Objectives: Raised intracranial pressure (ICP) is well known to be indicative of a poor outcome in traumatic brain injury (TBI). This phenomenon was quantified using a pressure time index (PTI) model of raised ICP burden in a paediatric population. Using the PTI methodology, this pilot study is aimed at investigating the relationship between raised ICP and length of stay (LOS) in adults admitted to a neurological intensive care unit (neuro-ICU).

Materials And Methods: In 10 patients admitted to the neuro-ICU following TBI, ICP was measured and data from the first 24 h were analysed. The PTI is a bounded area under the curve, where the bound is the threshold limit of interest for the signal. The upper bound of 20 mmHg for ICP is commonly used in clinical practice. To fully investigate the relationship between ICP and LOS, further bounds from 1 to 40 mmHg were used during the PTI calculations. A backwards step Poisson regression model with a log link function was used to find the important thresholds for the prediction of full LOS, measured in hours, in the neuro-ICU.

Results: The fit was assessed using a Chi-squared deviance goodness of fit method, which showed a non-significant p value of 0.97, indicating a correctly specified model. The backwards step strategy, minimising the model's Akaike information criteria (AIC) at each change, found that levels 13-16, 18 and 20-21 combined were the most predictive. From this model it can be shown that for every 1 mmHg/h increase in burden, as measured by the PTI, the LOS has a base exponential increase of approximately 2 h, with the largest increases in the LOS given at the 20-mmHg threshold level.

Conclusions: This model demonstrates that increased duration of raised ICP in the early monitoring period is associated with a prolonged LOS in the neuro-ICU. Further validation of the PTI model in a larger cohort is currently underway as part of the CHART-ADAPT project. Second, further adjustment with known predictors of outcome, such as severity of injury, would help to improve the fit and validate the current combination of predictors.

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http://dx.doi.org/10.1007/978-3-319-65798-1_42DOI Listing

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