  Existing methods to characterise the evolving condition of traumatic brain
injury (TBI) patients in the intensive care unit (ICU) do not capture the
context necessary for individualising treatment. Here, we integrate all
heterogenous data stored in medical records (1,166 pre-ICU and ICU variables)
to model the individualised contribution of clinical course to six-month
functional outcome on the Glasgow Outcome Scale - Extended (GOSE). On a
prospective cohort (n=1,550, 65 centres) of TBI patients, we train recurrent
neural network models to map a token-embedded time series representation of all
variables (including missing values) to an ordinal GOSE prognosis every two
hours. The full range of variables explains up to 52% (95% CI: 50%-54%) of the
ordinal variance in functional outcome. Up to 91% (95% CI: 90%-91%) of this
explanation is derived from pre-ICU and admission information (i.e., static
variables). Information collected in the ICU (i.e., dynamic variables)
increases explanation (by up to 5% [95% CI: 4%-6%]), though not enough to
counter poorer overall performance in longer-stay (>5.75 days) patients.
Highest-contributing variables include physician-based prognoses, CT features,
and markers of neurological function. Whilst static information currently
accounts for the majority of functional outcome explanation after TBI,
data-driven analysis highlights investigative avenues to improve dynamic
characterisation of longer-stay patients. Moreover, our modelling strategy
proves useful for converting large patient records into interpretable time
series with missing data integration and minimal processing.
