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Mild acquired factor XIII deficiency and clinical relevance at the ICU - a retrospective analysis
(2021)
Acquired FXIII deficiency is a relevant complication in the perioperative setting; however, we still have little evidence about the incidence and management of this rarely isolated coagulopathy. This study aims to help find the right value for the substitution of patients with an acquired mild FXIII deficiency. In this retrospective single-center cohort study, we enrolled critically ill patients with mild acquired FXIII deficiency (>5% and ≤70%) and compared clinical and laboratory parameters, as well as pro-coagulatory treatments. The results of the present analysis of 104 patients support the clinical relevance of FXIII activity out of the normal range. Patients with lower FXIII levels, beginning at <60%, had lower minimum and maximum hemoglobin values, corresponding to the finding that patients with a minimum FXIII activity of <50% needed significantly more packed red blood cells. FXIII activity correlated significantly with general coagulation markers such as prothrombin time, activated partial thromboplastin time, and fibrinogen. Nevertheless, comparing the groups with a cut-off of 50%, the amount of fresh frozen plasma, thrombocytes, PPSB, AT-III, and fibrinogen given did not differ. These results indicate that a mild FXIII deficiency occurring at any point of intensive care unit stay is also probably relevant for the total need of packed red blood cells, independent of pro-coagulatory management. In alignment with the ESAIC guidelines, the measurement of FXIII in critically ill patients with the risk of bleeding and early management, with the substitution of FXIII at levels <50%-60%, could be suggested.
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.