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Figure 1.
Calculating Optimal Blood Pressure and Limits of Autoregulation
Calculating Optimal Blood Pressure and Limits of Autoregulation

A, A 48-hour continuous recording of mean arterial pressure (MAP) and near-infrared spectroscopy–derived tissue oxygenation index (TOI) in a patient with large-vessel occlusion ischemic stroke. The second trend shows a time series of the tissue oxygenation autoregulatory index (TOx), calculated as a rolling Pearson correlation coefficient between 30 successive, time-averaged values of MAP and TOI. B, The limits of autoregulation were calculated by dividing MAP values into groups of 5 mm Hg and plotting them against corresponding TOx indices over a 4-hour monitoring period, resulting in a characteristic U-shaped curve on ICM+ software (University of Cambridge). By superimposing a threshold for impaired autoregulation (TOx = 0.30), the intersecting MAP values provide estimates of the lower and upper limits of autoregulation (LLA and ULA, respectively). The vertex of the curve corresponds to the MAP with the most preserved autoregulation (ie, MAPOPT). The orange shaded area indicates optimal cerebral blood flow (CBF). C, A continuous time trend of optimal MAP (orange line), ULA, and LLA (red lines surrounding MAPOPT) can be calculated in this manner, while superimposing the patient’s blood pressure (black line) in real time. This trend provides clinicians with a dynamically updating, visual MAP target. The gray shaded areas (vertical panels) highlight times the patient’s blood pressure deviated from personalized limits of autoregulation.

Figure 2.
Associations of Individualized Limits of Autoregulation With Functional Outcome and Hemorrhagic Transformation
Associations of Individualized Limits of Autoregulation With Functional Outcome and Hemorrhagic Transformation

A and B, The percentage of time spent above the upper limit of autoregulation (ULA) (% time > ULA) was plotted per each modified Rankin scale (mRS) score at discharge and 90 days. C, The percentage of time above the ULA is shown among degrees of hemorrhagic transformation (HT) and symptomatic intracerebral hemorrhage (sICH). The open circles represent the patient’s percentage of time outside LA outside the 95% confidence interval. HI indicates hemorrhagic infarction; MAP, mean arterial pressure; PH, parenchymal hematoma.

aIndicates significant differences.

1.
Goyal  M, Menon  BK, van Zwam  WH,  et al; HERMES collaborators.  Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials.  Lancet. 2016;387(10029):1723-1731. doi:10.1016/S0140-6736(16)00163-XPubMedGoogle ScholarCrossref
2.
Wang  A, Sheth  K, Marshall  R, Mampre  D, Hebert  R, Petersen  N.  Autoregulation-based blood pressure optimization after large-vessel ischemic stroke using non-invasive near-infrared spectroscopy monitoring (S21.008) [published online April 10, 2018].  Neurol. 2018;90(suppl 15). https://n.neurology.org/content/90/15_Supplement/S21.008. Accessed April 10, 2018.Google Scholar
3.
Donnelly  J, Czosnyka  M, Adams  H,  et al.  Individualizing thresholds of cerebral perfusion pressure using estimated limits of autoregulation.  Crit Care Med. 2017;45(9):1464-1471. doi:10.1097/CCM.0000000000002575PubMedGoogle ScholarCrossref
4.
van Mook  WNKA, Rennenberg  RJMW, Schurink  GW,  et al.  Cerebral hyperperfusion syndrome.  Lancet Neurol. 2005;4(12):877-888. doi:10.1016/S1474-4422(05)70251-9PubMedGoogle ScholarCrossref
5.
Galyfos  G, Sianou  A, Filis  K.  Cerebral hyperperfusion syndrome and intracranial hemorrhage after carotid endarterectomy or carotid stenting: a meta-analysis.  J Neurol Sci. 2017;381:74-82. doi:10.1016/j.jns.2017.08.020PubMedGoogle ScholarCrossref
6.
Hashimoto  T, Matsumoto  S, Ando  M, Chihara  H, Tsujimoto  A, Hatano  T.  Cerebral hyperperfusion syndrome after endovascular reperfusion therapy in a patient with acute internal carotid artery and middle cerebral artery occlusions.  World Neurosurg. 2018;110:145-151. doi:10.1016/j.wneu.2017.11.023PubMedGoogle ScholarCrossref
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Research Letter
July 29, 2019

Association of Personalized Blood Pressure Targets With Hemorrhagic Transformation and Functional Outcome After Endovascular Stroke Therapy

Author Affiliations
  • 1Department of Neurology, Yale University School of Medicine, New Haven, Connecticut
  • 2Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut
JAMA Neurol. 2019;76(10):1256-1258. doi:10.1001/jamaneurol.2019.2120

The loss of cerebral autoregulation in the acute phase of ischemic stroke leaves patients vulnerable to changes in blood pressure (BP),1 potentially resulting in additional injury from relative hypoperfusion or hyperperfusion. We have shown that near-infrared spectroscopy can be used to identify and track the BP range in individual patients at which autoregulation is optimally functioning.2 Such an autoregulation-derived, personalized BP range may provide a favorable physiologic environment for the injured brain. In this study, we used a novel approach to define and trend limits of autoregulation (LA) to determine patient-specific, dynamic BP targets. The aim of this study was to assess the feasibility of determining personalized BP targets as well as the association of deviating from these targets with radiographic and clinical outcomes.

Methods

We conducted a single-center prospective cohort study of patients with large-vessel occlusion ischemic stroke undergoing endovascular therapy (ET). Approval for the study was obtained from the Yale University human investigations committee. All patients or their legally authorized representatives provided written informed consents. Autoregulatory function was continuously measured for 24 hours following thrombectomy by interrogating changes in near-infrared spectroscopy–derived tissue oxygenation, a surrogate for cerebral blood flow, in response to changes in mean arterial pressure (MAP) using time-correlation analysis (Figure 1). The resulting autoregulatory index was used to trend the BP range at which autoregulation was most preserved, as previously described.3 The percentage of time that MAP exceeded the upper LA (ULA) was calculated for each patient and its association with outcomes on the modified Rankin scale was assessed using a multivariable ordinal logistic regression. All statistics were computed using SPSS, version 24 (IBM Corp), and statistical significance was set at 2-tailed P < .05.

Results

We enrolled 65 patients (mean [SD] age, 71.6 [16.5] years; 30 women [46%]; mean [SD] National Institutes of Health Stroke Scale score, 14.3 [6.2]; mean [SD] monitoring time, 25.6 [16.5] hours). Optimal BP and LA were calculated for 86.3% of the total monitoring period. Adjusting for age, admission National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT score, and the degree of reperfusion, the percentage of time above the ULA (% time > ULA) was independently associated with higher (worse) modified Rankin scale scores at discharge (adjusted odds ratio [OR] per 10% time >ULA, 1.6; 95% CI, 1.1-2.5, P = .03) and 90 days (adjusted OR per 10% time >ULA, 2.4; 95% CI, 1.4-4.1, P = .001; Figure 2A and B).

Hemorrhagic transformation (HT) was seen in 30 patients (46.1%) and was overall associated with poor outcomes. We observed a progressive increase in the percentage of time above the ULA with worsening grades of HT (9% for no HT, 13.5% for hemorrhagic infarction 1 and 2, and 20.9% for parenchymal hematoma 1 and 2; P = .01; Figure 2C). In addition, patients who developed symptomatic intracerebral hemorrhage spent significantly more time above the ULA compared with patients without symptomatic intracerebral hemorrhage (11% vs 24.6%; P = 0.01). In a binary logistic regression, the percentage of time above the ULA was significantly associated with HT (OR, 1.75; 95% CI, 1.11-2.78; P = .02).

Discussion

We showed that a continuous, noninvasive estimation of personalized BP targets is feasible and that exceeding individualized thresholds of autoregulation is associated with HT and worse functional outcomes even after adjusting for prognostic covariates. To our knowledge, there are no randomized clinical trials of optimal BP management after ET and data to guide treatment approaches are limited. Most patients enrolled in thrombectomy trials also received intravenous tissue plasminogen activator and were treated according to current guidelines of a BP of less than 180/105 mm Hg for 24 hours. However, recanalization rates with ET are much higher, and it remains unclear if the same BP target applies. Once recanalization is achieved, BP management above the ULA may lead to reperfusion injury with consequent development of cerebral edema and hemorrhage. This phenomenon is well described after carotid revascularization4,5 but may also occur in acute stroke.6 Accordingly, several thrombectomy trials aimed for lower BP targets if successful reperfusion was achieved. However, the optimal BP range after ET is likely associated with numerous factors, and stratifying by reperfusion status alone may not be sufficient. Further research is needed to test autoregulation-based treatment strategies, including tailored pharmacologic BP augmentation and lowering therapies based on patients’ real-time autoregulatory status.

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Article Information

Accepted for Publication: May 24, 2019.

Corresponding Author: Nils H. Petersen, MD, Department of Neurology, Yale Medical School, 15 York St, New Haven, CT 06510 (nils.petersen@yale.edu).

Published Online: July 29, 2019. doi:10.1001/jamaneurol.2019.2120

Author Contributions: Dr Petersen and Mr Silverman had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Petersen and Mr Silverman contributed equally to the manuscript.

Concept and design: Petersen, Sheth.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Petersen, Silverman.

Critical revision of the manuscript for important intellectual content: Wang, Strander, Kodali, Matouk, Sheth.

Statistical analysis: Petersen, Silverman, Wang, Strander, Kodali.

Obtained funding: Petersen, Sheth.

Administrative, technical, or material support: Strander, Sheth.

Supervision: Petersen, Wang, Matouk, Sheth.

Conflict of Interest Disclosures: Dr Petersen reported grants from National Center for Advancing Translational Science (NCATS) of the National Institutes of Health (NIH) and the American Heart Association (AHA) during the conduct of the study. Dr Sheth reported grants from Biogen, Bard, Hyperfine, and the NIH outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by AHA grant 17MCPRP33460188/Nils Petersen/2017 and Clinical and Translational Science Awards grant KL2 TR001862 from NCATS.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of AHA or NIH.

Additional Contributions: We thank the following individuals who contributed a critical review of our article: Lauren Sansing, MD, Joseph Schindler, MD, Guido Falcone, MD, Emily Gilmore, MD, and Adam Jasne, MD (Department of Neurology, Yale School of Medicine), Branden Cord, MD, and Ryan Hebert, MD (Department of Neurosurgery, Yale School of Medicine), and Michele Johnson, MD (Department of Radiology, Yale School of Medicine). None of these individuals were compensated for their contributions.

References
1.
Goyal  M, Menon  BK, van Zwam  WH,  et al; HERMES collaborators.  Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials.  Lancet. 2016;387(10029):1723-1731. doi:10.1016/S0140-6736(16)00163-XPubMedGoogle ScholarCrossref
2.
Wang  A, Sheth  K, Marshall  R, Mampre  D, Hebert  R, Petersen  N.  Autoregulation-based blood pressure optimization after large-vessel ischemic stroke using non-invasive near-infrared spectroscopy monitoring (S21.008) [published online April 10, 2018].  Neurol. 2018;90(suppl 15). https://n.neurology.org/content/90/15_Supplement/S21.008. Accessed April 10, 2018.Google Scholar
3.
Donnelly  J, Czosnyka  M, Adams  H,  et al.  Individualizing thresholds of cerebral perfusion pressure using estimated limits of autoregulation.  Crit Care Med. 2017;45(9):1464-1471. doi:10.1097/CCM.0000000000002575PubMedGoogle ScholarCrossref
4.
van Mook  WNKA, Rennenberg  RJMW, Schurink  GW,  et al.  Cerebral hyperperfusion syndrome.  Lancet Neurol. 2005;4(12):877-888. doi:10.1016/S1474-4422(05)70251-9PubMedGoogle ScholarCrossref
5.
Galyfos  G, Sianou  A, Filis  K.  Cerebral hyperperfusion syndrome and intracranial hemorrhage after carotid endarterectomy or carotid stenting: a meta-analysis.  J Neurol Sci. 2017;381:74-82. doi:10.1016/j.jns.2017.08.020PubMedGoogle ScholarCrossref
6.
Hashimoto  T, Matsumoto  S, Ando  M, Chihara  H, Tsujimoto  A, Hatano  T.  Cerebral hyperperfusion syndrome after endovascular reperfusion therapy in a patient with acute internal carotid artery and middle cerebral artery occlusions.  World Neurosurg. 2018;110:145-151. doi:10.1016/j.wneu.2017.11.023PubMedGoogle ScholarCrossref
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