The utility of bandemia in prognostication and prediction of mortality in sepsis
Author(s) -
Nivedita Prasanna,
Benjamin DelPrete,
Geoffrey Ho,
David Yamane,
Amira Elshikh,
Amir Rashed,
Andrew Sparks,
Danielle Davison,
Katrina Hawkins
Publication year - 2022
Publication title -
journal of the intensive care society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.551
H-Index - 14
eISSN - 2057-360X
pISSN - 1751-1437
DOI - 10.1177/17511437211069307
Subject(s) - medicine , septic shock , sepsis , sofa score , retrospective cohort study , risk stratification , intensive care medicine
Background: Bandemia, defined as a band count >10%, is indicative of underlying infection and is increasingly being used for early detection of sepsis. While an absolute band level has been linked to worse outcomes, its trend has not been extensively studied as a prognostic marker. In this study, we assessed patients admitted to the ICU with sepsis or septic shock and evaluated the correlation between bandemia trends and clinical trajectory among these patients. Methods: This study was a retrospective chart review. Band counts, serum lactate levels, and SOFA scores at 0 and 72 h after admission to the ICU were collected. Patients were risk stratified into groups depending on their SOFA trends, and corresponding band trends and serum lactate levels were compared. Results: 134 patients were included for analysis. There was a statistically significant decrease in bandemia trends for patients with a reduction in SOFA scores [median (IQR)−4.5 (−11, 0); p < 0.0001], and a statistically significant increase in bandemia trends for patients with worsening SOFA scores [median (IQR) 4 (0, 8); p = 0.0007]. Conclusion: Early trends of serum band levels in patients with sepsis or septic shock may help to predict a clinical trajectory and overall prognosis. More investigation is warranted as to whether incorporating bandemia trends, when used in conjunction with other known markers such as lactate levels, may help to guide bedside clinical decisions such as risk stratification, tailored therapies, and ultimately improve outcomes.
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