Cluster Analysis of Clinical Remission and Relapse Patterns in Chronic Urticaria: Results from the PREDICT-CU Study

Scritto il 17/03/2025
da Maria-Magdalena Balp

Dermatol Ther (Heidelb). 2025 Mar 17. doi: 10.1007/s13555-025-01376-4. Online ahead of print.

ABSTRACT

INTRODUCTION: Patients with chronic urticaria (CU) may have different clinical courses of disease including periods of active CU, clinical remission, and relapse. The objective of this study was to describe representative clinical remission and relapse profiles for patients with CU.

METHODS: Adults with a CU diagnosis and confirmation CU diagnosis/CU-related treatment at least 6 weeks later were identified in the OptumĀ® de-identified Electronic Health Record dataset (2007-2018). Active CU was a period during which a patient was not in clinical remission. Clinical remission was defined as at least 12 months without CU diagnosis and/or treatment. Relapse was defined as having a CU diagnosis and/or treatment following clinical remission. A data-driven clustering algorithm grouped patients on the basis of clinical remission and relapse patterns.

RESULTS: The 112,443 patients were grouped into four clusters. Cluster 1 (N = 36,690 [32.6%]) had the shortest median time to clinical remission (4.1 months) and lowest relapse rate (38.0%). Cluster 2 (N = 29,834 [26.5%]) reached clinical remission later (10.0 months), with a higher relapse rate (52.3%). Clusters 3 (N = 24,093 [21.4%]) and 4 (N = 21,826 [19.4%]) had the longest median times to clinical remission (33.8 and 44.6 months) and highest relapse rates (75%). Cluster 4 had the most frequent CU diagnoses and treatments, and highest comorbidity burden, polypharmacy, and resource use.

CONCLUSIONS: Patients in Clusters 3 and 4 had the lowest clinical remission and highest relapse rates relative to Clusters 1 and 2; additionally, Cluster 4 had higher resource use, more comorbidities, and polypharmacy. These cluster definitions could be used to develop a model to predict patients with relapsing and remitting patterns associated with higher disease burden who might require enhanced disease management.

PMID:40095234 | DOI:10.1007/s13555-025-01376-4