A proteomic survival predictor for COVID-19 patients in intensive care
Fig 2
Prediction of survival or death in critically ill patients, from the first sampling time point at intensive care treatment level (WHO grade 7).
a) Performance of established ICU risk assessment indices (APACHE II, SOFA and Charlson comorbidity index) calculated at the time of ICU admission (APACHE II, Charlson comorbidity index) or at the first time point at WHO grade 7 (SOFA score) in predicting the outcome in critically ill patients. b) Prediction of survival or death in critically ill patients using proteomics. A machine learning model based on parenclitic networks (Methods) was trained on the samples of the Charité cohort closest to the time point of treatment escalation during intensive care (start of ECMO, RRT or vasopressors, i.e. WHO grade 7). The performance was assessed on the test samples, which were held out during training. Upper panel: The ROC curve indicates correct classification of survival vs non-survival with an AUROC of 0.81 (95% CI 0.68–0.94). Middle panel: The proteomic classifier was used to predict the probability of survival and non-survival, which is significantly different between the groups. Lower panel: Kaplan-Meier survival curves using a threshold of predicted probability (0.678) chosen to maximize Youden’s J index (J = sensitivity + specificity—1). Log-rank test was used to compare survival rates between patients with predicted death risk < 0.678 (black) and > 0.678 (orange). c) (upper, middle, and lower panels): The model trained on the Charité cohort, was tested on an independent cohort (Innsbruck). d) Exemplary parenclitic networks from two patients in the independent Innsbruck cohort. Edges with weights > 0.5 are shown. Left panel: a network predicting low probability of death in a surviving patient. Right panel: a network predicting high probability of death in a non-survivor.