The international EAACI/GA²LEN/EuroGuiDerm/APAAACI guideline for the definition, classification, diagnosis, and management of urticaria

This update and revision of the international guideline for urticaria was developed following the methods recommended by Cochrane and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group. It is a joint initiative of the Dermatology Section of the European Academy of Allergology and Clinical Immunology (EAACI), the Global Allergy and Asthma European Network (GA²LEN) and its Urticaria and Angioedema Centers of Reference and Excellence (UCAREs and ACAREs), the European Dermatology Forum (EDF; EuroGuiDerm), and the Asia Pacific Association of Allergy, Asthma and Clinical Immunology with the participation of 64 delegates of 50 national and international societies and from 31 countries. The consensus conference was held on 3 December 2020. This guideline was acknowledged and accepted by the European Union of Medical Specialists (UEMS). Urticaria is a frequent, mast cell–driven disease that presents with wheals, angioedema, or both. The lifetime prevalence for acute urticaria is approximately 20%. Chronic spontaneous or inducible urticaria is disabling, impairs quality of life, and affects performance at work and school. This updated version of the international guideline for urticaria covers the definition and classification of urticaria and outlines expert-guided and evidence-based diagnostic and therapeutic approaches for the different subtypes of urticaria. © 2021 GA²LEN. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd

Urticaria exacerbations and adverse reactions in patients with chronic urticaria receiving COVID-19 vaccination : results of the UCARE COVAC-CU study

Background: Concern about disease exacerbations and fear of reactions after coronavirus disease 2019 (COVID-19) vaccinations are common in chronic urticaria (CU) patients and may lead to vaccine hesitancy. Objective: We assessed the frequency and risk factors of CU exacerbation and adverse reactions in CU patients after COVID-19 vaccination. Methods: COVAC-CU is an international multicenter study of Urticaria Centers of Reference and Excellence (UCAREs) that retrospectively evaluated the effects of COVID-19 vaccination in CU patients aged ≥18 years and vaccinated with ≥1 dose of any COVID-19 vaccine. We evaluated CU exacerbations and severe allergic reactions as well as other adverse events associated with COVID-19 vaccinations and their association with various CU parameters. Results: Across 2769 COVID-19–vaccinated CU patients, most (90%) received at least 2 COVID-19 vaccine doses, and most patients received CU treatment and had well-controlled disease. The rate of COVID-19 vaccination–induced CU exacerbation was 9%. Of 223 patients with CU exacerbation after the first dose, 53.4% experienced recurrence of CU exacerbation after the second dose. CU exacerbation most often started <48 hours after vaccination (59.2%), lasted for a few weeks or less (70%), and was treated mainly with antihistamines (70.3%). Factors that increased the risk for COVID-19 vaccination–induced CU exacerbation included female sex, disease duration shorter than 24 months, having chronic spontaneous versus inducible urticaria, receipt of adenovirus viral vector vaccine, having nonsteroidal anti-inflammatory drug/aspirin intolerance, and having concerns about getting vaccinated; receiving omalizumab treatment and Latino/Hispanic ethnicity lowered the risk. First-dose vaccine–related adverse effects, most commonly local reactions, fever, fatigue, and muscle pain, were reported by 43.5% of CU patients. Seven patients reported severe allergic reactions. Conclusions: COVID-19 vaccination leads to disease exacerbation in only a small number of CU patients and is generally well tolerated

Predicting outcomes of pelvic exenteration using machine learning

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay &gt;&nbsp;14&nbsp;days (LOS), major complication rates at 30&nbsp;days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS &gt;&nbsp;14&nbsp;days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods