Multi-centric study for development and validation of a CT head rule for mild traumatic brain injury in direct oral anticoagulants: the HERO-M nomogram.

BACKGROUND: Nomograms are easy-to-handle clinical tools which can help in estimating the risk of adverse outcome in certain population. This multi-center study aims to create and validate a simple and usable clinical prediction nomogram for individual risk of post-traumatic Intracranial Hemorrhage (ICH) after Mild Traumatic Brain Injury (MTBI) in patients treated with Direct Oral Anticoagulants (DOACs). METHODS: From January 1, 2016 to December 31, 2019, all patients on DOACs evaluated for an MTBI in five Italian Emergency Departments were enrolled. A training set to develop the nomogram and a test set for validation were identified. The predictive ability of the nomogram was assessed using AUROC, calibration plot, and decision curve analysis. RESULTS: Of the 1425 patients in DOACs in the study cohort, 934 (65.5%) were included in the training set and 491 (34.5%) in the test set. Overall, the rate of post-traumatic ICH was 6.9% (7.0% training and 6.9% test set). In a multivariate analysis, major trauma dynamic (OR: 2.73, p = 0.016), post-traumatic loss of consciousness (OR: 3.78, p = 0.001), post-traumatic amnesia (OR: 4.15, p < 0.001), GCS < 15 (OR: 3.00, p < 0.001), visible trauma above the clavicles (OR: 3. 44, p < 0.001), a post-traumatic headache (OR: 2.71, p = 0.032), a previous history of neurosurgery (OR: 7.40, p < 0.001), and post-traumatic vomiting (OR: 3.94, p = 0.008) were independent risk factors for ICH. The nomogram demonstrated a good ability to predict the risk of ICH (AUROC: 0.803; CI95% 0.721-0.884), and its clinical application showed a net clinical benefit always superior to performing CT on all patients. CONCLUSION: The Hemorrhage Estimate Risk in Oral anticoagulation for Mild head trauma (HERO-M) nomogram was able to predict post-traumatic ICH and can be easily applied in the Emergency Department (ED).

Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis.

INTRODUCTION: Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) software has already been widely used in the evaluation of interstitial lung diseases (ILD) but has not yet been tested in patients affected by COVID-19. Our aim was to use it to describe the relationship between Coronavirus Disease 2019 (COVID-19) outcome and the CALIPER-detected pulmonary vascular-related structures (VRS). MATERIALS AND METHODS: We performed a multicentric retrospective study enrolling 570 COVID-19 patients who performed a chest CT in emergency settings in two different institutions. Fifty-three age- and sex-matched healthy controls were also identified. Chest CTs were analyzed with CALIPER identifying the percentage of VRS over the total lung parenchyma. Patients were followed for up to 72 days recording mortality and required intensity of care. RESULTS: There was a statistically significant difference in VRS between COVID-19-positive patients and controls (median (iqr) 4.05 (3.74) and 1.57 (0.40) respectively, p = 0.0001). VRS showed an increasing trend with the severity of care, p < 0.0001. The univariate Cox regression model showed that VRS increase is a risk factor for mortality (HR 1.17, p < 0.0001). The multivariate analysis demonstrated that VRS is an independent explanatory factor of mortality along with age (HR 1.13, p < 0.0001). CONCLUSION: Our study suggests that VRS increases with the required intensity of care, and it is an independent explanatory factor for mortality. KEY POINTS: • The percentage of vascular-related structure volume (VRS) in the lung is significatively increased in COVID-19 patients. • VRS showed an increasing trend with the required intensity of care, test for trend p< 0.0001. • Univariate and multivariate Cox models showed that VRS is a significant and independent explanatory factor of mortality.

"Decision tree analysis for assessing the risk of post-traumatic haemorrhage after mild traumatic brain injury in patients on oral anticoagulant therapy".

BACKGROUND: The presence of oral anticoagulant therapy (OAT) alone, regardless of patient condition, is an indication for CT imaging in patients with mild traumatic brain injury (MTBI). Currently, no specific clinical decision rules are available for OAT patients. The aim of the study was to identify which clinical risk factors easily identifiable at first ED evaluation may be associated with an increased risk of post-traumatic intracranial haemorrhage (ICH) in OAT patients who suffered an MTBI. METHODS: Three thousand fifty-four patients in OAT with MTBI from four Italian centers were retrospectively considered. A decision tree analysis using the classification and regression tree (CART) method was conducted to evaluate both the pre- and post-traumatic clinical risk factors most associated with the presence of post-traumatic ICH after MTBI and their possible role in determining the patient's risk. The decision tree analysis used all clinical risk factors identified at the first ED evaluation as input predictor variables. RESULTS: ICH following MTBI was present in 9.5% of patients (290/3054). The CART model created a decision tree using 5 risk factors, post-traumatic amnesia, post-traumatic transitory loss of consciousness, greater trauma dynamic, GCS less than 15, evidence of trauma above the clavicles, capable of stratifying patients into different increasing levels of ICH risk (from 2.5 to 61.4%). The absence of concussion and neurological alteration at admission appears to significantly reduce the possible presence of ICH. CONCLUSIONS: The machine-learning-based CART model identified distinct prognostic groups of patients with distinct outcomes according to on clinical risk factors. Decision trees can be useful as guidance in patient selection and risk stratification of patients in OAT with MTBI.

Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.

BACKGROUND AND AIMS: There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. METHODS AND RESULTS: Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age ≥85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs ≥ 90 mL/min/1.73 m2; HR = 2.3; 1.5-3.6 for C-reactive protein levels ≥10 vs ≤ 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. CONCLUSIONS: Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.

Trauma Center model application in the University Hospital of Pisa: a single-center comparative study.

The Trauma Center, Hub, is a highly specialized hospital indicated for complex major trauma management after stabilization at a 1st level hospital, Spoke. Although in the United States this organization demonstrated its effectiveness in mortality, in the Italian context, data available are limited. On 30 September 2018, the University Hospital of Pisa formalized the introduction of the Trauma Center, optimizing Emergency Department (ED) organization to guarantee the highest standard of care. The aim of this study was to demonstrate that the new model led better outcomes. We conducted a comparative retrospective study on 1154 major traumas over 24 months: the first 12 months (576 patients) correspond to the period before Trauma Center introduction, and the following 12 (457 patients) to the subsequent period. Results showed increase in greater dynamics and primary centralization by helicopter (p < 0.001, p 0.006). A systematic assessment with ABCDE algorithm was performed in a higher number of patients in the most recent period, from 38.4% to 80.3% (p < 0.001). Focused Assessment with Sonography for Trauma (FAST) performed by the emergency doctor increased after Trauma Center introduction, p value < 0.001. The data show an increase of ATLS certification among staff from 51.9 to 71.4% and a reduction in early and late mortality after the Trauma Center introduction (p value 0.05 and < 0.01). Fewer patients required intensive and surgical treatments, with a shorter hospital stay. The results demonstrate the advantage in terms of outcomes in the organization of the Trauma Center in the Italian context.

Blood oxygenation state in COVID-19 patients: Unexplored role of 2,3-bisphosphoglycerate.

BACKGROUND: COVID-19 reduces lung functionality causing a decrease of blood oxygen levels (hypoxemia) often related to a decreased cellular oxygenation (hypoxia). Besides lung injury, other factors are implicated in the regulation of oxygen availability such as pH, partial arterial carbon dioxide tension (PaCO2), temperature, and erythrocytic 2,3-bisphosphoglycerate (2,3-BPG) levels, all factors affecting hemoglobin saturation curve. However, few data are currently available regarding the 2,3-BPG modulation in SARS-CoV-2 affected patients at the hospital admission. MATERIAL AND METHODS: Sixty-eight COVID-19 patients were enrolled at hospital admission. The lung involvement was quantified using chest-Computer Tomography (CT) analysed with automatic software (CALIPER). Haemoglobin concentrations, glycemia, and routine analysis were evaluated in the whole blood, while partial arterial oxygen tension (PaO2), PaCO2, pH, and HCO3- were assessed by arterial blood gas analysis. 2,3-BPG levels were assessed by specific immunoenzymatic assays in RBCs. RESULTS: A higher percentage of interstitial lung disease (ILD) and vascular pulmonary-related structure (VRS) volume on chest-CT quantified with CALIPER had been found in COVID-19 patients with a worse disease outcome (R = 0.4342; and R = 0.3641, respectively). Furthermore, patients with lower PaO2 showed an imbalanced acid-base equilibrium (pH, p = 0.0208; PaCO2, p = 0.0496) and a higher 2,3-BPG levels (p = 0.0221). The 2,3-BPG levels were also lower in patients with metabolic alkalosis (p = 0.0012 vs. no alkalosis; and p = 0.0383 vs. respiratory alkalosis). CONCLUSIONS: Overall, the data reveal a different pattern of activation of blood oxygenation compensatory mechanisms reflecting a different course of the COVID-19 disease specifically focusing on 2,3-BPG modulation.

Arterial lactate as a risk factor for death in respiratory failure related to coronavirus disease 2019: an observational study.

BACKGROUND: Arterial lactate is a recognized biomarker associated with death in critically ill patients. The prognostic role of arterial lactate in acute respiratory failure due to the novel coronavirus disease 2019 (COVID-19) is unclear. OBJECTIVES: We aimed to investigate the prognostic role of arterial lactate levels at admission in patients with COVID-19-related acute respiratory failure. DESIGN AND METHODS: Cohorts of consecutive patients admitted to nonintensive care units (ICU) at study centers for COVID-19-related respiratory failure were merged into a collaborative database. The prognostic role of lactate levels at admission was assessed for continuous values and values ⩾2.0 mmol/l, and lactate clearance at 24 h through delta-lactate (ΔLac). The study outcome was 30-day in-hospital death. Cox proportional regression model was used to assess independent predictors of the study outcome. RESULTS: At admission, 14.6% of patients had lactate levels ⩾2 mmol/l. In-hospital death at 30 days occurred in 57 out of 206 patients; 22.3% and 56.7% with normal or ⩾ 2 mmol/l lactate at admission, respectively. The median lactate level was 1.0 [interquartile range (IQR) 0.8-1.3] mmol/l and 1.3 (IQR 1.0-2.1) mmol/l in survivors and nonsurvivors, respectively (p-value < 0.001). After adjusting for age, relevant comorbidities, acidemia, and the severity of respiratory failure, lactate ⩾2.0 mmol/l was associated with in-hospital death (HR 2.53, 95% CI 1.29-4.95, p-value 0.0066), while Δ Lac ⩾0 was not (HR 1.37, 95% CI 0.42-4.49). These results were confirmed in patients with a pO2/FiO2-ratio (P/F ratio) ⩽300 mmHg. CONCLUSIONS: In our study, increased arterial lactate at admission was independently associated with in-hospital death at 30 days in non-ICU patients with acute respiratory failure related to COVID-19.

Long-term lung ultrasound follow-up in patients after COVID-19 pneumonia hospitalization: A prospective comparative study with chest computed tomography.

During COVID-19 pandemic, lung ultrasound (LUS) proved to be of great value in the diagnosis and monitoring of patients with pneumonia. However, limited data exist regarding its use to assess aeration changes during follow-up (FU). Our study aims to prospectively evaluate 232 subjects who underwent a 3-month-FU program after hospitalization for COVID-19 at the University Hospital of Pisa. The goals were to assess the usefulness of standardized LUS compared with the gold standard chest computed tomography (CT) to evaluate aeration changes and to verify LUS and CT agreement at FU. Patients underwent in the same day a standardized 16-areas LUS and high-resolution chest CT reported by expert radiologists, assigning interpretative codes. Based on observations distribution, LUS score cut-offs of 3 and 7 were selected, corresponding to the 50th and 75th percentile, respectively. Patients with LUS scores above both these thresholds were older and with longer hospital stay. Patients with a LUS score ≥3 had more comorbidities. LUS and chest CT showed a high agreement in identifying residual pathological findings, using both cut-off scores of 3 (OR 14,7; CL 3,6-64,5, Sensitivity 91%, Specificity 49%) and 7 (OR 5,8; CL 2,3-14,3, Sensitivity 65%, Specificity 79%). Our data suggest that LUS is very sensitive in identifying pathological findings at FU after a hospitalization for COVID-19 pneumonia, compared to CT. Given its low cost and safety, LUS could replace CT in selected cases, such as in contexts with limited resources or it could be used as a gate-keeper examination before more advanced techniques.

The role of prone positioning in patients with SARS-CoV-2-related respiratory failure in non-intensive care unit.

BACKGROUND: Prone positioning (PP) is an established and commonly used lung recruitment method for intubated patients with severe acute respiratory distress syndrome, with potential benefits in clinical outcome. The role of PP outside the intensive care unit (ICU) setting is debated. OBJECTIVES: We aimed at assessing the role of PP in death and ICU admission in non-intubated patients with acute respiratory failure related to COronaVIrus Disease-19 (COVID-19) pneumonia. DESIGN: This is a retrospective analysis of a collaborative multicenter database obtained by merging local non-interventional cohorts. METHODS: Consecutive adult patients with COVID-19-related respiratory failure were included in a collaborative cohort and classified based on the severity of respiratory failure according to the partial arterial oxygen pressure to fraction of inspired oxygen ratio (PaO2/FiO2) and on clinical severity by the quick Sequential Organ Failure Assessment (qSOFA) score. The primary study outcome was the composite of in-hospital death or ICU admission within 30 days from hospitalization. RESULTS: PP was used in 114 of 536 study patients (21.8%), more commonly in patients with lower PaO2/FiO2 or receiving non-invasive ventilation and less commonly in patients with known comorbidities. A primary study outcome event occurred in 163 patients (30.4%) and in-hospital death in 129 (24.1%). PP was not associated with death or ICU admission (HR 1.17, 95% CI 0.78-1.74) and not with death (HR 1.01, 95% CI 0.61-1.67) at multivariable analysis; PP was an independent predictor of ICU admission (HR 2.64, 95% CI 1.53-4.40). The lack of association between PP and death or ICU admission was confirmed at propensity score-matching analysis. CONCLUSION: PP is used in a non-negligible proportion of non-intubated patients with COVID-19-related severe respiratory failure and is not associated with death but with ICU admission. The role of PP in this setting merits further evaluation in randomized studies.

Minimal is not minor also in patients with mild traumatic brain injury on oral direct anticoagulant therapy.

Currently, all patients, regardless of the type of head injury, should undergo a head computerized tomography (CT) if on oral anticoagulant therapy. The aim of the study was to assess the different incidences of intracranial hemorrhage (ICH) between patients with minor head injury (mHI) and patients with mild traumatic brain injury (MTBI) and whether there were differences in the risk of death at 30 days as a result of trauma or neurosurgery. A retrospective multicenter observational study was conducted from January 1, 2016, to February 1, 2020. All patients on DOACs therapy who suffered head trauma and underwent a head CT were extracted from the computerized databases. Patients were divided into two groups MTBI vs mHI all in DOACs treatment. Whether a difference in the incidence of post-traumatic ICH was present was investigated, and pre- and post-traumatic risk factors were compared between the two groups to assess the possible association with ICH risk by propensity score matching. 1425 with an MTBI in DOACs were enrolled. Of these, 80.1% (1141/1425) had an mHI and 19.9% (284/1425) had an MTBI. Of these, 16.5% (47/284) patients with MTBI and 3.3% (38/1141) with mHI reported post-traumatic ICH. After propensity score matching, ICH was consistently found to be more associated with patients with MTBI than with mHI (12.5% vs 5.4%, p = 0.027). Risk factors associated with immediate ICH in mHI patients were high energy impact, previous neurosurgery, trauma above the clavicles, post-traumatic vomiting and headache. Patients on MTBI (5.4%) were found to be more associated with ICH than those with mHI (0.0%, p = 0.002). also when the need for neurosurgery or death within 30 days were considered. Patients on DOACs with mHI have a lower risk of presenting with post-traumatic ICH than patients with MTBI. Furthermore, patients with mHI have a lower risk of death or neurosurgery than patients with MTBI, despite the presence of ICH.

Prognostic Role of Sonographic Decongestion in Patients with Acute Heart Failure with Reduced and Preserved Ejection Fraction: A Multicentre Study.

BACKGROUND: We investigated the role of the dynamic changes of pulmonary congestion, as assessed by sonographic B-lines, as a tool to stratify prognosis in patients admitted for acute heart failure with reduced and preserved ejection fraction (HFrEF, HFpEF). METHODS: In this multicenter, prospective study, lung ultrasound was performed at admission and before discharge by trained investigators, blinded to clinical findings. RESULTS: We enrolled 208 consecutive patients (mean age 76 [95% confidence interval, 70-84] years), 125 with HFrEF, 83 with HFpEF (mean ejection fraction 32% and 57%, respectively). The primary composite endpoint of cardiovascular death or HF re-hospitalization occurred in 18% of patients within 6 months. In the overall population, independent predictors of the occurrence of the primary endpoint were the number of B-lines at discharge, NT-proBNP levels, moderate-to-severe mitral regurgitation, and inferior vena cava diameter on admission. B-lines at discharge were the only independent predictor in both HFrEF and HFpEF subgroups. A cut-off of B-lines > 15 at discharge displayed the highest accuracy in predicting the primary endpoint (AUC = 0.80, p < 0.0001). Halving B-lines during hospitalization further improved event classification (continuous net reclassification improvement = 22.8%, p = 0.04). CONCLUSIONS: The presence of residual subclinical sonographic pulmonary congestion at discharge predicts 6-month clinical outcomes across the whole spectrum of acute HF patients, independent of conventional biohumoral and echocardiographic parameters. Achieving effective pulmonary decongestion during hospitalization is associated with better outcomes.

A causal learning framework for the analysis and interpretation of COVID-19 clinical data.

We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features. The workflow consists in a multi-step approach that goes from identifying the main causes of patient's outcome through BSL, to the realization of a tool suitable for clinical practice, based on a Binary Decision Tree (BDT), to recognize patients at high-risk with information available already at hospital admission time. We evaluate our approach on a feature-rich dataset of Coronavirus disease (COVID-19), showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We compare our findings with current literature on COVID-19, showing that this approach allows to re-discover established cause-effect relationships about the disease. Further, our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features: age, a previous history of chronic obstructive pulmonary disease and the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and Sodium) increases predictive accuracy to 94.5%.

Clinical prediction models in hospitalized patients with COVID-19: A multicenter cohort study.

BACKGROUND: Clinical spectrum of novel coronavirus disease (COVID-19) ranges from asymptomatic infection to severe respiratory failure that may result in death. We aimed at validating and potentially improve existing clinical models to predict prognosis in hospitalized patients with acute COVID-19. METHODS: Consecutive patients with acute confirmed COVID-19 pneumonia hospitalized at 5 Italian non-intensive care unit centers during the 2020 outbreak were included in the study. Twelve validated prognostic scores for pneumonia and/or sepsis and specific COVID-19 scores were calculated for each study patient and their accuracy was compared in predicting in-hospital death at 30 days and the composite of death and orotracheal intubation. RESULTS: During hospital stay, 302 of 1044 included patients presented critical illness (28.9%), and 226 died (21.6%). Nine out of 34 items included in different prognostic scores were independent predictors of all-cause-death. The discrimination was acceptable for the majority of scores (APACHE II, COVID-GRAM, REMS, CURB-65, NEWS II, ROX-index, 4C, SOFA) to predict in-hospital death at 30 days and poor for the rest. A high negative predictive value was observed for REMS (100.0%) and 4C (98.7%) scores; the positive predictive value was poor overall, ROX-index having the best value (75.0%). CONCLUSIONS: Despite the growing interest in prognostic models, their performance in patients with COVID-19 is modest. The 4C, REMS and ROX-index may have a role to select high and low risk patients at admission. However, simple predictors as age and PaO2/FiO2 ratio can also be useful as standalone predictors to inform decision making.

Early outcome detection for COVID-19 patients.

With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Starting from a large number of clinical variables, we select six of them with largest predictive power, using a feature selection method based on genetic algorithms and starting from a set of COVID-19 patients from the first wave. The algorithm is designed to reduce the impact of missing values in the set of variables measured, and consider only variables that show good accuracy on validation data. The final predictive model provides accuracy larger than 85% on test data, including a new patient cohort from the second COVID-19 wave, and on patients with imputed missing values. The selected clinical variables are confirmed to be relevant by recent literature on COVID-19.

Diaphragmatic thickening fraction as a potential predictor of response to continuous positive airway pressure ventilation in Covid-19 pneumonia: A single-center pilot study.

BACKGROUND: In a variable number of Covid-19 patients with acute respiratory failure, non-invasive breathing support strategies cannot provide adequate oxygenation, thus making invasive mechanical ventilation necessary. Factors predicting this unfavorable outcome are unknown, but we hypothesized that diaphragmatic weakness may contribute. METHODS: We prospectively analyzed the data of 27 consecutive patients admitted to the general Intensive Care Unit (ICU) from March 19, 2020, to April 20, 2020 and submitted to continuous positive airway pressure (CPAP) before considering invasive ventilation. Diaphragmatic thickening fraction (DTF) inferred by ultrasound was determined before applying CPAP. RESULTS: Eighteen patients recovered with CPAP, whereas nine required invasive mechanical ventilation with longer stay in ICU (p < 0.001) and hospital (p = 0.003). At univariate logistic regression analysis, CPAP failure was significantly associated with low DTF [ß: -0.396; OR: 0.673; p < 0.001] and high respiratory rate [ß: 0.452; OR: 1.572; p < 0.001] but only DTF reached statistical significance at multivariate analysis [ß: -0.384; OR: 0.681; p < 0.001]. The DTF best threshold predicting CPAP failure was 21.4 % (AUC: 0.944; sensitivity: 94.4 %, specificity: 88.9 %). CONCLUSIONS: In critically ill patients with Covid-19 respiratory failure admitted to ICU, a reduced DTF could be a potential predictor of CPAP failure and requirement of invasive ventilation.

Low diaphragm muscle mass predicts adverse outcome in patients hospitalized for COVID-19 pneumonia: an exploratory pilot study.

BACKGROUND: The aim of this study was to evaluate whether measurement of diaphragm thickness (DT) by ultrasonography may be a clinically useful noninvasive method for identifying patients at risk of adverse outcomes defined as need of invasive mechanical ventilation or death. METHODS: We prospectively enrolled 77 patients with laboratory-confirmed COVID-19 infection admitted to our intermediate care unit in Pisa between March 5 and March 30, 2020, with follow-up until hospital discharge or death. Logistic regression was used identify variables potentially associated with adverse outcomes and those P<0.10 were entered into a multivariate logistic regression model. Cumulative probability for lack of adverse outcomes in patients with or without low baseline diaphragm muscle mass was calculated with the Kaplan-Meier product-limit estimator. RESULTS: The main findings of this study are that: 1) patients who developed adverse outcomes had thinner diaphragm than those who did not (2.0 vs. 2.2 mm, P=0.001); and 2) DT and lymphocyte count were independent significant predictors of adverse outcomes, with end-expiratory DT being the strongest (ß=-708; OR=0.492; P=0.018). CONCLUSIONS: Diaphragmatic ultrasound may be a valid tool to evaluate the risk of respiratory failure. Evaluating the need of mechanical ventilation treatment should be based not only on PaO2/FiO2, but on a more comprehensive assessment including DT because if the lungs become less compliant a thinner diaphragm, albeit free of intrinsic abnormality, may become exhausted, thus contributing to severe respiratory failure.

C-C motive chemokine ligand 2 and thromboinflammation in COVID-19-associated pneumonia: A retrospective study.

PURPOSE: A derangement of the coagulation process and thromboinflammatory events has emerged as pathologic characteristics of severe COVID-19, characterized by severe respiratory failure. CC motive chemokine ligand 2 (CCL2), a chemokine originally described as a chemotactic agent for monocytes, is involved in inflammation, coagulation activation and neoangiogenesis. We investigated the association of CCL2 levels with coagulation derangement and respiratory impairment in patients with COVID-19. METHODS: We retrospectively evaluated 281 patients admitted to two hospitals in Italy with COVID-19. Among them, CCL2 values were compared in different groups (identified according to D-dimer levels and the lowest PaO2/FiO2 recorded during hospital stay, P/Fnadir) by Jonckheere-Terpstra tests; linear regression analysis was used to analyse the relationship between CCL2 and P/Fnadir. We performed Mann-Whitney test and Kaplan-Meier curves to investigate the role of CCL2 according to different clinical outcomes (survival and endotracheal intubation [ETI]). RESULTS: CCL2 levels were progressively higher in patients with increasing D-dimer levels and with worse gas exchange impairment; there was a statistically significant linear correlation between log CCL2 and log P/Fnadir. CCL2 levels were significantly higher in patients with unfavourable clinical outcomes; Kaplan-Meier curves for the composite outcome death and/or need for ETI showed a significantly worse prognosis for patients with higher (> median) CCL2 levels. CONCLUSIONS: CCL2 correlates with both indices of activation of the coagulation cascade and respiratory impairment severity, which are likely closely related in COVID-19 pathology, thus suggesting that CCL2 could be involved in the thromboinflammatory events characterizing this disease.

The relationship between cardiac injury, inflammation and coagulation in predicting COVID-19 outcome.

High sensitivity troponin T (hsTnT) is a strong predictor of adverse outcome during SARS-CoV-2 infection. However, its determinants remain partially unknown. We aimed to assess the relationship between severity of inflammatory response/coagulation abnormalities and hsTnT in Coronavirus Disease 2019 (COVID-19). We then explored the relevance of these pathways in defining mortality and complications risk and the potential effects of the treatments to attenuate such risk. In this single-center, prospective, observational study we enrolled 266 consecutive patients hospitalized for SARS-CoV-2 pneumonia. Primary endpoint was in-hospital COVID-19 mortality. hsTnT, even after adjustment for confounders, was associated with mortality. D-dimer and CRP presented stronger associations with hsTnT than PaO2. Changes of hsTnT, D-dimer and CRP were related; but only D-dimer was associated with mortality. Moreover, low molecular weight heparin showed attenuation of the mortality in the whole population, particularly in subjects with higher hsTnT. D-dimer possessed a strong relationship with hsTnT and mortality. Anticoagulation treatment showed greater benefits with regard to mortality. These findings suggest a major role of SARS-CoV-2 coagulopathy in hsTnT elevation and its related mortality in COVID-19. A better understanding of the mechanisms related to COVID-19 might pave the way to therapy tailoring in these high-risk individuals.

Estimated pulse wave velocity improves risk stratification for all-cause mortality in patients with COVID-19.

Accurate risk stratification in COVID-19 patients consists a major clinical need to guide therapeutic strategies. We sought to evaluate the prognostic role of estimated pulse wave velocity (ePWV), a marker of arterial stiffness which reflects overall arterial integrity and aging, in risk stratification of hospitalized patients with COVID-19. This retrospective, longitudinal cohort study, analyzed a total population of 1671 subjects consisting of 737 hospitalized COVID-19 patients consecutively recruited from two tertiary centers (Newcastle cohort: n = 471 and Pisa cohort: n = 266) and a non-COVID control cohort (n = 934). Arterial stiffness was calculated using validated formulae for ePWV. ePWV progressively increased across the control group, COVID-19 survivors and deceased patients (adjusted mean increase per group 1.89 m/s, P < 0.001). Using a machine learning approach, ePWV provided incremental prognostic value and improved reclassification for mortality over the core model including age, sex and comorbidities [AUC (core model + ePWV vs. core model) = 0.864 vs. 0.755]. ePWV provided similar prognostic value when pulse pressure or hs-Troponin were added to the core model or over its components including age and mean blood pressure (p < 0.05 for all). The optimal prognostic ePWV value was 13.0 m/s. ePWV conferred additive discrimination (AUC: 0.817 versus 0.779, P < 0.001) and reclassification value (NRI = 0.381, P < 0.001) over the 4C Mortality score, a validated score for predicting mortality in COVID-19 and the Charlson comorbidity index. We suggest that calculation of ePWV, a readily applicable estimation of arterial stiffness, may serve as an additional clinical tool to refine risk stratification of hospitalized patients with COVID-19 beyond established risk factors and scores.

Lung ultrasound for the early diagnosis of COVID-19 pneumonia: an international multicenter study.

PURPOSE: To analyze the application of a lung ultrasound (LUS)-based diagnostic approach to patients suspected of COVID-19, combining the LUS likelihood of COVID-19 pneumonia with patient's symptoms and clinical history. METHODS: This is an international multicenter observational study in 20 US and European hospitals. Patients suspected of COVID-19 were tested with reverse transcription-polymerase chain reaction (RT-PCR) swab test and had an LUS examination. We identified three clinical phenotypes based on pre-existing chronic diseases (mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure at presentation. We defined the LUS likelihood of COVID-19 pneumonia according to four different patterns: high (HighLUS), intermediate (IntLUS), alternative (AltLUS), and low (LowLUS) probability. The combination of patterns and phenotypes with RT-PCR results was described and analyzed. RESULTS: We studied 1462 patients, classified in mild (n = 400), severe (n = 727), and mixed (n = 335) phenotypes. HighLUS and IntLUS showed an overall sensitivity of 90.2% (95% CI 88.23-91.97%) in identifying patients with positive RT-PCR, with higher values in the mixed (94.7%) and severe phenotype (97.1%), and even higher in those patients with objective respiratory failure (99.3%). The HighLUS showed a specificity of 88.8% (CI 85.55-91.65%) that was higher in the mild phenotype (94.4%; CI 90.0-97.0%). At multivariate analysis, the HighLUS was a strong independent predictor of RT-PCR positivity (odds ratio 4.2, confidence interval 2.6-6.7, p < 0.0001). CONCLUSION: Combining LUS patterns of probability with clinical phenotypes at presentation can rapidly identify those patients with or without COVID-19 pneumonia at bedside. This approach could support and expedite patients' management during a pandemic surge.