Critical Care Explorations (Aug 2019)

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

  • Patricia C. Liaw, PhD,
  • Alison E. Fox-Robichaud, MSc, MD, FRCPC,
  • Kao-Lee Liaw, PhD,
  • Ellen McDonald, RN,
  • Dhruva J. Dwivedi, PhD,
  • Nasim M. Zamir, MD,
  • Laura Pepler, PhD,
  • Travis J. Gould, PhD,
  • Michael Xu, MSc,
  • Nicole Zytaruk, RN,
  • Sarah K. Medeiros, BSc,
  • Lauralyn McIntyre, MD, FRCPC,
  • Jennifer Tsang, MD, PhD, FRCPC,
  • Peter M. Dodek, MD, MHSc,
  • Brent W. Winston, MD, FRCPC,
  • Claudio Martin, MSc, MD, FRCPC,
  • Douglas D. Fraser, MD, PhD, FRCPC,
  • Jeffrey I. Weitz, MD, FRCPC,
  • Francois Lellouche, MD, PhD,
  • Deborah J. Cook, MD, FRCPC,
  • John Marshall, MD, FRCPC,
  • for the Canadian Critical Care Translational Biology Group (CCCTBG) and the Canadian Critical Care Trials Group (CCCTG),
  • Jamie Hutchison,
  • Jane Batt,
  • Emmanuel Charbonney,
  • Jean-Francois Cailhier,
  • Rob Fowler,
  • Paul Hebert,
  • Kusum Menon,
  • Karen Burns,
  • Shane English,
  • John Drover,
  • Bram Rochwerg,
  • Dominique Piquette,
  • Margaret Herridge,
  • Sylvie Debigare,
  • Srinivas Murthy,
  • Michelle Kho,
  • Danae Tassy

DOI
https://doi.org/10.1097/CCE.0000000000000032
Journal volume & issue
Vol. 1, no. 8
p. e0032

Abstract

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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.