Discovery and integration of univariate patterns from daily individual organ-failure scores for intensive care mortality prediction

OBJECTIVES The current established mortality predictive models in the intensive care rely only on patient information gathered within the first 24 hours of admission. Recent research demonstrated the added prognostic value residing in the sequential organ-failure assessment (SOFA) score which quantifies on each day the cumulative patient organ derangement. The objective of this paper is to develop and study predictive models that also incorporate univariate patterns of the six individual organ systems underlining the SOFA score. A model for a given day d predicts the probability of in-hospital mortality. MATERIALS AND METHODS We use the logistic framework to combine a summary statistic of the historic SOFA information for a patient together with selected dummy variables indicating the occurrence of univariate frequent temporal patterns of individual organ system functioning. We demonstrate the application of our method to a large real-life data set from an intensive care unit (ICU) in a teaching hospital. Model performance is tested in terms of the AUC and the Brier score. RESULTS An algorithm for categorization, discovery, and selection of univariate patterns of individual organ scores and the induction of predictive models. The case-study resulted in six daily models corresponding to days 2-7. Their AUC ranged between 0.715 and 0.794 and the Brier scores between 0.161 and 0.216. Models using only admission data but recalibrated for days 2-7 generated AUC ranging between 0.643 and 0.761 and Brier scores ranged between 0.175 and 0.230. CONCLUSIONS The results show that temporal organ-failure episodes improve predictions' quality in terms of both discrimination and calibration. In addition, they enhance the interpretability of models. Our approach should be applicable to many other medical domains where severity scores and sub-scores are collected.