Change in Blood Flow Velocity Pulse Waveform during Plateau Waves of Intracranial Pressure

Haemosync: A synchronisation algorithm for multimodal haemodynamic signals

BACKGROUND

Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results.

METHODS

We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm's performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined.

RESULTS

The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° (median, IQR 0.13-0.2°, range 0.0038-1.1°) and 0.54° (median, IQR 0.23-1.7°, range 0.0088-5.6°) for the very low frequency and low frequency ranges, respectively.

DISCUSSION

This algorithm allows visually interpretable detection and accurate correction of time-shifts between pulsatile haemodynamic signals (ABP and CBv).

Non-Invasive Methods for Intracranial Pressure Monitoring in Traumatic Brain Injury Using Transcranial Doppler: A Scoping Review

Intracranial pressure (ICP) monitoring is necessary for managing patients with traumatic brain injury (TBI). Although gold-standard methods include intraventricular or intraparenchymal transducers, these systems cannot be used in patients with coagulopathies or in those who are at high risk of catheter-related infections, nor can they be used in resource-constrained settings. Therefore, a non-invasive modality that is more widely available, cost effective, and safe would have tremendous impact. Among such non-invasive choices, transcranial Doppler (TCD) provides indirect ICP estimates through waveform analysis of cerebral hemodynamic changes. The objective of this scoping review is to describe the existing evidence for the use of TCD-derived methods in estimating ICP in adult TBI patients as compared with gold-standard invasive methods. This review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews, with a main search of PubMed and Embase. The search was limited to studies conducted in adult TBI patients published in any language between 2012 and 2022. Twenty-two studies were included for analysis, with most being prospective studies conducted in high-income countries. TCD-derived non-invasive ICP (nICP) methods are either mathematical or non-mathematical, with the former having slightly better correlation with invasive methods, especially when using time-trending ICP dynamics over one-time estimated values. Nevertheless, mathematical methods are associated with greater cost and complexity in their application. Formula-based methods showed promise in excluding elevated ICP, exhibiting a high negative predictive value. Therefore, TCD-derived methods could be useful in assessing ICP changes instead of absolute ICP values for high-risk patients, especially in low-resource settings.

Cerebral Pulsatility Index and In-Hospital Mortality in Chinese Patients with Traumatic Brain Injury: A Retrospective Cohort Study

There are limited studies on the relationship between the vascular transcranial Doppler (TCD) pulsatility index (PI) and in-hospital mortality in patients with traumatic brain injury (TBI). To address this issue, we conducted this study to explore whether, in newly diagnosed Chinese TBI patients, the PI is an independent predictor of the in-hospital mortality rate after adjusting for other covariates. This study is a retrospective cohort study. From 24 March 2019 to 24 January 2020, we recruited 144 Chinese patients with newly diagnosed TBI from a Chinese hospital. The independent variable was the PI, and the dependent variable was in-hospital mortality in TBI patients. The relationship between the PI and in-hospital mortality in TBI patients was nonlinear and had an inflection point of 1.11. In the multivariate analysis, after adjusting for potential confounders, the effect sizes and confidence intervals per additional 0.1 units on the left and right sides of the inflection point were 4.09 (1.30–12.83) and 1.42 (0.93–2.17). The relationship between the PI and in-hospital mortality was nonlinear. The PI was positively related with in-hospital mortality when the PI was less than 1.11.