Open Access

Objectives: Novel formulations (gastro-resistant tablet and intravenous solution) of posaconazole (POS) have been approved in prophylaxis and therapy of invasive fungal diseases (IFDs). Study aim was to analyze treatment strategies and clinical effectiveness. Methods: We set up a web-based registry on www.ClinicalSurveys.net for documentation of comprehensive data of patients who received novel POS formulations. Data analysis was split into two groups of patients who received novel POS formulations for antifungal prophylaxis (posaconazole prophylaxis group) and antifungal therapy (posaconazole therapy group), respectively. Results: Overall, 180 patients (151 in the posaconazole prophylaxis group and 29 in the posaconazole therapy group) from six German tertiary care centers and hospitalized between 05/2014 – 03/2016 were observed. Median age was 58 years (range: 19 – 77 years) and the most common risk factor for IFD was chemotherapy (n = 136; 76%). In the posaconazole prophylaxis group and posaconazole therapy group, median POS serum levels at steady-state were 1,068 μg/L (IQR 573–1,498 μg/L) and 904 μg/L (IQR 728–1,550 μg/L), respectively (P = 0.776). During antifungal prophylaxis with POS, nine (6%) probable/proven fungal breakthroughs were reported and overall survival rate of hospitalization was 86%. The median overall duration of POS therapy was 18 days (IQR: 7 – 23 days). Fourteen patients (48%) had progressive IFD under POS therapy, of these five patients (36%) died related to or likely related to IFD. Conclusions: Our study demonstrates clinical effectiveness of antifungal prophylaxis with novel POS formulations. In patients treated for possible/probable/proven IFD, we observed considerable mortality in patients receiving salvage treatment and with infections due to rare fungal species.

Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77–0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77–0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the “first wave” of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78–0.87]; for full follow-up: 0.82 [95% CI: 0.78–0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.

Background: The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demanded the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties. Main body: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected in a consensus-based process by working groups of medical experts from the respective specialty to ensure that the contents are aligned with the research needs of the specialty. The selected data elements were mapped to international standardized vocabularies and data exchange specifications were created using HL7 FHIR profiles on the appropriate resources. All steps were performed in close interdisciplinary collaboration between medical domain experts, medical information scientists and FHIR developers. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. In that way, we defined dataset specifications for a total number of 23 (immunization), 59 (pediatrics), and 50 (cardiology) data elements that augment the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module. Conclusions: We here present extension modules for the GECCO core dataset that contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology. These extension modules were defined in an interdisciplinary, iterative, consensus-based approach that may serve as a blueprint for the development of further dataset definitions and GECCO extension modules. The here developed GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.

Correction to: Infection (2020) 48:723–733 https://doi.org/10.1007/s15010-020-01469-6. The original version of this article unfortunately contained a mistake. In this article the authors Dirk Schürmann at affiliation Charité, University Medicine, Berlin, Olaf Degen at affiliation University Clinic Hamburg Eppendorf, Hamburg and Heinz-August Horst at affiliation University Hospital Schleswig–Holstein, Kiel, Germany were missing from the author list. The original article has been corrected.

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.