Kinetic modelling of serum S100b after traumatic brain injury

A Ercole, E P Thelin, A Holst, B M Bellander, D W Nelson, A Ercole, E P Thelin, A Holst, B M Bellander, D W Nelson

Abstract

Background: An understanding of the kinetics of a biomarker is essential to its interpretation. Despite this, little kinetic modelling of blood biomarkers can be found in the literature. S100b is an astrocyte related marker of brain injury used primarily in traumatic brain injury (TBI). Serum levels are expected to be the net result of a multi-compartmental process. The optimal sample times for TBI prognostication, and to follow injury development, are unclear. The purpose of this study was to develop a kinetic model to characterise the temporal course of serum S100b concentration after primary traumatic brain injury.

Methods: Data of serial serum S100b samples from 154 traumatic brain injury patients in a neurointensive care unit were retrospectively analysed, including only patients without secondary peaks of this biomarker. Additionally, extra-cranial S100b can confound samples earlier than 12 h after trauma and were therefore excluded. A hierarchical, Bayesian gamma variate kinetic model was constructed and the parameters estimated by Markov chain Monte Carlo sampling.

Results: We demonstrated that S100b concentration changes dramatically over timescales that are clinically important for early prognostication with a peak at 27.2 h (95 % credible interval [25.6, 28.8]). Baseline S100b levels was found to be 0.11 μg/L (95 % credible interval [0.10, 0.12]).

Conclusions: Even small differences in injury to sample time may lead to marked changes in S100b during the first days after injury. This must be taken into account in interpretation. The model offers a way to predict the peak and trajectory of S100b from 12 h post trauma in TBI patients, and to identify deviations from this, possibly indicating a secondary event. Kinetic modelling, providing an equation for the peak and projection, may offer a way to reduce the ambiguity in interpretation of, in time, randomly sampled acute biomarkers and may be generally applicable to biomarkers with, in time, well defined hits.

Keywords: Biomarkers; Human; Kinetics; S100b protein; Traumatic brain injury.

Figures

Fig. 1
Fig. 1
Demographics. a Age (b) Glasgow Coma Scale (GCS) at admission (c) Pupil responses: Normal, One responsive, Non-responsive (d) Glasgow Outcome Scale (GOS) barplot.(GOS 1 = dead - GOS 5 = full recovery)
Fig. 2
Fig. 2
Fitted kinetic gamma variate model. Calculated mean of S100b posterior prediction distribution as a function of time from our model. The dotted lines represent 95 % prediction intervals and within our model are interpreted as the parameter uncertainties excluding the variation from individual patients
Fig. 3
Fig. 3
Plot of S100b trajectories for all patients. Re-scaled and translated to minimise mean-square deviation from the from the model curve. The model curve is overlaid

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