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Multinomial logistic regression analysis for differentiating 3 treatment outcome trajectory groups for
Kristin Nicole Lewis1, Bernadette Davantes Heckman, Lina Himawan
1Department of Psychology, Ohio University, Athens, OH, USA.
Pain
|March 23, 2011
Summary
Growth mixture modeling identified distinct patient groups for headache disability treatment. Attendance significantly impacts outcomes, suggesting targeted interventions for better headache management.
Area of Science:
- Neurology
- Clinical Psychology
- Health Services Research
Background:
- Headache disability significantly impacts patient quality of life.
- Identifying patient subgroups with distinct treatment trajectories is crucial for personalized care.
Purpose of the Study:
- To identify latent groups based on headache disability outcome trajectories.
- To determine predictors of treatment success in headache subspecialty clinics.
Main Methods:
- Longitudinal study design with 219 patients in headache subspecialty clinics.
- Growth mixture modeling (GMM) to identify treatment outcome trajectories.
- Multinomial logistic regression to identify significant predictors.
Main Results:
- Three distinct trajectory groups were identified: high-disability improvers (11%), high-disability nonimprovers (34%), and moderate-disability improvers (55%).
- Treatment appointment attendance was a significant predictor differentiating high-disability improvers from nonimprovers.
- Approximately 75% of high-disability patients did not show reduced disability after 5 months of preventive pharmacotherapy.
Conclusions:
- Preventive headache pharmacotherapies appear most effective for patients with moderate disability.
- High-disability patients benefit most when they attend all treatment appointments.
- Optimizing treatment adherence is critical for improving outcomes in high-disability headache patients.
