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Future research will explore various potential explanatory factors through multivariable modelling, including the timing of treatments received and of tumour biology, as these could go some way to explain the survival differences found between deprivation groups.
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According to these results, we built a multivariable model including the SOFA score, INR, and RRT.
Despite our attempt to overcome confounding by using multivariable models including well-established confounders, unmeasured residual confounding may have occurred.
We aimed to build a multivariable model including the best general ICU score, the best biological variable of hepatic failure, and the most relevant therapy performed at ICU admission.
In a multivariable model including the Injury Severity Score and the Simplified Acute Physiology Score and the presence of shock and cardiac arrest before admission, initial cTn-I elevation is not associated with ICU mortality.
In a second multivariable model including a global biosecurity score as a surrogate for a specific pattern of biosecurity measures, this score was not retained in the final model.
A factor was defined as a confounder if there was a discrepancy of 10% or more in the estimated coefficient of interest between the multivariable model including the factor and the model without it.
The multivariable model including the three significant variables derived from univariable analyses, arrived at a coefficient of –0.00046 for public spending on health, which was marginally insignificant at the 0.05 level (95% CI: –0.00095, 0.00003, p-value = 0.063).
Then multivariable models, including all predictors and confounders, were estimated.
We conducted a final multivariable model including all confounding variables.
As such, a multivariable model including all inflammatory biomarkers was not constructed.
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