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Abstract Rationale Health-related quality of life after surviving acute respiratory distress syndrome has come into focus in recent years, especially during the coronavirus disease 2019 pandemic. Objectives A total of 144 patients with acute respiratory distress syndrome caused by COVID-19 or of other origin were recruited in a randomized multicenter trial. Methods Clinical data during intensive care treatment and data up to 180 days after study inclusion were collected. Changes in the Sequential Organ Failure Assessment score were used to quantify disease severity. Disability was assessed using the Barthel index on days 1, 28, 90, and 180. Measurements Mortality rate and morbidity after 180 days were compared between patients with and without COVID-19. Independent risk factors associated with high disability were identified using a binary logistic regression. Main results The SOFA score at day 5 was an independent risk factor for high disability in both groups, and score dynamic within the first 5 days significantly impacted disability in the non-COVID group. Mortality after 180 days and impairment measured by the Barthel index did not differ between patients with and without COVID-19. Conclusions Resolution of organ dysfunction within the first 5 days significantly impacts long-term morbidity. Acute respiratory distress syndrome caused by COVID-19 was not associated with increased mortality or morbidity.

Abstract Background A profound inflammation-mediated lung injury with long-term acute respiratory distress and high mortality is one of the major complications of critical COVID-19. Immunoglobulin M (IgM)-enriched immunoglobulins seem especially capable of mitigating the inflicted inflammatory harm. However, the efficacy of intravenous IgM-enriched preparations in critically ill patients with COVID-19 is largely unclear. Methods In this retrospective multicentric cohort study, 316 patients with laboratory-confirmed critical COVID-19 were treated in ten German and Austrian ICUs between May 2020 and April 2021. The primary outcome was 30-day mortality. Analysis was performed by Cox regression models. Covariate adjustment was performed by propensity score weighting using machine learning-based SuperLearner to overcome the selection bias due to missing randomization. In addition, a subgroup analysis focusing on different treatment regimens and patient characteristics was performed. Results Of the 316 ICU patients, 146 received IgM-enriched immunoglobulins and 170 cases did not, which served as controls. There was no survival difference between the two groups in terms of mortality at 30 days in the overall cohort (HRadj: 0.83; 95% CI: 0.55 to 1.25; p = 0.374). An improved 30-day survival in patients without mechanical ventilation at the time of the immunoglobulin treatment did not reach statistical significance (HRadj: 0.23; 95% CI: 0.05 to 1.08; p = 0.063). Also, no statistically significant difference was observed in the subgroup when a daily dose of ≥ 15 g and a duration of ≥ 3 days of IgM-enriched immunoglobulins were applied (HRadj: 0.65; 95% CI: 0.41 to 1.03; p = 0.068). Conclusions Although we cannot prove a statistically reliable effect of intravenous IgM-enriched immunoglobulins, the confidence intervals may suggest a clinically relevant effect in certain subgroups. Here, an early administration (i.e. in critically ill but not yet mechanically ventilated COVID-19 patients) and a dose of ≥ 15 g for at least 3 days may confer beneficial effects without concerning safety issues. However, these findings need to be validated in upcoming randomized clinical trials. Trial registration DRKS00025794 , German Clinical Trials Register, https://www.drks.de . Registered 6 July 2021.

Abstract Background Severe COVID-19 induced acute respiratory distress syndrome (ARDS) often requires extracorporeal membrane oxygenation (ECMO). Recent German health insurance data revealed low ICU survival rates. Patient characteristics and experience of the ECMO center may determine intensive care unit (ICU) survival. The current study aimed to identify factors affecting ICU survival of COVID-19 ECMO patients. Methods 673 COVID-19 ARDS ECMO patients treated in 26 centers between January 1st 2020 and March 22nd 2021 were included. Data on clinical characteristics, adjunct therapies, complications, and outcome were documented. Block wise logistic regression analysis was applied to identify variables associated with ICU-survival. Results Most patients were between 50 and 70 years of age. PaO2/FiO2 ratio prior to ECMO was 72 mmHg (IQR: 58–99). ICU survival was 31.4%. Survival was significantly lower during the 2nd wave of the COVID-19 pandemic. A subgroup of 284 (42%) patients fulfilling modified EOLIA criteria had a higher survival (38%) (p = 0.0014, OR 0.64 (CI 0.41–0.99)). Survival differed between low, intermediate, and high-volume centers with 20%, 30%, and 38%, respectively (p = 0.0024). Treatment in high volume centers resulted in an odds ratio of 0.55 (CI 0.28–1.02) compared to low volume centers. Additional factors associated with survival were younger age, shorter time between intubation and ECMO initiation, BMI > 35 (compared to

Abstract 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.