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Updated: Jun 7, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Added value of inflammatory markers to vital signs for predicting mortality in patients with suspected infection:
Toshihiko Takada1,2,3, Jeroen Hoogland4, Kotaro Fujii5,6
1Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan. T.Takada@umcutrecht.nl.
Abstract:
It is crucial to identify high-risk patients with infectious conditions for appropriate management. We previously found that inflammatory markers added value to vital signs for predicting mortality in patients with suspected infection. In this study, the aim was to externally validate the added value of the inflammatory markers and to develop a new prediction model. For the external validation, consecutive adult patients with suspected infection admitted to the department of general medicine at two acute care hospitals were evaluated. A prognostic model for 30-day in-hospital mortality based on vital signs (systolic blood pressure, respiratory rate, and mental status) was compared with an extended model that also included four inflammatory markers (C-reactive protein, neutrophil-lymphocyte ratio, mean platelet volume, and red cell distribution width). Similar to the previous finding, all inflammatory markers except C-reactive protein showed significant contributions. Subsequently, a prediction model was developed using vital signs and markers with significant added value using a dataset that combined the external validation data with the data of the previous study. The new model was compared with a model based on the quick Sequential (sepsis-related) Organ Failure Assessment (qSOFA) score. The newly developed model showed a higher c-index than the qSOFA model [0.756 (95% CI 0.726-0.786) vs. 0.663 (0.630-0.696), p < 0.001]. Using the new model, 9.0% of patients who died were correctly reclassified compared with the qSOFA model at the threshold of 10% mortality risk. The new model including these markers showed potential to outperform the qSOFA model.
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