Clasificación de riesgo en Alopecia Areata

Published in Dermatology

Journal Scan / Research · April 10, 2019

Topographic Phenotypes of Alopecia Areata and Development of a Prognostic Prediction Model and Grading System

JAMA Dermatology

IMPORTANCE

Diverse assessment tools and classification have been used for alopecia areata; however, their prognostic values are limited.

OBJECTIVE

To identify the topographic phenotypes of alopecia areata using cluster analysis and to establish a prediction model and grading system for stratifying prognoses.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study of 321 patients with alopecia areata who visited a single tertiary referral center between October 2012 and February 2017 and underwent 4-view photographic assessment.

EXPOSURES

Clinical photographs were reviewed to evaluate hair loss using the Severity of Alopecia Tool 2. Topographic phenotypes of alopecia areata were identified using hierarchical clustering with Ward's method. Differences in clinical characteristics and prognosis were compared across the clusters. The model was evaluated for its performance, accuracy, and interobserver reliability by comparison to conventional methods.

MAIN OUTCOMES AND MEASURES

Topographic phenotypes of alopecia areata and their major (60%-89%) and complete regrowth probabilities (90%-100%) within 12 months.

RESULTS

A total of 321 patients were clustered into 5 subgroups. Grade 1 (n = 200; major regrowth, 93.4%; complete regrowth, 65.2%) indicated limited hair loss, whereas grades 2A (n = 66; major regrowth, 87.8%; complete regrowth, 64.2%) and 2B (n = 20; major regrowth, 73.3%; complete regrowth, 45.5%) exhibited greater hair loss than grade 1. The temporal area was predominantly involved in grade 2B, but not in grade 2A, despite being comparable in total extent of hair loss. Grade 3 (n = 20; major regrowth, 45.5%; complete regrowth, 25.5%) included diffuse or extensive alopecia areata, and grade 4 (n = 15; major regrowth, 28.2%; complete regrowth, 16.7%) corresponded to alopecia (sub)totalis. No significant differences in prognosis (hazard ratio [HR] for major regrowth, 0.79; 95% CI, 0.56-1.12) were found between grades 2A and 1, whereas grades 2B (HR, 0.41; 95% CI, 0.21-0.81), 3 (HR, 0.24; 95% CI, 0.12-0.50), and 4 (HR, 0.16; 95% CI, 0.06-0.39) had significantly poorer response. Among multiple models, the cluster solution had the greatest prognostic performance and accuracy. The tree model of the cluster solution was converted into the Topography-based Alopecia Areata Severity Tool (TOAST), which revealed an excellent interobserver reliability among 4 dermatologists (median quadratic-weighted κ, 0.89).

CONCLUSIONS AND RELEVANCE

Temporal area involvement should be independently measured for better prognostic stratification. The TOAST is an effective tool for describing the topographical characteristics and prognosis of hair loss and may enable clinicians to establish better treatment plans.

JAMA Dermatology

Topographic Phenotypes of Alopecia Areata and Development of a Prognostic Prediction Model and Grading System: A Cluster Analysis

JAMA Dermatol 2019 Mar 27;[EPub Ahead of Print], S Lee, BJ Kim, CH Lee, WS Lee 

TAKE-HOME MESSAGE

• The authors of this retrospective study developed a grading model to predict the prognosis of alopecia areata (AA). Standardized photos of 321 patients with AA were used to perform a cluster analysis, which lead to the identification of five distinct clusters (termed as grades 1, 2A, 2B, 3, and 4) based on location and severity of AA lesions. The authors further developed a decision-tree model for clinical use in grading AA.

• The five defined grades accurately predicted 12-month hair regrowth, with higher grades indicating poorer prognosis. Notably, involvement of the temporal area was associated with significantly poorer prognosis than when the temporal area was spared.

– Caitlyn T. Reed, MD

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Benjamin Hidalgo-Matlock
Skin Care Physicians of Costa Rica

Clinica Victoria en San Pedro: 4000-1054
Momentum Escazu: 2101-9574

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