  Objective: Acute intracranial hypertension is an important risk factor of
secondary brain damage after traumatic brain injury. Hypertensive episodes are
often diagnosed reactively, leading to late detection and lost time for
intervention planning. A pro-active approach that predicts critical events
several hours ahead of time could assist in directing attention to patients at
risk. Approach: We developed a prediction framework that forecasts onsets of
acute intracranial hypertension in the next 8 hours. It jointly uses cerebral
auto-regulation indices, spectral energies and morphological pulse metrics to
describe the neurological state of the patient. One-minute base windows were
compressed by computing signal metrics, and then stored in a multi-scale
history, from which physiological features were derived. Main results: Our
model predicted events up to 8 hours in advance with alarm recall rates of 90%
at a precision of 30.3% in the MIMIC-III waveform database, improving upon two
baselines from the literature. We found that features derived from
high-frequency waveforms substantially improved the prediction performance over
simple statistical summaries of low-frequency time series, and each of the
three feature classes contributed to the performance gain. The inclusion of
long-term history up to 8 hours was especially important. Significance: Our
results highlight the importance of information contained in high-frequency
waveforms in the neurological intensive care unit. They could motivate future
studies on pre-hypertensive patterns and the design of new alarm algorithms for
critical events in the injured brain.
