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. 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
Subject(s)
Lung Diseases , Severe Acute Respiratory Syndrome , Thrombosis , Learning Disabilities , COVID-19ABSTRACT
Rationale: COVID-19 convalescent plasma (CCP) has been considered a treatment option in COVID-19. Objectives:: To assess the efficacy of neutralizing antibody containing high-dose CCP in hospitalized adults with COVID-19 requiring respiratory support or intensive care treatment. Methods: Patients (n=105) were randomized 1:1 to either receive standard treatment and 3 units of CCP or standard treatment alone. Control group patients with progress on day 14 could cross over to the CCP group. Primary outcome was a dichotomous composite outcome of survival and no longer fulfilling criteria for severe COVID-19 on day 21. The trial is registered: clinicaltrials.gov #NCT04433910. Measurements and main results: The primary outcome occurred in 43.4% of patients in the CCP and 32.7% in the control group (p=0.32). The median time to clinical improvement was 26 days (IQR 15-not reached (n.r.)) in the CCP group and 66 days (IQR 13-n.r.) in the control group (p=0.27). Median time to discharge from hospital was 31 days (IQR 16-n.r.) in the CCP and 51 days (IQR 20-n.r.) in the control group (p=0.24). In the subgroup that received a higher cumulative amount of neutralizing antibodies the primary outcome occurred in 56.0% (versus 32.1%), with a shorter interval to clinical improvement, shorter time to hospital discharge and better survival compared to the control group. Conclusion: CCP added to standard treatment did not result in a significant difference in the primary and secondary outcomes. A pre-defined subgroup analysis showed a significant benefit for CCP among those who received a larger amount of neutralizing antibodies. Primary Funding Source: Bundesministerium fuer Gesundheit