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.

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.

Predictive Models for Very Preterm Birth: Developing a Point-of-Care Tool.

OBJECTIVE: The objective of this study was to create three point-of-care predictive models for very preterm birth using variables available at three different time points: prior to pregnancy, at the end of the first trimester, and mid-pregnancy. STUDY DESIGN: This is a retrospective cohort study of 359,396 Ohio Medicaid mothers from 2008 to 2015. The last baby for each mother was included in the final dataset. Births prior to 22 weeks were excluded. Multivariable logistic regression was used to create three models. These models were validated on a cohort that was set aside and not part of the model development. The main outcome measure was birth prior to 32 weeks. RESULTS: The final dataset contained 359,396 live births with 6,516 (1.81%) very preterm births. All models had excellent calibration. Goodness-of-fit tests suggested strong agreement between the probabilities estimated by the model and the actual outcome experience in the data. The mid-pregnancy model had acceptable discrimination with an area under the receiver operator characteristic curve of approximately 0.75 in both the developmental and validation datasets. CONCLUSION: Using data from a large Ohio Medicaid cohort we developed point-of-care predictive models that could be used before pregnancy, after the first trimester, and in mid-pregnancy to estimate the probability of very preterm birth. Future work is needed to determine how the calculator could be used to target interventions to prevent very preterm birth. KEY POINTS: · We developed predictive models for very preterm birth.. · All models showed excellent calibration.. · The models were integrated into a risk calculator..

Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test.

Evaluating the goodness of fit of logistic regression models is crucial to ensure the accuracy of the estimated probabilities. Unfortunately, such evaluation is problematic in large samples. Because the power of traditional goodness of fit tests increases with the sample size, practically irrelevant discrepancies between estimated and true probabilities are increasingly likely to cause the rejection of the hypothesis of perfect fit in larger and larger samples. This phenomenon has been widely documented for popular goodness of fit tests, such as the Hosmer-Lemeshow test. To address this limitation, we propose a modification of the Hosmer-Lemeshow approach. By standardizing the noncentrality parameter that characterizes the alternative distribution of the Hosmer-Lemeshow statistic, we introduce a parameter that measures the goodness of fit of a model but does not depend on the sample size. We provide the methodology to estimate this parameter and construct confidence intervals for it. Finally, we propose a formal statistical test to rigorously assess whether the fit of a model, albeit not perfect, is acceptable for practical purposes. The proposed method is compared in a simulation study with a competing modification of the Hosmer-Lemeshow test, based on repeated subsampling. We provide a step-by-step illustration of our method using a model for postneonatal mortality developed in a large cohort of more than 300 000 observations.

Model assisted sensitivity analyses for hidden bias with binary outcomes.

In medical and health sciences, observational studies are a major data source for inferring causal relationships. Unlike randomized experiments, observational studies are vulnerable to the hidden bias introduced by unmeasured confounders. The impact of unmeasured covariates on the causal effect can be assessed by conducting a sensitivity analysis. A comprehensive framework of sensitivity analyses has been developed for matching designs. Sensitivity parameters are introduced to capture the association between the missing covariates and the exposure or the outcome. Fixing sensitivity parameter values, it is possible to compute the bounds of the p-value of a randomization test on causal effects. We propose a model assisted sensitivity analysis with binary outcomes for the general 1:k matching design, which provides results equivalent to the conventional nonparametric approach in large sample. By introducing a conditional logistic outcome model, we substantially simplify the implementation and interpretation of the sensitivity analysis. More importantly, we are able to provide a closed form representation for the set of sensitivity parameters for which the maximum p-values are non-significant. This methodology can be easily extended to matching designs with multilevel treatments. We illustrate our method using a U.S. trauma care database to examine mortality difference between trauma care levels.

A new test and graphical tool to assess the goodness of fit of logistic regression models.

A prognostic model is well calibrated when it accurately predicts event rates. This is first determined by testing for goodness of fit with the development dataset. All existing tests and graphic tools designed for the purpose suffer several drawbacks, related mainly to the subgrouping of observations or to heavy dependence on arbitrary parameters. We propose a statistical test and a graphical method to assess the goodness of fit of logistic regression models, obtained through an extension of similar techniques developed for external validation. We analytically computed and numerically verified the distribution of the underlying statistic. Simulations on a set of realistic scenarios show that this test and the well-known Hosmer-Lemeshow approach have similar type I error rates. The main advantage of this new approach is that the relationship between model predictions and outcome rates across the range of probabilities can be represented in the calibration belt plot, together with its statistical confidence. By readily spotting any deviations from the perfect fit, this new graphical tool is designed to identify, during the process of model development, poorly modeled variables that call for further investigation. This is illustrated through an example based on real data.

A new calibration test and a reappraisal of the calibration belt for the assessment of prediction models based on dichotomous outcomes.

Calibration is one of the main properties that must be accomplished by any predictive model. Overcoming the limitations of many approaches developed so far, a study has recently proposed the calibration belt as a graphical tool to identify ranges of probability where a model based on dichotomous outcomes miscalibrates. In this new approach, the relation between the logits of the probability predicted by a model and of the event rates observed in a sample is represented by a polynomial function, whose coefficients are fitted and its degree is fixed by a series of likelihood-ratio tests. We propose here a test associated with the calibration belt and show how the algorithm to select the polynomial degree affects the distribution of the test statistic. We calculate its exact distribution and confirm its validity via a numerical simulation. Starting from this distribution, we finally reappraise the procedure to construct the calibration belt and illustrate an application in the medical context.

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.

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.

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.

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.

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.

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.

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.

Effect of a quality improvement program on compliance to the sepsis bundle in non-ICU patients: a multicenter prospective before and after cohort study.

Objective: Sepsis and septic shock are major challenges and economic burdens to healthcare, impacting millions of people globally and representing significant causes of mortality. Recently, a large number of quality improvement programs focused on sepsis resuscitation bundles have been instituted worldwide. These educational initiatives have been shown to be associated with improvements in clinical outcomes. We aimed to evaluate the impact of a multi-faceted quality implementing program (QIP) on the compliance of a "simplified 1-h bundle" (Sepsis 6) and hospital mortality of severe sepsis and septic shock patients out of the intensive care unit (ICU). Methods: Emergency departments (EDs) and medical wards (MWs) of 12 academic and non-academic hospitals in the Lombardy region (Northern Italy) were involved in a multi-faceted QIP, which included educational and organizational interventions. Patients with a clinical diagnosis of severe sepsis or septic shock according to the Sepsis-2 criteria were enrolled in two different periods: from May 2011 to November 2011 (before-QIP cohort) and from August 2012 to June 2013 (after-QIP cohort). Measurements and main results: The effect of QIP on bundle compliance and hospital mortality was evaluated in a before-after analysis. We enrolled 467 patients in the before-QIP group and 656 in the after-QIP group. At the time of enrollment, septic shock was diagnosed in 50% of patients, similarly between the two periods. In the after-QIP group, we observed increased compliance to the "simplified rapid (1 h) intervention bundle" (the Sepsis 6 bundle - S6) at three time-points evaluated (1 h, 13.7 to 18.7%, p = 0.018, 3 h, 37.1 to 48.0%, p = 0.013, overall study period, 46.2 to 57.9%, p < 0.001). We then analyzed compliance with S6 and hospital mortality in the before- and after-QIP periods, stratifying the two patients' cohorts by admission characteristics. Adherence to the S6 bundle was increased in patients with severe sepsis in the absence of shock, in patients with serum lactate <4.0 mmol/L, and in patients with hypotension at the time of enrollment, regardless of the type of admission (from EDs or MWs). Subsequently, in an observational analysis, we also investigated the relation between bundle compliance and hospital mortality by logistic regression. In the after-QIP cohort, we observed a lower in-hospital mortality than that observed in the before-QIP cohort. This finding was reported in subgroups where a higher adherence to the S6 bundle in the after-QIP period was found. After adjustment for confounders, the QIP appeared to be independently associated with a significant improvement in hospital mortality. Among the single S6 procedures applied within the first hour of sepsis diagnosis, compliance with blood culture and antibiotic therapy appeared significantly associated with reduced in-hospital mortality. Conclusion: A multi-faceted QIP aimed at promoting an early simplified bundle of care for the management of septic patients out of the ICU was associated with improved compliance with sepsis bundles and lower in-hospital mortality.

Changes in upper airways microbiota in ventilator-associated pneumonia.

BACKGROUND: The role of upper airways microbiota and its association with ventilator-associated pneumonia (VAP) development in mechanically ventilated (MV) patients is unclear. Taking advantage of data collected in a prospective study aimed to assess the composition and over-time variation of upper airway microbiota in patients MV for non-pulmonary reasons, we describe upper airway microbiota characteristics among VAP and NO-VAP patients. METHODS: Exploratory analysis of data collected in a prospective observational study on patients intubated for non-pulmonary conditions. Microbiota analysis (trough 16S-rRNA gene profiling) was performed on endotracheal aspirates (at intubation, T0, and after 72 h, T3) of patients with VAP (cases cohort) and a subgroup of NO-VAP patients (control cohort, matched according to total intubation time). RESULTS: Samples from 13 VAP patients and 22 NO-VAP matched controls were analyzed. At intubation (T0), patients with VAP revealed a significantly lower microbial complexity of the microbiota of the upper airways compared to NO-VAP controls (alpha diversity index of 84 ± 37 and 160 ± 102, in VAP and NO_VAP group, respectively, p-value < 0.012). Furthermore, an overall decrease in microbial diversity was observed in both groups at T3 as compared to T0. At T3, a loss of some genera (Prevotella 7, Fusobacterium, Neisseria, Escherichia-Shigella and Haemophilus) was found in VAP patients. In contrast, eight genera belonging to the Bacteroidetes, Firmicutes and Fusobacteria phyla was predominant in this group. However, it is unclear whether VAP caused dysbiosis or dysbiosis caused VAP. CONCLUSIONS: In a small sample size of intubated patients, microbial diversity at intubation was less in patients with VAP compared to patients without VAP.