Non-linear models for the detection of impaired cerebral blood flow autoregulation

Chacón, Máx; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B.

doi:10.1371/journal.pone.0191825

Cited by 8 publications

(4 citation statements)

References 34 publications

(44 reference statements)

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“…In this study, hypercapnia led to significant depression of dCA (figures 2(C) and (D)), as well as highly significant values of AUC, when compared to the null hypothesis of AUC = 0.5 (figure 4). Nevertheless, the values of AUC we obtained were lower than corresponding values in the literature, but those involved different physiological conditions (Katsogridakis et al 2013), or more complex mathematical models (Chacon et al 2018).…”

Section: Discussioncontrasting

confidence: 87%

“…As mentioned above, this assumption is not acceptable for frequencies below approximately 0.15 Hz because an active CA implies that cerebrovascular resistance is changing over time, thus representing a departure from the premise of linearity (Bendat and Piersol 1986). Although non-linear models have been proposed to address this inherent limitation of TFA (Chacon et al 2018), the jury is still out to determine the benefits of using these models in clinical applications, and the key differences that would result in comparison with classical TFA.…”

Section: Limitations Of the Studymentioning

confidence: 99%

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COHmax: an algorithm to maximise coherence in estimates of dynamic cerebral autoregulation

et al. 2020

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Objective: The reliability of dynamic cerebral autoregulation (dCA) parameters, obtained with transfer function analysis (TFA) of spontaneous fluctuations in arterial blood pressure (BP), require statistically significant values of the coherence function. A new algorithm (COHmax) is proposed to increase values of coherence by means of the automated, selective removal of sub-segments of data. Approach: Healthy subjects were studied at baseline (normocapnia) and during 5% breathing of CO2 (hypercapnia). BP (Finapres), cerebral blood flow velocity (CBFV, transcranial Doppler), end-tidal CO2 (EtCO2, capnography) and heart rate (ECG) were recorded continuously during 5 min in each condition. TFA was performed with sub-segments of data of duration (SEGD) 100 s, 50 s or 25 s and the autoregulation index (ARI) was obtained from the CBFV response to a step change in BP. The area-under-the curve (AUC) was obtained from the receiver-operating characteristic (ROC) curve for the detection of changes in dCA resulting from hypercapnia. Main results: In 120 healthy subjects (69 male, age range 20–77 years), CO2 breathing was effective in changing mean EtCO2 and CBFV (p < 0.001). For SEGD = 100 s, ARI changed from 5.8 ± 1.4 (normocapnia) to 4.0 ± 1.7 (hypercapnia, p < 0.0001), with similar differences for SEGD = 50 s or 25 s. Depending on the value of SEGD, in normocapnia, 15.8% to 18.3% of ARI estimates were rejected due to poor coherence, with corresponding rates of 8.3% to 13.3% in hypercapnia. With increasing coherence, 36.4% to 63.2% of these could be recovered in normocapnia (p < 0.001) and 50.0% to 83.0% in hypercapnia (p < 0.005). For SEGD = 100 s, ROC AUC was not influenced by the algorithm, but it was superior to corresponding values for SEGD = 50 s or 25 s. Significance: COHmax has the potential to improve the yield of TFA estimates of dCA parameters, without introducing a bias or deterioration of their ability to detect impairment of autoregulation. Further studies are needed to assess the behaviour of the algorithm in patients with different cerebrovascular conditions.

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Section: Discussioncontrasting

confidence: 87%

Section: Limitations Of the Studymentioning

confidence: 99%

COHmax: an algorithm to maximise coherence in estimates of dynamic cerebral autoregulation

et al. 2020

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“…The ROC analysis in our study revealed that a cut-off value of 4.0 had the best sensitivity and specificity for predicting response to therapy and is in agreement with previous publications suggesting an ARI < 4 to define impaired CA 22,23 . This cut-off value is a new finding in a stroke population, and should be further investigated and replicated in larger studies to be implemented as a valid assessment tool for detection of CA impairment.…”

Section: Discussionsupporting

confidence: 91%

Cerebral autoregulation and response to intravenous thrombolysis for acute ischemic stroke

Nogueira

Lam

Llwyd

et al. 2020

Sci Rep

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We hypothesized that knowledge of cerebral autoregulation (CA) status during recanalization therapies could guide further studies aimed at neuroprotection targeting penumbral tissue, especially in patients that do not respond to therapy. Thus, we assessed CA status of patients with acute ischemic stroke (AIS) during intravenous r-tPA therapy and associated CA with response to therapy. AIS patients eligible for intravenous r-tPA therapy were recruited. Cerebral blood flow velocities (transcranial Doppler) from middle cerebral artery and blood pressure (Finometer) were recorded to calculate the autoregulation index (ARI, as surrogate for CA). National Institute of Health Stroke Score was assessed and used to define responders to therapy (improvement of ≥ 4 points on NIHSS measured 24-48 h after therapy). CA was considered impaired if ARI < 4. In 38 patients studied, compared to responders, non-responders had significantly lower ARI values (affected hemisphere: 5.0 vs. 3.6; unaffected hemisphere: 5.4 vs. 4.4, p = 0.03) and more likely to have impaired CA (32% vs. 62%, p = 0.02) during thrombolysis. In conclusion, CA during thrombolysis was impaired in patients who did not respond to therapy. this variable should be investigated as a predictor of the response to therapy and to subsequent neurological outcome. The key objective of current acute ischemic stroke (AIS) treatment is based on rapid blood flow restoration by thrombolysis, using intravenous recombinant tissue plasminogen activator (r-tPA), and/or mechanical arterial recanalization techniques 1-3. Several factors predict stroke outcome including age, initial stroke severity, arterial blood pressure (BP), site of occlusion, collaterals, and others 4,5. Nevertheless, complete or partial successful recanalization may not necessarily result in favorable outcome, with a number of predictors hypothesized 6,7. In particular, BP control may affect penumbral lesion size, with an optimal strategy still lacking evidence 8-10. Therapeutic BP manipulation may further impact on microvascular autoregulatory failure, as a consequence of an increase in lactate and free oxygen radicals in the occluded and/or reperfused tissues 11. Cerebral autoregulation (CA) refers to a set of physiological mechanisms that maintain the constancy of cerebral blood flow (CBF) despite wide variations in arterial BP. CA can be impaired within the first hours of ischemic stroke onset 11 ; as a consequence, BP control may be important for improving both the ischemic area in the brain and clinical outcome. Therefore, assessment of CA during recanalization therapy for AIS is relevant, and may influence future strategies for personalized BP control and associated neuroprotection. The aims of the present study were to assess CA status of responder and non-responder AIS patients to intravenous r-tPA during the therapy and after 24-48 h, and to test the hypothesis that CA during thrombolysis is associated with early response to therapy.

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“…A number of studies were carried out to assess CA, using static and (or) dynamic methods (Aaslid et al 1989, Zhang et al 1998, Czosnyka et al 2001, Novak et al 2004, Peng et al 2010, Chacón et al 2018. Mainly due to the advent of non-invasive continuous measurement of ABP and CBFV, dynamic cerebral autoregulation (dCA) is more frequently studied and a number of autoregulatory parameters have been proposed to assess dCA, such as autoregulation index (Tiecks et al 1995), correlation coefficient analysis (Mx) (Czosnyka et al 1996), variables derived from transfer function analysis (TFA) (Giller 1990), etc. Transfer function analysis (TFA) is the most frequently used method to assess dCA.…”

Section: Introductionmentioning

confidence: 99%

Separation of normal and impaired dynamic cerebral autoregulation using deep embedded clustering: a proof-of-concept study

Zhang

Guo

et al. 2021

Physiol. Meas.

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Objective. A previous study has shown that a data-driven approach can significantly improve the discriminative power of transfer function analysis (TFA) used to differentiate between normal and impaired cerebral autoregulation (CA) in two groups of data. The data was collected from both healthy subjects (assumed to have normal CA) and symptomatic patients with severe stenosis (assumed to have impaired CA). However, the sample size of the labeled data was relatively small, owing to the difficulty in data collection. Therefore, in this proof-of-concept study, we investigate the feasibility of using an unsupervised learning model to differentiate between normal and impaired CA on TFA variables without requiring labeled data for learning. Approach. Continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV), which were recorded simultaneously for approximately 10 min, were included from 148 subjects (41 healthy subjects, 31 with mild stenosis, 13 with moderate stenosis, 22 asymptomatic patients with severe stenosis, and 41 symptomatic patients with severe stenosis). Tiecks’ model was used to generate surrogate data with normal and impaired CA. A recently proposed unsupervised learning model was optimized and applied to separate the normal and impaired CA for both the surrogate data and real data. Main results. It achieved 98.9% and 74.1% accuracy for the surrogate and real data, respectively. Significance. To our knowledge, this is the first attempt to employ an unsupervised data-driven approach to assess CA using TFA. This method enables the development of a classifier to determine the status of CA, which is currently lacking.

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Non-linear models for the detection of impaired cerebral blood flow autoregulation

Cited by 8 publications

References 34 publications

COHmax: an algorithm to maximise coherence in estimates of dynamic cerebral autoregulation

COHmax: an algorithm to maximise coherence in estimates of dynamic cerebral autoregulation

Cerebral autoregulation and response to intravenous thrombolysis for acute ischemic stroke

Separation of normal and impaired dynamic cerebral autoregulation using deep embedded clustering: a proof-of-concept study

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