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Evaluating the discriminating capacity of cell death (apoptotic) biomarkers in sepsis.

Publication ,  Journal Article
Duplessis, C; Gregory, M; Frey, K; Bell, M; Truong, L; Schully, K; Lawler, J; Langley, RJ; Kingsmore, SF; Woods, CW; Rivers, EP; Jaehne, AK ...
Published in: J Intensive Care
2018

BACKGROUND: Sepsis biomarker panels that provide diagnostic and prognostic discrimination in sepsis patients would be transformative to patient care. We assessed the mortality prediction and diagnostic discriminatory accuracy of two biomarkers reflective of cell death (apoptosis), circulating cell-free DNA (cfDNA), and nucleosomes. METHODS: The cfDNA and nucleosome levels were assayed in plasma samples acquired in patients admitted from four emergency departments with suspected sepsis. Subjects with non-infectious systemic inflammatory response syndrome (SIRS) served as controls. Samples were acquired at enrollment (T0) and 24 h later (T24). We assessed diagnostic (differentiating SIRS from sepsis) and prognostic (28-day mortality) predictive power. Models incorporating procalcitonin (diagnostic prediction) and APACHE II scores (mortality prediction) were generated. RESULTS: Two hundred three subjects were included (107 provided procalcitonin measurements). Four subjects exhibited uncomplicated sepsis, 127 severe sepsis, 35 septic shock, and 24 had non-infectious SIRS. There were 190-survivors and 13 non-survivors. Mortality prediction models using cfDNA, nucleosomes, or APACHEII yielded AUC values of 0.61, 0.75, and 0.81, respectively. A model combining nucleosomes with the APACHE II score improved the AUC to 0.84. Diagnostic models distinguishing sepsis from SIRS using procalcitonin, cfDNA(T0), or nucleosomes(T0) yielded AUC values of 0.64, 0.65, and 0.63, respectively. The three parameter model yielded an AUC of 0.74. CONCLUSIONS: To our knowledge, this is the first head-to-head comparison of cfDNA and nucleosomes in diagnosing sepsis and predicting sepsis-related mortality. Both cfDNA and nucleosome concentrations demonstrated a modest ability to distinguish sepsis survivors and non-survivors and provided additive diagnostic predictive accuracy in differentiating sepsis from non-infectious SIRS when integrated into a diagnostic prediction model including PCT and APACHE II. A sepsis biomarker strategy incorporating measures of the apoptotic pathway may serve as an important component of a sepsis diagnostic and mortality prediction tool.

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Published In

J Intensive Care

DOI

ISSN

2052-0492

Publication Date

2018

Volume

6

Start / End Page

72

Location

England

Related Subject Headings

  • 3202 Clinical sciences
  • 1103 Clinical Sciences
 

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Duplessis, C., Gregory, M., Frey, K., Bell, M., Truong, L., Schully, K., … Clark, D. (2018). Evaluating the discriminating capacity of cell death (apoptotic) biomarkers in sepsis. J Intensive Care, 6, 72. https://doi.org/10.1186/s40560-018-0341-5
Duplessis, Christopher, Michael Gregory, Kenneth Frey, Matthew Bell, Luu Truong, Kevin Schully, James Lawler, et al. “Evaluating the discriminating capacity of cell death (apoptotic) biomarkers in sepsis.J Intensive Care 6 (2018): 72. https://doi.org/10.1186/s40560-018-0341-5.
Duplessis C, Gregory M, Frey K, Bell M, Truong L, Schully K, et al. Evaluating the discriminating capacity of cell death (apoptotic) biomarkers in sepsis. J Intensive Care. 2018;6:72.
Duplessis, Christopher, et al. “Evaluating the discriminating capacity of cell death (apoptotic) biomarkers in sepsis.J Intensive Care, vol. 6, 2018, p. 72. Pubmed, doi:10.1186/s40560-018-0341-5.
Duplessis C, Gregory M, Frey K, Bell M, Truong L, Schully K, Lawler J, Langley RJ, Kingsmore SF, Woods CW, Rivers EP, Jaehne AK, Quackenbush EB, Fowler VG, Tsalik EL, Clark D. Evaluating the discriminating capacity of cell death (apoptotic) biomarkers in sepsis. J Intensive Care. 2018;6:72.
Journal cover image

Published In

J Intensive Care

DOI

ISSN

2052-0492

Publication Date

2018

Volume

6

Start / End Page

72

Location

England

Related Subject Headings

  • 3202 Clinical sciences
  • 1103 Clinical Sciences