Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction

Sci Data. 2019 Dec 19;6(1):328. doi: 10.1038/s41597-019-0337-6.


The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underway and the early drivers of clinical outcome cannot be readily determined. Thus, there is a need to better understand the immediate immune response to injury and how this might influence important patient outcomes such as multi-organ dysfunction syndrome (MODS). In this study, we have assessed the immune response to trauma in 61 patients at three different post-injury time points (ultra-early (<=1 h), 4-12 h, 48-72 h) and analysed relationships with the development of MODS. We developed a pipeline using Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods that were able to identify 3 physiological features (decrease in neutrophil CD62L and CD63 expression and monocyte CD63 expression and frequency) as possible biomarkers for MODS development. After univariate and multivariate analysis for each feature alongside a stability analysis, the addition of these 3 markers to standard clinical trauma injury severity scores yields a Generalized Liner Model (GLM) with an average Area Under the Curve value of 0.92 ± 0.06. This performance provides an 8% improvement over the Probability of Survival (PS14) outcome measure and a 13% improvement over the New Injury Severity Score (NISS) for identifying patients at risk of MODS.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antigens, CD
  • Area Under Curve
  • Biomarkers*
  • Datasets as Topic
  • Humans
  • Linear Models
  • Machine Learning*
  • Monocytes
  • Multiple Organ Failure / diagnosis*
  • Multiple Organ Failure / immunology*
  • Neutrophils
  • Probability
  • Severity of Illness Index
  • Survival Analysis


  • Antigens, CD
  • Biomarkers