Development and Validation of Algorithms to Identify Individuals With Cutaneous Lupus From Healthcare Databases.

Background: There are no validated methods to identify individuals with cutaneous lupus erythematosus (CLE) from large databases including claims data and electronic health records, severely limiting the study of the epidemiology of this disease.

Objectives: To develop and validate accurate algorithms to identify individuals with CLE from healthcare records.

Methods: Twelve case-finding algorithms were developed based on the International Classification of Diseases (ICD)-10 diagnosis codes, provider specialty, and medication prescription data.

Development of a working core outcome set for cutaneous lupus erythematosus: a practical approach to an urgent unmet need.

Objective: The lack of standardised outcomes and outcome measures for cutaneous lupus erythematosus (CLE) represents a substantial barrier to clinical trial design, comparative analysis and approval of novel investigative treatments. We aimed to develop a working core outcome set (COS) for CLE randomised controlled trials and longitudinal observational studies.

Methods: We conducted a multistage literature review of CLE and SLE studies to generate candidate domains and outcome measures.

Differences in Utilization of Nonvideo Telemedicine Visits for Dermatologic Concerns in Underserved Populations During the COVID-19 Pandemic.

Usage of telemedicine for virtual dermatology care during the COVID-19 pandemic on a national scale is poorly characterized, particularly for nonvideo encounters. We sought to compare utilization of telephone and asynchronous virtual care for dermatologic concerns 3 months before (December 2019-February 2020) and during the pandemic (March-May 2020) across patient populations. A retrospective study was performed using a national claims database with >280 million patients within the COVID-19 Research Database to identify monthly telephone and asynchronous virtual visits by diagnosis, age, income, and patient race/ethnicity.

Towards gender equity in artificial intelligence and machine learning applications in dermatology.

There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin cancers and assessment of other dermatologic conditions. As these technologies continue to expand, it is essential to ensure they do not create or widen sex- and gender-based disparities in care. While desirable bias may result from the explicit inclusion of sex or gender in diagnostic criteria of diseases with gender-based differences, undesirable biases can result from usage of datasets with an underrepresentation of certain groups.

Cutaneous lupus erythematosus and cardiovascular disease: current knowledge and insights into pathogenesis.

Multiple autoinflammatory diseases, including psoriasis, psoriatic arthritis, and systemic lupus erythematosus, have been linked to increased risk of cardiovascular disease. Inflammation is known to play a key role in the pathogenesis of atherosclerosis, thus the contribution of systemic immune dysregulation, which characterizes such inflammatory conditions, towards the development of cardiovascular disease has garnered considerable interest. Cutaneous lupus erythematosus (CLE) is a chronic inflammatory skin disease, but risk of cardiovascular disease amongst patients with cutaneous lupus is less well known.