A Study Proposing a Pattern-Based Dermoscopic Algorithm for Diagnosis of Nail Psoriasis

Ahu Yorulmaz

doi:10.5826/dpc.1601a5881

A Study Proposing a Pattern-Based Dermoscopic Algorithm for Diagnosis of Nail Psoriasis

Authors

Ahu Yorulmaz Ankara Bilkent City Hospital, Department of Dermatology, Ankara, Turkey

Keywords:

nail psoriasis,, dermoscopy, algorithm, diagnosis

Abstract

Background: Nail psoriasis (NP) presents with various morphological patterns. Identifying these patterns enhances the recognition and diagnosis of NP. Dermoscopy is the only modality capable of providing accurate information regarding these patterns.

Objective: This study aimed to identify and classify NP into distinct dermoscopic patterns and evaluate their relationships with disease severity and the risk of psoriatic arthritis. A secondary objective was to propose a pattern-based dermoscopic algorithm for NP diagnosis.

Methods: This prospective study included 208 patients diagnosed with NP and 418 individuals without any nail disorders as controls. Over 1 yr, all participants underwent a dermoscopic examination of each nail.

Results: Two primary dermoscopic patterns were observed in NP patients: the bed and matrix (BM) pattern and trachyonychia. The BM pattern, the prototypical presentation of NP, involved findings related to both the nail bed and nail matrix. Trachyonychia was characterized by rough nail surfaces and pitting. The BM pattern was more prevalent than trachyonychia. However, patients with trachyonychia exhibited more severe psoriasis and NP and were more likely to develop psoriatic arthritis. Leukonychia was more common in the control group.

Conclusion: Although dermoscopy is a highly reliable diagnostic tool, it remains underutilized in NP diagnosis. This study demonstrates that NP exhibits specific morphological patterns that distinguish it from other nail disorders. A pattern-based dermoscopic algorithm was proposed, potentially eliminating the need for histopathological examination when these characteristic patterns are detected.

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