Background: Of the existing sepsis markers, immature granulocytes (IG) most frequently reflect the presence of an infection. The importance of IG as an early predictor of sepsis and bacteremia is evaluated differently for each study. This study aimed to evaluate the effectiveness of the Sysmex XN series' IG% as an independent prognostic indicator of sepsis using machine learning.
Methods: A total of 2465 IG% results from 117 severe burn patients in the intensive care unit of one institution were retrospectively analyzed. We evaluated the IG% for sepsis using the receiver operating characteristic, logistic regression, and partial dependence plot analyses. Clinical characteristics and other laboratory markers associated with sepsis, including WBC, procalcitonin, and C-reactive protein, were compared with the IG% values.
Results: Twenty-six of the 117 patients were diagnosed with sepsis. The median IG% value was 2.6% (95% CI: 1.4-3.1). The area under the receiver operating characteristic curve was 0.77 (95% CI: 0.78-0.84) and the optimal cut-off value was 3%, with a sensitivity of 76.9% and specificity of 68.1%. The partial dependence plot of IG% on predicting sepsis showed that an IG% < 4% had low predictability, but increased thereafter. The interaction plot of IG% and C-reactive protein showed an increase in sepsis probability at an IG% of 6% and C-reactive protein of 160 mg/L.
Conclusions: IG% is moderately useful for predicting sepsis. However, since it can be determined from routine laboratory test results and requires no additional intervention or cost, it could be particularly useful as an auxiliary marker.
Keywords: Immature granulocytes; Machine learning; Sepsis; Severe burn.
© 2021. The Author(s).