There is a very high need for improving the management of pain. Acute and persistent pain of different origins represent a common medical, social, and economic burden, and its pharmacotherapy is often inadequate. To advance management of pain patients and support decision making in clinical practice, more predictive assessments of treatment success are needed. The development of analgesics is onerous because promising preclinical data often do not translate into the clinic. Improved pharmacodynamic biomarkers could define whether nociceptive signalling is adequately modulated by a new drug, so increasing the chance of successful translation and greatly reducing the risk in initiating clinical development. Further, the pathophysiology of chronic pelvic pain indications is poorly understood and no adequate preclinical models are available, precluding focused preclinical research and leaving affected patients with little hope of relief. IMI-PainCare aims at making advances in these three pain areas in a complementary manner. Three subprojects will address specific scientific challenges. Subproject PROMPT will identify Patient Reported Outcome Measures as tools to standardise assessments of treatment success of acute and chronic pain in Real World conditions and controlled trials, and so improve its management; subproject BioPain will validate the translatability of pharmacodynamic biomarkers and PK-PD modelling in pain pathways of healthy subjects and preclinical species, thereby offering tools to improve drug development; subprojectTRiPP will identify biomarkers and novel therapeutic pathways of clinical phenotypes of patients with chronic pelvic pain, which after back-translation, can improve how preclinical models reflecting human diseases. The goal of IMI-PainCare is to improve the care of patients with acute or chronic pain by providing a toolbox to streamline the development process for novel analgesic drugs and to improve treatment quality in clinical practice.

Adverse events in health care are suffered by between 8% and 12% of patients admitted to hospitals in the EU and are accountable for around EUR 21 billion in direct costs. Surgical adverse events are one of the most common types reported. The SAFEST project aims at improving and harmonising perioperative patient safety by establishing widely supported perioperative patient-centred EU standardised practices to address this challenge. SAFEST goes beyond state of the art in surgical patient safety by addressing the whole perioperative patient?s pathway (beyond the hospital care), thus ensuring the continuum of care. Patients will participate as co-equal partners throughout the project. Their engagement will start with the broad European consensus, jointly with other key stakeholders, to design the SAFEST standardised practices for perioperative care. The SAFEST standardised practices will be implemented in 10 hospitals from 5 EU countries through a Perioperative Quality Improvement Learning Collaborative (PQILC). Our evaluation of the implementation, both from a process and a results perspective, will allow assessing the impact of the implementation of standardised practices on the improvement of clinical outcomes and to identify contextual facilitators and barriers at micro, meso and macro levels, providing new evidence to translate standardised, evidence-based guidelines to clinical practice. The uptake of the SAFEST standardised practices will be taken a step further by inviting 100 hospitals across EU-27, associated countries and beyond, to self-evaluate and create an international benchmarking assessment. Furthermore, we will ensure that SAFEST makes an impact beyond its lifespan, with ambitious dissemination and exploitation plans that include launching an open-access benchmarking platform for European hospitals and constituting an international network on perioperative patient care.

Within only six months, over 7.4 million people have been diagnosed with SARS-CoV-2. In the most severely hit countries, more than 10% of infected patients have received treatment in Intensive Care Units (ICUs). Insufficient data and limited knowledge on the disease as well as the lack of tools to support the intensivist in making accurate, timely and informed decisions has led to high mortality rates. Continuous surveillance, the collection and intelligent analysis of data from many sources, including ventilators and electrical impedance tomography, would allow intensivists to decide on the best suitable treatment to accelerate the recovery of the often comorbid COVID-19 patients, while reducing the burden on clinical staff and healthcare costs. This information would also increase our understanding of the yet unknown course of disease, supporting other stakeholders in the quest for new therapies. In ENVISION, our multidsciplinary public-private consortium will advance an innovative digital tool, Sandman.MD, a real-time and plug-and-play monitoring app, to an intelligent decision-support system for monitoring, prediction and treatment of COVID-19 patients in ICUs – the Sandman.ICU – reaching Technology Readiness Level 9 and ready for CE marking by the end of the project. The app has been developed by our SME partner app@work and successfully introduced by several hospitals in Germany for use during the perioperative period. Sandman.ICU will be integrated into an AI-driven data analytics suite with predictive modelling tools and enhanced with a smart alert functionality. The digital tool will be validated and demonstrated in 14 hospitals across Europe. Our Health Technology Assessment expert partner will demonstrate the economic and societal value of Sandman.ICU, while an experienced SME will manage the innovation process in view of an immediate market uptake. The rollout will be supported by the European Society of Anaesthesiology and Intensive Care.

Invasive mechanical ventilation (MV) is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, when MV alone is insufficient, extracorporeal lung support (ELS) is initiated. However, MV is recognised as potentially harmful, because inappropriate MV settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that MV is often not properly provided despite clear evidence and guidelines. Furthermore, treatment decisions by the healthcare providers, especially regarding MV and ELS, often remain incomprehensible to the patients and their relatives, since flow of information from caregivers to patients is challenged by a number of factors, including limited time and resources, communication problems as well as patients? capacity to comprehend and memorise information. The project proposed herein aims at clinically validating and extending our IntelliLung Artificial Intelligence Decision Support System (AI-DSS) designed to optimise MV and ELS settings to improve the care of ICU patients, alongside caregiver-patient communication. Thereby, best practice evidence-based MV and ELS within safer therapy corridors for longer periods, faster weaning from MV, and improved survival could be achieved - even in non-experienced hands. Additionally, this project will improve the information flow from caregivers to patients and relatives in the ICU setting. Therefore, we will develop a digital solution that allows automatic generation of an extensive plain-language information package for patients and their relatives, communicating highly individualised information on diseases and knowledge-based disease-management strategies, thus facilitating high-quality current and subsequent care through health literacy empowerment and patient-centredness. We will perform a retrospective and prospective multi-centre study to validate our IntelliLung AI-DSS and the patient information software.

More than 2.7 million hospitalisations of COVID-19-infected patients have occurred in Europe alone since the outbreak of the coronavirus in 2020. An average 14% of those patients with mild or moderate illness develop severe symptoms and are eventually admitted to the Intensive Care Unit (ICU). The most common treatment of patients in the first two stages of the illness, such as remdesivir and antibody therapies, have neither shown the desired effect nor prevented the progression of the disease to severe and critical stages of the illness. Interventions against SARS-CoV-2 are in high need to prevent admissions to ICUs, and would reduce the burden on patients and their families, clinical staff and the healthcare sector. Additionally, such interventions would help to provide ICU beds for non-Covid patients requiring immediate interventions, such as for heart or cancer surgery, both of which are often in need of planned free ICU beds. In COVend, our multidsciplinary consortium will deliver a new effective therapy against SARS-CoV-2 for the clinical management of the COVID-19 disease during mild and moderate stages, including for the prevention of disease progression to severe illness. This will be achieved by advancing a promising therapeutic candidate, FX06, from Technology Readiness Level (TRL) 7 to TRL9 through a multi-centre phase II/III clinical trial. COVend will study the influence of COVID-19 on endothelial cells (ECs) and the potential protective effect of FX06, and will apply -omics technologies, generate new algorithms and open-source software to carry out data analytics and modelling, and will develop health economic models to assess the socio-economic benefits and cost effectiveness of the new therapy. The COVend consortium unites expertise from different disciplines, including (cell) biology, ICT expertise for AI-based evaluations, pharma, economics and social sciences, as well as clinical expertise from 13 European countries.