Spanish cohort of VEXAS syndrome: clinical manifestations, outcome of treatments and novel evidences about UBA1 mosaicism.

BACKGROUND: The vacuoles, E1-enzyme, X linked, autoinflammatory and somatic (VEXAS) syndrome is an adult-onset autoinflammatory disease (AID) due to postzygotic UBA1 variants. OBJECTIVES: To investigate the presence of VEXAS syndrome among patients with adult-onset undiagnosed AID. Additional studies evaluated the mosaicism distribution and the circulating cytokines. METHODS: Gene analyses were performed by both Sanger and amplicon-based deep sequencing. Patients' data were collected from their medical charts. Cytokines were quantified by Luminex. RESULTS: Genetic analyses of enrolled patients (n=42) identified 30 patients carrying UBA1 pathogenic variants, with frequencies compatible for postzygotic variants. All patients were male individuals who presented with a late-onset disease (mean 67.5 years; median 67.0 years) characterised by cutaneous lesions (90%), fever (66.7%), pulmonary manifestations (66.7%) and arthritis (53.3%). Macrocytic anaemia and increased erythrocyte sedimentation rate and ferritin were the most relevant analytical abnormalities. Glucocorticoids ameliorated the inflammatory manifestations, but most patients became glucocorticoid-dependent. Positive responses were obtained when targeting the haematopoietic component of the disease with either decitabine or allogeneic haematopoietic stem cell transplantation. Additional analyses detected the UBA1 variants in both haematopoietic and non-haematopoietic tissues. Finally, analysis of circulating cytokines did not identify inflammatory mediators of the disease. CONCLUSION: Thirty patients with adult-onset AID were definitively diagnosed with VEXAS syndrome through genetic analyses. Despite minor interindividual differences, their main characteristics were in concordance with previous reports. We detected for the first time the UBA1 mosaicism in non-haematopoietic tissue, which questions the previous concept of myeloid-restricted mosaicism and may have conceptual consequences for the disease mechanisms.

Maculopapular eruptions associated to COVID-19: A subanalysis of the COVID-Piel study.

A previous study has defined the maculopapular subtype of manifestations of COVID-19. The objective of our study was to describe and classify maculopapular eruptions associated with COVI-19. We carried out a subanalysis of the maculopapular cases found in the previous cross-sectional study. Using a consensus, we defined seven clinical patterns. We described patient demographics, the therapy received by the patient and the characteristics of each pattern. Consensus lead to the description of seven major maculopapular patterns: morbilliform (45.5%), other maculopapular (20.0%), purpuric (14.2%), erythema multiforme-like (9.7%), pytiriasis rosea-like (5.7%), erythema elevatum diutinum-like (2.3%), and perifollicular (2.3%). In most cases, maculopapular eruptions were coincident (61.9%) or subsequent (34.1%) to the onset of other COVID-19 manifestations. The most frequent were cough (76%), dyspnea (72%), fever (88%), and astenia (62%). Hospital admission due to pneumonia was frequent (61%). Drug intake was frequent (78%). Laboratory alterations associated with maculo-papular eruptions were high C-reactive protein, high D-Dimer, lymphopenia, high ferritin, high LDH, and high IL-6. The main limitation of our study was the impossibility to define the cause-effect relationship of each pattern. In conclusion, we provide a description of the cutaneous maculopapular manifestations associated with COVID-19. The cutaneous manifestations of COVID-19 are wide-ranging and can mimic other dermatoses.

Eruptive pruritic papular porokeratosis or inflammatory form of disseminated superficial porokeratosis: a new case and review of the literature.

Porokeratosis is a heterogeneous group of dermatoses with alterations of keratinization. Histologically, they are characterized by the presence of cornoid lamellae. Eruptive pruritic papular porokeratosis (EPPP) or the inflammatory form of disseminated superficial porokeratosis (or eruptive disseminated porokeratosis) is an infrequent variant, characterized by pruritic erythematous papules or annular lesions. We present a 72-year-old woman with EPPP, exhibited by pruritic lesions on the extremities and back, and review the literature concerning this condition. We found 32 cases of EPPP or inflammatory disseminated superficial porokeratosis (including the current case) reported in the literature, with a median age of 66 years (range, 13-84); 59.3% were men. Eruptive pruritic papular porokeratosis was associated with various neoplasms in 31.2% of cases. Six patients had an associated viral infection. Response to treatment was poor in most cases. Eruptive pruritic papular porokeratosis resolved spontaneously in 75% of cases. Median time to resolution was 6 months (range, 1-24). Eruptive pruritic papular porokeratosis (or inflammatory disseminated superficial porokeratosis/eruptive disseminated porokeratosis) is an infrequent variant of porokeratosis characterized by intense pruritus and spontaneous resolution in most individuals. Eruptive pruritic papular porokeratosis can be associated with neoplasms and screening for malignancies is recommended if clinically indicated.

Clinical and photobiological response in eight patients with solar urticaria under treatment with omalizumab, and review of the literature.

BACKGROUND: Solar urticaria (SU) is a rare photodermatosis. Treatment is challenging, and outcomes are often disappointing. Omalizumab is an anti-IgE, currently approved for treatment of chronic spontaneous urticaria. We sought to evaluate therapy with omalizumab in refractory SU and describe predictive factors for response. MATERIALS/METHODS: Patients with refractory SU under treatment with omalizumab were included in this study. Clinical outcome was evaluated using the Urticaria Activity Score 7 (UAS7), Dermatology Life Quality Index (DLQI) and Pruritus Visual Analogue Scale (VAS). Complete clinical response (CCR) was defined as having an UAS7 = 0, DLQI <6 and VAS = 0. Phototesting was performed and compared to baseline. We performed a PubMed search to identify reported cases of SU in adults treated with omalizumab, analysing their characteristics in order to predict response to omalizumab. RESULTS: Eight patients were included. Median age was 45.5 years (range, 23-64). Light spectrum most commonly implicated was UV-A. Clinical outcomes: 89% (7/8) achieved CCR with omalizumab. Phototesting was normal in 42.8% (3/7) of them. In our review, we identified 38 patients (including the current case series), and 68.4% showed favourable outcomes with omalizumab. Median time since onset of SU was lower in responders. CONCLUSIONS: Omalizumab can be an effective treatment in refractory SU.

Porokeratosis ptychotropica responding to photodynamic therapy: An alternative treatment for a refractory disease.

BACKGROUND: Porokeratosis ptychotropica (PP) is a rare variant of porokeratosis with a special predisposition to affect body folds, particularly the intergluteal cleft. This disease is resistant to most topical and systemic treatments, as shown in the review of the literature we provide here. Itching and discomfort are often a difficult problem to solve. PATIENTS AND METHODS: Two patients with PP that had not responded to multiple topical treatments were treated with photodynamic therapy (PDT). Changes in plaque size, thickness and symptoms were assessed after treatment. RESULTS: Pruritus disappearance was observed in both patients after treatment with PDT. Partial clearance of the plaques was observed in one case. In the other case, a moderate clearance of hyperkeratosis was observed, although the size of the lesions persisted unchanged. CONCLUSIONS: PDT seems to be a good therapeutic alternative in the treatment of PP, as it can provide symptomatic relief and clinical improvement of the lesions. However, it does not appear to be a curative treatment. Moreover, long-term response is still unknown.

Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care.

Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classification. The objective of this study was to perform a prospective validation of an image analysis ML model, which is capable of screening 44 skin diseases, comparing its diagnostic accuracy with that of General Practitioners (GPs) and teledermatology (TD) dermatologists in a real-life setting. Prospective, diagnostic accuracy study including 100 consecutive patients with a skin problem who visited a participating GP in central Catalonia, Spain, between June 2021 and October 2021. The skin issue was first assessed by the GPs. Then an anonymised skin disease picture was taken and uploaded to the ML application, which returned a list with the Top-5 possible diagnosis in order of probability. The same image was then sent to a dermatologist via TD for diagnosis, as per clinical practice. The GPs Top-3, ML model's Top-5 and dermatologist's Top-3 assessments were compared to calculate the accuracy, sensitivity, specificity and diagnostic accuracy of the ML models. The overall Top-1 accuracy of the ML model (39%) was lower than that of GPs (64%) and dermatologists (72%). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained (n = 82), the balanced Top-1 accuracy of the ML model increased (48%) and in the Top-3 (75%) was comparable to the GPs Top-3 accuracy (76%). The Top-5 accuracy of the ML model (89%) was comparable to the dermatologist Top-3 accuracy (90%). For the different diseases, the sensitivity of the model (Top-3 87% and Top-5 96%) is higher than that of the clinicians (Top-3 GPs 76% and Top-3 dermatologists 84%) only in the benign tumour pathology group, being on the other hand the most prevalent category (n = 53). About the satisfaction of professionals, 92% of the GPs considered it as a useful diagnostic support tool (DST) for the differential diagnosis and in 60% of the cases as an aid in the final diagnosis of the skin lesion. The overall diagnostic accuracy of the model in this study, under real-life conditions, is lower than that of both GPs and dermatologists. This result aligns with the findings of few existing prospective studies conducted under real-life conditions. The outcomes emphasize the significance of involving clinicians in the training of the model and the capability of ML models to assist GPs, particularly in differential diagnosis. Nevertheless, external testing in real-life conditions is crucial for data validation and regulation of these AI diagnostic models before they can be used in primary care.

Using Artificial Intelligence as a Diagnostic Decision Support Tool in Skin Disease: Protocol for an Observational Prospective Cohort Study.

BACKGROUND: Dermatological conditions are a relevant health problem. Each person has an average of 1.6 skin diseases per year, and consultations for skin pathology represent 20% of the total annual visits to primary care and around 35% are referred to a dermatology specialist. Machine learning (ML) models can be a good tool to help primary care professionals, as it can analyze and optimize complex sets of data. In addition, ML models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and classification. OBJECTIVE: This study aims to perform a prospective validation of an image analysis ML model as a diagnostic decision support tool for the diagnosis of dermatological conditions. METHODS: In this prospective study, 100 consecutive patients who visit a participant general practitioner (GP) with a skin problem in central Catalonia were recruited. Data collection was planned to last 7 months. Anonymized pictures of skin diseases were taken and introduced to the ML model interface (capable of screening for 44 different skin diseases), which returned the top 5 diagnoses by probability. The same image was also sent as a teledermatology consultation following the current stablished workflow. The GP, ML model, and dermatologist's assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the ML model. The results will be represented globally and individually for each skin disease class using a confusion matrix and one-versus-all methodology. The time taken to make the diagnosis will also be taken into consideration. RESULTS: Patient recruitment began in June 2021 and lasted for 5 months. Currently, all patients have been recruited and the images have been shown to the GPs and dermatologists. The analysis of the results has already started. CONCLUSIONS: This study will provide information about ML models' effectiveness and limitations. External testing is essential for regulating these diagnostic systems to deploy ML models in a primary care practice setting.