Mortality Risk Profiles for Sepsis: A Novel Longitudinal and Multivariable Approach. (August 2019)

• Record Type: Journal Article
• Authors: Liaw, Patricia C.; Fox-Robichaud, Alison E.; Liaw, Kao-Lee; McDonald, Ellen; Dwivedi, Dhruva J.; Zamir, Nasim M.; Pepler, Laura; Gould, Travis J.; Xu, Michael; Zytaruk, Nicole; Medeiros, Sarah K.; McIntyre, Lauralyn; Tsang, Jennifer; Dodek, Peter M.; Winston, Brent W.; Martin, Claudio; Fraser, Douglas D.; Weitz, Jeffrey I.; Lellouche, Francois; Cook, Deborah J.; Marshall, John
• Is Part Of: Critical care explorations. Volume 1:Number 8(2019)
• Publication Date: 2019-08
• Subjects: biomarkers -- longitudinal analysis -- mortality -- mortality risk profiles -- sepsis
• DOI: 10.1097/CCE.0000000000000032
• Languages: English
• ISSNs: 2639-8028

Abstract

Abstract : Objectives: To determine if a set of time-varying biological indicators can be used to: 1) predict the sepsis mortality risk over time and 2) generate mortality risk profiles. Design: Prospective observational study. Setting: Nine Canadian ICUs. Subjects: Three-hundred fifty-six septic patients. Interventions: None. Measurements and Main Results: Clinical data and plasma levels of biomarkers were collected longitudinally. We used a complementary log-log model to account for the daily mortality risk of each patient until death in ICU/hospital, discharge, or 28 days after admission. The model, which is a versatile version of the Cox model for gaining longitudinal insights, created a composite indicator (the daily hazard of dying) from the "day 1" and "change" variables of six time-varying biological indicators (cell-free DNA, protein C, platelet count, creatinine, Glasgow Coma Scale score, and lactate) and a set of contextual variables (age, presence of chronic lung disease or previous brain injury, and duration of stay), achieving a high predictive power (conventional area under the curve, 0.90; 95% CI, 0.86–0.94). Including change variables avoided misleading inferences about the effects of day 1 variables, signifying the importance of the longitudinal approach. We then generated mortality risk profiles that highlight the relative contributions among the time-varying biological indicators to overall mortality risk. The tool was validated in 28 nonseptic patients from the same ICUs who became septic later and was subject to 10-fold cross-validation, achieving similarly high area under the curve. Conclusions: Using a novel version of the Cox model, we created a prognostic tool for septic patients that yields not only a predicted probability of dying but also a mortality risk profile that reveals how six time-varying biological indicators differentially and longitudinally account for the patient's overall daily mortality risk. Abstract : Supplemental Digital Content is available in the text.