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

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.

Clinically Applicable Deep Learning Framework for Measurement of the Extent of Hair Loss in Patients With Alopecia Areata

“…The measurement errors significantly improved with use of the computer-assisted approach. Furthermore, the interrater reliability also significantly improved when SALT scores were graded using model-assisted metrics …”

“…Furthermore, by adding a temporal subset of SALT score (SALT temporal ) as an additional input variable, the iAUC was significantly increased further. With the categorical metrics, a similar pattern of change was observed, although with less prominent statistical significance. Similarly, the highest iAUC was reached when the classifications based on the computer-assisted measurements were used.…”

“…A Cox proportional hazard model for major hair regrowth was fitted to investigate whether the computer-assisted measurements could result in better prognostic predictions (Figure 2D). The explanatory power of the SALT score significantly increased with the computer-assisted approach.…”

“…The explanatory power of the SALT score significantly increased with the computer-assisted approach. Furthermore, the performance significantly improved by adding a temporal subset of SALT scores as an additional independent factor …”

The Evolving Pathogenesis of Alopecia Areata: Major Open Questions

“…For example, this ranges from a relatively high chance of spontaneous hair regrowth in non-atopic patients whose first hair loss episode occurred after puberty and without associated nail dystrophy or family history for AA and other autoimmune diseases, to a poor prognosis for spontaneous disease remission in pre-pubertal atopic patients with a family history for AA or other autoimmune diseases (Gilhar et al, 2012;Strazzulla et al, 2018). In addition, we still lack a satisfactory mechanistic explanation why exactly AA lesion topography (e.g., the presence of ophiasis or nail involvement) are reliable prognostic markers (Lee et al, 2019;Roest et al, 2018), and why various comorbidities (Lee et al, 2019a(Lee et al, , 2019b) such as Down Syndrome and lupus erythematosus are associated with a negative prognosis.…”