Measuring the crowding of emergency departments: an assessment of the NEDOCS in Lombardy, Italy, and the development of a new objective indicator based on the waiting time for the first clinical assessment.

BACKGROUND: There is no ubiquitous definition of Emergency Department (ED) crowding and several indicators have been proposed to measure it. The National ED Overcrowding Study (NEDOCS) score is among the most popular, even though it has been severely criticised. We used the waiting time for the physician's initial assessment to evaluate the performance of the NEDOCS and proposed a new crowding indicator based on this objective measure. METHODS: To evaluate the NEDOCS, we used the 2022 data of all the Lombardy EDs and compared the distribution of waiting times across the five levels of the NEDOCS at ED arrival. To construct the new indicator, we estimated the centre-specific relationship between the total number of ED patients and the waiting time of those with minor or deferrable urgency. We defined seven classes of waiting times and calculated how many patients corresponded to an average waiting time in the classes. These centre-specific cutoffs were used to define the 7-level crowding indicator. The indicator was then compared to the NEDOCS score and validated on the first six months of 2023 data. RESULTS: Patients' waiting time did not increase at the increase of the NEDOCS score, suggesting the absence of a relationship between this score and the effect of ED crowding on the ED capacity of evaluating new patients. The indicator we propose is easy to estimate in real-time and based on centre-specific cutoffs, which depend on the volume of yearly accesses. We observed minimal agreement between the proposed indicator and the NEDOCS in most EDs, both in the development and validation datasets. CONCLUSIONS: We proposed to quantify ED crowding using the waiting time for physician's initial assessment of patients with minor or deferrable urgency, which increases in crowding situations due to the prioritization of urgent patients. The centre-specific cutoffs avoid the problem of the heterogeneity of the volume of accesses and organization among EDs, while enabling a fair comparison between centres.

High-dimensional proteomic analysis for pathophysiological classification of traumatic brain injury.

Pathophysiology and outcomes after Traumatic Brain Injury (TBI) are complex and heterogenous. Current classifications are uninformative about pathophysiology. Proteomic approaches with fluid-based biomarkers are ideal for exploring complex disease mechanisms, as 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, 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 taken at a single timepoint up to 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, n=24 non-injured controls) also had subacute (10 days to 6 weeks post-injury) 3T 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 (calbindin2, UCH-L1, visinin-like protein1), astroglial markers (S100B, GFAP), neurodegenerative disease proteins (total tau, pTau231, PSEN1, amyloid-beta-42, 14-3-3γ), inflammatory cytokines (IL16, CCL2, ficolin2), cell signalling (SFRP1), cell metabolism (MDH1) and autophagy related (sequestome1) proteins. Acute plasma levels of UCH-L1, PSEN1, total tau and pTau231 correlated with subacute lesion volume. Sequestome1 was positively correlated, whilst CLL2 was inversely correlated, with white matter fractional anisotropy. Neuronal, astroglial, tau and neurodegenerative proteins correlated with each other, IL16, MDH1 and sequestome1. Exploratory clustering (k means) by acute protein expression identified 3 TBI subgroups that differed in injury patterns, but not age or outcome. One TBI cluster had significantly lower white matter fractional anisotropy than control-predominant clusters, but had significantly lower lesion subacute lesions 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 only studied 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.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

The Fenice project to evaluate and improve the quality of healthcare in high-dependency care units: results after the first year.

High-Dependency care Units (HDUs) have been introduced worldwide as intermediate wards between Intensive Care Units (ICUs) and general wards. Performing a comparative assessment of the quality of care in HDU is challenging because there are no uniform standards and heterogeneity among centers is wide. The Fenice network promoted a prospective cohort study to assess the quality of care provided by HDUs in Italy. This work aims at describing the structural characteristics and admitted patients of Italian HDUs. All Italian HDUs affiliated to emergency departments were eligible to participate in the study. Participating centers reported detailed structural information and prospectively collected data on all admitted adult patients. Patients' data are presented overall and analyzed to evaluate the heterogeneity across the participating centers. A total of 12 HDUs participated in the study and enrolled 3670 patients. Patients were aged 68 years on average, had multiple comorbidities and were on major chronic therapies. Several admitted patients had at least one organ failure (39%). Mortality in HDU was 8.4%, raising to 16.6% in hospital. While most patients were transferred to general wards, a small proportion required ICU transfer (3.9%) and a large group was discharged directly home from the HDU (31%). The expertise of HDUs in managing complex and fragile patients is supported by both the available equipment and the characteristics of admitted patients. The limited proportion of patients transferred to ICUs supports the hypothesis of preventing of ICU admissions. The heterogeneity of HDU admissions requires further research to define meaningful patients' outcomes to be used by quality-of-care assessment programs.

A Model Predicting the 6-Month Disability of Patients With Traumatic Brain Injury to Assess the Quality of Care in Intensive Care Units: Results from the CREACTIVE Study.

Assessing quality of care is essential for improving the management of patients experiencing traumatic brain injury (TBI). This study aimed at devising a rigorous framework to evaluate the quality of TBI care provided by intensive care units (ICUs) and applying it to the Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe (CREACTIVE) consortium, which involved 83 ICUs from seven countries. The performance of the centers was assessed in terms of patients' outcomes, as measured by the 6-month Glasgow Outcome Scale-Extended (GOS-E). To account for the between-center differences in the characteristics of the admitted patients, we developed a multinomial logistic regression model estimating the probability of a four-level categorization of the GOS-E: good recovery (GR), moderate disability (MD), severe disability (SD), and death or vegetative state (D/VS). A total of 5928 patients admitted to the participating ICUs between March 2014 and March 2019 were analyzed. The model included 11 predictors and demonstrated good discrimination (area under the receiver operating characteristic [ROC] curve in the validation set for GR: 0.836, MD: 0.802, SD: 0.706, D/VS: 0.890) and calibration, both overall (Hosmer-Lemeshow test p value: 0.87) and in several subgroups, defined by prognostically relevant variables. The model was used as a benchmark for assessing quality of care by comparing the observed number of patients experiencing GR, MD, SD, and D/VS to the corresponding numbers expected in each category by the model, computing observed/expected (O/E) ratios. The four center-specific ratios were assembled with polar representations and used to provide a multidimensional assessment of the ICUs, overcoming the loss of information consequent to the traditional dichotomizations of the outcome in TBI research. The proposed framework can help in identifying strengths and weaknesses of current TBI care, triggering the changes that are necessary to improve patient outcomes.

Assessing the heterogeneity of the impact of COVID-19 incidence on all-cause excess mortality among healthcare districts in Lombardy, Italy, to evaluate the local response to the pandemic: an ecological study.

OBJECTIVES: The fragmentation of the response to the COVID-19 pandemic at national, regional and local levels is a possible source of variability in the impact of the pandemic on society. This study aims to assess how much of this variability affected the burden of COVID-19, measured in terms of all-cause 2020 excess mortality. DESIGN: Ecological retrospective study. SETTING: Lombardy region of Italy, 2015-2020. OUTCOME MEASURES: We evaluated the relationship between the intensity of the epidemics and excess mortality, assessing the heterogeneity of this relationship across the 91 districts after adjusting for relevant confounders. RESULTS: The epidemic intensity was quantified as the COVID-19 hospitalisations per 1000 inhabitants. Five confounders were identified through a directed acyclic graph: age distribution, population density, pro-capita gross domestic product, restriction policy and population mobility.Analyses were based on a negative binomial regression model with district-specific random effects. We found a strong, positive association between COVID-19 hospitalisations and 2020 excess mortality (p<0.001), estimating that an increase of one hospitalised COVID-19 patient per 1000 inhabitants resulted in a 15.5% increase in excess mortality. After adjusting for confounders, no district differed in terms of COVID-19-unrelated excess mortality from the average district. Minimal heterogeneity emerged in the district-specific relationships between COVID-19 hospitalisations and excess mortality (6 confidence intervals out of 91 did not cover the null value). CONCLUSIONS: The homogeneous effect of the COVID-19 spread on the excess mortality in the Lombardy districts suggests that, despite the unprecedented conditions, the pandemic reactions did not result in health disparities in the region.

Alzheimer's disease marker phospho-tau181 is not elevated in the first year after moderate-to-severe TBI.

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.

Diagnosis of sepsis in the emergency department: a real-world analysis from Lombardy, Italy.

Early identification of sepsis is particularly important in the emergency department (ED). However, data on the diagnosis of sepsis in the ED are scanty, especially within the Italian context. To quantify sepsis incidence and recognition in the ED from Lombardy, Italy, we used EUOL data from the Regional Emergency Agency for the years 2017-2022. Sepsis was identified based on the ED discharge diagnosis; recognized sepsis cases were those assigned to a high-priority code at triage, while unrecognized ones were those assigned to a low priority code. Odds ratios (ORs) for sepsis recognition according to various patient characteristics were estimated using multivariable mixed-effects logistic regression models. The rate of sepsis diagnosis in ED was 1.9 per 1000 (6626 patients) in 2017 and increased to 3.4 per 1000 in 2022 (11,508 patients). In 2022, 67% of sepsis cases were correctly identified. Death in the ED was more frequent in patients with recognized sepsis (10.4%) than in those with unrecognized sepsis (2.3%). The probability of sepsis being recognized at ED admission was higher in men (multivariable OR: 1.06), in individuals with advanced age (OR: 1.71 for age ≥ 90 years vs < 60), and in those with access to the second (OR: 1.48) and third ED level (OR: 1.87). Conversely, it was lower in patients arriving at the ED through autonomous transportation (OR: 0.36). This large real-world analysis indicates an increase in sepsis cases referred to the ED in recent years. About one-third of sepsis cases are not correctly identified at triage, although more severe cases appear to be promptly recognized.

Strategies to convert hospital beds for COVID-19 patients to minimize emergency department overcrowding.

Background: The shortage of hospital beds for COVID-19 patients has been one critical cause of Emergency Department (ED) overcrowding. Purpose: We aimed at elaborating a strategy of conversion of hospital beds, from non-COVID-19 to COVID-19 care, minimizing both ED overcrowding and the number of beds eventually converted. Research Design: Observational retrospective study. Study Sample: We considered the centralized database of all ED admissions in the Lombardy region of Italy during the second "COVID-19 wave" (October to December 2020). Data collection and Analysis: We analyzed all admissions to 82 EDs. We devised a family of Monte Carlo simulations to evaluate the performance of hospital beds' conversion strategies triggered by ED crowding of COVID-19 patients, determining a critical number of beds to be converted when passing an ED-specific crowding threshold. Results: Our results suggest that the maximum number of patients waiting for hospitalization could have been decreased by 70% with the proposed strategy. Such a reduction would have been achieved by converting 30% more hospital beds than the total number converted in the region. Conclusions: The disproportion between reduction in ED crowding and additionally converted beds suggests that a wide margin to improve the efficiency of the conversions exists. The proposed simulation apparatus can be easily generalized to study management policies synchronizing ED output and in-hospital bed availability.

Who are the superheroes? A cross-sectional study on the determinants of good health among Italian older adults.

BACKGROUND: People who reach old age enjoying good physical and mental health can be defined as (health) "superheroes", given their relatively low impact on healthcare expenditure and the desirable model they represent. AIM: To evaluate prevalence and possible determinants of being "physical superheroes" (i.e., free from the ten major chronic conditions, plus obesity), "mental superheroes" (i.e., free from major mental symptoms), and "superheroes" (i.e., both mental and physical superheroes). METHODS: A telephone-based cross-sectional study (LOST in Lombardia) was conducted in November 2020 (i.e., during the COVID-19 pandemic) on a representative sample of 4,400 adults aged ≥ 65 years from Lombardy region, northern Italy. All participants provided both current data and data referring to one year before. RESULTS: Mental and physical superheroes were 59.0% and 17.6%, respectively. Superheroes were 12.8% overall, 15.1% among men, and 11.1% among women; 20.2% among individuals aged 65-69 years, 11.3% among 70-74, 10.0% among 75-79, and 8.3% among ≥ 80 years. Multivariable analysis showed that female sex, higher age, disadvantaged socio-economic status, and physical inactivity (p for trend < 0.001) were inversely related to being superheroes. People not smoking (adjusted odds ratio, aOR = 1.40), alcohol abstainers (aOR = 1.30), and those free from feelings of hopelessness (aOR = 5.92) more frequently met the definition of superheroes. During COVID-19 pandemic, the proportion of superheroes decreased by 16.3%. CONCLUSIONS: Differences in the older adults' health status are largely attributable to their lifestyles but are also likely due to gender, educational, and socio-economic disparities, which should be properly addressed by public health policies.

Comparative Effectiveness of Intracranial Pressure Monitoring on 6-Month Outcomes of Critically Ill Patients With Traumatic Brain Injury.

Importance: While the relationship between persistent elevations in intracranial pressure (ICP) and poorer outcomes is well established for patients with traumatic brain injury (TBI), there is no consensus on how ICP measurements should drive treatment choices, and the effectiveness of ICP monitoring remains unknown. Objective: To evaluate the effectiveness of ICP monitoring on short- and mid-term outcomes of patients with TBI. Design, Setting, and Participants: CREACTIVE was a prospective cohort study that started in March 2014 and lasted 5 years. More than 8000 patients with TBI were enrolled at 83 intensive care units (ICUs) from 7 countries who joined the CREACTIVE Consortium. Patients with TBI who met the Brain Trauma Foundation guidelines for ICP monitoring were selected for the current analyses, which were performed from January to November 2022. Exposure: Patients who underwent ICP monitoring within 2 days of injury (exposure group) were propensity score-matched to patients who were not monitored or who underwent monitoring 2 days after the injury (control group). Main Outcome and Measure: Functional disability at 6 months as indicated by Glasgow Outcome Scale-Extended (GOS-E) score. Results: A total of 1448 patients from 43 ICUs in Italy and Hungary were eligible for analysis. Of the patients satisfying the ICP-monitoring guidelines, 503 (34.7%) underwent ICP monitoring (median [IQR] age: 45 years [29-61 years]; 392 males [77.9%], 111 females [22.1%]) and 945 were not monitored (median [IQR] age: 66 years [48-78 years]; 656 males [69.4%], 289 females [30.6%]). After matching to balance the variables, worse 6-month recovery was observed for monitored patients compared with nonmonitored patients (death/vegetative state: 39.2% vs 40.6%; severe disability: 33.2% vs 25.4%; moderate disability: 15.7% vs 14.9%; good recovery: 11.9% vs 19.1%, respectively; P = .005). Monitored patients received medical therapies significantly more frequently. Conclusions and Relevance: In this cohort study, ICP monitoring was associated with poorer recovery and more frequent medical interventions with their relevant adverse effects. Optimizing the value of ICP monitoring for TBI requires further investigation on monitoring indications, clinical interventions, and management protocols.

Prognostic value of respiratory parameters for COVID-19 patients in the emergency department: results from the EC-COVID study.

While several studies have evaluated the prognostic weight of respiratory parameters in patients with COVID-19, few have focused on patients' clinical conditions at the first emergency department (ED) assessment. We analyzed a large cohort of ED patients recruited within the EC-COVID study over the year 2020, and assessed the association between key bedside respiratory parameters measured in room air (pO2, pCO2, pH, and respiratory rate [RR]) and hospital mortality, after adjusting for key confounding factors. Analyses were based on a multivariable logistic Generalized Additive Model (GAM). After excluding patients who did not perform a blood gas analysis (BGA) test in room air or with incomplete BGA results, a total of 2458 patients were considered in the analyses. Most patients were hospitalized on ED discharge (72.0%); hospital mortality was 14.3%. Strong, negative associations with hospital mortality emerged for pO2, pCO2 and pH (p-values: < 0.001, < 0.001 and 0.014), while a significant, positive association was observed for RR (p-value < 0.001). Associations were quantified with nonlinear functions, learned from the data. No cross-parameter interaction was significant (all p-values were larger than 0.10), suggesting a progressive, independent effect on the outcome as the value of each parameter departed from normality. Our results collide with the hypothesized existence of patterns of breathing parameters with specific prognostic weight in the early stages of the disease.

Investigating the characteristics and correlates of systemic inflammation after traumatic brain injury: the TBI-BraINFLAMM study.

INTRODUCTION: A significant environmental risk factor for neurodegenerative disease is traumatic brain injury (TBI). However, it is not clear how TBI results in ongoing chronic neurodegeneration. Animal studies show that systemic inflammation is signalled to the brain. This can result in sustained and aggressive microglial activation, which in turn is associated with widespread neurodegeneration. We aim to evaluate systemic inflammation as a mediator of ongoing neurodegeneration after TBI. METHODS AND ANALYSIS: TBI-braINFLAMM will combine data already collected from two large prospective TBI studies. The CREACTIVE study, a broad consortium which enrolled >8000 patients with TBI to have CT scans and blood samples in the hyperacute period, has data available from 854 patients. The BIO-AX-TBI study recruited 311 patients to have acute CT scans, longitudinal blood samples and longitudinal MRI brain scans. The BIO-AX-TBI study also has data from 102 healthy and 24 non-TBI trauma controls, comprising blood samples (both control groups) and MRI scans (healthy controls only). All blood samples from BIO-AX-TBI and CREACTIVE have already been tested for neuronal injury markers (GFAP, tau and NfL), and CREACTIVE blood samples have been tested for inflammatory cytokines. We will additionally test inflammatory cytokine levels from the already collected longitudinal blood samples in the BIO-AX-TBI study, as well as matched microdialysate and blood samples taken during the acute period from a subgroup of patients with TBI (n=18).We will use this unique dataset to characterise post-TBI systemic inflammation, and its relationships with injury severity and ongoing neurodegeneration. ETHICS AND DISSEMINATION: Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (17/LO/2066). Results will be submitted for publication in peer-review journals, presented at conferences and inform the design of larger observational and experimental medicine studies assessing the role and management of post-TBI systemic inflammation.

Centre for Statistical and Methodological Excellence (CESAME): A Consortium Initiative for Improving Methodology in Randomised Clinical Trials.

When conducting randomised clinical trials, the choice of methodology and statistical analyses will influence the results. If the planned methodology is not of optimal quality and predefined in detail, there is a risk of biased trial results and interpretation. Even though clinical trial methodology is already at a very high standard, there are many trials that deliver biased results due to the implementation of inadequate methodology, poor data quality and erroneous or biased analyses. To increase the internal and external validity of randomised clinical trial results, several international institutions within clinical intervention research have formed The Centre for Statistical and Methodological Excellence (CESAME). Based on international consensus, the CESAME initiative will develop recommendations for the proper methodological planning, conduct and analysis of clinical intervention research. CESAME aims to increase the validity of randomised clinical trial results which will ultimately benefit patients worldwide across medical specialities. The work of CESAME will be performed within 3 closely interconnected pillars: (1) planning randomised clinical trials; (2) conducting randomised clinical trials; and (3) analysing randomised clinical trials.

Cerebral Metabolic Dysfunction at the Acute Phase of Traumatic Brain Injury Correlates with Long-Term Tissue Loss.

Following traumatic brain injury (TBI), cerebral metabolic dysfunction, characterized by an elevated cerebral microdialysis (CMD) lactate/pyruvate (LP) ratio, is associated with poor outcome. However, the exact pathophysiological mechanisms underlying this association are not entirely established. In this pre-planned analysis of the BIOmarkers of AXonal injury after Traumatic Brain Injury (BIO-AX-TBI) prospective study, we investigated any associations of LP ratio with brain structure volume change rates at 1 year. Fourteen subjects underwent acute-phase (0-96 h post-TBI) CMD monitoring and had longitudinal magnetic resonance imaging (MRI) quantification of brain volume loss between the subacute phase (14 days to 6 weeks) and 1 year after TBI, recalculated as an annual rate. On average, CMD showed an elevated (>25) LP ratio (31 [interquartile range (IQR) 24-34]), indicating acute cerebral metabolic dysfunction. Annualized whole brain and total gray matter (GM) volume change rates were abnormally reduced (-3.2% [-9.3 to -2.2] and -1.9% [-4.4 to 1.7], respectively). Reduced annualized total GM volume correlated significantly with elevated CMD LP ratio (Spearman's ρ = -0.68, p-value = 0.01) and low CMD glucose (ρ = 0.66, p-value = 0.01). After adjusting for age, admission Glasgow Coma Scale (GCS) score and CT Marshall score, CMD LP ratio remained strongly associated with 1-year total GM volume change rate (p < 0.001; multi-variable analysis). No relationship was found between WM volume changes and CMD metabolites. We demonstrate a strong association between acute post-traumatic cerebral metabolic dysfunction and 1-year gray matter atrophy, reinforcing the role of CMD LP ratio as an early biomarker of poor long-term recovery after TBI.

Thoracic UltrasONOgraphy Reporting: The TUONO Study.

Lung ultrasound (LUS) is a validated technique for the prompt diagnosis and bedside monitoring of critically ill patients due to its availability, safety profile, and cost-effectiveness. The aim of this work is to detect similarities and differences among LUS reports performed in ICUs and to provide a common ground for an integrated report form. We collected all LUS reports during an index week in 21 ICUs from the GiViTI network. First, we considered signs, chest areas, and terminology reported. Then, we compared different report structures and categorized them as structured reports (SRs), provided with a predefined model form, and free unstructured text reports (FTRs) that had no predetermined structure. We analyzed 171 reports from 21 ICUs, and 59 reports from 5 ICUs were structured. All the reports presented a qualitative description that mainly focused on the presence of B-lines, consolidations, and pleural effusion. Zones were defined in 66 reports (39%). In SRs, a complete examination of all the regions was more frequently achieved (96% vs. 74%), and a higher impact on therapeutic strategies was observed (17% vs. 6%). LUS reports vary significantly among different centers. Adopting an integrated SR seems to promote a systematic approach in scanning and reporting, with a potential impact on LUS clinical applications.

Effectiveness of a Multifaced Antibiotic Stewardship Program: A Pre-Post Study in Seven Italian ICUs.

Multidrug resistance has become a serious threat for health, particularly in hospital-acquired infections. To improve patients' safety and outcomes while maintaining the efficacy of antimicrobials, complex interventions are needed involving infection control and appropriate pharmacological treatments in antibiotic stewardship programs. We conducted a multicenter pre-post study to assess the impact of a stewardship program in seven Italian intensive care units (ICUs). Each ICU was visited by a multidisciplinary team involving clinicians, microbiologists, pharmacologists, infectious disease specialists, and data scientists. Interventions were targeted according to the characteristics of each unit. The effect of the program was measured with a panel of indicators computed with data from the MargheritaTre electronic health record. The median duration of empirical therapy decreased from 5.6 to 4.6 days and the use of quinolones dropped from 15.3% to 6%, both p < 0.001. The proportion of multi-drug-resistant bacteria (MDR) in ICU-acquired infections fell from 57.7% to 48.8%. ICU mortality and length of stay remained unchanged, indicating that reducing antibiotic administration did not harm patients' safety. This study shows that our stewardship program successfully improved the management of infections. This suggests that policy makers should tackle multidrug resistance with a multidisciplinary approach based on continuous monitoring and personalised interventions.

Benchmark of Intraoperative Activity in Cardiac Surgery: A Comparison between Pre- and Post-Operative Prognostic Models.

OBJECTIVES: Despite its large diffusion and improvements in safety, the risks of complications after cardiac surgery remain high. Published predictive perioperative scores (EUROSCORE, STS, ACEF) assess risk on preoperative data only, not accounting for the intraopertive period. We propose a double-fold model, including data collected before surgery and data collected at the end of surgery, to evaluate patient risk evolution over time and assess the direct contribution of surgery. METHODS: A total of 15,882 cardiac surgery patients from a Margherita-Prosafe cohort study were included in the analysis. Probability of death was estimated using two logistic regression models (preoperative data only vs. post-operative data, also including information at discharge from the operatory theatre), testing calibration and discrimination of each model. RESULTS: Pre-operative and post-operative models were built and demonstrate good discrimination and calibration with AUC = 0.81 and 0.87, respectively. Relative difference in pre- and post-operative mortality in separate centers ranged from -0.36 (95% CI: -0.44--0.28) to 0.58 (95% CI: 0.46-0.71). The usefulness of this two-fold preoperative model to benchmark medical care in single hospital is exemplified in four cases. CONCLUSIONS: Predicted post-operative mortality differs from predicted pre-operative mortality, and the distance between the two models represent the impact of surgery on patient outcomes. A double-fold model can assess the impact of the intra-operative team and the evolution of patient risk over time, and benchmark different hospitals on patients subgroups to promote an improvement in medical care in each center.

Hospitals with and without neurosurgery: a comparative study evaluating the outcome of patients with traumatic brain injury.

BACKGROUND: We leveraged the data of the international CREACTIVE consortium to investigate whether the outcome of traumatic brain injury (TBI) patients admitted to intensive care units (ICU) in hospitals without on-site neurosurgical capabilities (no-NSH) would differ had the same patients been admitted to ICUs in hospitals with neurosurgical capabilities (NSH). METHODS: The CREACTIVE observational study enrolled more than 8000 patients from 83 ICUs. Adult TBI patients admitted to no-NSH ICUs within 48 h of trauma were propensity-score matched 1:3 with patients admitted to NSH ICUs. The primary outcome was the 6-month extended Glasgow Outcome Scale (GOS-E), while secondary outcomes were ICU and hospital mortality. RESULTS: A total of 232 patients, less than 5% of the eligible cohort, were admitted to no-NSH ICUs. Each of them was matched to 3 NSH patients, leading to a study sample of 928 TBI patients where the no-NSH and NSH groups were well-balanced with respect to all of the variables included into the propensity score. Patients admitted to no-NSH ICUs experienced significantly higher ICU and in-hospital mortality. Compared to the matched NSH ICU admissions, their 6-month GOS-E scores showed a significantly higher prevalence of upper good recovery for cases with mild TBI and low expected mortality risk at admission, along with a progressively higher incidence of poor outcomes with increased TBI severity and mortality risk. CONCLUSIONS: In our study, centralization of TBI patients significantly impacted short- and long-term outcomes. For TBI patients admitted to no-NSH centers, our results suggest that the least critically ill can effectively be managed in centers without neurosurgical capabilities. Conversely, the most complex patients would benefit from being treated in high-volume, neuro-oriented ICUs.