Part 1 - Electrode design. Part 2 - Experimental design Quarterly report, Nov. 1968 - Feb. 1969

Electrode development for long-term implantation and measurement of evoked response in rat

Instrumentation for Biological Research, Volume I, Sections 1 to 3 Final Report, Nov. 9, 1964 - Mar. 31, 1966

Bioinstrumentation for controlling and measuring parameters interacting with biological syste

A stochastic differential equation analysis of cerebrospinal fluid dynamics

<p>Abstract</p> <p>Background</p> <p>Clinical measurements of intracranial pressure (ICP) over time show fluctuations around the deterministic time path predicted by a classic mathematical model in hydrocephalus research. Thus an important issue in mathematical research on hydrocephalus remains unaddressed--modeling the effect of noise on CSF dynamics. Our objective is to mathematically model the noise in the data.</p> <p>Methods</p> <p>The classic model relating the temporal evolution of ICP in pressure-volume studies to infusions is a nonlinear differential equation based on natural physical analogies between CSF dynamics and an electrical circuit. Brownian motion was incorporated into the differential equation describing CSF dynamics to obtain a nonlinear stochastic differential equation (SDE) that accommodates the fluctuations in ICP.</p> <p>Results</p> <p>The SDE is explicitly solved and the dynamic probabilities of exceeding critical levels of ICP under different clinical conditions are computed. A key finding is that the probabilities display strong threshold effects with respect to noise. Above the noise threshold, the probabilities are significantly influenced by the resistance to CSF outflow and the intensity of the noise.</p> <p>Conclusions</p> <p>Fluctuations in the CSF formation rate increase fluctuations in the ICP and they should be minimized to lower the patient's risk. The nonlinear SDE provides a scientific methodology for dynamic risk management of patients. The dynamic output of the SDE matches the noisy ICP data generated by the actual intracranial dynamics of patients better than the classic model used in prior research.</p

The BRAIN TRIAL: a randomised, placebo controlled trial of a Bradykinin B2 receptor antagonist (Anatibant) in patients with traumatic brain injury

BACKGROUND: Cerebral oedema is associated with significant neurological damage in patients with traumatic brain injury. Bradykinin is an inflammatory mediator that may contribute to cerebral oedema by increasing the permeability of the blood-brain barrier. We evaluated the safety and effectiveness of the non-peptide bradykinin B2 receptor antagonist Anatibant in the treatment of patients with traumatic brain injury. During the course of the trial, funding was withdrawn by the sponsor. METHODS: Adults with traumatic brain injury and a Glasgow Coma Scale score of 12 or less, who had a CT scan showing an intracranial abnormality consistent with trauma, and were within eight hours of their injury were randomly allocated to low, medium or high dose Anatibant or to placebo. Outcomes were Serious Adverse Events (SAE), mortality 15 days following injury and in-hospital morbidity assessed by the Glasgow Coma Scale (GCS), the Disability Rating Scale (DRS) and a modified version of the Oxford Handicap Scale (HIREOS). RESULTS: 228 patients out of a planned sample size of 400 patients were randomised. The risk of experiencing one or more SAEs was 26.4% (43/163) in the combined Anatibant treated group, compared to 19.3% (11/57) in the placebo group (relative risk = 1.37; 95% CI 0.76 to 2.46). All cause mortality in the Anatibant treated group was 19% and in the placebo group 15.8% (relative risk 1.20, 95% CI 0.61 to 2.36). The mean GCS at discharge was 12.48 in the Anatibant treated group and 13.0 in the placebo group. Mean DRS was 11.18 Anatibant versus 9.73 placebo, and mean HIREOS was 3.94 Anatibant versus 3.54 placebo. The differences between the mean levels for GCS, DRS and HIREOS in the Anatibant and placebo groups, when adjusted for baseline GCS, showed a non-significant trend for worse outcomes in all three measures. CONCLUSION: This trial did not reach the planned sample size of 400 patients and consequently, the study power to detect an increase in the risk of serious adverse events was reduced. This trial provides no reliable evidence of benefit or harm and a larger trial would be needed to establish safety and effectiveness. TRIAL REGISTRATION: This study is registered as an International Standard Randomised Controlled Trial, number ISRCTN23625128

Effect of resting pressure on the estimate of cerebrospinal fluid outflow conductance

<p>Abstract</p> <p>Background</p> <p>A lumbar infusion test is commonly used as a predictive test for patients with normal pressure hydrocephalus and for evaluation of cerebrospinal fluid (CSF) shunt function. Different infusion protocols can be used to estimate the outflow conductance (<it>C</it>_out) or its reciprocal the outflow resistance (<it>R</it>_out), with or without using the baseline resting pressure, <it>P</it>_r. Both from a basic physiological research and a clinical perspective, it is important to understand the limitations of the model on which infusion tests are based. By estimating <it>C</it>_out using two different analyses, with or without <it>P</it>_r, the limitations could be explored. The aim of this study was to compare the <it>C</it>_out estimates, and investigate what effect <it>P</it>_rhad on the results.</p> <p>Methods</p> <p>Sixty-three patients that underwent a constant pressure infusion protocol as part of their preoperative evaluation for normal pressure hydrocephalus, were included (age 70.3 ± 10.8 years (mean ± SD)). The analysis was performed without (<it>C</it>_{excl Pr}) and with (<it>C</it>_{incl Pr}) P_r. The estimates were compared using Bland-Altman plots and paired sample <it>t</it>-tests (<it>p </it>< 0.05 considered significant).</p> <p>Results</p> <p>Mean <it>C</it>_out for the 63 patients was: <it>C</it>_{excl Pr}= 7.0 ± 4.0 (mean ± SD) μl/(s kPa) and <it>C</it>_{incl Pr} = 9.1 ± 4.3 μl/(s kPa) and <it>R</it>_out was 19.0 ± 9.2 and 17.7 ± 11.3 mmHg/ml/min, respectively. There was a positive correlation between methods (r = 0.79, n = 63, <it>p </it>< 0.01). The difference, Δ<it>C</it>_out= -2.1 ± 2.7 μl/(s kPa) between methods was significant (<it>p </it>< 0.01) and Δ<it>R</it>_outwas 1.2 ± 8.8 mmHg/ml/min). The Bland-Altman plot visualized that the variation around the mean difference was similar all through the range of measured values and there was no correlation between Δ<it>C</it>_outand <it>C</it>_out.</p> <p>Conclusions</p> <p>The difference between <it>C</it>_outestimates, obtained from analyses with or without <it>P</it>_r, needs to be taken into consideration when comparing results from studies using different infusion test protocols. The study suggests variation in CSF formation rate, variation in venous pressure or a pressure dependent <it>C</it>_outas possible causes for the deviation from the CSF absorption model seen in some patients.</p

Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes

<p>Abstract</p> <p>Background</p> <p>Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models.</p> <p>Methods</p> <p>We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC.</p> <p>Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted.</p> <p>Results</p> <p>The packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient.</p> <p>Conclusions</p> <p>On relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen "non-informative" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain.</p

ICP curve morphology and intracranial flow-volume changes: a simultaneous ICP and cine phase contrast MRI study in humans

Background: The intracranial pressure (ICP) curve with its different peaks has been extensively studied, but the exact physiological mechanisms behind its morphology are still not fully understood. Both intracranial volume change (ΔICV) and transmission of the arterial blood pressure have been proposed to shape the ICP curve. This study tested the hypothesis that the ICP curve correlates to intracranial volume changes. Methods: Cine phase contrast magnetic resonance imaging (MRI) examinations were performed in neuro-intensive care patients with simultaneous ICP monitoring. The MRI was set to examine cerebral arterial inflow and venous cerebral outflow as well as flow of cerebrospinal fluid over the foramen magnum. The difference in total flow into and out from the cranial cavity (Flowtot) over time provides the ΔICV. The ICP curve was compared to the Flowtot and the ΔICV. Correlations were calculated through linear and logarithmic regression. Student’s t test was used to test the null hypothesis between paired samples. Results: Excluding the initial ICP wave, P1, the mean R2 for the correlation between the ΔICV and the ICP was 0.75 for the exponential expression, which had a higher correlation than the linear (p = 0.005). The first ICP peaks correlated to the initial peaks of Flowtot with a mean R2 = 0.88. Conclusion: The first part, or the P1, of the ICP curve seems to be created by the first rapid net inflow seen in Flowtot while the rest of the ICP curve seem to correlate to the ΔICV