|
|
Critical Care Monitoring
Transcranial Doppler Based Cerebrovascular Reactivity Indices in Adult Traumatic Brain Injury: A Scoping Review of Associations With Patient Oriented Outcomes
Background: Disruption in cerebrovascular reactivity following traumatic brain injury (TBI) is a known phenomenon that may hold prognostic value and clinical relevance. Ultimately, improved knowledge of this process and more robust means of continuous assessment may lead to advances in precision medicine following TBI. One such method is transcranial Doppler (TCD), which has been employed to evaluate cerebrovascular reactivity following injury utilizing a continuous time-series approach.
Objective: The present study undertakes a scoping review of the literature on the association of continuous time-domain TCD based indices of cerebrovascular reactivity, with global functional outcomes, cerebral physiologic correlates, and imaging evidence of lesion change.
2021-07-12
Background: Disruption in cerebrovascular reactivity following traumatic brain injury (TBI) is a known phenomenon that may hold prognostic value and clinical relevance. Ultimately, improved knowledge of this process and more robust means of continuous assessment may lead to advances in precision medicine following TBI. One such method is transcranial Doppler (TCD), which has been employed to evaluate cerebrovascular reactivity following injury utilizing a continuous time-series approach.
Objective: The present study undertakes a scoping review of the literature on the association of continuous time-domain TCD based indices of cerebrovascular reactivity, with global functional outcomes, cerebral physiologic correlates, and imaging evidence of lesion change.
2021-07-12
Assessment of Cerebrovascular Autoregulation in Head-Injured Patients: A Validation Study
Background and Purpose—Cerebrovascular autoregulation is frequently measured in head-injured patients.
We attempted to validate 4 bedside methods used for assessment of autoregulation
2020-02-27
Background and Purpose—Cerebrovascular autoregulation is frequently measured in head-injured patients.
We attempted to validate 4 bedside methods used for assessment of autoregulation
2020-02-27
Optimal cerebral perfusion pressure via transcranial Doppler in TBI: application of robotic technology
Abstract Individualized cerebral perfusion pressure (CPP) targets may be derived via assessing the minimum of the parabolic relationship between an index of cerebrovascular reactivity and CPP. This minimum is termed the optimal CPP (CPPopt), and literature suggests that the further away CPP is from CPPopt, the worse is clinical outcome in adult traumatic brain injury (TBI). Typically, CPPopt estimation is based on intracranial pressure (ICP)-derived cerebrovascular reactivity indices, given ICP is commonly measured and provides continuous long duration data streams. The goal of this study is to describe for the first time the application of robotic transcranial Doppler (TCD) and the feasibility of determining CPPopt based on TCD autoregulation indices.
2020-02-27
Abstract Individualized cerebral perfusion pressure (CPP) targets may be derived via assessing the minimum of the parabolic relationship between an index of cerebrovascular reactivity and CPP. This minimum is termed the optimal CPP (CPPopt), and literature suggests that the further away CPP is from CPPopt, the worse is clinical outcome in adult traumatic brain injury (TBI). Typically, CPPopt estimation is based on intracranial pressure (ICP)-derived cerebrovascular reactivity indices, given ICP is commonly measured and provides continuous long duration data streams. The goal of this study is to describe for the first time the application of robotic transcranial Doppler (TCD) and the feasibility of determining CPPopt based on TCD autoregulation indices.
2020-02-27
Non-Invasive Pressure Reactivity Index Using Doppler Systolic Flow Parameters: A Pilot Analysis
Abstract The goal was to predict pressure reactivity index (PRx) using non-invasive transcranial Doppler (TCD) based indices of cerebrovascular reactivity, systolic flow index (Sx_a), and mean flow index (Mx_a). Continuous extended duration time series recordings of middle cerebral artery cerebral blood flow velocity (CBFV) were obtained using robotic TCD in parallel with direct intracranial pressure (ICP). PRx, Sx_a, and Mx_a were derived from high frequency archived signals. Using time-series techniques, autoregressive integrative moving average (ARIMA) structure of PRx was determined and embedded in the following linear mixed effects (LME) models of PRx: PRx * Sx_a and PRx * Sx_a + Mx_a. Using 80% of the recorded patient data, the LME models were created and trained. Model superiority was assessed via Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood (LL). The superior two models were then used to predict PRx using the remaining 20% of the signal data. Predicted and observed PRx were compared via Pearson correlation, linear models, and Bland–Altman (BA) analysis. Ten patients had 3–4 h of continuous uninterrupted ICP and TCD data and were used for this pilot analysis. Optimal ARIMA structure for PRx was determined to be (2,0,2), and this was embedded in all LME models. The top two LME models of PRx were determined to be: PRx * Sx_a and PRx * Sx_a + Mx_a. Estimated and observed PRx values from both models were strongly correlated (r > 0.9; p < 0.0001 for both), with acceptable agreement on BA analysis. Predicted PRx using these two models was also moderately correlated with observed PRx, with acceptable agreement (r = 0.797, p = 0.006; r = 0.763, p = 0.011; respectively). With application of ARIMA and LME modeling, it is possible to predict PRx using non-invasive TCD measures. These are the first and as well as being preliminary attempts at doing so. Much further work is required.
2020-02-14
Abstract The goal was to predict pressure reactivity index (PRx) using non-invasive transcranial Doppler (TCD) based indices of cerebrovascular reactivity, systolic flow index (Sx_a), and mean flow index (Mx_a). Continuous extended duration time series recordings of middle cerebral artery cerebral blood flow velocity (CBFV) were obtained using robotic TCD in parallel with direct intracranial pressure (ICP). PRx, Sx_a, and Mx_a were derived from high frequency archived signals. Using time-series techniques, autoregressive integrative moving average (ARIMA) structure of PRx was determined and embedded in the following linear mixed effects (LME) models of PRx: PRx * Sx_a and PRx * Sx_a + Mx_a. Using 80% of the recorded patient data, the LME models were created and trained. Model superiority was assessed via Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood (LL). The superior two models were then used to predict PRx using the remaining 20% of the signal data. Predicted and observed PRx were compared via Pearson correlation, linear models, and Bland–Altman (BA) analysis. Ten patients had 3–4 h of continuous uninterrupted ICP and TCD data and were used for this pilot analysis. Optimal ARIMA structure for PRx was determined to be (2,0,2), and this was embedded in all LME models. The top two LME models of PRx were determined to be: PRx * Sx_a and PRx * Sx_a + Mx_a. Estimated and observed PRx values from both models were strongly correlated (r > 0.9; p < 0.0001 for both), with acceptable agreement on BA analysis. Predicted PRx using these two models was also moderately correlated with observed PRx, with acceptable agreement (r = 0.797, p = 0.006; r = 0.763, p = 0.011; respectively). With application of ARIMA and LME modeling, it is possible to predict PRx using non-invasive TCD measures. These are the first and as well as being preliminary attempts at doing so. Much further work is required.
2020-02-14
Application of robotic transcranial Doppler for extended duration recording in moderate/severe traumatic brain injury:
first experiences
Long duration application of transcranial Doppler (TCD) for recording of middle cerebral artery (MCA) cerebral blood fow velocity (CBFV) has been fraught with difculties [1, 2]. Classically, TCD has been labor-intensive, with limited ability to obtain uninterrupted recordings for extended periods. Furthermore, application of TCD within neurocritically ill for long durations has been limited given the complexity of care, regular bedside nursing care/patient manipulations, and presence of various other multi-modal monitoring devices. Tis is especially the case in traumatic brain injury (TBI) patients, with the adoption of extensive multi-modal monitoring. Within TBI, most TCD recordings, using standard widely available probes and holders, range from 30 min to 1-h duration and are frequently interrupted due to shifting of the probe and signal loss [3, 4]. Tus, we are typically left with a “snap-shot” recording with TCD examination, limiting our ability to extract valuable continuous variables, such as autoregulatory capacity [3–5]. Recent advances in robotics have led to the development of robotically driven TCD examination probes, integrated with automated algorithms for MCA CBFV detection and optimization of recorded signal intensity. To date, these devices have not been readily applied to the neurocritically ill, particularly moderate and severe traumatic brain injury (TBI) patients. However, given this advancement, they provide the potential to improve dramatically our ability to obtain longer, uninterrupted, TCD recordings in this population. Within, we provide a review of our initial experience with the application of a new robotic TCD system, the Delica EMS 9D robotic TCD, in 10 moderate and severe TBI patients undergoing multi-modal invasive/non-invasive cranial monitoring. To our knowledge, this is the frst account of the application of this device within the critically ill TBI population.
first experiences
Long duration application of transcranial Doppler (TCD) for recording of middle cerebral artery (MCA) cerebral blood fow velocity (CBFV) has been fraught with difculties [1, 2]. Classically, TCD has been labor-intensive, with limited ability to obtain uninterrupted recordings for extended periods. Furthermore, application of TCD within neurocritically ill for long durations has been limited given the complexity of care, regular bedside nursing care/patient manipulations, and presence of various other multi-modal monitoring devices. Tis is especially the case in traumatic brain injury (TBI) patients, with the adoption of extensive multi-modal monitoring. Within TBI, most TCD recordings, using standard widely available probes and holders, range from 30 min to 1-h duration and are frequently interrupted due to shifting of the probe and signal loss [3, 4]. Tus, we are typically left with a “snap-shot” recording with TCD examination, limiting our ability to extract valuable continuous variables, such as autoregulatory capacity [3–5]. Recent advances in robotics have led to the development of robotically driven TCD examination probes, integrated with automated algorithms for MCA CBFV detection and optimization of recorded signal intensity. To date, these devices have not been readily applied to the neurocritically ill, particularly moderate and severe traumatic brain injury (TBI) patients. However, given this advancement, they provide the potential to improve dramatically our ability to obtain longer, uninterrupted, TCD recordings in this population. Within, we provide a review of our initial experience with the application of a new robotic TCD system, the Delica EMS 9D robotic TCD, in 10 moderate and severe TBI patients undergoing multi-modal invasive/non-invasive cranial monitoring. To our knowledge, this is the frst account of the application of this device within the critically ill TBI population.