Estimation of pulsatile cerebral arterial blood volume based on transcranial doppler signals

Calviello, Leanne; Zeiler, Frederick A.; Donnelly, Joseph E.; Uryga, Agnieszka; Riva, Nicolás de; Smielewski, Peter; Czosnyka, Marek

doi:10.1016/j.medengphy.2019.07.019

Cited by 11 publications

(11 citation statements)

References 37 publications

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“…There have been different interpretations of which particular vessel properties influence PI TCD measurements. Some have proposed that PI TCD measures cerebrovascular resistance (CRV) at the reflection site [11,[13][14][15][16], while other studies have reported inconsistent results [17][18][19][20]. Following the discovery of vascular compliance, it has been hypothesized that as the vessels lose their compliance and become stiffer peak systolic (maximum) flow velocity increases and end-diastolic (minimum) flow velocity decreases which elevates calculated PI TCD values [10].…”

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confidence: 99%

Indexing Cerebrovascular Health Using Transcranial Doppler Ultrasound

Afkhami

Wong

Ramadan

et al. 2021

Ultrasound in Medicine & Biology

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Transcranial Doppler (TCD) blood flow velocity has been extensively used in biomedical research as it provides a cost-effective and relatively simple approach to assess changes in cerebral blood flow dynamics and track cerebrovascular health status. In this paper, we introduce a new TCD based timing index, TI TCD , as an indicator of vascular stiffening and vascular health. We investigate the correlations of the new index and the existing indices, namely the pulsatility index (PI TCD ) and the augmentation index (AI TCD ), with age, cardiorespiratory fitness (CRF) and magnetic resonance imaging (MRI) blood flow pulsatility index (PI MRI ). Notably, the new TI TCD index showed stronger correlations with CRF (r = −0.79) and PI MRI (r = 0.53) compared to AI TCD (r = −0.65 with CRF and no significant correlation with PI MRI ) and PI TCD (no significant correlations with CRF or PI MRI ) and similar correlations with age as AI TCD . The clearer relationship of the proposed timing index with vascular aging factors represented by cerebrovascular resistance (CVR) suggests its utility as an early indicator of vascular stiffening.

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confidence: 99%

Indexing Cerebrovascular Health Using Transcranial Doppler Ultrasound

Afkhami

Wong

Ramadan

et al. 2021

Ultrasound in Medicine & Biology

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“…Retrospective analysis of previously published data was performed [5,[16][17][18][19][20]. From among 160 head-injured patients treated in the Neurocritical Care Unit, Addenbrooke's Hospital in the years 1992-1995, 31 presented ICP plateau waves, defined as a sudden rise in ICP above the value of 40 mmHg that lasted at least 5 min.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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A reliable method for non-invasive detection of dangerous intracranial pressure (ICP) elevations is still unavailable. In this preliminary study, we investigate quantitatively our observation that superimposing waveforms of transcranial Doppler blood flow velocity (FV) and arterial blood pressure (ABP) may help in non-invasive identification of ICP plateau waves. Recordings of FV, ABP and ICP in 160 patients with severe head injury (treated in the Neurocritical Care Unit at Addenbrookes Hospital, Cambridge, UK) were reviewed retrospectively. From that cohort, we identified 18 plateau waves registered in eight patients. A “measure of dissimilarity” (Dissimilarity/Difference Index, DI) between ABP and FV waveforms was calculated in three following steps: 1. fragmentation of ABP and FV signal according to cardiac cycle; 2. obtaining the normalised representative ABP and FV cycles; and finally; 3. assessing their difference, represented by the area between both curves. DI appeared to discriminate ICP plateau waves from baseline episodes slightly better than conventional pulsatility index did: area under ROC curve 0.92 vs. 0.90, sensitivity 0.81 vs. 0.69, accuracy 0.88 vs. 0.84, respectively. The concept of DI, if further tested and improved, might be used for non-invasive detection of ICP plateau waves.

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“…No. of studies References ABP-management Arterial blood pressure-management 4 (Jakkula et al, 2018;Calviello et al, 2019;Sekhon et al, 2019;Kovacs et al, 2021) b Biomarkers Neuroglobin, prostacyclin, succinate 3 (Ding et al, 2019;Koskinen et al, 2019;Khellaf et al, 2022) a Fluid management CSF drainage, hypertonic saline, and/or fluid management 5 (Akbik et al, 2017;Carteron et al, 2018;Hoiland et al, 2021;Rass et al, 2021;Bernini et al, 2022) Mixed interventions Nimodipine, ICP/CPP management (vasopressors, sedation, etc. ), hyperoxia, glucose, head elevation/flat position 11 (Bele et al, 2015;Lin et al, 2015;Okonkwo et al, 2017;Sekhon et al, 2017;Rass et al, 2019;Veldeman et al, 2020a;Veldeman et al, 2020b;Fergusson et al, 2021;Gouvea Bogossian et al, 2021;Khellaf et al, 2022;Winberg et al, 2022) a…”

Section: Intervention Group Interventionsmentioning

confidence: 99%

Cerebral multimodality monitoring in adult neurocritical care patients with acute brain injury: A narrative review

et al. 2022

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Cerebral multimodality monitoring (MMM) is, even with a general lack of Class I evidence, increasingly recognized as a tool to support clinical decision-making in the neuroscience intensive care unit (NICU). However, literature and guidelines have focused on unimodal signals in a specific form of acute brain injury. Integrating unimodal signals in multiple signal monitoring is the next step for clinical studies and patient care. As such, we aimed to investigate the recent application of MMM in studies of adult patients with traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), acute ischemic stroke (AIS), and hypoxic ischemic brain injury following cardiac arrest (HIBI). We identified continuous or daily updated monitoring modalities and summarized the monitoring setting, study setting, and clinical characteristics. In addition, we discussed clinical outcome in intervention studies. We identified 112 MMM studies, including 11 modalities, over the last 7 years (2015–2022). Fifty-eight studies (52%) applied only two modalities. Most frequently combined were ICP monitoring (92 studies (82%)) together with PbtO2 (63 studies (56%). Most studies included patients with TBI (59 studies) or SAH (53 studies). The enrollment period of 34 studies (30%) took more than 5 years, whereas the median sample size was only 36 patients (q1- q3, 20–74). We classified studies as either observational (68 studies) or interventional (44 studies). The interventions were subclassified as systemic (24 studies), cerebral (10 studies), and interventions guided by MMM (11 studies). We identified 20 different systemic or cerebral interventions. Nine (9/11, 82%) of the MMM-guided studies included clinical outcome as an endpoint. In 78% (7/9) of these MMM-guided intervention studies, a significant improvement in outcome was demonstrated in favor of interventions guided by MMM. Clinical outcome may be improved with interventions guided by MMM. This strengthens the belief in this application, but further interdisciplinary collaborations are needed to overcome the heterogeneity, as illustrated in the present review. Future research should focus on increasing sample sizes, improved data collection, refining definitions of secondary injuries, and standardized interventions. Only then can we proceed with complex outcome studies with MMM-guided treatment.

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Estimation of pulsatile cerebral arterial blood volume based on transcranial doppler signals

Cited by 11 publications

References 37 publications

Indexing Cerebrovascular Health Using Transcranial Doppler Ultrasound

Indexing Cerebrovascular Health Using Transcranial Doppler Ultrasound

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

Cerebral multimodality monitoring in adult neurocritical care patients with acute brain injury: A narrative review

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