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Background: Proportions of patients dying from the coronavirus disease-19 (COVID-19) vary between different countries. We report the characteristics; clinical course and outcome of patients requiring intensive care due to COVID-19 induced acute respiratory distress syndrome (ARDS).
Methods: This is a retrospective, observational multicentre study in five German secondary or tertiary care hospitals. All patients consecutively admitted to the intensive care unit (ICU) in any of the participating hospitals between March 12 and May 4, 2020 with a COVID-19 induced ARDS were included.
Results: A total of 106 ICU patients were treated for COVID-19 induced ARDS, whereas severe ARDS was present in the majority of cases. Survival of ICU treatment was 65.0%. Median duration of ICU treatment was 11 days; median duration of mechanical ventilation was 9 days. The majority of ICU treated patients (75.5%) did not receive any antiviral or anti-inflammatory therapies. Venovenous (vv) ECMO was utilized in 16.3%. ICU triage with population-level decision making was not necessary at any time. Univariate analysis associated older age, diabetes mellitus or a higher SOFA score on admission with non-survival during ICU stay.
Conclusions: A high level of care adhering to standard ARDS treatments lead to a good outcome in critically ill COVID-19 patients.
Background
Coronavirus disease 2019 (COVID-19) associated coagulopathy (CAC) leads to thromboembolic events in a high number of critically ill COVID-19 patients. However, specific diagnostic or therapeutic algorithms for CAC have not been established. In the current study, we analyzed coagulation abnormalities with point-of-care testing (POCT) and their relation to hemostatic complications in patients suffering from COVID-19 induced Acute Respiratory Distress Syndrome (ARDS). Our hypothesis was that specific diagnostic patterns can be identified in patients with COVID-19 induced ARDS at risk of thromboembolic complications utilizing POCT.
Methods
This is a single-center, retrospective observational study. Longitudinal data from 247 rotational thromboelastometries (Rotem®) and 165 impedance aggregometries (Multiplate®) were analysed in 18 patients consecutively admitted to the ICU with a COVID-19 induced ARDS between March 12th to June 30th, 2020.
Results
Median age was 61 years (IQR: 51–69). Median PaO2/FiO2 on admission was 122 mmHg (IQR: 87–189), indicating moderate to severe ARDS. Any form of hemostatic complication occurred in 78 % of the patients with deep vein/arm thrombosis in 39 %, pulmonary embolism in 22 %, and major bleeding in 17 %. In Rotem® elevated A10 and maximum clot firmness (MCF) indicated higher clot strength. The delta between EXTEM A10 minus FIBTEM A10 (ΔA10) > 30 mm, depicting the sole platelet-part of clot firmness, was associated with a higher risk of thromboembolic events (OD: 3.7; 95 %CI 1.3–10.3; p = 0.02). Multiplate® aggregometry showed hypoactive platelet function. There was no correlation between single Rotem® and Multiplate® parameters at intensive care unit (ICU) admission and thromboembolic or bleeding complications.
Conclusions
Rotem® and Multiplate® results indicate hypercoagulability and hypoactive platelet dysfunction in COVID-19 induced ARDS but were all in all poorly related to hemostatic complications..
Background
Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.
Methods
A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported.
Results
1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy.
Conclusions
Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models.
Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.