Mortality Risk Profiles for Sepsis: A Novel Longitudinal and Multivariable Approach

Patricia C Liaw; Alison E Fox-Robichaud; Kao-Lee Liaw; Ellen McDonald; Dhruva J Dwivedi; Nasim M Zamir; Laura Pepler; Travis J Gould; Michael Xu; Nicole Zytaruk; Sarah K Medeiros; Lauralyn McIntyre; Jennifer Tsang; Peter M Dodek; Brent W Winston; Claudio Martin; Douglas D Fraser; Jeffrey I Weitz; Francois Lellouche; Deborah J Cook; John Marshall

doi:10.1097/CCE.0000000000000032

Mortality Risk Profiles for Sepsis: A Novel Longitudinal and Multivariable Approach

Crit Care Explor. 2019 Aug 1;1(8):e0032. doi: 10.1097/CCE.0000000000000032. eCollection 2019 Aug.

Authors

Patricia C Liaw^{1

2}, Alison E Fox-Robichaud^{1

2}, Kao-Lee Liaw³, Ellen McDonald^{1

2}, Dhruva J Dwivedi^{1

2}, Nasim M Zamir², Laura Pepler¹, Travis J Gould¹, Michael Xu¹, Nicole Zytaruk², Sarah K Medeiros¹, Lauralyn McIntyre⁴, Jennifer Tsang⁵, Peter M Dodek⁶, Brent W Winston⁷, Claudio Martin⁸, Douglas D Fraser⁸, Jeffrey I Weitz^{1

2}, Francois Lellouche⁹, Deborah J Cook², John Marshall⁵

Affiliations

¹ Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton, ON, Canada.
² Department of Medicine, McMaster University, Hamilton, ON, Canada.
³ School of Geography and Earth Sciences, McMaster University, Hamilton, ON, Canada.
⁴ Department of Medicine, University of Ottawa, Ottawa, ON, Canada.
⁵ Department of Surgery, University of Toronto, ON, Canada.
⁶ Centre for Health Evaluation and Outcomes Sciences, University of British Columbia, Vancouver, BC, Canada.
⁷ Department of Critical Care Medicine, University of Calgary, Calgary, AB, Canada.
⁸ Department of Medicine, University of Western Ontario, London, ON, Canada.
⁹ Department of Medicine, Laval University, Quebec City, QC, Canada.

Abstract

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.

Keywords: biomarkers; longitudinal analysis; mortality; mortality risk profiles; sepsis.