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 (<=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.

The Immunologic Effect of Early Intravenous Two and Four Gram Bolus Dosing of Tranexamic Acid Compared to Placebo in Patients With Severe Traumatic Bleeding (TAMPITI): A Randomized, Double-Blind, Placebo-Controlled, Single-Center Trial

“…More recently, machine learning was used to identify molecular signatures associated with MODS in traumatically injured patients. In this study, they found that decreases in CD62L expression on fMLF-activated neutrophils, neutrophil CD63 expression, and monocyte frequency were associated with MODS development (58). These data again highlight that the immune modulating effects of TXA are most likely highly dependent on disease etiology and timing of TXA administration (pre-or post-insult).…”

Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India

“…We the applied elastic net (EN) machine learning method [ 37 ] to help select important features which may discriminate between the urban and rural population, and BMI groups. Elastic net automatically selects the best features linked with the outcome or response variable from the dataset-based penalty applied, and hence provides a sparse solution [ 38 , 39 , 40 ]. Penalty parameters, λ (Range of : 0 to 1), are optimized using 10-fold cross validation.…”

“…These selected features were then further modeled by generating area under curve (AUC) curves. We performed stability analysis [ 39 ] (also called a permutation analysis) after randomizing the class label (for rural vs. urban populations). We compared a random AUC based on each iteration and averaged over 100 iterations with the true AUC (without changing the class label).…”

PowerTools: A web based user-friendly tool for future translational study design

“…PowerTools was applied to perform power analysis using previously published freely available omics datasets. To assess the two different modes, regression and classification, we have employed the data published by Acharjee et al, 2017 [18] and Bravo-Merodio et al, 2019 [19].…”

“…We used three physiological features (decrease neutrophil CD62L and CD63 expression as well as monocyte CD63 expression and frequency) [19] as potential biomarkers for multi organ dysfunction (MODS) development. These features were identified by Bravo-Merodio et al, 2019 [19] as biomarker of immune response to trauma in 51 patients at three different post-injury time points (ultra-early (<=1 h), 4-12 h, 48-72 h). Following a power analysis, we found CD62L requires 40 samples in each category to achieve a power of 0.86.…”