Improving Prediction of Favourable Outcome After 6\xa0Months in Patients with Severe Traumatic Brain Injury Using Physiological Cerebral Parameters in a Multivariable Logistic Regression Model

Frank C Bennis,Jan Willem Elting,Bibi Teeuwen,Tammo Delhaas,Marcel J Aries,Frederick A Zeiler,Pietro Bonizzi,Joukje Van Der Naalt

doi:10.1007/s12028-020-00930-6

Abstract

Background/ObjectiveCurrent severe traumatic brain injury (TBI) outcome prediction models calculate the chance of unfavourable outcome after 6 months based on parameters measured at admission. We aimed to improve current models with the addition of continuously measured neuromonitoring data within the first 24 h after intensive care unit neuromonitoring.MethodsForty-five severe TBI patients with intracranial pressure/cerebral perfusion pressure monitoring from two teaching hospitals covering the period May 2012 to January 2019 were analysed. Fourteen high-frequency physiological parameters were selected over multiple time periods after the start of neuromonitoring (0–6 h, 0–12 h, 0–18 h, 0–24 h). Besides systemic physiological parameters and extended Corticosteroid Randomisation after Significant Head Injury (CRASH) score, we added estimates of (dynamic) cerebral volume, cerebral compliance and cerebrovascular pressure reactivity indices to the model. A logistic regression model was trained for each time period on selected parameters to predict outcome after 6 months. The parameters were selected using forward feature selection. Each model was validated by leave-one-out cross-validation.ResultsA logistic regression model using CRASH as the sole parameter resulted in an area under the curve (AUC) of 0.76. For each time period, an increased AUC was found using up to 5 additional parameters. The highest AUC (0.90) was found for the 0–6 h period using 5 parameters that describe mean arterial blood pressure and physiological cerebral indices.ConclusionsCurrent TBI outcome prediction models can be improved by the addition of neuromonitoring bedside parameters measured continuously within the first 24 h after the start of neuromonitoring. As these factors might be modifiable by treatment during the admission, testing in a larger (multicenter) data set is warranted.

Highlights

Severe traumatic brain injury (TBI) is defined as severe trauma to the brain and skull due to an external force
We aim to develop a model that combines the prediction of the Corticosteroid Randomisation after Significant Head Injury (CRASH) model with continuously measured general and brain-specific monitoring parameters in severe TBI patients on day one after the start of neuromonitoring
Our results are in line with our hypothesis that current TBI outcome prediction models can be improved by the addition of early neuromonitoring data

Summary

Introduction

Severe traumatic brain injury (TBI) is defined as severe trauma to the brain and skull due to an external force. TBI is the leading cause of death and severe disability in young adults [2]. The external force to the brain may result in ischaemia, contusions and haematomas. These processes lead to swelling, rise in intracranial pressure (ICP), decrease in cerebral perfusion pressure (CPP) and cerebral ischaemia [2]. Intensive care unit (ICU) admission with organ support is necessary in comatose TBI patients to overcome secondary damage. Severe or moderate disability is common in surviving patients, which makes TBI a large burden for patients, families and society [3]. An accurate prediction of outcome would be helpful, as it would support the clinical team in decision-making and discussions with the family during ICU admission

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