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Background: Peritonitis is responsible for thousands of deaths annually in Germany alone. Even source control (SC) and antibiotic treatment often fail to prevent severe sepsis or septic shock, and this situation has hardly improved in the past two decades. Most experimental immunomodulatory therapeutics for sepsis have been aimed at blocking or dampening a specific pro-inflammatory immunological mediator. However, the patient collective is large and heterogeneous. There are therefore grounds for investigating the possibility of developing personalized therapies by classifying patients into groups according to biomarkers. This study aims to combine an assessment of the efficacy of treatment with a preparation of human immunoglobulins G, A, and M (IgGAM) with individual status of various biomarkers (immunoglobulin level, procalcitonin, interleukin 6, antigen D-related human leucocyte antigen (HLA-DR), transcription factor NF-κB1, adrenomedullin, and pathogen spectrum).
Methods/design: A total of 200 patients with sepsis or septic shock will receive standard-of-care treatment (SoC). Of these, 133 patients (selected by 1:2 randomization) will in addition receive infusions of IgGAM for 5 days. All patients will be followed for approximately 90 days and assessed by the multiple-organ failure (MOF) score, by the EQ QLQ 5D quality-of-life scale, and by measurement of vital signs, biomarkers (as above), and survival.
Discussion: This study is intended to provide further information on the efficacy and safety of treatment with IgGAM and to offer the possibility of correlating these with the biomarkers to be studied. Specifically, it will test (at a descriptive level) the hypothesis that patients receiving IgGAM who have higher inflammation status (IL-6) and poorer immune status (low HLA-DR, low immunoglobulin levels) have a better outcome than patients who do not receive IgGAM. It is expected to provide information that will help to close the knowledge gap concerning the association between the effect of IgGAM and the presence of various biomarkers, thus possibly opening the way to a personalized medicine.
Trial registration: EudraCT, 2016–001788-34; ClinicalTrials.gov, NCT03334006. Registered on 17 Nov 2017.
Trial sponsor: RWTH Aachen University, represented by the Center for Translational & Clinical Research Aachen (contact Dr. S. Isfort).
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.