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Background & Aims: In ACLF patients, an adequate risk stratification is essential, especially for liver transplant allocation, since ACLF is associated with high short-term mortality. The CLIF-C ACLF score is the best prognostic model to predict outcome in ACLF patients. While lung failure is generally regarded as signum malum in ICU care, this study aims to evaluate and quantify the role of pulmonary impairment on outcome in ACLF patients.
Methods: In this retrospective study, 498 patients with liver cirrhosis and admission to IMC/ICU were included. ACLF was defined according to EASL-CLIF criteria. Pulmonary impairment was classified into three groups: unimpaired ventilation, need for mechanical ventilation and defined pulmonary failure. These factors were analysed in different cohorts, including a propensity score-matched ACLF cohort.
Results: Mechanical ventilation and pulmonary failure were identified as independent risk factors for increased short-term mortality. In matched ACLF patients, the presence of pulmonary failure showed the highest 28-day mortality (83.7%), whereas mortality rates in ACLF with mechanical ventilation (67.3%) and ACLF without pulmonary impairment (38.8%) were considerably lower (p < .001). Especially in patients with pulmonary impairment, the CLIF-C ACLF score showed poor predictive accuracy. Adjusting the CLIF-C ACLF score for the grade of pulmonary impairment improved the prediction significantly.
Conclusions: This study highlights that not only pulmonary failure but also mechanical ventilation is associated with worse prognosis in ACLF patients. The grade of pulmonary impairment should be considered in the risk assessment in ACLF patients. The new score may be useful in the selection of patients for liver transplantation.
In high-energy heavy-ion collisions, partonic collectivity is evidenced by the constituent quark number scaling of elliptic flow anisotropy for identified hadrons. A breaking of this scaling and dominance of baryonic interactions is found for identified hadron collective flow measurements in √sNN = 3 GeV Au+Au collisions. In this paper, we report measurements of the first- and second-order azimuthal anisotropic parameters, v1 and v2, of light nuclei (d, t, 3He, 4He) produced in √sNN = 3 GeV Au+Au collisions at the STAR experiment. An atomic mass number scaling is found in the measured v1 slopes of light nuclei at mid-rapidity. For the measured v2 magnitude, a strong rapidity dependence is observed. Unlike v2 at higher collision energies, the v2 values at mid-rapidity for all light nuclei are negative and no scaling is observed with the atomic mass number. Calculations by the Jet AA Microscopic Transport Model (JAM), with baryonic mean-field plus nucleon coalescence, are in good agreement with our observations, implying baryonic interactions dominate the collective dynamics in 3 GeV Au+Au collisions at RHIC.
Measurement of cold nuclear matter effects for inclusive J/ψ in p+Au collisions at √sNN = 200 GeV
(2022)
Measurement by the STAR experiment at RHIC of the cold nuclear matter (CNM) effects experienced by inclusive J/ψ at mid-rapidity in 0-100% p+Au collisions at √sNN = 200 GeV is presented. Such effects are quantified utilizing the nuclear modification factor, RpAu, obtained by taking a ratio of J/ψ yield in p+Au collisions to that in p+p collisions scaled by the number of binary nucleon-nucleon collisions. The differential J/ψ yield in both p+p and p+Au collisions is measured through the dimuon decay channel, taking advantage of the trigger capability provided by the Muon Telescope Detector in the RHIC 2015 run. Consequently, the J/ψ RpAu is derived within the transverse momentum (pT) range of 0 to 10 GeV/c. A suppression of approximately 30% is observed for pT < 2 GeV/c, while J/ψ RpAu becomes compatible with unity for pT greater than 3 GeV/c, indicating the J/ψ yield is minimally affected by the CNM effects at high pT. Comparison to a similar measurement from 0-20% central Au+Au collisions reveals that the observed strong J/ψ suppression above 3 GeV/c is mostly due to the hot medium effects, providing strong evidence for the formation of the quark-gluon plasma in these collisions. Several model calculations show qualitative agreement with the measured J/ψ RpAu, while their agreement with the J/ψ yields in p+p and p+Au collisions is worse.
We report the first multi-differential measurements of strange hadrons of K −, φ and − yields as well as the ratios of φ/K − and φ/− in Au+Au collisions at √sNN = 3 GeV with the STAR experiment fixed target configuration at RHIC. The φ mesons and − hyperons are measured through hadronic decay channels, φ → K + K − and Ξ− → Λπ−. Collision centrality and rapidity dependence of the transverse momentum spectra for these strange hadrons are presented. The 4π yields and ratios are compared to thermal model and hadronic transport model predictions. At this collision energy, thermal model with grand canonical ensemble (GCE) under-predicts the φ/K − and φ/− ratios while the result of canonical ensemble (CE) calculations reproduce φ/K −, with the correlation length rc ∼ 2.7 fm, and φ/−, rc ∼ 4.2 fm, for the 0-10% central collisions. Hadronic transport models including high mass resonance decays could also describe the ratios. While thermal calculations with GCE work well for strangeness production in high energy collisions, the change to CE at 3 GeV implies a rather different medium property at high baryon density.
We report on the measurements of directed flow v1 and elliptic flow v2 for hadrons (π±, K ±, K0 S , p, φ, Λ and ) from Au+Au collisions at √sN N = 3 GeV and v2 for (π±, K ±, p and p) at 27 and 54.4 GeV with the STAR experiment. While at the two higher energy midcentral collisions the numberof-constituent-quark (NCQ) scaling holds, at 3 GeV the v2 at midrapidity is negative for all hadrons and the NCQ scaling is absent. In addition, the v1 slopes at midrapidity for almost all observed hadrons are found to be positive, implying dominant repulsive baryonic interactions. The features of negative v2 and positive v1 slope at 3 GeV can be reproduced with a baryonic mean-field in transport model calculations. These results imply that the medium in such collisions is likely characterized by baryonic interactions.
Background and Objectives: Proteins of the coagulation system contribute to autoimmune inflammation in patients with multiple sclerosis (MS). On blood-brain barrier (BBB) disruption, fibrinogen enters the CNS and is rapidly converted to fibrin, unfolding pleiotropic autoimmune mechanisms. Fibrin accumulation leads to subsequent proteolytic degradation that results in D-dimer generation. The primary objective of this study was to determine intrathecal levels of D-dimer in CSF as a measure of intrathecal coagulation cascade activation and to evaluate its diagnostic utility in patients with MS in contrast to healthy subjects. Key secondary objectives included analysis of CSF D-dimer in differential diagnoses of MS and its relation to routine clinical markers of disease activity.
Methods: Patients admitted for the assessment of suspected MS were prospectively recruited from October 2017 to December 2020. Blood plasma and citrated CSF samples were analyzed using a highly sensitive luminescent oxygen channeling immunoassay. Intrathecal generation of D-dimer was analyzed by adjusting for CSF/serum albumin (Qalb) and CSF/plasma D-dimer quotients (QD-dimer), and corresponding CSF fibrinogen levels were determined. Final diagnoses after full evaluation and clinical data were recorded.
Results: Of 187 patients, 113 patients received a diagnosis of MS or clinically/radiologically isolated syndrome. We found increased intrathecal CSF D-dimer generation levels (QD-dimer/Qalb-index) for patients with relapsing-remitting MS (RRMS; n = 71, median 4.7, interquartile range [IQR] 2.5–8.0) when compared with those for disease controls (n = 22, median 2.6, IQR 2.1–4.8, p = 0.031). Absolute CSF D-dimer values correlated with CSF fibrinogen levels (r = 0.463; p < 0 .001) and CSF leukocytes (r = 0.273; p = 0.003) and were elevated in MS patients with contrast enhancement (CE) compared with MS patients without CE on MRI (n = 48, median 6 ng/mL, and IQR 3–15.25 vs n = 41, median 4 ng/mL, and IQR 2–7; p = 0.026). Exploratory subgroup analyses indicated a correlation of intrathecal inflammatory activity and CSF D-dimer levels.
Discussion: D-dimer in CSF can be reliably determined and correlates with markers of CNS inflammation and CSF fibrinogen levels. Adjusted for BBB dysfunction, CSF D-dimer may allow the identification of intrathecal coagulation cascade activation in patients with MS.
Classification of Evidence: This study provides Class I evidence that CSF D-dimer levels are elevated in patients with RRMS.
(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in critically ill COVID-19 patients. (2) Methods: Our data set consisted of 840 patients enclosed in the LEOSS registry. Using lasso regression for variable selection, a multifactorial logistic regression model was fitted to the response variable survival. Specific risk factors and their odds ratios were derived. A nomogram was developed as a graphical representation of the model. (3) Results: 14 variables were identified as independent factors contributing to the risk of death for critically ill COVID-19 patients: age (OR 1.08, CI 1.06–1.10), cardiovascular disease (OR 1.64, CI 1.06–2.55), pulmonary disease (OR 1.87, CI 1.16–3.03), baseline Statin treatment (0.54, CI 0.33–0.87), oxygen saturation (unit = 1%, OR 0.94, CI 0.92–0.96), leukocytes (unit 1000/μL, OR 1.04, CI 1.01–1.07), lymphocytes (unit 100/μL, OR 0.96, CI 0.94–0.99), platelets (unit 100,000/μL, OR 0.70, CI 0.62–0.80), procalcitonin (unit ng/mL, OR 1.11, CI 1.05–1.18), kidney failure (OR 1.68, CI 1.05–2.70), congestive heart failure (OR 2.62, CI 1.11–6.21), severe liver failure (OR 4.93, CI 1.94–12.52), and a quick SOFA score of 3 (OR 1.78, CI 1.14–2.78). The nomogram graphically displays the importance of these 14 factors for mortality. (4) Conclusions: There are risk factors that are specific to the subpopulation of critically ill COVID-19 patients.
We report first results on elliptic flow of identified particles at midrapidity in Au+Au collisions at sqrt[sNN] = 130 GeV using the STAR TPC at RHIC. The elliptic flow as a function of transverse momentum and centrality differs significantly for particles of different masses. This dependence can be accounted for in hydrodynamic models, indicating that the system created shows a behavior consistent with collective hydrodynamical flow. The fit to the data with a simple model gives information on the temperature and flow velocities at freeze-out.
Purpose: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.
Methods: We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).
Results: The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.
Conclusion: We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.