Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two childrenu0027s hospitals and used subgraphaugmented nonnegative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.

OneYear Mortality Prediction in ICU Patients with Diagnosis of Sepsis Driven by Population Similarities Traditional Intensive Care Unit (ICU) outcome prediction models are based on the analysis of large populations, and often provide statistically rigorous results for an average patient but are also expensive, timeconsuming, and prone to selection bias; moreover, these indicators usually lack the precision required for use at the individual level, since they present significant errors at patient data away from the average. In this work we present an evaluation of the impact and relevance of three different patient similarity metrics on the oneyear mortality prediction when patients are related by the same diagnosis, sepsis. We use data of 16.000 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMICIII) database.