RESUMO
BACKGROUND: Bullous pemphigoid affects elderly individuals with multiple comorbidities, making conventional treatments unsuitable. OBJECTIVE: Evaluate the effectiveness and safety of dupilumab in the treatment of bullous pemphigoid. METHODS: A multicenter ambispective cohort study was conducted in 34 hospitals. Patients with bullous pemphigoid treated with Dupilumab were included. Most of patients (97.1%) received an initial 600 mg dose followed by 300 mg every two weeks. OUTCOMES AND MEASURES: The primary outcome was the proportion of patients achieving complete remission within 4 weeks, defined as Investigator Global Assessment score of 0 or 1. Complete remission at weeks 16, 24, and 52, adverse events, reductions in peak pruritus numerical rating scale, and systemic glucocorticoid use were also assessed. RESULTS: The study included 103 patients with a median age of 77.3 years, 58.0% male. Complete remission was achieved by 53.4% within 4 weeks and 95.7% by week 52. Peak pruritus scale reduced by 70.0% by week 4 and was completely controlled by week 24. Thirteen patients presented adverse events, most of which were mild. Systemic glucocorticoid use reduced by 82.1% by week 52. Shorter disease duration and exclusive cutaneous involvement predicted better response at 16 weeks. No differences in response rates to dupilumab were observed between drug-associated bullous pemphigoid and idiopathic cases. No significant difference in response rates was observed between patients treated with dupilumab in monotherapy and those receiving dupilumab with concomitant treatments. CONCLUSIONS: Dupilumab is effective, rapid, and safe in managing bullous pemphigoid, reducing the need for corticosteroids and other treatments. Early initiation and exclusive skin involvement predict better outcomes.
RESUMO
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.