Inhaled prostacyclin therapy in the acute respiratory distress syndrome: a randomized controlled multicenter trial.

BACKGROUND: Acute respiratory distress syndrome (ARDS) results in significant hypoxia, and ARDS is the central pathology of COVID-19. Inhaled prostacyclin has been proposed as a therapy for ARDS, but data regarding its role in this syndrome are unavailable. Therefore, we investigated whether inhaled prostacyclin would affect the oxygenation and survival of patients suffering from ARDS. METHODS: We performed a prospective randomized controlled single-blind multicenter trial across Germany. The trial was conducted from March 2019 with final follow-up on 12th of August 2021. Patients with moderate to severe ARDS were included and randomized to receive either inhaled prostacyclin (3 times/day for 5 days) or sodium chloride (Placebo). The primary outcome was the oxygenation index in the intervention and control groups on Day 5 of therapy. Secondary outcomes were mortality, secondary organ failure, disease severity and adverse events. RESULTS: Of 707 patients approached 150 patients were randomized to receive inhaled prostacyclin (n = 73) or sodium chloride (n = 77). Data from 144 patients were analyzed. The baseline PaO2/FiO2 ratio did not differ between groups. The primary analysis of the study was negative, and prostacyclin improved oxygenation by 20 mmHg more than Placebo (p = 0.17). Secondary analysis showed that the oxygenation was significantly improved in patients with ARDS who were COVID-19-positive (34 mmHg, p = 0.04). Mortality did not differ between groups. Secondary organ failure and adverse events were similar in the intervention and control groups. CONCLUSIONS: The primary result of our study was negative. Our data suggest that inhaled prostacyclin might be beneficial treatment in patients with COVID-19 induced ARDS. TRIAL REGISTRATION: The study was approved by the Institutional Review Board of the Research Ethics Committee of the University of Tübingen (899/2018AMG1) and the corresponding ethical review boards of all participating centers. The trial was also approved by the Federal Institute for Drugs and Medical Devices (BfArM, EudraCT No. 2016003168-37) and registered at clinicaltrials.gov (NCT03111212) on April 6th 2017.

Membrane oxygenator longevity was higher in argatroban-treated patients undergoing vvECMO.

BACKGROUND: In severe acute respiratory distress syndrome (ARDS), venovenous extracorporeal membrane oxygenation (vvECMO) can be a lifesaver. However, anticoagulation therapy is mandatory because the nonendothelial extracorporeal surface increases the risk of thromboembolic problems. Heparin is still the most common anticoagulant, but argatroban could be an alternative. This work investigates whether argatroban offers a therapeutic advantage over heparin during vvECMO. METHODS: We performed a retrospective cohort study of patients who underwent vvECMO for severe ARDS and received heparin or argatroban as anticoagulation therapy. Demographic variables, intensive care unit (ICU) treatment and outcome parameters were evaluated. The primary outcome parameter was the operating time of the membrane oxygenator normalized to the duration of vvECMO treatment. Secondary outcome parameters were transfusion requirements normalized to the duration of vvECMO therapy. RESULTS: Fifty seven patients from January 2019 to February 2021 underwent vvECMO and were included in this study. Thirty three patients received heparin and 24 patients argatroban as anticoagulatory therapy. The groups did not differ in demographics, ICU scoring systems, or comorbidities. Platelet counts and partial prothrombin time did not differ between the two groups during the first 6 days of vvECMO. The argatroban group had lower requirements for red blood cells, platelets and fresh frozen plasma. The mean runtime of the individual membrane oxygenator increased from 12.3 days (heparin group) to 16.6 days in the argatroban group. CONCLUSIONS: Our findings suggest that argatroban can be considered as anticoagulant during vvECMO.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

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.