Computational protocol: Prehospital immune responses and development of multiple organ dysfunction syndrome following traumatic injury: A prospective cohort study

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Protocol publication

[…] The current study is an exploratory investigation using a small convenience sample of trauma patients in order to generate hypotheses. There was no hypothesised effect upon which to power the study. Data were checked for normality using the Shapiro-Wilk test. A one-way ANOVA with Bonferroni post hoc test or a Kruskal-Wallis test with Dunn’s post hoc test was used to assess differences between patients and HCs. Relationships between continuous variables were assessed using a Pearson’s correlation. Comparisons of MODS versus no MODS patients were made on 34 variables; differences in continuous variables were assessed by Mann-Whitney U tests or independent samples t tests, whilst Chi-squared tests were performed to compare categorical variables. The resulting p-values from these 34 tests were compared to their Benjamini-Hochberg critical values to control for a false discovery rate of 5% []. Binary logistic regression analyses were used to explore the relationships between immune parameters and the development of MODS. In these models, the reference level of MODS was coded as “No MODS” (versus “MODS”). Model performance was measured through the proportion of variation explained by the model via R2 statistics and Brier scores, the level of calibration using the le Cessie-van Houwelingen goodness-of-fit test, and the level of discrimination using the concordance (or C) statistic []. Bias-corrected estimates of the C statistic were produced to account for model overfitting []. This internal validation consisted of 9,999 bootstrap resamples. Odds ratios (ORs) were calculated for the immune parameters in each model. The analysis was performed using the statistical software packages SPSS (IBM, New York, United States), R version 3.3.2 ( together with the ggplot2, effects, and rms packages, and GraphPad Prism software (GraphPad Software, California, US) on data that were available for each given time point. The threshold for significance was considered to be p ≤ 0.05, with nominal p-values reported with no adjustment for multiple testing unless otherwise stated. In all figures, the horizontal line displayed in the data points collected from HCs depicts the median value. […]

Pipeline specifications

Software tools SPSS, Ggplot2
Application Miscellaneous
Organisms Homo sapiens
Diseases Wounds and Injuries