Purpose: Lactate is an established prognosticator in critical care. However, there still is insufficient evidence about its role in predicting outcome in COVID-19. This is of particular concern in older patients who have been mostly affected during the initial surge in 2020. Methods: This prospective international observation study (The COVIP study) recruited patients aged 70 years or older (ClinicalTrials.gov ID: NCT04321265) admitted to an intensive care unit (ICU) with COVID-19 disease from March 2020 to February 2021. In addition to serial lactate values (arterial blood gas analysis), we recorded several parameters, including SOFA score, ICU procedures, limitation of care, ICU- and 3-month mortality. A lactate concentration ≥ 2.0 mmol/L on the day of ICU admission (baseline) was defined as abnormal. The primary outcome was ICU-mortality. The secondary outcomes 30-day and 3-month mortality. Results: In total, data from 2860 patients were analyzed. In most patients (68%), serum lactate was lower than 2 mmol/L. Elevated baseline serum lactate was associated with significantly higher ICU- and 3-month mortality (53% vs. 43%, and 71% vs. 57%, respectively, p < 0.001). In the multivariable analysis, the maximum lactate concentration on day 1 was independently associated with ICU mortality (aOR 1.06 95% CI 1.02–1.11; p = 0.007), 30-day mortality (aOR 1.07 95% CI 1.02–1.13; p = 0.005) and 3-month mortality (aOR 1.15 95% CI 1.08–1.24; p < 0.001) after adjustment for age, gender, SOFA score, and frailty. In 826 patients with baseline lactate ≥ 2 mmol/L sufficient data to calculate the difference between maximal levels on days 1 and 2 (∆ serum lactate) were available. A decreasing lactate concentration over time was inversely associated with ICU mortality after multivariate adjustment for SOFA score, age, Clinical Frailty Scale, and gender (aOR 0.60 95% CI 0.42–0.85; p = 0.004). Conclusion: In critically ill old intensive care patients suffering from COVID-19, lactate and its kinetics are valuable tools for outcome prediction. Trial registration number: NCT04321265.
With Brexit looming, start-ups in the London ecosystem may ask themselves whether they are still in the right place for their business. Are they considering a move to the continent due to the ambiguous Brexit developments? This research analyzes the probability of international start-ups based in the London region relocating to another European entrepreneurial ecosystem. We use location decision theory and secondary data from the European Digital City Index to rank the most attractive eco-systems for the possible relocation of London-based start-ups. In addition, we interview London start-up founders asking how likely they are to leave and where they envision continuing their entrepreneurial endeavors. This study examines whether London will lose its top rank as the most attractive entrepreneurial ecosystem in Europe. We ask which of the competing ecosystems of Europe stands to gain from London’s possible loss. Our quantitative analyses show that Amsterdam is the most likely hub to benefit from any exodus. The qualitative analyses conveyed a mixture of concern and ambivalence as only three of the startups considered relocating their headquarters to another ecosystem. Six of the startups have either opened an office in another European ecosystem or are in the process of doing so. This allows them to watch and wait as they want to remain. The attractiveness of the London region, the social capital investments by team and partners, and the lack of finances to leave are the main reasons for not considering relocation of their headquarters currently.
MULTIFILE
As the first order of business in the RIGHT project, each region produced and published its own regional report, using an underlying format developed in work package 3 in this project (Manickam & van Lieshout, 2018). The format and the regional work consisted of three parts. Part 1 is the Regional Innovation Ecosystems (RIE) mapping to provide a qualitative understanding of the region’s innovation ecosystem with regards to its Smart Specialisation Strategies (S3). This part is divided into a socio-economic and R&D profile mapping and a SWOT analysis. The RIE is an adaptation of a methodology and tool used by the eDIGIREGION Project. This part is to be filled in by desk research and consulting regional experts (through interviews and/or focus groups). This part is used for mapping the own regional ecosystems, information for the partners to get to know the other regions and to be able to identify relevant similarities and differences across the regions, which in turn, will be reported in part 1 of this trans-regional report. Regions themselves chose their own sector focus. One could focus on either energy of the blue sector, or both. Part 2 focuses on the innovation capacity and needs of SMEs from the chosen sector(s). The questions are adapted from a systemic study on cluster developments, in which an analysis model was developed (Manickam, 2018). It is based on (on average) six face-to-face interviews with SMEs from the sector. The outputs of these interviews were summarised into one template, in English, by each partner region to allow for joint analysis and comparison that is in turn reported in part 2 of this report Part 3 introduced the Job Forecasting and Skills Gaps mapping using the JOES templates as developed by van Lieshout et al. (2017). To gain an appreciation of the extent and nature of skills gap, each region was asked to analyse current and potential future labour demand, workforce, and discrepancies between the two, in up to 2 businesses. For obvious reasons (confidentiality and privacy), the JOEs will not be published separately, nor will their information be used in the report in a way that would be traceable to specific businesses. We will use exemplary information from them for illustrative purposes in Parts 1 and 2 of this report where relevant.
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