  When a patient is admitted to the intensive care unit (ICU) after a traumatic
brain injury (TBI), an early prognosis is essential for baseline risk
adjustment and shared decision making. TBI outcomes are commonly categorised by
the Glasgow Outcome Scale-Extended (GOSE) into 8, ordered levels of functional
recovery at 6 months after injury. Existing ICU prognostic models predict
binary outcomes at a certain threshold of GOSE (e.g., prediction of survival
[GOSE>1] or functional independence [GOSE>4]). We aimed to develop ordinal
prediction models that concurrently predict probabilities of each GOSE score.
From a prospective cohort (n=1,550, 65 centres) in the ICU stratum of the
Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI)
patient dataset, we extracted all clinical information within 24 hours of ICU
admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of
2 design elements on ordinal model performance: (1) the baseline predictor set,
ranging from a concise set of 10 validated predictors to a token-embedded
representation of all possible predictors, and (2) the modelling strategy, from
ordinal logistic regression to multinomial deep learning. With repeated k-fold
cross-validation, we found that expanding the baseline predictor set
significantly improved ordinal prediction performance while increasing
analytical complexity did not. Half of these gains could be achieved with the
addition of 8 high-impact predictors (2 demographic variables, 4 protein
biomarkers, and 2 severity assessments) to the concise set. At best, ordinal
models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability
(ordinal c-index) and 57% (95% CI: 54%-60%) explanation of ordinal variation in
6-month GOSE (Somers' D). Our results motivate the search for informative
predictors for higher GOSE and the development of ordinal dynamic prediction
models.
