Traumatic Brain Injury (TBI) triggers an acute systemic inflammatory response, which may impact outcomes. This response may interact with pre-existing factors linked to inflammation, such as age, to influence outcomes. Previous studies have typically measured few cytokines, but high-dimensional proteomic approaches can sensitively detect a broad range of inflammatory markers, to better characterise post-TBI inflammation. We analysed plasma from BIO-AX-TBI study participants (n = 37 acute moderate-severe TBI (Mayo Criteria), n = 22 acute non-TBI trauma (NTT), n = 28 non-injured controls (CON)) using the Alamar NULISA™ panel (>200 inflammatory markers). The NTT group enabled differentiation of TBI-specific versus general injury-related responses. Inflammatory markers were correlated with plasma NFL, GFAP, total tau, UCH-L1 (Simoa®), S100B (Millipore), and subacute (10 days-6 weeks) 3T MRI measures of lesion volume and white matter injury. Differential expression analysis identified four markers elevated specifically in TBI (VSNL1, IL1RN/IL-1Ra, GFAP, IKBKG), while other derangements reflected non-specific injury responses. Higher VSNL1 correlated with greater lesion volume (rs = 0.53) and higher IL1RN/IL-1Ra with greater white matter injury (rs = -0.66, both FDR-adjusted p < 0.05). IL33, part of the non-specific injury response was higher in participants with good (GOS-E 5-8) versus poor (GOS-E 1-4) outcomes (W = 47, FDR-adjusted p = 0.0024). Using an Elastic Net model trained on healthy controls, we show that "inflammation age" exceeded chronological age in TBI, particularly in younger participants. In summary, acute post-TBI inflammation includes both TBI-specific and non-specific components, linked to structural brain injury and functional outcome. Age modulates the inflammatory response. VSNL1, IL1RN/IL-1Ra, and IL33 are potential mediators of post-TBI pathophysiology.

Emergency medicine generates vast quantities of electronic health record (EHR) data across hospitals and countries, but leveraging these datasets for research and quality improvement is challenging due to privacy regulations, data silos, and heterogeneity of systems. Here, we describe how the Medical Informatics Platform (MIP) operationalizes cross-border federated analytics, combining governance, privacy-preserving data preparation, secure deployment, and federated execution as illustrated through the FERES and eCREAM federations. Each participating site runs a local MIP “node” containing its anonymized dataset behind its firewall; analysis queries are executed locally, and only aggregated results are shared to a central interface. Through this approach, sensitive patient data never leave their site of origin, yet clinicians and researchers can collaboratively analyze large multi-centric datasets in real time via a web-based interface. The MIP provides an intuitive, visualization-rich environment where users can select variables, apply statistical or machine learning algorithms, and interactively review results through charts and graphs. Robust governance and security measures are built-in: data remain under the control of the original institutions (who act as data controllers), all datasets are harmonized to common data models and irreversibly anonymized prior to analysis, and the platform enforces strict privacy safeguards to protect against re-identification. The MIP has been deployed in EU funded initiatives including the Federating European REgistries for Stroke (FERES) project, which is part of the larger EBRAINS initiative, and the eCREAM (enabling Clinical Research in Emergency and Acute care Medicine) retrospective observational multicenter study, allowing cross-border analyses of stroke outcomes and emergency department data while complying with GDPR and national regulations. By enabling international EHR collaborations without compromising patient privacy, the MIP shows how electronic records can support cross-border research and quality-improvement analyses in emergency medicine. This manuscript primarily reports the implementation approach and operational blueprint; it is not presented as a clinical outcomes study or a usability evaluation.

Inflammation following traumatic brain injury (TBI) may contribute to long-term morbidity. We aimed to characterize plasma interleukin-6 (IL6) trajectory after TBI and assess associations with imaging, biomarkers, and outcomes. Secondary analysis of three prospective multicenter observational cohorts: BIO-AX-TBI (United Kingdom/Europe), CREACTIVE (Europe), and TRACK-TBI (United States). Adults (≥18 years) with TBI were enrolled at trauma centers, with non-TBI trauma (NTT) and non-injured controls (CON) included in BIO-AX-TBI and TRACK-TBI. Blood was obtained at acute (≤10 days), subacute (10 days-6 weeks), and chronic (6 and 12 months) timepoints. IL6 was measured on OLINK® (BIO-AX-TBI, CREACTIVE) or MSD S-PLEX (TRACK-TBI) platforms. The Glasgow Outcome Scale-Extended (GOS-E) assessed functional outcome at chronic time points, dichotomized as unfavorable (1-4) versus favorable (5-8). Additional outcomes included neuropsychiatric symptom scores, magnetic resonance imaging (MRI) measures (lesion volume, fractional anisotropy [FA]), and neuronal/astroglial injury markers. BIO-AX-TBI included n = 195 TBI, n = 24 NTT, and n = 89 CON; CREACTIVE included n = 1146 TBI, TRACK-TBI included n = 387 TBI, n = 98 NTT, and n = 67 CON. IL6 was significantly elevated acutely in both TBI and NTT compared with CON but highest in TBI. In BIO-AX-TBI, IL6 remained elevated at 6 months (TBI median = 2.47, IQR = 1.98-2.87 vs. CON median = 2.13, IQR = 1.74-2.58; t = 2.50; p = 0.014) and 12 months (TBI median = 2.53, IQR = 2.08-3.06; t = 4.11; p < 0.001). Acute IL6 correlated with intracranial injury (GFAP; t/z = 5.14-8.14; p < 0.001), extracranial injury (t/z = 3.89-9.08; p < 0.005), and other plasma markers (rs = 0.2-0.67; false discovery rate-corrected p < 0.05). Higher peak IL-6 was associated with greater lesion volume (t = 2.82; p = 0.0057) and reduced white matter FA (t = 2.47-2.54; p < 0.05). Elevated subacute IL6 was associated with unfavorable GOS-E across all cohorts. No associations were observed with neuropsychiatric symptoms. Post-TBI IL6 elevation persists up to 12 months and is associated with greater tissue injury and worse outcomes, suggesting IL6 as a potential therapeutic target.

In recent years, several stakeholders in Italy have suggested a crisis of vocation for Emergency Medicine (EM). This study aimed to verify the accuracy of such claims. We conducted an observational cross-sectional study on data from the Italian national test for residency positions assignments from 2019 to 2025. We analyzed trends in the number of medicine graduates, participants in the national test and available and filled training positions. We then compared EM to other residency programs and four "competitors" considering the ratio among filled and available positions; the absolute and relative variation in available and filled positions; the rate of filled positions over the number of exams' participants. From 2019 to 2025 training positions grew, while the number of medical school graduates remained stable, the number of candidates decrease and was outnumbered by training positions in 2023-2024. The rate of filled positions in EM dropped from 90% in 2019 to 25% in 2024, then increased to 47% in 2025. Available positions in EM increased from 391 in 2019 to 954 in 2025, at a faster rate than most residency programs. EM absolute filled positions grew from 2022 to 2025, at a faster rate than most competitors. Recent years high rates of unfilled positions in EM is related to the abrupt increase in available positions. Our findings do not confirm a vocational crisis for EM in Italy.

The lack of cross-border patient health data exchange in Europe is an obstacle in many ways and can negatively affect patient care and health. When clinicians have incomplete information about patients traveling or residing abroad, for example, continuity of care cannot be assured, potentially leading to poorer health outcomes. The European Electronic Health Record Exchange Format (EEHRxF) is a system being established in Europe to permit the interoperability of different healthcare systems, such as electronic health records (EHRs) and medical devices, so that they can share data to support patient care and research. The system is currently being introduced for electronic prescriptions and dispensations, patient summaries, which are part of the larger collection of health data known as the electronic health record, laboratory results and medical imaging studies and their reports, and hospital discharge reports. In emergency medicine, where research is challenging due to time and resource constraints, the EHR should no longer be seen solely as a tool to support clinical practice; it is also a source of valuable information to fuel research and improve patient care. The use of data for research, one of the stated secondary goals of the EEHRxF, thus becomes paramount here and deserves to be properly developed. It is in this context that the eCREAM (enabling Clinical Research in Emergency and Acute care Medicine through automated data extraction) project, a 5-year Horizon Europe project, was established. eCREAM will develop a system to exploit EHRs to enable research and improve decision-making, resource allocation and patient outcomes. It will address this target in two ways. First, by creating a new EHR that simultaneously meets clinical and research needs, collecting reliable, structured data that facilitate the clinical process and are readily usable for research purposes. Second, by developing an advanced natural language processing tool tailored to the specific needs of emergency medicine to automatically extract accurate, structured data from the free texts contained in EHRs. The project's innovative approach addresses current challenges in data extraction and utilization and sets a new standard for emergency medicine in Europe in the digital age. This article provides a general overview of the eCREAM project.

The eCREAM project seeks to enhance emergency department (ED) care quality and research capacity by developing tools to extract and analyse electronic health record (EHR) data using artificial intelligence-based natural language processing. This involves creating interoperable databases for research and quality-of-care improvements across multiple European countries, which presents significant legal and ethical challenges due to the cross-jurisdictional processing of sensitive health information. A dedicated legal and regulatory task force was established to address these challenges. The methodological approach included the development of an ethical, legal, and social implications (ELSI) document, a legal and operational survey to map data flows and identify regulatory requirements, the creation of template documents for regulatory submissions, and multidisciplinary consultations with national experts. The main challenge was the legal classification of studies that did not fit the conventional clinical study categories. The data reuse was confirmed to rely on consent, but obtaining fresh consent was impracticable and incompatible with the study design. Varying national interpretations of the GDPR necessitated case-by-case analyses. Regulatory pathways primarily involved submissions to local ethics committees, which subsequently approved the approach under strict safeguards. This experience demonstrates that multinational ED research using EHR data can be conducted in a legally and ethically compliant manner through a proactive, tailored strategy to navigate the legal and regulatory landscape. Early engagement of a multidisciplinary legal and regulatory task force is critical. The framework developed provides a replicable model for future large-scale emergency care research initiatives within the EU while respecting patient privacy and regulatory requirements.

This study aimed to assess whether delivering Continuous Positive Airway Pressure (CPAP) through a Helmet interface (H-CPAP) reduces common carotid artery flow (CCAF), compared to breathing room air (RA) or using an oronasal mask (M-CPAP). This trial is an unblinded, randomized, controlled crossover trial. The primary outcome was CCAF, measured using Doppler ultrasound. The secondary outcome was mean arterial pressure (MAP). A convenient sample of adult healthy volunteers was enrolled. Subjects were enrolled and randomized to receive either H-CPAP or M-CPAP first at + 10 cmH2O, followed by the alternate intervention, each for 5 min. CCAF, mean arterial pressure (MAP), heart rate (HR), respiratory rate (RR), oxygen saturation (SpO₂), and anxiety score (AS) were recorded at baseline (RA) and after 5 min under each CPAP condition. Results showed a significant 14

This study assesses the feasibility of collecting real-time perceptions of emergency department (ED) crowding from different professional roles and compares the recorded perceptions with two crowding indicators: the NEDOCS and the recently proposed Fenice score. We conducted a prospective observational study in an Italian university hospital. ED staff received two SMS questionnaires daily, assessing perceived crowding and workload pressure on 0–10 scales. Simultaneously, the NEDOCS and Fenice scores were recorded every 15 min. Spearman correlations, regression models, and generalized additive models were used to assess associations. Among 49 staff members, 830 valid on-shift responses were collected (response rate: 62.6%). Perceived crowding was generally high (median: 7; Q 1– Q 3: 5–9) and varied significantly by role ( p -value < 0.001), with healthcare assistants and boarding nurses perceiving the ED as more crowded and process management nurses perceiving it as less crowded. The Fenice and NEDOCS scores correlated strongly with perceived crowding ( ρ : 0.71 and 0.69; p value = 0.06) and moderately with workload pressure ( ρ < 0.60). The Fenice score explained slightly more variance in perceived crowding ( R 2 : 0.65 vs. 0.59). While the correlation between the two scores in the study ED was high ( ρ : 0.85), a wide range of correlation coefficients emerged in a cohort of 83 Italian EDs (average ρ : 0.80; min–max: 0.24–0.99; Q 1– Q 3: 0.73–0.93). Real-time monitoring of ED staff perceptions is feasible and informative. Both the NEDOCS and Fenice scores correlate well with staff perception, but the simplicity and fully automatable implementation of the Fenice score make it a promising alternative to be evaluated in future studies.

Emergency departments (EDs) are critical healthcare settings that often face challenges related to patient overcrowding and sub-optimal resource use, and these issues have been linked to reduced effectiveness of care and increased patient mortality. This protocol describes a study to enhance electronic health records (EHR) with real-time dashboards developed for healthcare staff, administrators, and other stakeholders in order to address these issues. These dashboards will be analytic tools, fed with data directly from the electronic health records, allowing healthcare indicators to be monitored dynamically and interactively. The indicators will provide a comprehensive overview of patient flow, crowding levels, waiting times, and other key operational metrics to support healthcare providers by enabling real-time monitoring and decision-making. Administrators will also benefit from the dashboards by gaining insight into system bottlenecks and overall ED performance, facilitating more effective management of resources. This study is part of the eCREAM (enabling Clinical Research in Emergency and Acute care Medicine through automated data extraction) project, and 32 EDs from six different European countries will be involved in this 36-month study. A panel of experts will test the usefulness of the dashboards, set up for each end-user type, using questionnaires and semi-structured interviews. The EHRs, integrated with these ad-hoc dashboards, will empower clinical teams and policymakers to understand department operations dynamics better, ultimately contributing to improved patient outcomes and reduced crowding in emergency settings.Trial registration:Clinicaltrials.gov, identifier: NCT06372379.

Rapid Response Systems (RRSs) are designed to assist hospitalized patients who become unstable, aiming to address “failure to rescue” and prevent cardiac arrests. Despite global implementation, evidence of RRS effectiveness is controversial. This study evaluates the effectiveness and safety of an RRS at Maggiore Hospital in Lodi, Italy, focusing on the Medical Emergency Team (MET) organization. The RRS at Maggiore Hospital was established in 2017 using the National Early Warning Score (NEWS) for monitoring. The MET, consisting of an emergency physician and one nurse, operates from 8:00 PM to 8:00 AM. Data from 2014-2019, divided into PRE (2014-2016) and POST (2017-2019) periods, were analyzed. The primary outcomes were unplanned ICU transfers and in-hospital mortality. A difference-in-differences (DiD) design compared outcomes in MET and non-MET wards before and after RRS implementation. Hospitalizations were similar in the PRE and POST periods. A significant reduction in Intensive Care Unit (ICU) transfers was observed overall (0.26%, p=0.005), but not in mortality (0.10%, p=0.47). Both MET and non-MET wards showed reduced ICU transfers, but the decrease was statistically significant only in MET wards. DiD analysis showed no significant reductions in either ICU transfer (p=0.77) or mortality rates (p=0.15) between MET and non-MET wards. The RRS at Maggiore Hospital effectively reduced ICU transfers without increasing mortality, demonstrating its safety. The MET organization did not significantly impact ICU transfers compared to non-MET wards. Further studies should explore additional measures of clinical deterioration to fully assess the impact of RRS.

Some patients with moderate to severe traumatic brain injury (TBI) make a full recovery, while others remain severely disabled. Accurate prognostication is important, because withdrawal of life-sustaining therapy based on perceived poor prognosis is the leading cause of death after TBI. Synchronized activity between brain regions, measurable with resting-state functional MRI (rs-fMRI), may underlie neurological recovery. However, which functional connections are critical for recovery, and whether functional connectivity measured shortly after brain injury predicts long-term recovery, is unknown. Here, we analyzed data from three prospective cohorts of patients with moderate or severe TBI (N = 116 patients; 134 controls) who underwent rs-fMRI shortly after injury. The strongest predictor of 6-mo functional outcomes in the Training Cohort (mean cross validation AUC 0.94) and independent Testing Cohort (AUC 0.78; P = 0.001) was functional connectivity between three pairs of brain regions from functionally distinct networks, two of which were anticorrelated. Results were robust to controlling for sedation ( P = 0.02) and level of consciousness at time of MRI ( P = 0.02). Finally, preserved anticorrelations improved the leave-one-out outcome prediction accuracy of an established prognostic score (AUC 0.90 vs. 0.80; P = 0.02). Preserved functional anticorrelations in acutely traumatized brains identify patients with the neurological substrate required for recovery. This biomarker can inform prognostic decisions in patients at high risk for death from withdrawal of life-sustaining therapy.

Pathophysiology and outcomes after traumatic brain injury (TBI) are complex and heterogeneous. Current classifications are uninformative about pathophysiology. Proteomic approaches with fluid-based biomarkers are ideal for exploring complex disease mechanisms, because they enable sensitive assessment of an expansive range of processes potentially relevant to TBI pathophysiology. We used novel high-dimensional, multiplex proteomic assays to assess altered plasma protein expression in acute TBI. We analysed samples from 88 participants from the BIO-AX-TBI cohort [n = 38 moderate-severe TBI (Mayo Criteria), n = 22 non-TBI trauma and n = 28 non-injured controls] on two platforms: Alamar NULISA™ CNS Diseases and OLINK® Target 96 Inflammation. Patient participants were enrolled after hospital admission, and samples were taken at a single time point ≤10 days post-injury. Participants also had neurofilament light, GFAP, total tau, UCH-L1 (all Simoa®) and S100B (Millipore) data. The Alamar panel assesses 120 proteins, most of which were previously unexplored in TBI, plus proteins with known TBI specificity, such as GFAP. A subset (n = 29 TBI and n = 24 non-injured controls) also had subacute (10 days to 6 weeks post-injury) 3 T MRI measures of lesion volume and white matter injury (fractional anisotropy). Differential expression analysis identified 16 proteins with TBI-specific significantly different plasma expression. These were neuronal markers (calbindin 2, UCH-L1 and visinin-like protein 1), astroglial markers (S100B and GFAP), neurodegenerative disease proteins (total tau, pTau231, PSEN1, amyloid-beta-42 and 14-3-3γ), inflammatory cytokines (IL16, CCL2 and ficolin 2) and cell signalling- (SFRP1), cell metabolism- (MDH1) and autophagy-related (sequestome 1) proteins. Acute plasma levels of UCH-L1, PSEN1, total tau and pTau231 were correlated with subacute lesion volume. Sequestome 1 was positively correlated with white matter fractional anisotropy, whereas CCL2 was inversely correlated. Neuronal, astroglial, tau and neurodegenerative proteins were correlated with each other, IL16, MDH1 and sequestome 1. Exploratory clustering (k means) by acute protein expression identified three TBI subgroups that differed in injury patterns, but not in age or outcome. One TBI cluster had significantly lower white matter fractional anisotropy than control-predominant clusters but had significantly lower lesion subacute lesion volumes than another TBI cluster. Proteins that overlapped on two platforms had excellent (r > 0.8) correlations between values. We identified TBI-specific changes in acute plasma levels of proteins involved in neurodegenerative disease, inflammatory and cellular processes. These changes were related to patterns of injury, thus demonstrating that processes previously studied only in animal models are also relevant in human TBI pathophysiology. Our study highlights how proteomic approaches might improve classification and understanding of TBI pathophysiology, with implications for prognostication and treatment development.

INTRODUCTION Traumatic brain injury (TBI) is a leading cause of morbidity and mortality worldwide [1, 2]. It is now recognized as a condition involving multiorgan dysfunction, characterized by non-neurological complications, particularly respiratory ones such as ventilator-associated pneumonia (VAP), being common and associated with worse outcomes. VAP occurs frequently in intensive care unit (ICU) patients, and the incidence among those with TBI ranges from 21% to 60% and an average of 36% [3]. Prevention strategies for VAP include daily sedation interruption, spontaneous breathing trials, oral decontamination, continuous monitoring of endotracheal tube cuff pressure, the use of an endotracheal tube with subglottic drainage ports, and, most importantly, antibiotic prophylaxis (AP) [4, 5]. However, the role of AP in preventing VAP remains unclear. While some studies suggested that AP has a protective effect, particularly against early-onset VAP [6-9], others found no association between AP and VAP occurrence, length of hospital stay, or mortality [9-12]. Moreover, prolonged AP use has been associated with an increased incidence of antibiotic-resistant Gram-negative pathogens and other complications [13]. OBJECTIVES To investigate the effect of AP on the incidence of VAP in patients with TBI admitted to ICU. We also assessed the role of AP on secondary outcomes, including the duration of mechanical ventilation, ICU and hospital length of stay, ICU and hospital mortality, and the six-month Glasgow Outcome Scale-Extended (GOS-E), using data from the large, multicenter, prospective CREACTIVE cohort [14]. METHODS We included adult TBI patients requiring mechanical ventilation for more than 48 hours. AP was defined as administration of antibiotics in the absence of documented infection within the first 7 days of ICU stay. The primary outcome was the incidence of VAP, defined according to international criteria. To create well-balanced AP and no-AP groups for all relevant confounding factors, we used a propensity score-matched design, a robust methodology for estimating causal effects in observational studies [15]. Propensity scores were estimated for each patient using a logistic regression model based on 22 covariates, including variables that were identified to impact both the decision to administer AP and the patient outcome (i.e., demographics, TBI severity, extracranial injuries, and ICU characteristics). We used the full matching algorithm [16], which requires weighted post-matching analyses, in which the weights depend on the size and composition of the matched sets [17]. Differences between no AP and AP groups for the primary and secondary outcomes were investigated using opportune weighted tests. The probability of experiencing VAP was assessed using the weighted Kaplan-Meier analysis, and a time-to-event comparison was conducted using the log-rank test. RESULTS A total of 2,518 patients from 70 European ICUs were included, of whom 1,392 (54%) received AP, while 1,183 (46%) did not. Compared to patients in the no-AP group, those with AP at ICU admission were younger, had fewer comorbidities, presented lower Glasgow Coma Scale scores, higher Marshall scores, more injuries in body areas other than TBI, and were more frequently involved in high-impact or traffic-related trauma. After weighting, the groups were well balanced, with weighted standardized mean differences below 10% for all variables used in model to estimate the propensity score, except for country (11.8%) and penetrating trauma (10.4%). After weighting, patients in the no-AP group had higher probability of experiencing early VAP than those in the AP group (18.9% vs. 14.7%, p-value<0.01), although there was no significant difference in the overall occurrence of VAP (Table 1). Time-to-event analysis confirmed a reduced risk of early VAP in the AP group, particularly during the first days of mechanical ventilation (Log-rank p-value<0.05). Compared to AP patients, those without AP had higher ICU mortality (35.0% vs. 27.1%, p-value<0.01) and higher hospital mortality (43.5% vs. 37.1%, p-value<0.01). ICU and hospital stays were significantly longer for AP patients, while no difference was detected in the duration of mechanical ventilation. There were no differences between groups in the 6-month GOS-E. Among patients who developed VAP and had available microbiological data, those in the AP group reported a lower proportion of Gram-positive bacteria compared to the no-AP group (29.3% vs. 47.2%), and a higher proportion of Gram-negative bacteria (80.9% vs. 71.4%). Moreover, AP patients showed higher rates of MDR bacteria, both Gram-positive (17.4% vs. 11.9%) and Gram-negative (32.3% vs. 15.8%). CONCLUSIONS Our findings suggest that AP is effective in reducing early-onset VAP among TBI patients, consistent with previous studies [6, 8, 12, 18, 19]. The benefit is pronounced during the early phase of mechanical ventilation, when patients are especially vulnerable. Patients who received AP had more Gram-negative infections and fewer Gram-positive ones but also showed higher rates of MDR in both types. The higher MDR rates in the AP group may be attributable to longer antibiotic courses, which was also evident in our results. This finding aligns with existing literature, which indicates that greater antibiotic exposure may promote the selection of resistant strains, complicating future treatment [20-23]. These results underscore the need to balance the benefits of VAP prevention with the risks of antimicrobial resistance. In conclusion, AP appears effective in reducing the incidence of VAP in TBI patients, but its use should be carefully considered. Clinicians are encouraged to apply AP selectively in high-risk cases, aiming to prevent infection while preserving antibiotic efficacy.

Background and Objectives Severe Traumatic Brain Injury (TBI) is associated with secondary injury and poor outcomes, but the underlying mechanisms are poorly understood. Vascular mechanisms may be important. We aimed to characterise how blood vascular endothelial growth factor A (VEGF-A) levels are affected by TBI, and its associations with secondary injury and functional outcome. Methods We retrospectively analysed data from two multi-centre, international, prospective observational studies (CREACTIVE and BIO-AX-TBI) with follow-up of up to 1 year. These cohorts comprised adults with moderate-severe TBI (Mayo classification), recruited on admission to hospital (BIO-AX-TBI) and the intensive care unit (ICU) (CREACTIVE). Controls included non-TBI trauma (NTT) and uninjured adults. Plasma VEGF-A levels and TBI biomarkers (Neurofilament light [NFL], glial fibrillary acidic protein [GFAP], total Tau, UCH-L1, S100B) were measured on ICU admission and ~5 days later (CREACTIVE), or at 5 timepoints from admission to 12 months post-TBI (BIO-AX-TBI), and compared to NTT and control groups. In BIO-AX-TBI, MRI assessment was performed at subacute and chronic timepoints. Functional outcomes (Glasgow Outcome Scale-Extended) were measured at 6 and 12 months. Plasma VEGF-A was measured using the OLINK® Target 96 Inflammatory platform, which reports in arbitrary standardised units (NPX), and TBI biomarkers were measured using Simoa® or Millipore platforms. Results Data was available from 195 TBI (21% female, mean age 45.30years), 24 NTT (8%, 43.98) and 89 CON (44%, 42.39) in BIO-AX-TBI, and 1146 TBI (25%, 56.29) in CREACTIVE. Plasma VEGF-A was elevated acutely after both TBI (estimated mean difference=0.45NPX, SE=0.09, p<0.001) and NTT (estimated mean difference=0.74NPX, SE=0.16, p<0.001), but remained raised after the initial timepoint only in TBI patients, peaking at day 16. Higher acute VEGF-A was associated with increased odds of refractory raised intracranial pressure (r-rICP) (maximum Odds Ratio for r-rICP=1.69, p=0.031), higher lesion volume (estimated increased lesion volume=20.14ml, SE=8.20ml, p=0.02), and worse functional outcomes (maximum Odds Ratio for worse outcome category=2.51, p<0.001). Discussion There is a sustained rise in plasma VEGF-A after TBI, which is associated with r-rICP and chronic injury markers, suggesting vascular pathophysiology is important after TBI. Further research is needed to explore mechanisms. ### Competing Interest Statement Conflicts of interest HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). DJS has received an honorarium from the Rugby Football Union for participation in an expert concussion panel. DJS receives payment by Rugby Football Union, The Football Association and Premiership Rugby for private clinical services at the Institute of Sports Exercise and Health. There are no other conflicts of interest. ### Funding Statement ERA-NET NEURON Cofund (MR/R004528/1), a part of the European Research Projects on External Insults to the Nervous System call, within the Horizon 2020 funding framework, and the European Union Seventh Framework Programme (FP7/2007-2013, Grant Agreement number 602714) provided the core funds for the project. The UK Dementia Research Institute provided additional funds. LML and NG are supported by NIHR academic clinical lectureships, and acknowledges the support of the Imperial NIHR BRC. NG acknowledges support of Academy of Medical Sciences. HZ is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356; #2022-01018 and #2019-02397), the European Union Horizon Europe research and innovation programme under grant agreement No 101053962, Swedish State Support for Clinical Research (#ALFGBG-71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund and the Alzheimers Association (#ADSF-21-831376-C, #ADSF-21-831381-C, #ADSF-21-831377-C, and #ADSF-24-1284328-C), the European Partnership on Metrology, co-financed from the European Unions Horizon Europe Research and Innovation Programme and by the Participating States (NEuroBioStand, #22HLT07), the Bluefield Project, Cure Alzheimers Fund, the Olav Thon Foundation, the Erling-Persson Family Foundation, Familjen Ronstroms Stiftelse, Stiftelsen for Gamla Tjanarinnor, Hjarnfonden, Sweden (#FO2022-0270), the European Unions Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme Neurodegenerative Disease Research (JPND2021-00694), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI-1003). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: NHS Research Ethics Committee I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Statement: the datasheets, R workspace and R code scripts will be made available to any reasonable request, and subject to data sharing agreements.

BackgroundSevere Acquired Brain Injury (sABI) presents significant challenges in clinical management and rehabilitation due to its diverse and complex nature. The primary objective of the Tiresia project is to identify medium and medium-to-long-term prognostic factors for patients with sABI and to develop outcome indicators to evaluate the quality of care in rehabilitation units.MethodsThis paper outlines the protocol for a prospective observational multicenter study conducted within the Tiresia Network. The study relies on a comprehensive data collection with stringent quality control measures. Ethical considerations emphasize patient privacy protection and adherence to regulatory standards. All of the Italian intensive rehabilitation units dedicated to sABI patients were eligible to participate in the study. Twenty-seven of them joined the project and started the data collection.DiscussionThe present study represents a comprehensive effort to address critical gaps in sABI research and practice through a multicenter, prospective study design. Through rigorous data collection, analysis, and ethical oversight, the Tiresia project endorses the commitment of the research community to advancing evidence-based care and optimizing patient outcomes in sABI rehabilitation.Clinical trial registrationThe study was registered on ClinicalTrials.gov (ID: NCT04905264), on 24 May 2021.

Background: he surge in the use of Pre-hospital Emergency Medical Systems (EMS) and Emergency Departments (ED) has become a pressing issue worldwide after the COVID-19 pandemic. To address this challenge, we developed an experimental and innovative care pathway supported by telemedicine. The aim of this study is to describe the activity of the Integrated Medical Center (CMI): a new telemedicine-based care model for patients referring to the Emergency Medical System. Methods: A prospective observational study was conducted from January 2022 to December 2022. The CMI was established to manage patients referring to the Emergency Medical System. Results: From January to December 2022, a total of 8680 calls were managed by CMI, with an average of 24 calls per day. 6243 patients (71.9%) were managed without ED access of whom 4884 patients (78.2%) were managed through telemedicine evaluation only, and 1359 (21.8%) with telemedicine evaluation and dispatch of the Home Rapid Response Team (HRRT). The population treated by the HRRT exhibited a higher age. The mean satisfaction score was 9.1/10. Conclusions: Telemedicine evaluation allowed for remote assessments, treatment prescriptions, and teleconsultation for HRRT and was associated with high patient satisfaction. This model could be useful in future pandemics for managing patients with non-urgent illnesses at home, preventing hospital admissions for potentially infectious patients, and thereby reducing in-hospital transmission.

Increasing demands on emergency departments (EDs) call for optimized decision-making processes to improve patient outcomes and resource allocation. Overcrowding is a significant issue, and the propensity of EDs to hospitalize patients is a key contributing factor to limiting in-patient bed availability, with inappropriate decisions negatively impacting healthcare quality and costs. In this setting research in emergency medicine to improve these difficulties is challenging. The main obstacles are the large volume of cases handled, the paucity of staff availability, and the resulting lack of time to dedicate to data entry. Furthermore, the electronic health record (EHR) systems currently used in EDs are not optimized for collection of data for research. Even retrospective data analyses cannot be performed due to the lack of robust data. Moreover, the EHR contains not only structured data but also abundant information in a free-text format which is challenging to use for research purposes. This protocol describes a study, the Use Case 1 study, which is part of the more general Horizon Europe eCREAM (enabling Clinical Research in Emergency and Acute-care Medicine) project. The study will test the reliability of an advanced natural language processing model set up in eCREAM to exploit EHRs by extracting robust, structured data to enable research in EDs. Specifically, the study will test the validity of the data extracted from the EHRs by addressing the issue of hospitalization rate. We will develop a predictive model to assess emergency department hospitalization rates, thereby enabling standardized comparisons across centers, ultimately leading to improved decision-making and reduced unnecessary hospital admissions. Retrospective patient data from 2021 to 2023 from 30 centers across Europe will be analyzed, and multivariable models will be employed to predict hospitalization and adjust comparisons between centers. The results are expected to improve decision-making in these departments. More generally, should the data extraction system prove valid, our results would serve as a practical demonstration that, despite the abundance of free-text data, EHRs can be exploited to conduct research in the emergency medicine field.

BACKGROUND:Traumatic brain injury (TBI) is associated with the tauopathies Alzheimer's disease and chronic traumatic encephalopathy. Advanced immunoassays show significant elevations in plasma total tau (t-tau) early post-TBI, but concentrations subsequently normalise rapidly. Tau phosphorylated at serine-181 (p-tau181) is a well-validated Alzheimer's disease marker that could potentially seed progressive neurodegeneration. We tested whether post-traumatic p-tau181 concentrations are elevated and relate to progressive brain atrophy. METHODS:Plasma p-tau181 and other post-traumatic biomarkers, including total-tau (t-tau), neurofilament light (NfL), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP), were assessed after moderate-to-severe TBI in the BIO-AX-TBI cohort (first sample mean 2.7 days, second sample within 10 days, then 6 weeks, 6 months and 12 months, n=42). Brain atrophy rates were assessed in aligned serial MRI (n=40). Concentrations were compared patients with and without Alzheimer's disease, with healthy controls. RESULTS:Plasma p-tau181 concentrations were significantly raised in patients with Alzheimer's disease but not after TBI, where concentrations were non-elevated, and remained stable over one year. P-tau181 after TBI was not predictive of brain atrophy rates in either grey or white matter. In contrast, substantial trauma-associated elevations in t-tau, NfL, GFAP and UCH-L1 were seen, with concentrations of NfL and t-tau predictive of brain atrophy rates. CONCLUSIONS:Plasma p-tau181 is not significantly elevated during the first year after moderate-to-severe TBI and levels do not relate to neuroimaging measures of neurodegeneration.