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1.
Infection ; 2023 Mar 23.
Article in English | MEDLINE | ID: covidwho-2280180

ABSTRACT

PURPOSE: Symptom control for patients who were severely ill or dying from COVID-19 was paramount while resources were strained and infection control measures were in place. We aimed to describe the characteristics of SARS-CoV-2 infected patients who received specialized palliative care (SPC) and the type of SPC provided in a larger cohort. METHODS: From the multi-centre cohort study Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS), data of patients hospitalized with SARS-CoV-2 infection documented between July 2020 and October 2021 were analysed. RESULTS: 273/7292 patients (3.7%) received SPC. Those receiving SPC were older and suffered more often from comorbidities, but 59% presented with an estimated life expectancy > 1 year. Main symptoms were dyspnoea, delirium, and excessive tiredness. 224/273 patients (82%) died during the hospital stay compared to 789/7019 (11%) without SPC. Symptom control was provided most common (223/273; 95%), followed by family and psychological support (50% resp. 43%). Personal contact with friends or relatives before or during the dying phase was more often documented in patients receiving SPC compared to patients without SPC (52% vs. 30%). CONCLUSION: In 3.7% of SARS-CoV-2 infected hospitalized patients, the burden of the acute infection triggered palliative care involvement. Besides complex symptom management, SPC professionals also focused on psychosocial and family issues and aimed to enable personal contacts of dying patients with their family. The data underpin the need for further involvement of SPC in SARS-CoV-2 infected patients but also in other severe chronic infectious diseases.

2.
Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen ; 2023.
Article in German | EuropePMC | ID: covidwho-2236410

ABSTRACT

Hintergrund Diese Studie beschreibt die Entwicklung und Validierung von Strukturindikatoren für das klinisch-infektiologische Versorgungsangebot in deutschen Krankenhäusern. Ein solches ist notwendig, um den künftigen Herausforderungen in der Infektionsmedizin adäquat begegnen zu können. Methode Ein Expert*innenteam entwickelte die Strukturindikatoren im Rahmen eines dreistufigen Entscheidungsverfahrens: (1) Identifizierung potenzieller Strukturindikatoren basierend auf einer Literaturrecherche, (2) schriftliches Bewertungsverfahren sowie eine (3) persönliche Diskussion zur Konsensfindung und finalen Auswahl geeigneter Strukturindikatoren. Zur Pilotierung der entwickelten Strukturindikatoren wurde eine Feldstudie durchgeführt. Ein auf den Strukturindikatoren basierender Score wurde für jedes Krankenhaus ermittelt und über eine Receiver-Operator-Charakteristik-Kurve (ROC) anhand extern validierter infektiologischer Expertise (Zentrum der Deutschen Gesellschaft für Infektiologie [DGI]) validiert. Ergebnisse Auf der Basis einer Liste von 45 potenziellen Strukturindikatoren wurden 18 geeignete Strukturindikatoren für das klinisch-infektiologische Versorgungsangebot entwickelt. Von diesen wurden zehn Schlüsselindikatoren für das allgemeine bzw. Coronavirus-Krankheit-2019 (COVID-19)-spezifische klinisch-infektiologische Versorgungsangebot definiert. Bei der Felderhebung des Versorgungsangebots für COVID-19-Patient*innen in 40 deutschen Krankenhäusern erreichten die teilnehmenden Einrichtungen 0 bis 9 Punkte (Median 4) im ermittelten Score. Die Fläche unter der ROC-Kurve betrug 0,893 (95%-Konfidenzintervall (KI): 0,797, 0,988;p < 0,001). Diskussion/Schlussfolgerung Die im Rahmen eines transparenten und etablierten Entwicklungsprozesses entwickelten Strukturindikatoren können perspektivisch genutzt werden, um den aktuellen Zustand und zukünftige Entwicklungen der infektiologischen Versorgungsqualität in Deutschland zu erfassen und Vergleiche zu ermöglichen.

3.
Methods Inf Med ; 62(S 01): e47-e56, 2023 Jun.
Article in English | MEDLINE | ID: covidwho-2237390

ABSTRACT

BACKGROUND: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. OBJECTIVES: The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. METHODS: All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. RESULTS: Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. CONCLUSION: An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.


Subject(s)
COVID-19 , Data Accuracy , Humans , Consensus , Pandemics , Quality Indicators, Health Care , COVID-19/epidemiology , Data Collection
4.
Z Evid Fortbild Qual Gesundhwes ; 176: 12-21, 2023 Feb.
Article in German | MEDLINE | ID: covidwho-2236412

ABSTRACT

INTRODUCTION: This study describes the development and validation of structure indicators for clinical infectious disease (ID) care in German hospitals, which is important to adequately face the future challenges in ID medicine. METHODS: A team of experts developed the structure indicators in a three-stage, multicriteria decision-making process: (1) identification of potential structure indicators based on a literature review, (2) written assessment process, and (3) face-to-face discussion to reach consensus and final selection of appropriate structure indicators. A field study was conducted to assess the developed structure indicators. A score based on the structure indicators was determined for each hospital and validated via receiver operator characteristic (ROC) curves using externally validated ID expertise (German Society of ID (DGI) Centre). RESULTS: Based on a list of 45 potential structure indicators, 18 suitable indicators were developed for clinical ID care structures in German hospitals. Out of these, ten key indicators were defined for the general and coronavirus disease 2019- (COVID-19-) specific clinical ID care structures. In the field survey of clinical ID care provision for COVID-19 patients in 40 German hospitals, the participating facilities achieved 0 to 9 points (median 4) in the determined score. The area under the ROC curve was 0.893 (95% CI: 0.797, 0.988; p < 0.001). DISCUSSION/CONCLUSION: The structure indicators developed within the framework of a transparent and established development process can be used in the future to both capture the current state and future developments of ID care quality in Germany and enable comparisons.


Subject(s)
COVID-19 , Communicable Diseases , Humans , Germany , Pandemics , Hospitals
5.
Sci Rep ; 12(1): 19035, 2022 Nov 09.
Article in English | MEDLINE | ID: covidwho-2106464

ABSTRACT

Establishing the optimal treatment for COVID-19 patients remains challenging. Specifically, immunocompromised and pre-diseased patients are at high risk for severe disease course and face limited therapeutic options. Convalescent plasma (CP) has been considered as therapeutic approach, but reliable data are lacking, especially for high-risk patients. We performed a retrospective analysis of 55 hospitalized COVID-19 patients from University Hospital Duesseldorf (UKD) at high risk for disease progression, in a substantial proportion due to immunosuppression from cancer, solid organ transplantation, autoimmune disease, dialysis. A matched-pairs analysis (1:4) was performed with 220 patients from the Lean European Open Survey on SARS-CoV-2-infected Patients (LEOSS) who were treated or not treated with CP. Both cohorts had high mortality (UKD 41.8%, LEOSS 34.1%). A matched-pairs analysis showed no significant effect on mortality. CP administration before the formation of pulmonary infiltrates showed the lowest mortality in both cohorts (10%), whereas mortality in the complicated phase was 27.8%. CP administration during the critical phase revealed the highest mortality: UKD 60.9%, LEOSS 48.3%. In our cohort of COVID-19 patients with severe comorbidities CP did not significantly reduce mortality in a retrospective matched-pairs analysis. However, our data supports the concept that a reduction in mortality is achievable by early CP administration.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , COVID-19/therapy , Matched-Pair Analysis , Retrospective Studies , Renal Dialysis , Immunization, Passive , COVID-19 Serotherapy
6.
J Med Virol ; 93(12): 6703-6713, 2021 12.
Article in English | MEDLINE | ID: covidwho-1544323

ABSTRACT

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.


Subject(s)
COVID-19/diagnosis , Adult , Age Factors , Aged , Aged, 80 and over , C-Reactive Protein/analysis , COVID-19/mortality , COVID-19/pathology , Female , Fibrin Fibrinogen Degradation Products/analysis , Humans , Male , Middle Aged , Reproducibility of Results , Risk Assessment , Risk Factors , Severity of Illness Index , Urea/blood , Young Adult
7.
Social Science Open Access Repository; 2020.
Non-conventional in English | Social Science Open Access Repository | ID: grc-747861

ABSTRACT

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.

8.
Gesundheitswesen ; 83(S 01): S45-S53, 2021 Nov.
Article in German | MEDLINE | ID: covidwho-1500783

ABSTRACT

OBJECTIVE: The Coronavirus Disease-2019 (COVID-19) pandemic has brought opportunities and challenges, especially for health services research based on routine data. In this article we will demonstrate this by presenting lessons learned from establishing the currently largest registry in Germany providing a detailed clinical dataset on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected patients: the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS). METHODS: LEOSS is based on a collaborative and integrative research approach with anonymous recruitment and collection of routine data and the early provision of data in an open science context. The only requirement for inclusion was a SARS-CoV-2 infection confirmed by virological diagnosis. Crucial strategies to successfully realize the project included the dynamic reallocation of available staff and technical resources, an early and direct involvement of data protection experts and the ethics committee as well as the decision for an iterative and dynamic process of improvement and further development. RESULTS: Thanks to the commitment of numerous institutions, a transsectoral and transnational network of currently 133 actively recruiting sites with 7,227 documented cases could be established (status: 18.03.2021). Tools for data exploration on the project website, as well as the partially automated provision of datasets according to use cases with varying requirements, enabled us to utilize the data collected within a short period of time. Data use and access processes were carried out for 97 proposals assigned to 27 different research areas. So far, nine articles have been published in peer-reviewed international journals. CONCLUSION: As a collaborative effort of the whole network, LEOSS developed into a large collection of clinical data on COVID-19 in Germany. Even though in other international projects, much larger data sets could be analysed to investigate specific research questions through direct access to source systems, the uniformly maintained and technically verified documentation standard with many discipline-specific details resulted in a large valuable data set with unique characteristics. The lessons learned while establishing LEOSS during the current pandemic have already created important implications for the design of future registries and for pandemic preparedness and response.


Subject(s)
COVID-19 , Pandemics , Germany/epidemiology , Health Services Research , Humans , Pandemics/prevention & control , Registries , SARS-CoV-2
9.
BMC Med Inform Decis Mak ; 20(1): 341, 2020 12 21.
Article in English | MEDLINE | ID: covidwho-992476

ABSTRACT

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.


Subject(s)
Biomedical Research , COVID-19 , Datasets as Topic , Medicine , Consensus , Humans , Pandemics
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