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Arch Dermatol. 2001 Aug;137(8):1063-8.

Predictive model for immunotherapy of alopecia areata with diphencyprone.

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1
Division of Dermatology, Vancouver General Hospital, University of British Columbia, 835 W 10th Ave, Vancouver, British Columbia, Canada V5Z 4E8.

Abstract

BACKGROUND:

Immunotherapy with diphencyprone (diphenylcyclopropenone) is used in the treatment of alopecia areata (AA). Response rates have varied in the literature.

OBJECTIVES:

To determine the efficacy of diphencyprone therapy for AA in the largest reported cohort of patients; to identify patient and treatment factors predictive of therapeutic success; and to develop a practical model for predicting patient response.

METHODS:

The medical records of 148 consecutive patients treated with diphencyprone were reviewed. A clinically significant response to diphencyprone therapy was defined as a cosmetically acceptable response or greater than 75% terminal hair regrowth. Survival analyses using the Kaplan-Meier method and the Cox proportional hazards model were performed to determine significant factors predictive of regrowth and relapse.

RESULTS:

Using a survival analysis model, the cumulative patient response at 32 months was 77.9% (95% confidence interval, 56.8%-98.9%). Variables independently associated with clinically significant regrowth were age at onset of disease and baseline extent of AA. Older age at onset of AA portended a better prognosis. A cosmetically acceptable end point was obtained in 17.4% of patients with alopecia totalis/universalis, 60.3% with 75% to 99% AA, 88.1% with 50% to 74% AA, and 100% with 25% to 49% AA. A lag of 3 months was present between initiation of therapy and development of significant hair regrowth in the first responders. Relapse after achieving significant regrowth developed in 62.6% of patients.

CONCLUSIONS:

Response to diphencyprone treatment in AA is affected by baseline extent of AA and age at disease onset. A prolonged treatment course might be necessary. A predictive model has been developed to assist with patient prognostication and counseling.

PMID:
11493099
[Indexed for MEDLINE]
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