“Multimorbidity states with high sepsis-related deaths: a data-driven analysis in critical care”

Zsolt Zador, Alexander Landry, Michael D. Cusimano, Nophar Geifman
2018 · doi:10.1101/491712
preprint OA: closed CC-BY-NC-ND-4.0
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Abstract

Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients’ medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations (“multimorbidity”). Thus, it is imperative we incorporate morbidity state in our healthcare models. We investigate effects of multimorbidity on the occurrence of sepsis and associated mortality in critical care (CC) through analysis of 36390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. Using latent class analysis (LCA) we identified six clinically distinct subgroups based on demographics, admission type and morbidity compositions. Subgroup of middle-aged patients with health consequences of drug and alcohol addiction had the highest mortality rate, over 2-fold greater compared to other groups with older patients and complex multimorbid patterns. The findings promote incorporation of multimorbidity in healthcare models and the shift away from current single-disease paradigm in clinical practice, training and trial design.

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License: CC-BY-NC-ND-4.0