Next Article in Journal
Open vs. Percutaneous Achilles Tendon Repair: Experience of Single Orthopedic Institute with Long-Term Follow-Up
Previous Article in Journal
Tryptase: The Silent Witness of Past and Ongoing Systemic Events
Previous Article in Special Issue
Comparison of Effectiveness of Two Different Practical Approaches to Teaching Basic Life Support and Use of an Automated External Defibrillator in Primary School Children
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medicina
Volume 60
Issue 9
10.3390/medicina60091381
medicina-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Monitoring of Cerebral Blood Flow Autoregulation after Cardiac Arrest

by
Rok Petrovčič
1,*,
Martin Rakusa
2 and
Andrej Markota
3
1
Emergency Department, University Medical Centre Maribor, Ljubljanska ulica 5, 2000 Maribor, Slovenia
2
Department of Neurologic Diseases, University Medical Centre Maribor, Ljubljanska ulica 5, 2000 Maribor, Slovenia
3
Department of Intensive Internal Medicine, Division of Internal Medicine, University Medical Centre Maribor, Ljubljanska ulica 5, 2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Medicina 2024, 60(9), 1381; https://doi.org/10.3390/medicina60091381
Submission received: 21 July 2024 / Revised: 19 August 2024 / Accepted: 20 August 2024 / Published: 23 August 2024
(This article belongs to the Special Issue Cardiopulmonary Resuscitation: Recent Advances and Future Challenges)
Browse Figure
Versions Notes

Abstract

:
Background: Cardiac arrest remains one of the leading causes of death. After successful resuscitation of patients in cardiac arrest, post-cardiac arrest syndrome develops, part of it being an impaired cerebral blood flow autoregulation. Monitoring cerebral blood flow autoregulation after cardiac arrest is important for optimizing patient care and prognosticating patients’ survival, yet remains a challenge. There are still gaps in clinical implications and everyday use. In this article, we present a systematic review of studies with different methods of monitoring cerebral blood flow autoregulation after non-traumatic cardiac arrest. Methods: A comprehensive literature search was performed from 1 June 2024 to 27 June 2024 by using multiple databases: PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials. Inclusion criteria were studies with an included description of the measurement of cerebral blood flow autoregulation in adult patients after non-traumatic cardiac arrest. Results: A total of 16 studies met inclusion criteria. Our data show that the most used methods in the reviewed studies were near-infrared spectroscopy and transcranial Doppler. The most used mathematical methods for calculating cerebral autoregulation were cerebral oximetry index, tissue oxygenation reactivity index, and mean flow index. Conclusions: The use of various monitoring and mathematical methods for calculating cerebral blood flow autoregulation poses a challenge for standardization, validation, and daily use in clinical practice. In the future studies, focus should be considered on clinical validation and transitioning autoregulation monitoring techniques to everyday clinical practice, which could improve the survival outcomes of patients after cardiac arrest.

1. Introduction

Cardiac arrest is one of the leading causes of death in Europe [1,2,3]. Appropriate treatment after cardiac arrest consists of immediate basic life support, followed by advanced life support [4]. The chain of survival is important, connecting basic and advanced life support with post-resuscitation care [5]. Part of post-cardiac arrest syndrome is post-cardiac arrest brain injury—hypoxic brain injury, due to impaired cerebral blood flow autoregulation [6].
Cerebral blood flow autoregulation (CBFA) is a “homeostatic process that regulates and maintains cerebral blood flow constantly across a range of blood pressures” [7]. Niels Lassen proposed the original concept in 1959 with a figure of a lower limit and the plateau of mean arterial pressure in which cerebral blood flow remains constant [8]. Through the years and with the advances in monitoring techniques, the concept has been challenged in terms of the smaller cerebral antiregulatory plateau and CBFA being more pressure-passive. To this day, there are important knowledge gaps in understanding CBFA [9,10]. Assessment of CBFA can be divided into static and dynamic. The static method assesses relationships between CBFA and mean arterial pressure (MAP) when they reach a steady state without time changes. The dynamic method assesses changes in CBFA in response to dynamic changes in MAP [7,10].
In clinical practice, CBFA is monitored in settings of patients after cardiac arrest, traumatic brain injury, brain hemorrhage, stroke, during anesthesia, sepsis, and other critically ill patients [11]. The most commonly used techniques include near-infrared spectroscopy (NIRS), transcranial Doppler ultrasound (TCD), intracranial pressure, and diffuse correlation spectroscopy (DCA) [12]. To current date, none of the monitoring techniques are defined as a golden standard, and a lack of implication in clinical management protocols exists [13].
Mathematical methods used for calculation of CBFA.
There is a large variety of mathematical methods or indices used to calculate and define impaired CBFA by data derived from monitoring techniques [12]. Some of the frequently used mathematical methods include [14] the following.
Autoregulation index (ARI) uses arterial blood pressure (ABP) and blood flow velocity (Fv) as input signals. The signals are interpreted as an ARI value of 0 (meaning absent autoregulation) and an ARI value of 9 (meaning normal autoregulation) [15,16].
The flow index uses ABP (as cerebral perfusion pressure) and Fv as an input signal. The signals are interpreted as higher values of mean flow index (Mx), systolic flow index (Sx), or diastolic flow index (Dx) means impaired autoregulation [16,17,18].
The transfer function uses ABP and Fv as input signals. It uses Fourier decomposition of stationary input and output signals and phase, gain, and coherence. Impaired autoregulation is defined as low phase, high gain, and high coherence [19].
The cerebral oximetry index (COx) and tissue oxygenation reactivity index (TOx) use ABP (as cerebral perfusion pressure) and NIRS-measured brain oxygenation as input signals. It is calculated as a Pearson correlation coefficient between 30 consecutive 10-s means of ABP and tissue oxygenation. Higher values of COx or TOx mean impaired autoregulation [20,21,22,23].
The pressure reactivity index (PRx) uses ABP and ICP as input signals. It is calculated as a moving correlation of 30 consecutive 10-s means of ABP and ICP. Higher values of PRx mean impaired autoregulation [24].
The oxygen reactivity index (ORx) uses cerebral perfusion pressure and brain tissue oxygen (PBTO2) pressure as the input signal. It is calculated as a correlation between 30 consecutive 10-s means of ABP and PBTO2. Higher values of ORx mean impaired autoregulation [25,26].
Monitoring CBFA in patients after cardiac arrest has clinical implications regarding prognostication of cerebral performance and tailoring patients’ cerebral perfusion pressure [27].
This systematic review aims to identify and list monitoring techniques and mathematical methods used in clinical trials to calculate and define impaired cerebral blood flow autoregulation in patients after non-traumatic cardiac arrest.

2. Materials and Methods

2.1. Data Sources and Search Strategy

In this study, we used Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines (Figure 1) [28]. A systematic literature search was performed from 1 June 2024 to 27 June 2024 by using the following databases to identify relevant studies: PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials. The following search terms were used: monitoring, cardiac arrest, cerebral, blood flow, and autoregulation, with the following derivatives and combinations: (“Monitoring” OR “Measurement” OR “Assessment” OR “Evaluation”) AND (“Cerebral Blood Flow” OR “Cerebral Perfusion” OR “Brain Blood Flow”) AND (“Cardiac Arrest” OR “Heart Arrest” OR “Cardiopulmonary Arrest”); (“Cerebral Blood Flow” OR “Cerebral Perfusion” OR “Brain Blood Flow”) AND (“Autoregulation” OR “Self-Regulation” OR “Cerebral Autoregulation”) AND (“Cardiac Arrest” OR “Heart Arrest” OR “Cardiopulmonary Arrest”); and (“Monitoring” OR “Measurement” OR “Assessment” OR “Evaluation”) AND (“Autoregulation” OR “Self-Regulation” OR “Cerebral Autoregulation”) AND (“Cardiac Arrest” OR “Heart Arrest” OR “Cardiopulmonary Arrest”). The following selected filters were used: humans, language (English), and time of publication (from 1 January 1990). Inclusion criteria were studies with an included description of the measurement of cerebral blood flow autoregulation in adult patients after non-traumatic cardiac arrest. The exclusion criteria were adolescents (under 18 years of age) and traumatic cardiac arrest. Searches were re-run and updated before the final analyses.

2.2. Study Selection and Data Collection

Two authors (RP and AM) independently searched articles, screening titles, abstracts, and full texts. The articles were categorized into three groups: “included” and “excluded” (if both examiners agree) or “uncertain” (in case of disagreement). If the article was classified as uncertain, the decision was made by future examination of a third author (MR). A standardized electronic spreadsheet (Microsoft Excel, version 2016; Microsoft, Redmond, WA, USA) was used to extract data. Data were synthesized in a standardized electronic spreadsheet, with columns including the following titles: study, main finding, type of study, setting, patients (number), monitoring method, maneuver used (if applicable), main limitation, and autoregulation indices (mathematical method used) (Table 1).

2.3. Endpoints

Representation of monitoring techniques and mathematical methods used to calculate and define impaired cerebral blood flow autoregulation in patients after non-traumatic cardiac arrest.
Risk of Bias Assessment in the Included Studies.
Two examiners (RP and AM) independently assessed the risk of bias in the included studies using a Mixed Methods Appraisal Tool (MMAT), version 2018.

3. Results

The Prisma literature review flow diagram is presented in Figure 1. We identified 182 studies, of which 16 were analyzed (Table 1). Three studies were excluded as they used the same data set as in already included studies. One was a protocol for post hoc analyses. In total, 13 studies were prospective and 3 were retrospective. Altogether, 531 patients were included.
In the analyzed studies, a large variety of methods and mathematical models were used to assess cerebral autoregulation (Table 2 and Table 3). The most commonly used methods were NIRS and TCD. Other methods were also used. In total, 3 out of 16 studies used invasive monitoring methods. CBFA was calculated using data derived from these methods and autoregulation metrics.
Several mathematical methods or autoregulation metrics were described for the calculation of CBFA in the included studies. The most commonly used methods were COx, Tox, and Mx. They correlate well with the mostly used monitoring methods in studies—NIRS and TCD. In the majority of studies, blood pressure was monitored invasively through an arterial line.

4. Discussion

Our analysis demonstrates heterogeneity in assessing CBFA. Several different monitoring techniques and outcome indices (COx, PRx, TOx, and Mx) were used to better understand the brain’s autoregulatory function. While each study design contributes unique insights, the heterogeneity poses challenges when attempting to synthesize and draw overarching conclusions.
  • Heterogeneity in Monitoring Approaches
The most used method of CBFA measurement in CA patients is NIRS. The strength of this method is the noninvasive real-time monitoring of brain oxygenation based on the principle of transluminal spectroscopy. Despite some of its limitations, for example, the limitation of signal capture only to the area of the frontal cortex and the oxygenation value changes, as a result of the changes in blood flow in the skin, NIRS is adopted in various clinical practices [45]. Because of its strengths, it is widely used to monitor autoregulation in neonatology, surgery, and neurocritical care [45]. In most analyzed studies, data derived from NIRS are analyzed using ICM+ ® software in the form of COx. It also is used in a continuous assessment of CBFA in adults [22].
The second most used method is TCD, a noninvasive real-time monitoring method of cerebral blood flow velocity in the main intracranial vessels [46]. TCD is a valid method for determining the lower limit of cerebral autoregulation [47]. TCD has been used to monitor autoregulation in different settings, similar to NIRS, and also to evaluate patients with postpartum angiopathy, eclampsia, and syncope [48]. Limitations of its use are being operator-dependent, its reproducibility, and its variability [49].
Multimodal monitoring of CBFA can provide additional insight into complex interrelated neurophysiologic determinants of CBFA but can be challenging in terms of cost and clinical efficiency [50].
  • Association with Clinical Outcomes
Several studies demonstrated associations between impaired CBFA and clinical outcomes in CA patients. Ameloot et al. reported that CBFA was disturbed in one-third of CA patients. The survival rate was negatively associated with the time spent under optimal mean arterial pressure (MAP) [29]. Similarly, Pham et al. reported that impaired CBFA was independently associated with mortality at three months follow-up [34]. In another study, Laurikkala et al. reported disturbances in CBFA in a significant proportion of CA patients, which were correlated with worse outcomes [36]. Therefore, we may conclude that monitoring CBFA in this population is important and may help predict survival outcomes. Monitoring of CBFA can also be used as a prediction tool in other cerebrovascular pathologies [51,52,53,54]. There is still a lack of definite evidence, which makes everyday use of CBFA as a prediction tool for clinical outcomes difficult [55].
However, it is necessary to use a reliable and robust method for CBFA monitoring. One possibility is NIRS. When comparing left- and right-sided NIRS recordings, there were no differences between sides or CBFA estimation in CA patients, indicating the reliability of NIRS as a monitoring tool [40]. Monitoring CBFA with NIRS-derived COx correlated well with previously validated TCD-based methods, suggesting the reliability of NIRS in assessing CBFA [42].
In research on pediatric patients resuscitated after cardiac arrest, monitoring of CBFA is often used. Similarly, there is a high heterogeneity of methods in adult patients after cardiac arrest, which includes NIRS, ICP, and TCD, with a variety of mathematical methods used [56,57,58,59,60].
Autoregulation after cardiac arrest is experimentally explored in swine models using NIRS, ICP, and laser-Doppler flowmetry [61,62,63].
Different pathologies cause cerebral dysfunction and CBFA is monitored in other clinical settings. In sepsis, cerebral autoregulation is mostly monitored using NIRS and TCD [64,65,66]. In subarachnoid hemorrhage, cerebral autoregulation is monitored similarly and uses data derived from ICP [26,67,68,69,70]. In cardiac surgery, autoregulation is widely monitored with the same techniques [71,72,73].
Other systematic reviews found similar conclusions. In the review of determining optimal mean arterial pressure after cardiac arrest by Rikhraj et al., a high heterogeneity of monitoring methods and calculation was found [74]. Longhitano et. al. reviewed CBFA in non-brain injured patients. NIRS and TCD assessed CBFA in the included studies. When NIRS was used, cerebral autoregulation was monitored using Pearson’s correlation coefficient between MAP and NIRS signals to generate the variable COx. When TCD was used, the Mx index was calculated to evaluate cerebral autoregulation as a correlation between systemic MAP and mean flow velocity in the middle cerebral artery [75]. A review by Caldas et al. of CBFA in cardiopulmonary bypass surgery found that cerebral autoregulation was mostly assessed by NIRS and TCD with a wide variety of indices of static and dynamic autoregulation [76]. A similarity was found in the assessment of the risk of perioperative stroke and CBFA monitoring by Santos et al. [77]. By the analysis of predictors of outcome with cerebral autoregulation monitoring by Rivera-Lara et al., invasive (ICP monitor) and noninvasive (NIRS, TCD) methods were identified to monitor cerebral autoregulation in different pathologies, with different autoregulation indices—the PRx, ARI, ORx, and flow reactivity index [78]. In a systematic review and meta-analysis by De-Lima-Oliveira, with the purpose of establishing the relation between cerebral autoregulation and intracranial hypertension, identified techniques were divided into static and dynamic. Static techniques of cerebral autoregulation monitoring identified in studies were Xe133, arterio-jugular differences in oxygen, positron emission tomography flow, and N2O. Dynamic techniques were used to move the correlation index to assess CA. The techniques to determine the correlations with blood pressure variation were invasive intracranial pressure (PRx), TCD (Mx), cerebral tissue PtbO2 (ORx), and CBF measurements through a laser catheter (Lx) or a thermal sensor catheter (CBFx) [79].
  • Strengths and Limitations
The strength of this study is the systematic review of autoregulation monitoring techniques employed in studies that involve patients who have experienced non-traumatic cardiac arrest. We were able to identify different monitoring methods and mathematical methods used to measure cerebral autoregulation.
The limitations of this study are the high heterogeneity of different monitoring, mathematical methods, and methodological limitations observed, which makes comparison and reproducibility difficult. There is a small overall size of patient cohorts.
Future research should focus on finding noninvasive reproducible monitoring methods of cerebral blood autoregulation, comparing different metrics, and transitioning the monitoring of cerebral autoregulation to everyday clinical practice. There is still an important gap in the everyday standard clinical use of cerebral autoregulation monitoring in patients after cardiac arrest, which can be addressed with the findings of additional studies.

5. Conclusions

In the presenting article, we systematically reviewed different methods of monitoring cerebral blood flow autoregulation after non-traumatic cardiac arrest. The use of diverse monitoring methods and definitions introduces challenges in standardization. The choice of modality may influence the observed outcomes and make comparisons between studies difficult.
Our results demonstrate that the most used methods were NIRS and TCD, with a wide variety of mathematical methods for calculating cerebral autoregulation. The most common were COx, Tox, and Mx.
Future studies should focus on clinical validation and transitioning those methods to everyday clinical practice to improve the survival outcomes of patients after CA.

Author Contributions

Conceptualization, R.P., M.R. and A.M.; methodology, R.P., M.R. and A.M.; formal analysis, R.P., M.R. and A.M.; investigation, R.P., M.R. and A.M.; resources, R.P., M.R. and A.M.; data curation, R.P., M.R. and A.M.; writing—original draft preparation, R.P.; writing—review and editing, M.R. and A.M.; visualization, R.P.; supervision, M.R. and A.M.; project administration, R.P. All authors have read and agreed to the published version of the manuscript.

Funding

Funded by University Medical Centre Maribor, Maribor, Slovenia.

Institutional Review Board Statement

Ethical review Board approval was not required due to not using personal information and open access of data analyzed.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Gräsner, J.-T.; Wnent, J.; Herlitz, J.; Perkins, G.D.; Lefering, R.; Tjelmeland, I.; Koster, R.W.; Masterson, S.; Rossell-Ortiz, F.; Maurer, H.; et al. Survival after Out-of-Hospital Cardiac Arrest in Europe-Results of the EuReCa TWO Study. Resuscitation 2020, 148, 218–226. [Google Scholar] [CrossRef] [PubMed]
  2. Gräsner, J.-T.; Lefering, R.; Koster, R.W.; Masterson, S.; Böttiger, B.W.; Herlitz, J.; Wnent, J.; Tjelmeland, I.B.M.; Ortiz, F.R.; Maurer, H.; et al. EuReCa ONE⿿27 Nations, ONE Europe, ONE Registry. Resuscitation 2016, 105, 188–195. [Google Scholar] [CrossRef]
  3. Gräsner, J.-T.; Herlitz, J.; Tjelmeland, I.B.M.; Wnent, J.; Masterson, S.; Lilja, G.; Bein, B.; Böttiger, B.W.; Rosell-Ortiz, F.; Nolan, J.P.; et al. European Resuscitation Council Guidelines 2021: Epidemiology of Cardiac Arrest in Europe. Resuscitation 2021, 161, 61–79. [Google Scholar] [CrossRef]
  4. Perkins, G.D.; Graesner, J.-T.; Semeraro, F.; Olasveengen, T.; Soar, J.; Lott, C.; Van de Voorde, P.; Madar, J.; Zideman, D.; Mentzelopoulos, S.; et al. European Resuscitation Council Guidelines 2021: Executive Summary. Resuscitation 2021, 161, 1–60. [Google Scholar] [CrossRef] [PubMed]
  5. Semeraro, F.; Greif, R.; Böttiger, B.W.; Burkart, R.; Cimpoesu, D.; Georgiou, M.; Yeung, J.; Lippert, F.; S Lockey, A.; Olasveengen, T.M.; et al. European Resuscitation Council Guidelines 2021: Systems Saving Lives. Resuscitation 2021, 161, 80–97. [Google Scholar] [CrossRef] [PubMed]
  6. Sekhon, M.S.; Ainslie, P.N.; Griesdale, D.E. Clinical Pathophysiology of Hypoxic Ischemic Brain Injury after Cardiac Arrest: A “Two-Hit” Model. Crit. Care 2017, 21, 90. [Google Scholar] [CrossRef] [PubMed]
  7. Armstead, W.M. Cerebral Blood Flow Autoregulation and Dysautoregulation. Anesthesiol. Clin. 2016, 34, 465–477. [Google Scholar] [CrossRef]
  8. Lassen, N.A. Cerebral Blood Flow and Oxygen Consumption in Man. Physiol. Rev. 1959, 39, 183–238. [Google Scholar] [CrossRef]
  9. Brassard, P.; Labrecque, L.; Smirl, J.D.; Tymko, M.M.; Caldwell, H.G.; Hoiland, R.L.; Lucas, S.J.E.; Denault, A.Y.; Couture, E.J.; Ainslie, P.N. Losing the Dogmatic View of Cerebral Autoregulation. Physiol. Rep. 2021, 9, e14982. [Google Scholar] [CrossRef]
  10. Claassen, J.A.H.R.; Thijssen, D.H.J.; Panerai, R.B.; Faraci, F.M. Regulation of Cerebral Blood Flow in Humans: Physiology and Clinical Implications of Autoregulation. Physiol. Rev. 2021, 101, 1487–1559. [Google Scholar] [CrossRef]
  11. Moerman, A.; De Hert, S. Why and How to Assess Cerebral Autoregulation? Best. Pract. Res. Clin. Anaesthesiol. 2019, 33, 211–220. [Google Scholar] [CrossRef]
  12. Czosnyka, M.; Miller, C. Monitoring of Cerebral Autoregulation. Neurocrit. Care 2014, 21, 95–102. [Google Scholar] [CrossRef]
  13. Depreitere, B.; Citerio, G.; Smith, M.; Adelson, P.D.; Aries, M.J.; Bleck, T.P.; Bouzat, P.; Chesnut, R.; De Sloovere, V.; Diringer, M.; et al. Cerebrovascular Autoregulation Monitoring in the Management of Adult Severe Traumatic Brain Injury: A Delphi Consensus of Clinicians. Neurocrit. Care 2021, 34, 731–738. [Google Scholar] [CrossRef]
  14. Donnelly, J.; Budohoski, K.P.; Smielewski, P.; Czosnyka, M. Regulation of the Cerebral Circulation: Bedside Assessment and Clinical Implications. Crit. Care 2016, 20, 129. [Google Scholar] [CrossRef]
  15. Aaslid, R.; Lindegaard, K.F.; Sorteberg, W.; Nornes, H. Cerebral Autoregulation Dynamics in Humans. Stroke 1989, 20, 45–52. [Google Scholar] [CrossRef]
  16. Tiecks, F.P.; Lam, A.M.; Aaslid, R.; Newell, D.W. Comparison of Static and Dynamic Cerebral Autoregulation Measurements. Stroke 1995, 26, 1014–1019. [Google Scholar] [CrossRef]
  17. Czosnyka, M.; Smielewski, P.; Kirkpatrick, P.; Menon, D.K.; Pickard, J.D. Monitoring of Cerebral Autoregulation in Head-Injured Patients. Stroke 1996, 27, 1829–1834. [Google Scholar] [CrossRef]
  18. Olsen, M.H.; Riberholt, C.G.; Plovsing, R.R.; Møller, K.; Berg, R.M.G. Reliability of the Mean Flow Index (Mx) for Assessing Cerebral Autoregulation in Healthy Volunteers. Physiol. Rep. 2021, 9, e14923. [Google Scholar] [CrossRef]
  19. Claassen, J.A.; Meel-van den Abeelen, A.S.; Simpson, D.M.; Panerai, R.B. Transfer Function Analysis of Dynamic Cerebral Autoregulation: A White Paper from the International Cerebral Autoregulation Research Network. J. Cereb. Blood Flow Metab. 2016, 36, 665–680. [Google Scholar] [CrossRef]
  20. Moerman, A.; De Hert, S. Recent Advances in Cerebral Oximetry. Assessment of Cerebral Autoregulation with near-Infrared Spectroscopy: Myth or Reality? F1000Res 2017, 6, 1615. [Google Scholar] [CrossRef]
  21. Brady, K.M.; Lee, J.K.; Kibler, K.K.; Smielewski, P.; Czosnyka, M.; Easley, R.B.; Koehler, R.C.; Shaffner, D.H. Continuous Time-Domain Analysis of Cerebrovascular Autoregulation Using Near-Infrared Spectroscopy. Stroke 2007, 38, 2818–2825. [Google Scholar] [CrossRef]
  22. Steiner, L.A.; Pfister, D.; Strebel, S.P.; Radolovich, D.; Smielewski, P.; Czosnyka, M. Near-Infrared Spectroscopy Can Monitor Dynamic Cerebral Autoregulation in Adults. Neurocrit. Care 2009, 10, 122–128. [Google Scholar] [CrossRef] [PubMed]
  23. Zweifel, C.; Castellani, G.; Czosnyka, M.; Carrera, E.; Brady, K.M.; Kirkpatrick, P.J.; Pickard, J.D.; Smielewski, P. Continuous Assessment of Cerebral Autoregulation With Near-Infrared Spectroscopy in Adults After Subarachnoid Hemorrhage. Stroke 2010, 41, 1963–1968. [Google Scholar] [CrossRef]
  24. Czosnyka, M.; Smielewski, P.; Kirkpatrick, P.; Laing, R.J.; Menon, D.; Pickard, J.D. Continuous Assessment of the Cerebral Vasomotor Reactivity in Head Injury. Neurosurgery 1997, 41, 11–19. [Google Scholar] [CrossRef]
  25. Jaeger, M.; Schuhmann, M.U.; Soehle, M.; Meixensberger, J. Continuous Assessment of Cerebrovascular Autoregulation after Traumatic Brain Injury Using Brain Tissue Oxygen Pressure Reactivity*. Crit. Care Med. 2006, 34, 1783–1788. [Google Scholar] [CrossRef]
  26. Jaeger, M.; Soehle, M.; Schuhmann, M.U.; Meixensberger, J. Clinical Significance of Impaired Cerebrovascular Autoregulation After Severe Aneurysmal Subarachnoid Hemorrhage. Stroke 2012, 43, 2097–2101. [Google Scholar] [CrossRef]
  27. van den Brule, J.M.D.; van der Hoeven, J.G.; Hoedemaekers, C.W.E. Cerebral Perfusion and Cerebral Autoregulation after Cardiac Arrest. Biomed. Res. Int. 2018, 2018, 4143636. [Google Scholar] [CrossRef]
  28. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Antes, G.; Atkins, D.; Barbour, V.; Barrowman, N.; Berlin, J.A.; Clark, J.; et al. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Open Med. 2009, 3, 172–181. [Google Scholar] [CrossRef]
  29. Ameloot, K.; Genbrugge, C.; Meex, I.; Jans, F.; Boer, W.; Vander Laenen, M.; Ferdinande, B.; Mullens, W.; Dupont, M.; Dens, J.; et al. An Observational Near-Infrared Spectroscopy Study on Cerebral Autoregulation in Post-Cardiac Arrest Patients: Time to Drop ‘One-Size-Fits-All’ Hemodynamic Targets? Resuscitation 2015, 90, 121–126. [Google Scholar] [CrossRef] [PubMed]
  30. Nishizawa, H.; Kudoh, I. Cerebral Autoregulation Is Impaired in Patients Resuscitated after Cardiac Arrest. Acta Anaesthesiol. Scand. 1996, 40, 1149–1153. [Google Scholar] [CrossRef]
  31. Sundgreen, C.; Larsen, F.S.; Herzog, T.M.; Knudsen, G.M.; Boesgaard, S.; Aldershvile, J. Autoregulation of Cerebral Blood Flow in Patients Resuscitated From Cardiac Arrest. Stroke 2001, 32, 128–132. [Google Scholar] [CrossRef]
  32. Sekhon, M.S.; Gooderham, P.; Menon, D.K.; Brasher, P.M.A.; Foster, D.; Cardim, D.; Czosnyka, M.; Smielewski, P.; Gupta, A.K.; Ainslie, P.N.; et al. The Burden of Brain Hypoxia and Optimal Mean Arterial Pressure in Patients With Hypoxic Ischemic Brain Injury After Cardiac Arrest*. Crit. Care Med. 2019, 47, 960–969. [Google Scholar] [CrossRef]
  33. Sekhon, M.S.; Smielewski, P.; Bhate, T.D.; Brasher, P.M.; Foster, D.; Menon, D.K.; Gupta, A.K.; Czosnyka, M.; Henderson, W.R.; Gin, K.; et al. Using the Relationship between Brain Tissue Regional Saturation of Oxygen and Mean Arterial Pressure to Determine the Optimal Mean Arterial Pressure in Patients Following Cardiac Arrest: A Pilot Proof-of-Concept Study. Resuscitation 2016, 106, 120–125. [Google Scholar] [CrossRef] [PubMed]
  34. Pham, P.; Bindra, J.; Chuan, A.; Jaeger, M.; Aneman, A. Are Changes in Cerebrovascular Autoregulation Following Cardiac Arrest Associated with Neurological Outcome? Results of a Pilot Study. Resuscitation 2015, 96, 192–198. [Google Scholar] [CrossRef] [PubMed]
  35. van den Brule, J.M.D.; van Kaam, C.R.; van der Hoeven, J.G.; Claassen, J.A.H.R.; Hoedemaekers, C.W.E. Influence of Induced Blood Pressure Variability on the Assessment of Cerebral Autoregulation in Patients after Cardiac Arrest. Biomed. Res. Int. 2018, 2018, 8153241. [Google Scholar] [CrossRef] [PubMed]
  36. Laurikkala, J.; Aneman, A.; Peng, A.; Reinikainen, M.; Pham, P.; Jakkula, P.; Hästbacka, J.; Wilkman, E.; Loisa, P.; Toppila, J.; et al. Association of Deranged Cerebrovascular Reactivity with Brain Injury Following Cardiac Arrest: A Post-Hoc Analysis of the COMACARE Trial. Crit. Care 2021, 25, 350. [Google Scholar] [CrossRef]
  37. Bindra, J.; Pham, P.; Aneman, A.; Chuan, A.; Jaeger, M. Non-Invasive Monitoring of Dynamic Cerebrovascular Autoregulation Using Near Infrared Spectroscopy and the Finometer Photoplethysmograph. Neurocrit. Care 2016, 24, 442–447. [Google Scholar] [CrossRef]
  38. Calviello, L.A.; Cardim, D.; Czosnyka, M.; Preller, J.; Smielewski, P.; Siyal, A.; Damian, M.S. Feasibility of Non-Invasive Neuromonitoring in General Intensive Care Patients Using a Multi-Parameter Transcranial Doppler Approach. J. Clin. Monit. Comput. 2022, 36, 1805–1815. [Google Scholar] [CrossRef]
  39. Balu, R.; Rajagopalan, S.; Baghshomali, S.; Kirschen, M.; Amurthur, A.; Kofke, W.A.; Abella, B.S. Cerebrovascular Pressure Reactivity and Intracranial Pressure Are Associated with Neurologic Outcome after Hypoxic-Ischemic Brain Injury. Resuscitation 2021, 164, 114–121. [Google Scholar] [CrossRef]
  40. Hazenberg, L.; Aries, M.; Beqiri, E.; Mess, W.; van Mook, W.; Delnoij, T.; Zeiler, F.; van Kuijk, S.; Tas, J. Are NIRS-Derived Cerebral Autoregulation and ABPopt Values Different between Hemispheres in Hypoxic-Ischemic Brain Injury Patients Following Cardiac Arrest? J. Clin. Monit. Comput. 2023, 37, 1427–1430. [Google Scholar] [CrossRef]
  41. Griesdale, D.E.G.; Sekhon, M.S.; Wood, M.D.; Cardim, D.; Brasher, P.M.A.; McCredie, V.; Sirounis, D.; Foster, D.; Krasnogolova, Y.; Smielewski, P.; et al. Near-Infrared Spectroscopy to Assess Cerebral Autoregulation and Optimal Mean Arterial Pressure in Patients With Hypoxic-Ischemic Brain Injury: A Prospective Multicenter Feasibility Study. Crit. Care Explor. 2020, 2, e0217. [Google Scholar] [CrossRef] [PubMed]
  42. Rivera-Lara, L.; Geocadin, R.; Zorrilla-Vaca, A.; Healy, R.; Radzik, B.R.; Palmisano, C.; Mirski, M.; Ziai, W.C.; Hogue, C. Validation of Near-Infrared Spectroscopy for Monitoring Cerebral Autoregulation in Comatose Patients. Neurocrit. Care 2017, 27, 362–369. [Google Scholar] [CrossRef]
  43. Crippa, I.A.; Vincent, J.-L.; Zama Cavicchi, F.; Pozzebon, S.; Annoni, F.; Cotoia, A.; Njimi, H.; Gaspard, N.; Creteur, J.; Taccone, F.S. Cerebral Autoregulation in Anoxic Brain Injury Patients Treated with Targeted Temperature Management. J. Intensive Care 2021, 9, 67. [Google Scholar] [CrossRef]
  44. Tachino, J.; Nonomiya, Y.; Taniuchi, S.; Shintani, A.; Nakao, S.; Takegawa, R.; Hirose, T.; Sakai, T.; Ohnishi, M.; Shimazu, T.; et al. Association between Time-Dependent Changes in Cerebrovascular Autoregulation after Cardiac Arrest and Outcomes: A Prospective Cohort Study. J. Cereb. Blood Flow Metab. 2023, 43, 1942–1950. [Google Scholar] [CrossRef]
  45. Murkin, J.M.; Arango, M. Near-Infrared Spectroscopy as an Index of Brain and Tissue Oxygenation. Br. J. Anaesth. 2009, 103, i3–i13. [Google Scholar] [CrossRef]
  46. Author, A.; Tetičkovič, E.; Menih, M.; Magdič, J.; Prispel, Č.; Avtor, I. TCD monitoring of Cerebral Blood Flow. Acta Med.-Biotech. 2009, 2, 11–18. [Google Scholar] [CrossRef]
  47. Larsen, F.S.; Olsen, K.S.; Hansen, B.A.; Paulson, O.B.; Knudsen, G.M. Transcranial Doppler Is Valid for Determination of the Lower Limit of Cerebral Blood Flow Autoregulation. Stroke 1994, 25, 1985–1988. [Google Scholar] [CrossRef]
  48. Purkayastha, S.; Sorond, F. Transcranial Doppler Ultrasound: Technique and Application. Semin. Neurol. 2013, 32, 411–420. [Google Scholar] [CrossRef]
  49. Panerai, R.B. Transcranial Doppler for Evaluation of Cerebral Autoregulation. Clin. Auton. Res. 2009, 19, 197–211. [Google Scholar] [CrossRef]
  50. Vitt, J.R.; Loper, N.E.; Mainali, S. Multimodal and Autoregulation Monitoring in the Neurointensive Care Unit. Front. Neurol. 2023, 14, 1155986. [Google Scholar] [CrossRef] [PubMed]
  51. Nogueira, R.C.; Lam, M.Y.; Llwyd, O.; Salinet, A.S.M.; Bor-Seng-Shu, E.; Panerai, R.B.; Robinson, T.G. Cerebral Autoregulation and Response to Intravenous Thrombolysis for Acute Ischemic Stroke. Sci. Rep. 2020, 10, 10554. [Google Scholar] [CrossRef]
  52. Rasulo, F.A.; Girardini, A.; Lavinio, A.; De Peri, E.; Stefini, R.; Cenzato, M.; Nodari, I.; Latronico, N. Are Optimal Cerebral Perfusion Pressure and Cerebrovascular Autoregulation Related to Long-Term Outcome in Patients With Aneurysmal Subarachnoid Hemorrhage? J. Neurosurg. Anesthesiol. 2012, 24, 3–8. [Google Scholar] [CrossRef]
  53. Johnson, U.; Engquist, H.; Lewén, A.; Howells, T.; Nilsson, P.; Ronne-Engström, E.; Rostami, E.; Enblad, P. Increased Risk of Critical CBF Levels in SAH Patients with Actual CPP below Calculated Optimal CPP. Acta Neurochir. (Wien.) 2017, 159, 1065–1071. [Google Scholar] [CrossRef]
  54. Reinhard, M.; Neunhoeffer, F.; Gerds, T.A.; Niesen, W.-D.; Buttler, K.-J.; Timmer, J.; Schmidt, B.; Czosnyka, M.; Weiller, C.; Hetzel, A. Secondary Decline of Cerebral Autoregulation Is Associated with Worse Outcome after Intracerebral Hemorrhage. Intensive Care Med. 2010, 36, 264–271. [Google Scholar] [CrossRef]
  55. Sarwal, A.; Robba, C.; Venegas, C.; Ziai, W.; Czosnyka, M.; Sharma, D. Are We Ready for Clinical Therapy Based on Cerebral Autoregulation? A Pro-Con Debate. Neurocrit. Care 2023, 39, 269–283. [Google Scholar] [CrossRef]
  56. Lee, J.K.; Brady, K.M.; Chung, S.-E.; Jennings, J.M.; Whitaker, E.E.; Aganga, D.; Easley, R.B.; Heitmiller, K.; Jamrogowicz, J.L.; Larson, A.C.; et al. A Pilot Study of Cerebrovascular Reactivity Autoregulation after Pediatric Cardiac Arrest. Resuscitation 2014, 85, 1387–1393. [Google Scholar] [CrossRef]
  57. Kirschen, M.P.; Majmudar, T.; Beaulieu, F.; Burnett, R.; Shaik, M.; Morgan, R.W.; Baker, W.; Ko, T.; Balu, R.; Agarwal, K.; et al. Deviations from NIRS-Derived Optimal Blood Pressure Are Associated with Worse Outcomes after Pediatric Cardiac Arrest. Resuscitation 2021, 168, 110–118. [Google Scholar] [CrossRef]
  58. Kurz, J.E.; Smith, C.M.; Wainwright, M.S. Thermoregulate, Autoregulate and Ventilate. Curr. Opin. Pediatr. 2017, 29, 259–265. [Google Scholar] [CrossRef]
  59. Zipfel, J.; Hegele, D.; Hockel, K.; Kerscher, S.R.; Heimberg, E.; Czosnyka, M.; Neunhoeffer, F.; Schuhmann, M.U. Monitoring of Cerebrovascular Pressure Reactivity in Children May Predict Neurologic Outcome after Hypoxic-Ischemic Brain Injury. Child’s Nerv. Syst. 2022, 38, 1717–1726. [Google Scholar] [CrossRef] [PubMed]
  60. Lovett, M.E.; Maa, T.; Chung, M.G.; O’Brien, N.F. Cerebral Blood Flow Velocity and Autoregulation in Paediatric Patients Following a Global Hypoxic-Ischaemic Insult. Resuscitation 2018, 126, 191–196. [Google Scholar] [CrossRef]
  61. Lee, J.K.; Brady, K.M.; Mytar, J.O.; Kibler, K.K.; Carter, E.L.; Hirsch, K.G.; Hogue, C.W.; Easley, R.B.; Jordan, L.C.; Smielewski, P.; et al. Cerebral Blood Flow and Cerebrovascular Autoregulation in a Swine Model of Pediatric Cardiac Arrest and Hypothermia*. Crit. Care Med. 2011, 39, 2337–2345. [Google Scholar] [CrossRef]
  62. Liu, X.; Hu, X.; Brady, K.M.; Koehler, R.; Smielewski, P.; Czosnyka, M.; Donnelly, J.; Lee, J.K. Comparison of Wavelet and Correlation Indices of Cerebral Autoregulation in a Pediatric Swine Model of Cardiac Arrest. Sci. Rep. 2020, 10, 5926. [Google Scholar] [CrossRef]
  63. Lee, J.K.; Yang, Z.-J.; Wang, B.; Larson, A.C.; Jamrogowicz, J.L.; Kulikowicz, E.; Kibler, K.K.; Mytar, J.O.; Carter, E.L.; Burman, H.T.; et al. Noninvasive Autoregulation Monitoring in a Swine Model of Pediatric Cardiac Arrest. Anesth. Analg. 2012, 114, 825–836. [Google Scholar] [CrossRef]
  64. Berg, R.M.G.; Plovsing, R.R. Effects of Short-Term Mechanical Hyperventilation on Cerebral Blood Flow and Dynamic Cerebral Autoregulation in Critically Ill Patients with Sepsis. Scand. J. Clin. Lab. Investig. 2016, 76, 226–233. [Google Scholar] [CrossRef]
  65. de Azevedo, D.S.; Salinet, A.S.M.; de Lima Oliveira, M.; Teixeira, M.J.; Bor-Seng-Shu, E.; de Carvalho Nogueira, R. Cerebral Hemodynamics in Sepsis Assessed by Transcranial Doppler: A Systematic Review and Meta-Analysis. J. Clin. Monit. Comput. 2017, 31, 1123–1132. [Google Scholar] [CrossRef]
  66. Crippa, I.A.; Subirà, C.; Vincent, J.-L.; Fernandez, R.F.; Hernandez, S.C.; Cavicchi, F.Z.; Creteur, J.; Taccone, F.S. Impaired Cerebral Autoregulation Is Associated with Brain Dysfunction in Patients with Sepsis. Crit. Care 2018, 22, 327. [Google Scholar] [CrossRef] [PubMed]
  67. Al-Kawaz, M.; Cho, S.-M.; Gottesman, R.F.; Suarez, J.I.; Rivera-Lara, L. Impact of Cerebral Autoregulation Monitoring in Cerebrovascular Disease: A Systematic Review. Neurocrit. Care 2022, 36, 1053–1070. [Google Scholar] [CrossRef]
  68. Budohoski, K.P.; Czosnyka, M.; Smielewski, P.; Varsos, G.V.; Kasprowicz, M.; Brady, K.M.; Pickard, J.D.; Kirkpatrick, P.J. Monitoring Cerebral Autoregulation After Subarachnoid Hemorrhage. In Intracranial Pressure and Brain Monitoring XV; Springer: Cham, Switzerland, 2016; pp. 199–203. [Google Scholar]
  69. Budohoski, K.P.; Czosnyka, M.; Smielewski, P.; Kasprowicz, M.; Helmy, A.; Bulters, D.; Pickard, J.D.; Kirkpatrick, P.J. Impairment of Cerebral Autoregulation Predicts Delayed Cerebral Ischemia After Subarachnoid Hemorrhage. Stroke 2012, 43, 3230–3237. [Google Scholar] [CrossRef]
  70. Soehle, M.; Czosnyka, M.; Pickard, J.D.; Kirkpatrick, P.J. Continuous Assessment of Cerebral Autoregulation in Subarachnoid Hemorrhage. Anesth. Analg. 2004, 98, 1133–1139. [Google Scholar] [CrossRef]
  71. Brown, C.H.; Neufeld, K.J.; Tian, J.; Probert, J.; LaFlam, A.; Max, L.; Hori, D.; Nomura, Y.; Mandal, K.; Brady, K.; et al. Effect of Targeting Mean Arterial Pressure During Cardiopulmonary Bypass by Monitoring Cerebral Autoregulation on Postsurgical Delirium Among Older Patients. JAMA Surg. 2019, 154, 819. [Google Scholar] [CrossRef]
  72. Liu, X.; Donnelly, J.; Brady, K.M.; Akiyoshi, K.; Bush, B.; Koehler, R.C.; Lee, J.K.; Hogue, C.W.; Czosnyka, M.; Smielewski, P.; et al. Comparison of Different Metrics of Cerebral Autoregulation in Association with Major Morbidity and Mortality after Cardiac Surgery. Br. J. Anaesth. 2022, 129, 22–32. [Google Scholar] [CrossRef] [PubMed]
  73. Nakano, M.; Nomura, Y.; Whitman, G.; Sussman, M.; Schena, S.; Kilic, A.; Choi, C.W.; Akiyoshi, K.; Neufeld, K.J.; Lawton, J.; et al. Cerebral Autoregulation in the Operating Room and Intensive Care Unit after Cardiac Surgery. Br. J. Anaesth. 2021, 126, 967–974. [Google Scholar] [CrossRef] [PubMed]
  74. Rikhraj, K.J.K.; Wood, M.D.; Hoiland, R.L.; Thiara, S.; Griesdale, D.E.G.; Sekhon, M.S. Determining Optimal Mean Arterial Pressure After Cardiac Arrest: A Systematic Review. Neurocrit. Care 2021, 34, 621–634. [Google Scholar] [CrossRef]
  75. Longhitano, Y.; Iannuzzi, F.; Bonatti, G.; Zanza, C.; Messina, A.; Godoy, D.; Dabrowski, W.; Xiuyun, L.; Czosnyka, M.; Pelosi, P.; et al. Cerebral Autoregulation in Non-Brain Injured Patients: A Systematic Review. Front. Neurol. 2021, 12, 732176. [Google Scholar] [CrossRef] [PubMed]
  76. Caldas, J.R.; Haunton, V.J.; Panerai, R.B.; Hajjar, L.A.; Robinson, T.G. Cerebral Autoregulation in Cardiopulmonary Bypass Surgery: A Systematic Review. Interact. Cardiovasc. Thorac. Surg. 2018, 26, 494–503. [Google Scholar] [CrossRef]
  77. Santos, D.P.D.A.D.; Thirumala, P.D.; Reddy, G.; Barros, D.F.D.; Faria, V.N.R.; Shandal, V.; Kurtz, P. Risk of Perioperative Stroke and Cerebral Autoregulation Monitoring: A Systematic Review. Arq. Neuropsiquiatr. 2022, 80, 1196–1203. [Google Scholar] [CrossRef]
  78. Rivera-Lara, L.; Zorrilla-Vaca, A.; Geocadin, R.; Ziai, W.; Healy, R.; Thompson, R.; Smielewski, P.; Czosnyka, M.; Hogue, C.W. Predictors of Outcome With Cerebral Autoregulation Monitoring. Crit. Care Med. 2017, 45, 695–704. [Google Scholar] [CrossRef]
  79. de-Lima-Oliveira, M.; Salinet, A.S.M.; Nogueira, R.C.; de Azevedo, D.S.; Paiva, W.S.; Teixeira, M.J.; Bor-Seng-Shu, E. Intracranial Hypertension and Cerebral Autoregulation: A Systematic Review and Meta-Analysis. World Neurosurg. 2018, 113, 110–124. [Google Scholar] [CrossRef]
Medicina 60 01381 g001
Figure 1. PRISMA literature review flow diagram.
Figure 1. PRISMA literature review flow diagram.
Medicina 60 01381 g001
Table 1. Studies analyzed.
Table 1. Studies analyzed.
StudyType of StudyMain FindingSettingStudy PopulationMonitoring MethodManeuverMain LimitationAutoregulation Indices
Ameloot K et al. [29]Prospective observational studyCBFA disturbed in one-third CA patients, correlated with worse outcomes. Time spent under optimal MAP is negatively associated with survivalTertiary care hospital51 patients after CANIRS, invasive blood pressure monitoring/In a small sample size, COX predicted optimal MAP as an average value,
insufficient data on drugs used		COx
Nishizawa H et al. [30] Prospective interventional study CBFA impaired in patients after CADepartment of Anesthesiology, University school of medicineEight patients after CAA catheter was inserted percutaneously into the right internal jugular vein, the tip positioned in the jugular bulb for venous blood gas measurement. Arterial pressure measurement and arterial blood gas samples were obtained MAP changed to a value of 30% lower or higher than baseline by infusing trimethaphan or methoxamineSmall sample sizeArterial-jugular bulb venous oxygen content difference (AVDO2) calculated at each MAP level. After that, 1/AVDO2, cerebral blood flow indices (CBFI) were calculated. The
changes in CBFI and oxygen saturation of jugular venous blood seen after the decrease or increase in
MAP indicates impairment of CBFA
Sundgreen C et al. [31]Case-control interventional studyCBFA is absent or right-shifted in the majority of patients after CAUniversity Hospital ICU18 patients after CA in and out of hospital, 6 healthy control subjectsTCD, invasive blood pressure monitoringA stepwise rise in MAP by use of norepinephrine infusionSmall sample sizeMAP was plotted against the mean Fv,
and a possible lower limit of autoregulation was identified
Sekhon MS et al. [32]Prospective interventional studyA
linear relationship between increased MAP and PBTO2
in HIBI patients. Perfusion within proximity
of optimal MAP may be associated with
improved PBTO2		Quaternary ICUTen patients after CAMultimodal: brain tissue oxygenation, intracranial
pressure, jugular venous continuous oximetry, NIRS		/Small sample size, definition of brain hypoxia from the traumatic brain injury literature, patient cohort with pulseless circulatory arrestPRx
Sekhon MS et al. [33]Proof-of-concept feasibility studyFeasibility of determining optimal MAP using cerebral oximetry in patients after CAGeneral hospital ICU20 patients after CANIRS, invasive blood pressure monitoring/Proof of concept study, no granular data on carbon dioxideCOx
Pham P et al. [34]Case-control studyEarly impairment of CBFA after CA independently associated with mortality at three months follow-upsGeneral hospital ICU23 patients after CA, 28 healthy volunteersNIRS, invasive blood pressure monitoring, fino-meter for noninvasive blood pressure monitoring in healthy volunteersBlood pressure changes induced in healthy volunteers
through bed-tilt positional changes, Valsalva maneuvers and short
immersion of one hand in ice-cold water		Small sample size, intermittent measurement TOx
Van den Brule JMD et al. [35] Prospective observational studyChanges in experimental settings strongly influence the results
of estimation of CBFA		University Hospital ICU13 patients after CATCD, invasive blood pressure monitoringRepeated changes in the position of the bed from horizontal to a maximum of 30 degrees Trendelenburg and 30 degrees anti-TrendelenburgSmall sample size, changes in quality of the recorded signal, the influence of bed position on venous outflowCBFA was calculated in the time domain (Mx) and frequency domain (transfer function analysis)
Laurikkala J et al. [36]Post-hoc analysis of multicenter srandomised controlled pilot studyImpaired cerebrovascular reactivity is common after CA, especially in patients with chronic hypertension. Decreased upper MAP bound and a narrower MAP range for maintained cerebrovascular reactivity associated with poor outcome, severe brain injury assessed by NfLSix ICUs in Finland and one ICU in
Denmark		120 patients after CANIRS, invasive blood pressure monitoring/Limitations of NIRS and TOx to monitor cerebrovascular reactivityTOx
Bindra J et al. [37]Prospective observational studyDynamic CBFA can be continuously assessed noninvasivelyGeneral hospital ICUDiverse cohort, including 1 patient after CANIRS, invasive blood pressure monitoring, finometer for noninvasive blood pressure monitoring/Small sample size, finometer being operator dependedCBFA as a correlation coefficient between invasive arterial blood pressure and rSO2 (iTOx) or noninvasive arterial blood pressure
and rSO2 (nTOx)
Calviello LA et al. [38]Prospective studyMultiparameter TCD neuromonitoring increases outcome predictive power. TCD-based indices can be applied to general intensive care monitoringUniversity Hospital ICUDiverse cohort, including 14 patients after CATCD and ABP were continuously monitored non-invasively using a finger probe or a pressure monitoring kit/Small sample size, preliminary results, patients not separated by condition Mx
Balu R et al. [39]Retrospective observational studyCerebrovascular pressure reactivity and ICP appear to be associated with neurologic outcomes in patients with HIBINCCU at University HospitalDiverse cohort, including 32 patients after CAICP monitoring and interstitial brain tissue oxygen measurements through quad-lumen bolt invasive blood pressure monitoring /Single-center
retrospective design, selection
and indication biases, monitoring only the frontal lobe, temporal trends not analyzed, not using cerebral performance score as an outcome 		PRx
Hazenberg L et al. [40]Prospective observational study No differences between left and right-sided NIRS recordings or CBFA estimation in HIBI patientsICU University Medical Center11 patients after CANIRS, invasive blood pressure monitoring/Small sample size, short monitoring time, possibility of local pathologyCOx
Griesdale DEG et al. [41]Prospective multicenter cohort studyIt is feasible to recruit and collect high-frequency physiological data in patients after CA. Time below optimal MAP and duration of dysfunctional CBFA not associated with an unfavorable neurologic outcomeICUs in three teaching hospitals59 patients after CANIRS, invasive blood pressure monitoring/Underpowered analyses, inability to control for potential confoundsCOx
Rivera-Lara L et al. [42] Prospective observational cohort studyMonitoring CBFA with NIRS-derived COx is correlated and agrees well with previously validated TCD-based methodsNCCU at the teaching hospitalDiverse cohort, including one patient after CANIRS, TCD, invasive blood pressure monitoring/Small sample size, heterogeneous lesions in a population with acute comaCOx, Mx
Crippa IA et al. [43]Retrospective analysis of prospectively collected dataCBFA is frequently altered in CA patients treated by targeted temperature management. Altered CBFA during normothermia was independently associated with poor outcomeDepartment of intensive care at University hospital50 patients after CATCD, invasive blood pressure monitoring/Cerebrovascular resistance or absolute CBF values were not measured directly MAP in relation to individual CBFA curves not investigated Mx
Tachino J et al. [44]A prospective, observational cohort studyMortality increased significantly with longer non-CBFA time within 96 h after the return of spontaneous circulationTrauma and Acute Critical Care Center100 consecutive patients after CANIRS, invasive blood pressure monitoring/Observational research and differences in patient characteristics and clinical protocol did not account for the potential influence of targeted temperature control and CO2 on cerebral circulationCOx
Abbreviations: NCCU: Neurocritical care unit, ICU: Intensive care unit, CA: cardiac arrest, NIRS: near-infrared spectroscopy, TCD: transcranial Doppler, ICP: intracranial pressure, ABP: arterial blood pressure, MAP: mean arterial pressure, CBFA: Cerebral blood flow autoregulation, rSO2: regional saturation of oxygen, Fv: blood flow velocity, Mx: mean flow index, COx: cerebral oximetry index, TOx: tissue oxygenation reactivity index, PRx: Pressure reactivity index, PBTO2: pressure of brain tissue oxygen, HIBI: hypoxic-ischemic brain injury, NfL: neurofilament light chain.
Table 2. Monitoring methods used in research.
Table 2. Monitoring methods used in research.
Monitoring MethodNumber of Research
NIRS8
TCD4
ICP and interstitial brain tissue oxygen measurements through quad-lumen bolt1
Internal jugular vein catheter1
Multimodal2
Abbreviations: NIRS: near-infrared spectroscopy, TCD: transcranial Doppler, ICP: intracranial pressure.
Table 3. Mathematical models used in research.
Table 3. Mathematical models used in research.
Mathematical ModelNumber of Research
COx5
TOx
Mx3
PRx2
Multimodal1
Other2
Abbreviations: COx: cerebral oximetry index, TOx: tissue oxygenation reactivity index, Mx: mean flow index, PRx—pressure reactivity index.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Petrovčič, R.; Rakusa, M.; Markota, A. Monitoring of Cerebral Blood Flow Autoregulation after Cardiac Arrest. Medicina 2024, 60, 1381. https://doi.org/10.3390/medicina60091381

AMA Style

Petrovčič R, Rakusa M, Markota A. Monitoring of Cerebral Blood Flow Autoregulation after Cardiac Arrest. Medicina. 2024; 60(9):1381. https://doi.org/10.3390/medicina60091381

Chicago/Turabian Style

Petrovčič, Rok, Martin Rakusa, and Andrej Markota. 2024. "Monitoring of Cerebral Blood Flow Autoregulation after Cardiac Arrest" Medicina 60, no. 9: 1381. https://doi.org/10.3390/medicina60091381

Article Metrics

Back to TopTop