Plasma RIPK3 And HMGB1 Predict Severe COVID-19 Progression In ICU Patients: A Single-Center Cohort Study (preprint)

Background: Severe progression of coronavirus disease 2019 (COVID‑19) causes respiratory failure and critical illness. Recently, these pathologies have been associated with necroptosis, a receptor‑interacting serine/threonine‑protein kinase 3 (RIPK3) dependent regulated form of inflammatory cell death. Investigations of indicator necroptosis proteins like RIPK3, mixed lineage kinase domain‑like pseudokinase (MLKL), receptor‑interacting serine/threonine‑protein kinases 1 (RIPK1), and high‑mobility group box 1 (HMGB1) in clinical COVID‑19 manifestations are lacking. Methods: : A prospective prolonged cohort study including 46 intensive care unit (ICU) patients classified with moderate and severe COVID‑19 was conducted with daily measured plasma levels of indicator necroptosis proteins like RIPK3, MLKL, RIPK1, and HMGB1 by enzyme‑linked immunosorbent assay (ELISA). On this basis, a multiple logistic (regression) classification for the prediction of severe COVID‑19 progression was performed. Results: : We found significantly elevated RIPK3, MLKL, HMGB1, and RIPK1 levels in COVID‑19 patients admitted to the ICU compared to healthy controls throughout the ongoing disease, indicating necroptotic processes. Above all, with combined measurements of RIPK3 and HMGB1 plasma levels, we were able to time‑independently predict COVID‑19 severity with 84% accuracy, 90% sensitivity, and 76% specificity. Conclusion: We suggest that HMGB1 and RIPK3 are potential biomarkers to identify high‑risk COVID‑19 patients and developed a classifier for COVID‑19 severity.

Machine Learning Identifies ICU Outcome Predictors in a Multicenter COVID-19 Cohort. (preprint)

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. 1,039 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