Long-Term Outcomes with Subcutaneous C1-Inhibitor Replacement Therapy for Prevention of Hereditary Angioedema Attacks

Background For the prevention of attacks of hereditary angioedema (HAE), the efficacy and safety of subcutaneous human C1-esterase inhibitor (C1-INH[SC]; HAEGARDA, CSL Behring) was established in the 16-week Clinical Study for Optimal Management of Preventing Angioedema with Low-Volume Subcutaneous C1-Inhibitor Replacement Therapy (COMPACT). Objective To assess the long-term safety, occurrence of angioedema attacks, and use of rescue medication with C1-INH(SC). Methods Open-label, randomized, parallel-arm extension of COMPACT across 11 countries. Patients with frequent angioedema attacks, either study treatment-naive or who had completed COMPACT, were randomly assigned (1:1) to 40 IU/kg or 60 IU/kg C1-INH(SC) twice per week, with conditional uptitration to optimize prophylaxis (ClinicalTrials.gov registration no. NCT02316353). Results A total of 126 patients with a monthly attack rate of 4.3 in 3 months before entry in COMPACT were enrolled and treated for a mean of 1.5 years; 44 patients (34.9%) had more than 2 years of exposure. Mean steady-state C1-INH functional activity increased to 66.6% with 60 IU/kg. Incidence of adverse events was low and similar in both dose groups (11.3 and 8.5 events per patient-year for 40 IU/kg and 60 IU/kg, respectively). For 40 IU/kg and 60 IU/kg, median annualized attack rates were 1.3 and 1.0, respectively, and median rescue medication use was 0.2 and 0.0 times per year, respectively. Of 23 patients receiving 60 IU/kg for more than 2 years, 19 (83%) were attack-free during months 25 to 30 of treatment. Conclusions In patients with frequent HAE attacks, long-term replacement therapy with C1-INH(SC) is safe and exhibits a substantial and sustained prophylactic effect, with the vast majority of patients becoming free from debilitating disease symptoms

Results of the COVID-19 mental health international for the general population (COMET-G) study.

INTRODUCTION: There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study. MATERIAL AND METHODS: During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively. STATISTICAL ANALYSIS: Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables. RESULTS: Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed. CONCLUSIONS: The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them

Overview of the photo annotation task in ImageCLEF@ICPR

The Photo Annotation Task poses the challenge for automated annotation of 53 visual concepts in Flickr photos and was organized as part of the ImageCLEF@ICPR contest. In total, 12 research teams participated in the multilabel classification challenge while initially 17 research groups were interested and got access to the data. The participants were provided with a training set of 5,000 Flickr images with annotations, a validation set of 3,000 Flickr images with annotations and the test was performed on 10,000 Flickr images. The evaluation was carried out twofold: first the evaluation per concept was conducted by utilizing the Equal Error Rate (EER) and the Area Under Curve (AUC) and second the evaluation per example was performed with the Ontology Score (OS). Summarizing the results, an average AUC of 86.5% could be achieved, including concepts with an AUC of 96%. The classification performance for each image ranged between 59% and 100% with an average score of 85%. In comparison to the results achieved in ImageCLEF 2009, the detection performance increased for the concept-based evaluation by 2.2% EER and 2.5% AUC and showed a slight decrease for the example-based evaluation