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

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Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction. / Bravo, Laura; Acharjee, Animesh; Hazeldine, Jon; Bentley, Conor; Foster, Mark; Gkoutos, Georgios; Lord, Janet.

In: Scientific Data, Vol. 6, 328, 19.12.2019, p. 1-10.

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@article{2e88efd2f02b47ca9368441fb2439154,
title = "Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction",
abstract = "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 (<=1h), 4-12h, 48-72h) 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.",
author = "Laura Bravo and Animesh Acharjee and Jon Hazeldine and Conor Bentley and Mark Foster and Georgios Gkoutos and Janet Lord",
year = "2019",
month = dec,
day = "19",
doi = "10.1038/s41597-019-0337-6",
language = "English",
volume = "6",
pages = "1--10",
journal = "Scientific Data",
issn = "2052-4463",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction

AU - Bravo, Laura

AU - Acharjee, Animesh

AU - Hazeldine, Jon

AU - Bentley, Conor

AU - Foster, Mark

AU - Gkoutos, Georgios

AU - Lord, Janet

PY - 2019/12/19

Y1 - 2019/12/19

N2 - 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 (<=1h), 4-12h, 48-72h) 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.

AB - 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 (<=1h), 4-12h, 48-72h) 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.

U2 - 10.1038/s41597-019-0337-6

DO - 10.1038/s41597-019-0337-6

M3 - Article

C2 - 31857590

VL - 6

SP - 1

EP - 10

JO - Scientific Data

JF - Scientific Data

SN - 2052-4463

M1 - 328

ER -