Assessing the prediction potential of an in silico computer model of intracranial pressure dynamics

Abstract

T raumatic brain injury (TBI) is a leading cause of death and disability in children younger than 18 years, causing more than 50% of all childhood deaths in the United States. Each year, more than 150,000 pediatric brain injuries result in about 7000 deaths and 29,000 children with new, permanent disabilities. The death rate for severe TBI (defined as a Glasgow Coma Scale score 8) remains between 30% and 45% at major children’s hospitals (1, 2). A published evidence-based medicine review reports that elevated intracranial pressure (ICP) is a main determinant of outcome following TBI and is strongly correlated with both death and disability (3). The underlying mechanism is that persistent elevated ICP leads to reduced blood flow, which can result in insufficient tissue perfusion (ischemia), secondary injuries, and generally poor outcomes. Despite the availability of many treatment options for reducing elevated ICP (defined as 20 mm Hg) (3), poor outcomes frequently result, often because of elevated ICP that is unresponsive to therapy. Treatment options for severe TBI include draining cerebral spinal fluid (CSF) via a ventriculostomy catheter, raising the head-of-bed (HOB) elevation to 30° to promote jugular venous drainage, and inducing mild hyperventilation (2, 4, 5). The underlying pathophysiologic mechanisms governing ICP regulation and the mechanisms by which various treatments affect ICP remain only partially understood (6). It is known, for example, that HOB directly impacts arterial and venous pressure by changing the Objective: Traumatic brain injury (TBI) frequently results in poor outcome, suggesting that new approaches are needed. We hypothesized that a patient-specific in silico computer model of intracranial pressure (ICP) dynamics may predict the ICP response to therapy. Design: In silico model analysis of prospectively collected data. Setting: Twenty-three and 16-bed pediatric intensive care units in two tertiary care academic hospitals. Patients: Nine subjects with severe TBI undergoing ICP monitoring (7 M/2 F, age range 3–17 years). Interventions: Random changes in head-of-bed (HOB) (0°, 10°, 20°, 30°, 40°) elevation and respiratory rate (to achieve a ETCO2 3–4 mm Hg) were performed daily according to a study protocol as long as an intracerebral monitoring device was in place. Methods and Main Outcome Measures: A six-compartment dynamic ICP model was developed based on published equations and parametric data (baseline model parameter values). For each of 24 physiologic challenge sessions, patient-specific model parameter values were estimated that minimized the model fitness error, the difference between model-calculated ICP and observed ICP, both for baseline parameters and patient-specific parameter. Next, model prediction error was measured using two analyses. First, a “within” session analysis estimated parameter values using data from an initial Segment A, and then used those parameter values to predict the ICP during a later Segment B. The predicted ICP for B was compared with the observed ICP for B. Second, a “between” session analysis was performed. This analysis used parameter values estimated from earlier sessions to predict the ICP in later sessions. Fitness and prediction errors were measured in terms of mean absolute error (MAE). To normalize the errors, MAE was divided by the mean absolute deviation (MAD) for the associated segment or session, yielding a measure for both model fitness error and model prediction error that is favorable when 18 mm Hg) (n 6; 25%) ICP had lower error than moderate ICP (12–18 mm Hg) (n 10; 42%). MAE/MAD was <1 for 6 of 22 (27%) for within-session predictions and 3 of 31 (10%) for between-session predictions. Conclusions: The protocol for collecting physiologic data in subjects with severe TBI was feasible. The in silico ICP model with session-specific parameters accurately reproduced observed ICP response to changes in head-of-bed and respiration rate. We demonstrated modest success at predicting future ICP within a session and to a lesser extent between sessions. (Crit Care Med 2009; 37: 1079–1089)