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Zusammenfassung Hintergrund Psoriasisplaques an den Unterschenkeln zeigen sich oftmals besonders therapierefraktär. Eine Kompressionstherapie könnte an dieser Lokalisation möglicherweise eine sinnvolle ergänzende Therapiemaßnahme darstellen. Jedoch bestehen oft Bedenken, dass ein Köbner-Phänomen zu einer Verschlechterung des Hautbefundes führen könnte. Daher sollten in dieser Studie die Effekte einer Kompressionstherapie auf Psoriasisplaques bei gleichzeitig bestehenden Ödemen an den Unterschenkeln untersucht werden. Patienten und Methoden Es erfolgte ein Halbseitenversuch bei dem zusätzlich zum „standard of care“ eine 4‑wöchige Kompressionstherapie durchgeführt wurde. Der primäre Endpunkt war das klinische Ansprechen der Psoriasis-Plaques an den Unterschenkeln im Seitenvergleich gemessen mittels Lesion Severity Score (LSS) und der lokal betroffenen Körperoberfläche in Woche 4 im Vergleich zum Ausgangsbefund. Sekundäre Endpunkte bezogen sich auf „patient-reported outcomes“. Ergebnisse In die finale Analyse gingen die Daten von 30 Patienten ein. Die mittleren LSS-Befunde sowie die subjektiven Schmerzen der Patienten zeigten in der rein deskriptiven Auswertung eine geringgradig stärkere Verbesserung im Bereich des komprimierten im Vergleich zum nicht komprimierten Unterschenkel. Es ergaben sich keine Hinweise auf ein Köbner-Phänomen. Diskussion Es handelt sich um die erste klinische Studie, die systematisch den Einfluss einer Kompressionstherapie auf Psoriasisplaques untersuchte. Im Untersuchungszeitraum von 4 Wochen ergaben sich zwar keine signifikanten Besserungen der Psoriasisplaques, allerdings auch keine Hinweise für eine Verschlechterung des Hautbefundes. Folglich kann eine antiödematöse Kompressionstherapie bei Patienten mit Psoriasis unter Beachtung der grundlegenden Kontraindikationen ohne Komplikationen durchgeführt werden.

BackgroundThe rate of seroconversion after COVID-19 vaccination in patients with moderate to severe psoriasis requiring systemic treatment is poorly understood.ObjectivesThe aim of this prospective single-center cohort study performed between May 2020 and October 2021 was to determine the rate of seroconversion after COVID-19 vaccination in patients under active systemic treatment for moderate to severe psoriasis.MethodsInclusion criteria were systemic treatment for moderate to severe psoriasis, known COVID-19 vaccination status, and repetitive anti-SARS-CoV-2-S IgG serum quantification. The primary outcome was the rate of anti-SARS-CoV-2-S IgG seroconversion after complete COVID-19 vaccination.Results77 patients with a median age of 55.9 years undergoing systemic treatment for moderate to severe psoriasis were included. The majority of patients received interleukin- (n=50, 64.9%) or tumor necrosis factor (TNF)-α inhibitors (n=16, 20.8%) as systemic treatment for psoriasis; nine patients (11.7%) were treated with methotrexate (MTX) monotherapy, and one patient each received dimethyl fumarate (1.3%), respectively apremilast (1.3%). All included patients completed COVID-19 vaccination with two doses over the course of the study. Serum testing revealed that 74 patients (96.1%) showed an anti-SARS-CoV-2-S IgG seroconversion. While all patients on IL-17A, -12 or -12/23 inhibitors (n=50) achieved seroconversion, three of 16 patients (18.8%) receiving MTX and/or a TNF-α inhibitor as main anti-psoriatic treatment did not. At follow-up, none of the patients had developed symptomatic COVID-19 or died from COVID-19.ConclusionsAnti-SARS-CoV-2-S IgG seroconversion rates following COVID-19 vaccination in psoriasis patients under systemic treatment were high. An impaired serological response, however, was observed in patients receiving MTX and/or TNF-α inhibitors, in particular infliximab.

Background In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems.