Depression (CDSS-D)

The CDSS for depression was based on an up-to-date model of the Texas Medication Algorithm Project that employs the principles of Measurement Based Care (MBC), while at the same time having a user interface for providers that is easy-to-use. MBC is the systematic use of measuring clinical outcomes at routine visits to guide treatment management. These outcomes may include symptoms, side effects, and medication adherence. Recent efforts from the large, multi-site effectiveness study, Sequenced Treatment Alternatives to Relieve Depression (STAR\*D), show that a treatment plan guided by MBC is integral in implementing algorithm based care.

OTHERUsual Care (UC)

Usual Care was up to the discretion of the study physician and the patient treated. These physicians were provided with up-to-date treatment protocols for depression, but were not instructed to strictly adhere to a treatment algorithm. Therefore, they treated depressed patients as they usually would.

Sponsors

Pfizer
CollaboratorINDUSTRY
University of Texas Southwestern Medical Center
Lead SponsorOTHER

Study design

Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Outpatients aged 18 and over * Met Criteria for Non-Psychotic Major Depressive Disorder * Had a baseline HRSD-17 score of at least 14

Exclusion criteria

* Patients with a current Axis I diagnosis of somatization disorder, anorexia nervosa, bulimia, or obsessive-compulsive disorder * Patients with current alcohol or substance dependence * Women with a positive pregnancy test or who are lactating * Women of child-bearing potential who are not practicing a clinically accepted method of contraception * Patients with general medical conditions that contraindicate antidepressant medications * Patient whose clinical status requires inpatient or day hospital treatment

Design outcomes

Primary

MeasureTime frame
Mean change from baseline in the 17-item Hamilton Rating Scale for Depression (HRSD) Score24 weeks

Secondary

MeasureTime frame
Mean change from baseline on the 16-item Quick Inventory of Depressive Symptomatology - Self Report (QIDS-SR-16)24 weeks
Mean change from baseline on the 30-item Inventory of Depressive Symptomatology - Clinician's version (IDS-C-30)24 weeks

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Apr 3, 2026