Entwicklung und Validierung potenzieller Strukturindikatoren zur Evaluierung des klinisch-infektiologischen Versorgungsangebots in deutschen Krankenhäusern während der COVID-19-Pandemie Development and validation of potential structure indicators for clinical infectious disease (ID) care in German hospitals during the COVID-19 pandemic

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.

[Development and validation of potential structure indicators for clinical infectious disease (ID) care in German hospitals during the COVID-19 pandemic]. / Entwicklung und Validierung potenzieller Strukturindikatoren zur Evaluierung des klinisch-infektiologischen Versorgungsangebots in deutschen Krankenhäusern während der COVID-19-Pandemie.

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.

[The COVID-19 Pandemic as an Opportunity and Challenge for Registries in Health Services Research: Lessons Learned from the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS)]. / Die COVID-19 Pandemie als Herausforderung und Chance für Register in der Versorgungsforschung: Erfahrungen aus Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS).

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.

First results of the "Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)".

PURPOSE: Knowledge regarding patients' clinical condition at severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is sparse. Data in the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort study may enhance the understanding of COVID-19. METHODS: Sociodemographic and clinical characteristics of SARS-CoV-2-infected patients, enrolled in the LEOSS cohort study between March 16, 2020, and May 14, 2020, were analyzed. Associations between baseline characteristics and clinical stages at diagnosis (uncomplicated vs. complicated) were assessed using logistic regression models. RESULTS: We included 2155 patients, 59.7% (1,287/2,155) were male; the most common age category was 66-85 years (39.6%; 500/2,155). The primary COVID-19 diagnosis was made in 35.0% (755/2,155) during complicated clinical stages. A significant univariate association between age; sex; body mass index; smoking; diabetes; cardiovascular, pulmonary, neurological, and kidney diseases; ACE inhibitor therapy; statin intake and an increased risk for complicated clinical stages of COVID-19 at diagnosis was found. Multivariable analysis revealed that advanced age [46-65 years: adjusted odds ratio (aOR): 1.73, 95% CI 1.25-2.42, p = 0.001; 66-85 years: aOR 1.93, 95% CI 1.36-2.74, p < 0.001; > 85 years: aOR 2.38, 95% CI 1.49-3.81, p < 0.001 vs. individuals aged 26-45 years], male sex (aOR 1.23, 95% CI 1.01-1.50, p = 0.040), cardiovascular disease (aOR 1.37, 95% CI 1.09-1.72, p = 0.007), and diabetes (aOR 1.33, 95% CI 1.04-1.69, p = 0.023) were associated with complicated stages of COVID-19 at diagnosis. CONCLUSION: The LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data. Further data regarding outcomes of the natural course of COVID-19 and the influence of treatment are required.