Cross-border clinical-practice-variation-based development of COvid-19 DAta-driven consensus treatment guidelines and the creation of an Euregio COVID-19 data Platform to anticipate a new wave. (Q4296995)
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Project Q4296995 in Germany, Belgium, Netherlands
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Cross-border clinical-practice-variation-based development of COvid-19 DAta-driven consensus treatment guidelines and the creation of an Euregio COVID-19 data Platform to anticipate a new wave. |
Project Q4296995 in Germany, Belgium, Netherlands |
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806,156.27 Euro
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895,729.19 Euro
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90.0 percent
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1 July 2020
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30 June 2021
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Ziekenhuis Oost-Limburg
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50°55'33.46"N, 5°20'31.56"E
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50°38'46.10"N, 5°32'7.37"E
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50°57'27.86"N, 5°31'2.03"E
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50°46'36.66"N, 6°2'36.20"E
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50°50'3.30"N, 5°42'48.96"E
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Euregio, with a high population density, intense cross-border passage and only an estimated 5-10% of inhabitants that ran through infection during this first pandemic wave, is likely struck hard again in a second COVID-19 wave. If so, we aim to provide all available care, including admission to Intensive Care Unit (ICU), to those patients that will benefit and withhold care for those who do not, while focussing on supportive care for both them and their family. We have to learn lessons now to guide decisions on future care far better. The cross-border perspective in learning lessons is likely to unravel additional ones. As inhabitants of Euregio are alike, differences in outcomes are also driven by system factors. Within Euregio, healthcare systems, hospital infrastructure, admission criteria and treatment choices vary considerably. Notably, availability of ICU beds in the Netherlands is 6.4, compared to 29.2 and 15.9 per 100.000 inhabitants for Germany and Belgium, resp. Scarce ICU resources should be optimally used during a pandemic, in agreement with regular care, which inevitably affects physician decisions. We learned a great heterogeneity in disease course of COVID-19 infection. This heterogeneity is amplified since no specific treatment for COVID-19 exists (experimental off-label therapies e.g. hydroxychloroquine, antiviral drugs, steroids, interleukin receptor blockers were used variably between centres and countries). With a major focus on cross-border differences, a solid evaluation on heterogeneity that describes baseline demographics, evaluates disease courses over time, addresses sex differences, investigates relationships with outcome and evaluates treatment differences will show unique results of effects of different choices/settings (including health care systems). A cross-border prospectively collected ICU COVID-19 cohort does not exist, but is urgently needed. A large well-characterised Euregio COVID-19 cohort that incorporates the "natural" variation between countries can reveal best practice throughout our region to benefit future COVID-19 patients, at least, throughout our region and countries. Conversely, while taking different settings (including health care systems) into account, Euregio data can be used to predict outcome. Established prediction scores for general ICU populations appear inappropriate for COVID-19 population, which differ clinically. Fortunately, many COVID-19 prediction scores become available. Euregio data has major advantages to investigate their external validity cross-border, and, particularly, could establish whether certain scores perform better in one hospital or country over the other. Development of new, or adaptation of existing models for Euregio specifically will help to inform physicians better. Aims: (1) study heterogeneity differences in ICU patient populations, (2) improve clinical decisions guidelines, and (3) build IT-infrastructure for secure and efficient data-exchange and analysis. Obj 1. We intend to connect isolated efforts into a single Euregio cohort network and pioneer new concepts of patient characterisation, machine learning and outcome evaluation (Table 1). Network partners will be granted access to the common data model, and algorithms will be made available to the entire cohort network, boosting collaborative research efforts using a common data platform. To start, the euregionally prespecified, and prospectively collected data on COVID-19 will identify differences between countries. First, description of data combined with advanced statistical modelling with outcome data will unravel heterogeneity and reveal results on best practices throughout our region. Next, we will validate existing prediction models, adapting them for Euregio and develop new prediction models with outcome. Obj 2. Review current guidelines and adapt these using data-driven results to inform physicians on COVID-19 patients. Results from analyses that use "natural" variation throughout Euregio and reveal best practice will form a basis for guideline refinement. This will guide physicians to admit patients to an ICU or not and to continue life-sustaining treatment or forgo life-sustaining support if considered futile in a more uniform way throughout the region. Obj 3. The overarching ambition is to build a growing data platform that will host the dataset, making shared access possible, that respects best technical and regulatory practices, to benefit target groups (Table 2). In addition to enabling research, this infrastructure will create a dashboard sharing near-real-time ICU data on patient admissions and Euregio bed availability. By detecting effects on ICU capacity and clinical course earlier, hospitals will have more time to collaborate, reorganise and prepare for the upcoming wave. (English)
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