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Objective: Interventional stroke therapy made thrombi available for histological analysis. Unfortunately, simple composition aspects such as erythrocyte versus fibrin/platelet rich did not allow a feasible allocation to thrombi's cardiac or carotid origin. Since the mentioned criteria represent characteristics of thrombus age, we used established histological criteria for determining thrombus age in patients who had an atherosclerotic (TOAST (Trial of Org 10172 in Acute stroke Treatment) 1) stroke versus patients who had a cardioembolic (TOAST 2) stroke., Methods: We assessed prospectively data from stroke patients presenting with occlusion of the middle cerebral artery eligible for catheter-based intervention. Besides patient characteristics and stroke workup, extracted thrombi were classified into different age categories according to their cellular to fibrotic transition. Thrombi were collected in an erythrocyte lysing solution to reduce acute clotting effects. Statistics were done with a non-parametric Kolmogorov-Smirnov test., Results: 170 patients were included, of which 50 (38 men; 73±12 years) had a TOAST 1 and 99 (59 women; 75±10 years) had a TOAST 2 categorised stroke. Age, National Institutes of Health Stroke Score (13±7 vs 15±7), Alberta Stroke Program Early CT Score (9±3 vs 9±2), Thrombolysis in Cerebral Infarction Score (2.9±0.2 vs 2.9±0.3), modified Rankin Score on discharge (3.2±2 vs 3.2±2), number of vascular risk factors (0.9±1.4 vs 1.0±1.1) or time span between symptom onset to reperfusion (266±115 vs 260±128 min) remained non-significant. Also, thrombus age did not differ between the groups. The mean age of thrombi was 5-8 days. However, the male-female ratio differed significantly (p<0.0005) between groups, with more men in TOAST 1 group and more women in TOAST 2 group., Conclusion: Age aspects of thrombi seem not feasible to allow reliable source allocation. However, the young age of thrombi points to a rapid detachment. The difference in sex relation is in line with previous reports., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: In a previously introduced mathematical model, intracranial pressure (ICP) was noninvasively assessed using cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP). In this study this method is evaluated using new data from patients with traumatic brain injury (TBI)., Materials and Methods: Three hundred fifteen data recordings of 137 patients (114 men; age 14-78 years, mean age 37 ± 17 years) with severe TBI were studied. CBFV, ABP, and invasively assessed ICP were simultaneously recorded for 1 h. Noninvasive ICP (nICP) was calculated and compared with ICP., Results: On 315 recordings, average deviation between ICP and nICP (± standard deviation) was 4.9 ± 3.3 mmHg. The standard deviation of differences (ICP - nICP) was 5.6 mmHg. The 95 % confidence interval of ICP prediction ranged from -9.6 to 12.3 mmHg. Mean ICP was 16.7 mmHg and mean nICP was 18.0 mmHg. When nICP was adjusted by their difference 1.3 mmHg (nICPadj = nICP - 1.3), the 95 % confidence limits of ICP prediction became ±11.0 mmHg. In recordings with highly dynamic ICP signals (n = 27), ICP and nICP correlated on average with R = 0.51 ± 0.47., Conclusions: nICP assessment showed reasonable accuracy and may be used in clinical studies of patients without any indication for ICP probe implantation.

Objective: The authors previously introduced a method in which intracranial pressure (ICP) was estimated using parameters (TCD characteristics) derived from cerebral blood flow velocity (FV) and arterial blood pressure (ABP). Some results suggested that this model might be influenced by the patient's state of cerebral autoregulation and other clinical parameters. Hence, it was the aim of the present study to improve the method by modifying the previously used global procedure in certain subgroups of patients., Methods: In 103 traumatic brain injured patients (3-76 years, mean: 31 +/- 16 years) signal data of FV, ABP and ICP were used to generate samples of TCD characteristics together with time corresponding ICP. Fuzzy Pattern Classification was used to identify cluster subsets (classes) of the sample space. On each class a local estimator of ICP was defined. This approach provides a non-invasive assessment of ICP (nICP) as follows: Using FV and ABP the TCD characteristics were computed and related to the matching classes. nICP was calculated as a weighted sum of local ICP estimations., Results: ICP A and B waves and long-term trends could be visibly assessed. The median absolute difference between ICP and nICP was 5.7 mmHg., Conclusions: The class structure of the model facilitates nICP assessment in heterogeneous patient groups and supports a stepwise extension of the target patient group without affecting the former validity.

Background and Purpose: A mathematical model has previously been introduced to estimate noninvasively intracranial pressure (nICP). In the present multicenter study, we investigated the ability of model to adapt to the state of cerebral autoregulation (SCA). This modification was intended to improve the quality of nICP estimation and noninvasive assessment of pressure reactivity of the cerebrovascular system., Methods: We studied 145 patients after severe head injuries or stroke. All patients had direct ICP, arterial blood pressure (ABP), and transcranial Doppler middle cerebral artery blood flow velocity (FV) monitored. The SCA was assessed by moving correlation (Mx index) of cerebral perfusion pressure (CPP=ABP-ICP) and cerebral blood flow velocity and correlation of ABP and ICP (PRx index). nICP was calculated from ABP and FV waveforms. When nICP was used instead of ICP, the SCA was continuously estimated, and the model was dynamically adapted to the SCA., Results: High and moderate correlations between invasively (Mx, PRx) and noninvasively (nMx, nPRx) estimated autoregulation indexes were observed (Mx: R=0.90, P<0.001; PRx: R=0.62, P<0.001). Values of Mx and nMx indicated contradictory SCA in 4 of 167 evaluated recordings; values of PRx and nPRx were contradictory in 27 recordings. When the model was adapted to the SCA, the mean error of ICP estimation decreased significantly (P<0.005)., Conclusions: Continuous adaptation of the model to SCA improves the accuracy of noninvasive estimation of ICP and ICP dynamics. The same model provides a noninvasive and continuous assessment of SCA.

Object: A mathematical model previously introduced by the authors allowed noninvasive intracranial pressure (nICP) assessment. In the present study the authors investigated this model as an aid in predicting the time course of raised ICP during infusion tests in patients with hydrocephalus and its suitability for estimating the resistance to outflow of cerebrospinal fluid (Rcsf)., Methods: Twenty-one patients with hydrocephalus were studied. The nICP was calculated from the arterial blood pressure (ABP) waveform by using a linear signal transformation, which was dynamically modified by the relationship between ABP and cerebral blood flow velocity. This model was verified by comparison of nICP with "real" ICP measured during lumbar infusion tests. In all simulations, parallel increases in real ICP and nICP were evident. The simulated Rcsf was computed using nICP and then compared with Rcsf computed from real ICP. The mean absolute error between real and simulated Rcsf was 4.1 +/- 2.2 mm Hg minute/ml. By the construction of simulations specific to different subtypes of hydrocephalus arising from various causes, the mean error decreased to 2.7 +/- 1.7 mm Hg minute/ml, whereas the correlation between real and simulated Rcsf increased from R = 0.73 to R = 0.89 (p < 0.001)., Conclusions: The validity of the mathematical model was confirmed in this study. The creation of type-specific simulations resulted in substantial improvements in the accuracy of ICP assessment. Improvement strategies could be important because of a potential clinical benefit from this method.

Deep spontaneous vasodilatatory events are frequently recorded in various cerebral diseases, causing dramatic increases (A-waves) in intracranial pressure (ICP) and subsequently provoking ischemic brain insults. The relationship between fluctuations in CBF, ICP, and arterial blood pressure (ABP) is influenced by properties of cerebrovascular control mechanisms and the cerebrospinal pressure-volume compensation. The goal of this study was to construct a mathematical model of this relationship and to assess its ability to predict the occurrence and time course of A-waves. A group of 17 severely head-injured patients were included in the study. In our model ICP was derived from the ABP waveform using a linear signal transformation. The transformation was modified during the simulation by a relationship between ABP and flow velocity, i.e., by the characterization of the cerebrovascular bed. In this way the ICP could be calculated from the ABP waveform. This model was verified by comparison of simulated and directly measured ICP during A-waves recorded in seven of the patients. In all simulations, plateau elevations of ICP were well replicated. The mean absolute error between real and simulated ICP was 8.3 +/- 5.4 mm Hg at the baseline and 7.9 +/- 4.3 mm Hg at the top of plateau waves. The correlation coefficient between real and simulated increase in ICP was R = 0.98; P < .001. Similarly, correlation between real and simulated increase in pulse amplitude of ICP was highly significant (R = 0.94; P < .001). The mathematical model of the relationship between ABP, flow velocity, and ICP is of potential clinical use for the noninvasive detection of A-waves in patients in whom invasive ICP assessment is not conducted.

The study introduces a method to simulate continuously an intracranial pressure (ICP) wave form. In a system analysis approach the intracranial compartment was viewed as a black box with arterial blood pressure (ABP) as an input signal and ICP as an output. A weight function was used to transform the ABP curve into the ICP curve. The output ICP waveform was generated using a weight function derived from the transcranial Doppler blood flow velocity (FV) and ABP curves. In order to establish the relationship between TCD characteristics and weight functions simultaneous recordings of FV, ABP, and ICP curves of a defined group of patients were used. A linear function between the TCD characteristics and the weight functions was obtained by calculating a series of multiple regression analyses. Given examples demonstrate the procedure's capabilities in predicting the mean ICP, the pulse and respiratory waveform modulations, and the trends of ICP changes.

To evaluate early changes in collateral flow through the anterior communicating artery, 11 patients undergoing carotid endarterectomy were monitored. All had angiographically demonstrated collateral flow towards the hemisphere on the side of surgery. The middle cerebral artery ipsilateral to the operated side and the contralateral anterior cerebral artery were simultaneously insonated with a transcranial Doppler instrument equipped with dual transducers, and flow velocity values were obtained at specific surgical stages. Mean flow velocity in the contralateral anterior cerebral artery's A1 segment increased at clamp placement (p = 0.036), did not change during clamping, and decreased at clamp release (p = 0.004). The rise in velocity occurred within seconds of clamp placement in all patients with an increase, reaching the 10 cm/s threshold within 1 min. No consistent increase was detected after 5 min. A decrease in pulsatility index, indicating a drop in resistance, was detected at clamp placement in the middle cerebral artery on the side of surgery (p = 0.012). The index did not change during clamping, but increased at clamp release (p = 0.002). Our findings indicate that significant changes in anterior communicating artery collateral flow occur during carotid endarterectomy, starting within seconds of carotid artery clamping. These changes are associated with arterial resistance alterations in the territory of the middle cerebral artery on the endarterectomy side. Our observations should be useful to assess intracranial early collateral flow changes in surgical and non-surgical settings.

The natural history of intracranial arterial stenoses remains relatively unknown. To monitor the progression of these lesions over time, the authors reviewed transcranial Doppler (TCD) laboratory reports at five hospitals for patients with angiographically documented intracranial arterial stenoses along the internal carotid artery distribution, and at least two TCD studies conducted more than 2 months apart. Twenty-two patients (19 men and 3 women; mean age, 64 years) with 29 stenoses were identified. The findings were compared to reproducibility data obtained from 11 age-matched control subjects with repeat TCD studies. During a mean follow-up period of 21 months, peak systolic flow velocities corresponding to the areas of stenosis increased in 9 arteries with lesions, and new collateral flow patterns, indicating further hemodynamic compromise distal to the lesions, developed in 2; one of the latter also had increased corresponding velocities. Thus, 10 (35%) arteries with lesions had TCD evidence of progression. Flow velocities remained the same in 13 (45%) stenotic vessels and dropped in 2 (7%). Findings were considered inconclusive for 4 lesions (14%). These findings suggest that intracranial arterial stenoses are dynamic lesions, and that they can evolve and cause further reductions of the arterial diameters after relatively short periods of time. TCD can noninvasively detect their hemodynamic effects.

The medical complications that commonly occur following cerebral infarction, intracerebral hemorrhage, and subarachnoid hemorrhage are reviewed, and the available treatment options are outlined. A literature review is undertaken to assess the effect stroke units have on morbidity and mortality following stroke., (Copyright © 1994 National Stroke Association. Published by Elsevier Inc. All rights reserved.)