Continuous sepsis trajectory prediction using tensor-reduced physiological signals

Abstract

Abstract The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual’s risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, $$p = 0.01$$ p = 0.01 ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, $$p < 0.01$$ p < 0.01 ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, $$p = 0.01$$ p = 0.01 ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, $$p < 0.01$$ p < 0.01 ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.