Multinomial logistic regression analysis for differentiating 3 treatment outcome trajectory groups for headache-associated disability.

Abstract:

:Growth mixture modeling (GMM) identified latent groups based on treatment outcome trajectories of headache disability measures in patients in headache subspecialty treatment clinics. Using a longitudinal design, 219 patients in headache subspecialty clinics in 4 large cities throughout Ohio provided data on their headache disability at pretreatment and 3 follow-up assessments. GMM identified 3 treatment outcome trajectory groups: (1) patients who initiated treatment with elevated disability levels and who reported statistically significant reductions in headache disability (high-disability improvers; 11%); (2) patients who initiated treatment with elevated disability but who reported no reductions in disability (high-disability nonimprovers; 34%); and (3) patients who initiated treatment with moderate disability and who reported statistically significant reductions in headache disability (moderate-disability improvers; 55%). Based on the final multinomial logistic regression model, a dichotomized treatment appointment attendance variable was a statistically significant predictor for differentiating high-disability improvers from high-disability nonimprovers. Three-fourths of patients who initiated treatment with elevated disability levels did not report reductions in disability after 5 months of treatment with new preventive pharmacotherapies. Preventive headache agents may be most efficacious for patients with moderate levels of disability and for patients with high disability levels who attend all treatment appointments.

journal_name

Pain

journal_title

Pain

authors

Lewis KN,Heckman BD,Himawan L

doi

10.1016/j.pain.2011.02.001

subject

Has Abstract

pub_date

2011-08-01 00:00:00

pages

1718-1726

issue

8

eissn

0304-3959

issn

1872-6623

pii

00006396-201108000-00010

journal_volume

152

pub_type

杂志文章,多中心研究

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