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Synergistic Effect of Static Compliance and D-dimers to Predict Outcome of Patients with COVID-19-ARDS: A Prospective Multicenter Study

TLDR
In this article, the synergic combination of high D-dimer and low static compliance identifies a clinical phenotype with high mortality in patients with COVID-19-ARDS, while no difference was found between LD and HD-HC.
Abstract
The synergic combination of D-dimer (as proxy of thrombotic/vascular injury) and static compliance (as proxy of parenchymal injury) in predicting mortality in COVID-19-ARDS has not been systematically evaluated. The objective is to determine whether the combination of elevated D-dimer and low static compliance can predict mortality in patients with COVID-19-ARDS. A "training sample" (March-June 2020) and a "testing sample" (September 2020-January 2021) of adult patients invasively ventilated for COVID-19-ARDS were collected in nine hospitals. D-dimer and compliance in the first 24 h were recorded. Study outcome was all-cause mortality at 28-days. Cut-offs for D-dimer and compliance were identified by receiver operating characteristic curve analysis. Mutually exclusive groups were selected using classification tree analysis with chi-square automatic interaction detection. Time to death in the resulting groups was estimated with Cox regression adjusted for SOFA, sex, age, PaO2/FiO2 ratio, and sample (training/testing). "Training" and "testing" samples amounted to 347 and 296 patients, respectively. Three groups were identified: D-dimer ≤ 1880 ng/mL (LD); D-dimer > 1880 ng/mL and compliance > 41 mL/cmH2O (LD-HC); D-dimer > 1880 ng/mL and compliance ≤ 41 mL/cmH2O (HD-LC). 28-days mortality progressively increased in the three groups (from 24% to 35% and 57% (training) and from 27% to 39% and 60% (testing), respectively; p < 0.01). Adjusted mortality was significantly higher in HD-LC group compared with LD (HR = 0.479, p < 0.001) and HD-HC (HR = 0.542, p < 0.01); no difference was found between LD and HD-HC. In conclusion, combination of high D-dimer and low static compliance identifies a clinical phenotype with high mortality in COVID-19-ARDS.

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Deciphering the balance of IL-6/IL-10 cytokines in severe to critical COVID-19 patients

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D-dimer levels in non-COVID-19 ARDS and COVID-19 ARDS patients: A systematic review with meta-analysis

TL;DR: In this article , the authors search PubMed, EMBASE, and ClinicalTrails.gov databases looking for studies reporting D-dimer levels in patients without or with COVID-19 acute respiratory distress syndrome (ARDS).
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Extracorporeal CO2 Removal During Renal Replacement Therapy to Allow Lung-Protective Ventilation in Patients With COVID-19–Associated Acute Respiratory Distress Syndrome

TL;DR: In this paper , the feasibility and safety of a combined extracorporeal CO2 removal (ECCO2R) plus renal replacement therapy (RRT) system to use an ultraprotective ventilator setting while maintaining an effective support of renal function and values of pH within the physiologic limits in a cohort of coronavirus infectious disease 2019 (COVID-19) patients were evaluated.
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Clinical Characteristics of Invasively Ventilated Covid-19 Patients: An Overview of Clinical Experience in Pauls Stradiņš Clinical University Hospital, Rīga, Latvia

TL;DR: In survivors, the duration of time between the onset of symptoms and hospitalisation, and time betweenThe onset of Symptoms and intubation, were found to be shorter than in non-survivors.
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TL;DR: Wang et al. as discussed by the authors used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death, including older age, high SOFA score and d-dimer greater than 1 μg/mL.
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TL;DR: In patients with acute lung injury and the acute respiratory distress syndrome, mechanical ventilation with a lower tidal volume than is traditionally used results in decreased mortality and increases the number of days without ventilator use.
Journal ArticleDOI

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TL;DR: The updated and revised Berlin Definition for ARDS addresses a number of the limitations of the AECC definition and may serve as a model to create more accurate, evidence-based, critical illness syndrome definitions and to better inform clinical care, research, and health services planning.
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