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Multisided platforms are new organizing forms that can boost innovation in the healthcare sector by empowering patients as innovators and facilitating the commercialization of innovations by and for patients. However, applying the playbook script for the platform model from other sectors may prove challenging given the distinctive nature of the healthcare services. Drawing on the case of a leading healthcare innovation platform in Europe, this article examines the platform's role in identifying and enabling the development and diffusion of patient innovations. This involves three main roles (community organizer, market matchmaker, and innovation manager) and corresponding orchestration activities. [ABSTRACT FROM AUTHOR]

In Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools. [ABSTRACT FROM AUTHOR]

Background: Prospective, single-center trials have shown that the implementation of the Kidney Disease: Improving Global Outcomes (KDIGO) recommendations in high-risk patients significantly reduced the development of acute kidney injury (AKI) after surgery. We sought to evaluate the feasibility of implementing a bundle of supportive measures based on the KDIGO guideline in high-risk patients undergoing cardiac surgery in a multicenter setting in preparation for a large definitive trial., Methods: In this multicenter, multinational, randomized controlled trial, we examined the adherence to the KDIGO bundle consisting of optimization of volume status and hemodynamics, functional hemodynamic monitoring, avoidance of nephrotoxic drugs, and prevention of hyperglycemia in high-risk patients identified by the urinary biomarkers tissue inhibitor of metalloproteinases-2 [TIMP-2] and insulin growth factor-binding protein 7 [IGFBP7] after cardiac surgery. The primary end point was the adherence to the bundle protocol and was evaluated by the percentage of compliant patients with a 95% confidence interval (CI) according to Clopper-Pearson. Secondary end points included the development and severity of AKI., Results: In total, 278 patients were included in the final analysis. In the intervention group, 65.4% of patients received the complete bundle as compared to 4.2% in the control group (absolute risk reduction [ARR] 61.2 [95% CI, 52.6-69.9]; P < .001). AKI rates were statistically not different in both groups (46.3% intervention versus 41.5% control group; ARR -4.8% [95% CI, -16.4 to 6.9]; P = .423). However, the occurrence of moderate and severe AKI was significantly lower in the intervention group as compared to the control group (14.0% vs 23.9%; ARR 10.0% [95% CI, 0.9-19.1]; P = .034). There were no significant effects on other specified secondary outcomes., Conclusions: Implementation of a KDIGO-derived treatment bundle is feasible in a multinational setting. Furthermore, moderate to severe AKI was significantly reduced in the intervention group., Competing Interests: Conflicts of Interest: See Disclosures at the end of the article., (Copyright © 2021 International Anesthesia Research Society.)