Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit

• Published in: PLOS digital health 2022-03, Vol.1 (3), p.e0000019-e0000019
• Main Authors: Kausch, Sherry L, Sullivan, Brynne, Spaeder, Michael C, Keim-Malpass, Jessica
• Format: Article
• Language: English
• Subjects: Blood pressure; College admissions; Computer and Information Sciences; Creatinine; Critical care; Electrocardiography; Engineering and Technology; Entropy; Heart rate; Hospitals; Illnesses; Intensive care; Laboratories; Markov analysis; Medicine and Health Sciences; Mortality; Oxygen saturation; Patients; Pediatrics; Physical Sciences; Physiology; Prediction models; Regression analysis; Research and Analysis Methods; Sepsis; Stochastic models; Time series; Ventilators; Vital signs
• ISSN: 2767-3170; 2767-3170
• DOI: 10.1371/journal.pdig.0000019

Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness.