Prevalence of Patients with Uncontrolled Asthma Despite NVL/GINA Step 4/5 Treatment in Germany.

Purpose: Asthma is one of the most prevalent chronic diseases in Germany affecting 4-5% of all adults and 10% of children. Despite the availability of biologicals in recent years, studies show patients with inadequately controlled severe asthma in real life. The aim of the current study was to characterize and estimate the number of patients with NVL/GINA level 4 or 5 asthma and signs of poor control in Germany. Patients and Methods: In 2021, we retrospectively analyzed data collected during 2019 using the IQVIA™ LRx and IQVIA™ Disease Analyzer databases which contain anonymized longitudinal data covering approximately 80% of statutory health insurance (GKV) prescriptions in Germany with most relevant information about prescriptions, basic patient demographics or location of the prescriber; the IQVIA™ Disease Analyzer anonymized electronic medical records from a representative sample of office-based GPs and specialists. An expert committee of pulmonologists from different hospitals and expert practices supported the study. Asthma patients treated according to NVL/GINA 4/5 who used SABAs frequently (≥3 on days with no ICS-containing prescriptions/year) and/or received prescriptions for oral corticosteroids (OCS) (score of ≥2/year, a pulmonologist prescription scored 1.0, GP 0.75) were classified as severe, uncontrolled asthma. Results: In 2019, 3.4 million patients received at least two prescriptions of respiratory medications and 2.4 million patients on maintenance respiratory treatment have asthma. A total of 625,000 asthma patients were treated according to NVL/GINA step 4 or 5. Among these, 54,000 were uncontrolled according to the pre-defined OCS and/or SABA use, which corresponds to approximately 15% of patients in certain regions. Conclusion: In 2019, approximately 54,000 patients in Germany treated according to NVL/GINA step 4/5 had evidence suggestive for poor asthma control, up to 15% of patients in certain regions. Yet, only 12,000 patients overall were being treated with biologicals suggesting a possible treatment gap that requires further investigation.

Longitudinal Neurocognitive and Pulmonological Profile of Long COVID-19: Protocol for the COVIMMUNE-Clin Study.

BACKGROUND: There is a dearth of information about "brain fog," characterized by concentration, word-finding, or memory problems, which has been listed in the new World Health Organization provisional classification "U09.9 Post-COVID-19 Condition." Moreover, the extent to which these symptoms may be associated with neurological, pulmonary, or psychiatric difficulties is unclear. OBJECTIVE: This ongoing cohort study aims to carefully assess neurocognitive function in the context of the neurological, psychiatric, and pulmonary sequelae of SARS-CoV-2 infection among patients with asymptomatic/mild and severe cases of COVID-19 after remission, including actively recruited healthy controls. METHODS: A total of 150 participants will be included in this pilot study. The cohort will comprise patients who tested positive for SARS-CoV-2 infection with either an asymptomatic course or a mild course defined as no symptoms except for olfactory and taste dysfunction (n=50), patients who tested positive for SARS-CoV-2 infection with a severe disease course (n=50), and a healthy control group (n=50) with similar age and sex distribution based on frequency matching. A comprehensive neuropsychological assessment will be performed comprising nuanced aspects of complex attention, including language, executive function, verbal and visual learning, and memory. Psychiatric, personality, social and lifestyle factors, sleep, and fatigue will be evaluated. Brain magnetic resonance imaging, neurological and physical assessment, and pulmonological and lung function examinations (including body plethysmography, diffusion capacity, clinical assessments, and questionnaires) will also be performed. Three visits are planned with comprehensive testing at the baseline and 12-month visits, along with brief neurological and neuropsychological examinations at the 6-month assessment. Blood-based biomarkers of neurodegeneration will be quantified at baseline and 12-month follow-up. RESULTS: At the time of submission, the study had begun recruitment through telephone and in-person screenings. The first patient was enrolled in the study at the beginning of April 2021. Interim data analysis of baseline information is expected to be complete by December 2021 and study completion is expected at the end of December 2022. Preliminary group comparisons indicate worse word list learning, short- and long-delayed verbal recall, and verbal recognition in both patient cohorts compared with those of the healthy control group, adjusted for age and sex. Initial volumetric comparisons show smaller grey matter, frontal, and temporal brain volumes in both patient groups compared with those of healthy controls. These results are quite robust but are neither final nor placed in the needed context intended at study completion. CONCLUSIONS: To the best of our knowledge, this is the first study to include objective and comprehensive longitudinal analyses of neurocognitive sequelae of COVID-19 in an extreme group comparison stratified by disease severity with healthy controls actively recruited during the pandemic. Results from this study will contribute to the nascent literature on the prolonged effects of COVID-19 on neurocognitive performance via our coassessment of neuroradiological, neurological, pulmonary, psychiatric, and lifestyle factors. TRIAL REGISTRATION: International Clinical Trials Registry Platform DRKS00023806; https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS00023806. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30259.

Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients.

BACKGROUND: The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system. METHODS: In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings. RESULTS: Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host. CONCLUSIONS: Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.