@article{MaguniaLedererVerbuechelnetal.2021,
  author    = {Magunia, Harry and Lederer, Simone and Verbuecheln, Raphael and Gilot, Bryant Joseph and Koeppen, Michael and Haeberle, Helene A. and Mirakaj, Valbona and Hofmann, Pascal and Marx, Gernot and Bickenbach, Johannes and Nohe, Boris and Lay, Michael and Spies, Claudia and Edel, Andreas and Schiefenh{\"o}vel, Fridtjof and Rahmel, Tim and Putensen, Christian and Sellmann, Timur and Koch, Thea and Brandenburger, Timo and Kindgen-Milles, Detlef and Brenner, Thorsten and Berger, Marc and Zacharowski, Kai and Adam, Elisabeth and Posch, Matthias and Moerer, Onnen and Scheer, Christian S. and Sedding, Daniel and Weigand, Markus A. and Fichtner, Falk and Nau, Carla and Pr{\"a}tsch, Florian and Wiesmann, Thomas and Koch, Christian and Schneider, Gerhard and Lahmer, Tobias and Straub, Andreas and Meiser, Andreas and Weiss, Manfred and Jungwirth, Bettina and Wappler, Frank and Meybohm, Patrick and Herrmann, Johannes and Malek, Nisar and Kohlbacher, Oliver and Biergans, Stephanie and Rosenberger, Peter},
  title     = {Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort},
  series = {Critical Care},
  volume    = {25},
  journal   = {Critical Care},
  doi       = {10.1186/s13054-021-03720-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-306766},
  year      = {2021},
  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.},
  language  = {en}
}
@article{HerrmannNotzSchlesingeretal.2021,
  author    = {Herrmann, Johannes and Notz, Quirin and Schlesinger, Tobias and Stumpner, Jan and Kredel, Markus and Sitter, Magdalena and Schmid, Benedikt and Kranke, Peter and Schulze, Harald and Meybohm, Patrick and Lotz, Christopher},
  title     = {Point of care diagnostic of hypercoagulability and platelet function in COVID-19 induced acute respiratory distress syndrome: a retrospective observational study},
  series = {Thrombosis Journal},
  volume    = {19},
  journal   = {Thrombosis Journal},
  number    = {1},
  doi       = {10.1186/s12959-021-00293-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260739},
  year      = {2021},
  abstract  = {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..},
  language  = {en}
}
@article{NotzLotzHerrmannetal.2021,
  author    = {Notz, Quirin and Lotz, Christopher and Herrmann, Johannes and Vogt, Marius and Schlesinger, Tobias and Kredel, Markus and Muellges, Wolfgang and Weismann, Dirk and Westermaier, Thomas and Meybohm, Patrick and Kranke, Peter},
  title     = {Severe neurological complications in critically ill COVID‑19 patients},
  series = {Journal of Neurology},
  journal   = {Journal of Neurology},
  issn      = {0340-5354},
  doi       = {10.1007/s00415-020-10152-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232429},
  pages     = {1576-1579},
  year      = {2021},
  abstract  = {No abstract available.},
  language  = {en}
}