Solam Lee1, Beom Jun Kim1, Chung-Hyeok Lee1, Won-Soo Lee1. 1. Institute of Hair and Cosmetic Medicine, Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea.
Abstract
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.
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.