A method for estimating zero-flow pressure and intracranial pressure

The method described by Czosnyka is particularly suitable for measuring CPPe in patients undergoing cerebral angiography

Background

The primary objective of this study was to estimate the effective cerebral perfusion pressure (CPPe), critical closing pressure (CrCP), and resistance-area product (RAP) of the intravascular common carotid artery using three different methods. These estimates were then compared to the reference method of linear regression (LR).

Methods

In our previous study, we employed linear regression to evaluate the values of CrCP and RAP. To assess the consistency of results obtained from alternative assessment methods (CPPe, CrCP, and RAP) with the linear regression LR, we conducted a secondary analysis of the previously collected data. We estimated the CPPe, CrCP, and RAP of the intravascular common carotid artery using three different methods: Belford's method (mean/diastolic pressure), Czosnyka's method (systolic/diastolic pressure, CZO), and Schmidt's method (systolic/diastolic pressure, SCH), and compared these estimates with LR. CPPe is calculated as the difference between mean arterial pressure and CrCP. The primary outcome was the mean differences and biases between CPPe, CrCP, and RAP of intravascular common carotid artery, the secondary outcome was correlations and agreement among these various estimates of CPPe measurements.

Results

Nineteen patients were included in this analysis. The median age was 53.5 ± 11.6 years, with 73.7% being men. There were no significant differences in CPPe, RAP and CrCP between the right common carotid artery (RCCA) and the left common carotid artery (LCCA) by using three different methods. Compared to the LR, the mean differences in CPPe and CrCP values were no significant for LCCA according to SCH, CZO and BEL method. But for RAP, the three methods are different in terms of mean differences compared with the LR. CPPe and CrCP revealed a small mean bias compared CPPCZO with CPPLR. Comparing CPPLR measurements with CPPBEL, the mean bias was higher with wider LoA. BEL and CZO showed a strong correlation with LR in Pearson correlation coefficients.

Conclusion

The CPPe, CrCP, and RAP values obtained using the CZO calculation methods are comparable to those measured using the reference method. These findings may provide valuable insights for patients undergoing digital subtraction brain angiography, aiding in the determination of the most suitable approach for individualized blood pressure management.

Influence of angiotensin II and telmisartan on in vivo high-resolution renal arterial impedance in rats

Angiotensin II (ANG II) is known to play an important role in regulating renal hemodynamics. We sought to quantify this effect in an in vivo rat model with high-resolution renal arterial (RA) impedance. This study examines the effects of ANG II and its type 1 receptor blocker telmisartan (TELM) on RA impedance. In baroreflex-deactivated rats, we measured RA pressure (Pr) and blood flow (Fr) during random ventricular pacing to induce pressure fluctuation at three different mean Pr (60, 80, and 100 mmHg). We then estimated RA impedance as the transfer function from Fr to Pr. The RA impedance was found to align with a three-element Windkessel model consisting of proximal (Rp) and distal (Rd) resistance and compliance (C). Our study showed Rd reflected the composite characteristics of afferent and efferent arterioles. Rd increased with increasing Pr under the baseline condition with a slope of 1.03 ± 0.21 (× 10-1) min·mL-1. ANG II significantly increased the slope by 0.72 ± 0.29 (× 10-1) min·mL-1 (P < 0.05) without affecting the intercept. TELM significantly reduced the intercept by 34.49 ± 4.86 (× 10-1) mmHg·min·mL-1 (P < 0.001) from the baseline value of 37.93 ± 13.36 (× 10-1) mmHg·min·mL-1, whereas it did not affect the slope. In contrast, Rp was less sensitive than Rd to ANG II or TELM, suggesting Rp may represent the characteristics of elastic large arteries. Our findings provide valuable insights into the influence of ANG II on the dynamics of the renal vasculature.NEW & NOTEWORTHY This present method of quantifying high-resolution renal arterial impedance could contribute to elucidating the characteristics of renal vasculature influenced by physiological mechanisms, renal diseases, or pharmacological effects. The present findings help construct a lumped-parameter renal hemodynamic model that reflects the influence of angiotensin II.

Non-Invasive Intracranial Pressure Monitoring and Its Applicability in Spaceflight

INTRODUCTION: Neuro-ophthalmic findings collectively defined as Spaceflight-Associated Neuro-ocular Syndrome (SANS) are one of the leading health priorities in astronauts engaging in long duration spaceflight or prolonged microgravity exposure. Though multifactorial in etiology, similarities to terrestrial idiopathic intracranial hypertension (IIH) suggest these changes may result from an increase or impairing in intracranial pressure (ICP). Finding a portable, accessible, and reliable method of monitoring ICP is, therefore, crucial in long duration spaceflight. A review of recent literature was conducted on the biomedical literature search engine PubMed using the search term “non-invasive intracranial pressure”. Studies investigating accuracy of noninvasive and portable methods were assessed. The search retrieved different methods that were subsequently grouped by approach and technique. The majority of publications included the use of ultrasound-based methods with variable accuracies. One of which, noninvasive ICP estimation by optical nerve sheath diameter measurement (nICP_ONSD), presented the highest statistical correlation and prediction values to invasive ICP, with area under the curve (AUC) ranging from 0.75 to 0.964. One study even considers a combination of ONSD with transcranial Doppler (TCD) for an even higher performance. Other methods, such as near-infrared spectroscopy (NIRS), show positive and promising results [good statistical correlation with invasive techniques when measuring cerebral perfusion pressure (CPP): r = 0.83]. However, for its accessibility, portability, and accuracy, ONSD seems to present itself as the up to date, most reliable, noninvasive ICP surrogate and a valuable spaceflight asset.Félix H, Santos Oliveira E. Non-invasive intracranial pressure monitoring and its applicability in spaceflight. Aerosp Med Hum Perform. 2022; 93(6):517–531.

Mixture Models for Estimating Maximum Blood Flow Velocity

OBJECTIVES

A gaussian mixture model (GMM) was recently developed for estimating the probability density function of blood flow velocity measured with transcranial Doppler ultrasound data. In turn, the quantiles of the probability density function allow one to construct estimators of the "maximum" blood flow velocity. However, GMMs assume gaussianity, a feature that is not omnipresent in observed data. The objective of this work was to develop mixture models that do not invoke the gaussian assumption.

METHODS

Here, GMMs were extended to a skewed GMM and a nongaussian kernel mixture model. All models were developed on data from 59 patients with closed head injuries from multiple hospitals in the United States, with ages ranging from 13 to 81 years and Glasgow Coma Scale scores ranging from 3 to 11. The models were assessed in terms of the log likelihood (a goodness-of-fit measure) and via visual comparison with the underlying spectrograms.

RESULTS

Among the models examined, the skewed GMM showed a significantly (P< .05) higher log likelihood for 56 of the 59 patients and produced maximum flow velocity estimates consistent with the observed spectrograms for all patients. Kernel mixture models are generally less "robust" in that their quality is inconsistent across patients.

CONCLUSIONS

Among the models examined, it was found that the skewed GMM provided a better model of the data both in terms of the quality of the fit and in terms of visual comparison of the underlying spectrogram and the estimated maximum blood flow velocity. Nongaussian mixture models have potential for even higher-quality assessment of blood flow, but further development is called for.

Neuroanesthesiology update

We review topics pertinent to the perioperative care of patients with neurological disorders. Our review addresses topics not only in the anesthesiology literature, but also in basic neurosciences, critical care medicine, neurology, neurosurgery, radiology, and internal medicine literature. We include literature published or available online up through December 8, 2013. As our review is not able to include all manuscripts, we focus on recurring themes and unique and pivotal investigations. We address the broad topics of general neuroanesthesia, stroke, traumatic brain injury, anesthetic neurotoxicity, neuroprotection, pharmacology, physiology, and nervous system monitoring.

A double-gaussian, percentile-based method for estimating maximum blood flow velocity

OBJECTIVES

Transcranial Doppler sonography allows for the estimation of blood flow velocity, whose maximum value, especially at systole, is often of clinical interest. Given that observed values of flow velocity are subject to noise, a useful notion of "maximum" requires a criterion for separating the signal from the noise. All commonly used criteria produce a point estimate (ie, a single value) of maximum flow velocity at any time and therefore convey no information on the distribution or uncertainty of flow velocity. This limitation has clinical consequences especially for patients in vasospasm, whose largest flow velocities can be difficult to measure. Therefore, a method for estimating flow velocity and its uncertainty is desirable.

METHODS

A gaussian mixture model is used to separate the noise from the signal distribution. The time series of a given percentile of the latter, then, provides a flow velocity envelope. This means of estimating the flow velocity envelope naturally allows for displaying several percentiles (e.g., 95th and 99th), thereby conveying uncertainty in the highest flow velocity.

RESULTS

Such envelopes were computed for 59 patients and were shown to provide reasonable and useful estimates of the largest flow velocities compared to a standard algorithm. Moreover, we found that the commonly used envelope was generally consistent with the 90th percentile of the signal distribution derived via the gaussian mixture model.

CONCLUSIONS

Separating the observed distribution of flow velocity into a noise component and a signal component, using a double-gaussian mixture model, allows for the percentiles of the latter to provide meaningful measures of the largest flow velocities and their uncertainty.