Effect of Targeting Mean Arterial Pressure During Cardiopulmonary Bypass by Monitoring Cerebral Autoregulation on Postsurgical Delirium Among Older Patients: A Nested Randomized Clinical Trial | Cardiothoracic Surgery | JAMA Surgery | JAMA Network
[Skip to Content]
[Skip to Content Landing]
Figure 1.  Patient Enrollment
Patient Enrollment
Figure 2.  Delirium Incidence by Randomization Group
Delirium Incidence by Randomization Group

Mean arterial pressure during cardiopulmonary bypass was managed according to standard care or autoregulation-targeted goals. Delirium incidence by randomization group is shown.

Table 1.  Patient and Perioperative Characteristics for Patients Randomized to Standard Care vs Autoregulation-Targeted Management of Mean Arterial Pressure During Cardiopulmonary Bypass
Patient and Perioperative Characteristics for Patients Randomized to Standard Care vs Autoregulation-Targeted Management of Mean Arterial Pressure During Cardiopulmonary Bypass
Table 2.  Characteristics of Management During Cardiopulmonary Bypass for Patients Randomized to Standard Care vs Autoregulation-Targeted Management of Mean Arterial Pressure
Characteristics of Management During Cardiopulmonary Bypass for Patients Randomized to Standard Care vs Autoregulation-Targeted Management of Mean Arterial Pressure
Table 3.  Delirium Outcomes by Treatment Group
Delirium Outcomes by Treatment Group
1.
Rudolph  JL, Jones  RN, Levkoff  SE,  et al.  Derivation and validation of a preoperative prediction rule for delirium after cardiac surgery.  Circulation. 2009;119(2):229-236. doi:10.1161/CIRCULATIONAHA.108.795260PubMedGoogle ScholarCrossref
2.
Martin  BJ, Buth  KJ, Arora  RC, Baskett  RJ.  Delirium as a predictor of sepsis in post-coronary artery bypass grafting patients: a retrospective cohort study.  Crit Care. 2010;14(5):R171. doi:10.1186/cc9273PubMedGoogle ScholarCrossref
3.
Brown  CH  IV, Laflam  A, Max  L,  et al.  The impact of delirium after cardiac surgical procedures on postoperative resource use.  Ann Thorac Surg. 2016;101(5):1663-1669. doi:10.1016/j.athoracsur.2015.12.074PubMedGoogle ScholarCrossref
4.
Brown  CH  IV, Probert  J, Healy  R,  et al.  Cognitive decline after delirium in patients undergoing cardiac surgery.  Anesthesiology. 2018;129(3):406-416. doi:10.1097/ALN.0000000000002253PubMedGoogle ScholarCrossref
5.
Gottesman  RF, Grega  MA, Bailey  MM,  et al.  Delirium after coronary artery bypass graft surgery and late mortality.  Ann Neurol. 2010;67(3):338-344.PubMedGoogle Scholar
6.
Brady  K, Joshi  B, Zweifel  C,  et al.  Real-time continuous monitoring of cerebral blood flow autoregulation using near-infrared spectroscopy in patients undergoing cardiopulmonary bypass.  Stroke. 2010;41(9):1951-1956. doi:10.1161/STROKEAHA.109.575159PubMedGoogle ScholarCrossref
7.
Joshi  B, Ono  M, Brown  C,  et al.  Predicting the limits of cerebral autoregulation during cardiopulmonary bypass.  Anesth Analg. 2012;114(3):503-510. doi:10.1213/ANE.0b013e31823d292aPubMedGoogle ScholarCrossref
8.
Hori  D, Ono  M, Rappold  TE,  et al.  Hypotension after cardiac operations based on autoregulation monitoring leads to brain cellular injury.  Ann Thorac Surg. 2015;100(2):487-493. doi:10.1016/j.athoracsur.2015.03.036PubMedGoogle ScholarCrossref
9.
Ono  M, Brady  K, Easley  RB,  et al.  Duration and magnitude of blood pressure below cerebral autoregulation threshold during cardiopulmonary bypass is associated with major morbidity and operative mortality.  J Thorac Cardiovasc Surg. 2014;147(1):483-489. doi:10.1016/j.jtcvs.2013.07.069PubMedGoogle ScholarCrossref
10.
Wesselink  EM, Kappen  TH, van Klei  WA, Dieleman  JM, van Dijk  D, Slooter  AJ.  Intraoperative hypotension and delirium after on-pump cardiac surgery.  Br J Anaesth. 2015;115(3):427-433. doi:10.1093/bja/aev256PubMedGoogle ScholarCrossref
11.
Rudiger  A, Begdeda  H, Babic  D,  et al.  Intra-operative events during cardiac surgery are risk factors for the development of delirium in the ICU.  Crit Care. 2016;20:264. doi:10.1186/s13054-016-1445-8PubMedGoogle ScholarCrossref
12.
Siepe  M, Pfeiffer  T, Gieringer  A,  et al.  Increased systemic perfusion pressure during cardiopulmonary bypass is associated with less early postoperative cognitive dysfunction and delirium.  Eur J Cardiothorac Surg. 2011;40(1):200-207. doi:10.1016/j.ejcts.2010.11.024PubMedGoogle ScholarCrossref
13.
Palmbergen  WA, van Sonderen  A, Keyhan-Falsafi  AM, Keunen  RW, Wolterbeek  R.  Improved perioperative neurological monitoring of coronary artery bypass graft patients reduces the incidence of postoperative delirium: the Haga Brain Care Strategy.  Interact Cardiovasc Thorac Surg. 2012;15(4):671-677. doi:10.1093/icvts/ivs317PubMedGoogle ScholarCrossref
14.
McKhann  GM, Grega  MA, Borowicz  LM  Jr,  et al.  Encephalopathy and stroke after coronary artery bypass grafting: incidence, consequences, and prediction.  Arch Neurol. 2002;59(9):1422-1428. doi:10.1001/archneur.59.9.1422PubMedGoogle Scholar
15.
Ono  M, Joshi  B, Brady  K,  et al.  Risks for impaired cerebral autoregulation during cardiopulmonary bypass and postoperative stroke.  Br J Anaesth. 2012;109(3):391-398. doi:10.1093/bja/aes148PubMedGoogle ScholarCrossref
16.
Ono  M, Arnaoutakis  GJ, Fine  DM,  et al.  Blood pressure excursions below the cerebral autoregulation threshold during cardiac surgery are associated with acute kidney injury.  Crit Care Med. 2013;41(2):464-471. doi:10.1097/CCM.0b013e31826ab3a1PubMedGoogle ScholarCrossref
17.
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA: American Psychiatric Association; 2013.
18.
Trzepacz  PT, Mittal  D, Torres  R, Kanary  K, Norton  J, Jimerson  N.  Validation of the Delirium Rating Scale-Revised-98: comparison with the delirium rating scale and the cognitive test for delirium.  J Neuropsychiatry Clin Neurosci. 2001;13(2):229-242. doi:10.1176/jnp.13.2.229PubMedGoogle ScholarCrossref
19.
Inouye  SK, van Dyck  CH, Alessi  CA, Balkin  S, Siegal  AP, Horwitz  RI.  Clarifying confusion: the confusion assessment method: a new method for detection of delirium.  Ann Intern Med. 1990;113(12):941-948. doi:10.7326/0003-4819-113-12-941PubMedGoogle ScholarCrossref
20.
Ely  EW, Margolin  R, Francis  J,  et al.  Evaluation of delirium in critically ill patients: validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU).  Crit Care Med. 2001;29(7):1370-1379. doi:10.1097/00003246-200107000-00012PubMedGoogle ScholarCrossref
21.
Inouye  SK, Leo-Summers  L, Zhang  Y, Bogardus  ST  Jr, Leslie  DL, Agostini  JV.  A chart-based method for identification of delirium: validation compared with interviewer ratings using the confusion assessment method.  J Am Geriatr Soc. 2005;53(2):312-318. doi:10.1111/j.1532-5415.2005.53120.xPubMedGoogle ScholarCrossref
22.
Sessler  CN, Gosnell  MS, Grap  MJ,  et al.  The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients.  Am J Respir Crit Care Med. 2002;166(10):1338-1344.PubMedGoogle ScholarCrossref
23.
Robinson  TN, Raeburn  CD, Tran  ZV, Angles  EM, Brenner  LA, Moss  M.  Postoperative delirium in the elderly: risk factors and outcomes.  Ann Surg. 2009;249(1):173-178. doi:10.1097/SLA.0b013e31818e4776PubMedGoogle ScholarCrossref
24.
Meagher  DJ, Morandi  A, Inouye  SK,  et al.  Concordance between DSM-IV and DSM-5 criteria for delirium diagnosis in a pooled database of 768 prospectively evaluated patients using the delirium rating scale-revised-98.  BMC Med. 2014;12:164. doi:10.1186/s12916-014-0164-8PubMedGoogle ScholarCrossref
25.
Pandharipande  PP, Pun  BT, Herr  DL,  et al.  Effect of sedation with dexmedetomidine vs lorazepam on acute brain dysfunction in mechanically ventilated patients: the MENDS randomized controlled trial.  JAMA. 2007;298(22):2644-2653. doi:10.1001/jama.298.22.2644PubMedGoogle ScholarCrossref
26.
Folstein  MF, Folstein  SE, McHugh  PR.  “Mini-Mental State”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198.PubMedGoogle ScholarCrossref
27.
Roques  F, Michel  P, Goldstone  AR, Nashef  SA.  The logistic EuroSCORE.  Eur Heart J. 2003;24(9):881-882.PubMedGoogle ScholarCrossref
28.
Lassen  NA.  Cerebral blood flow and oxygen consumption in man.  Physiol Rev. 1959;39(2):183-238. doi:10.1152/physrev.1959.39.2.183PubMedGoogle ScholarCrossref
29.
Inouye  SK, Westendorp  RG, Saczynski  JS.  Delirium in elderly people.  Lancet. 2014;383(9920):911-922. doi:10.1016/S0140-6736(13)60688-1PubMedGoogle ScholarCrossref
30.
Chan  B, Aneman  A.  A prospective, observational study of cerebrovascular autoregulation and its association with delirium following cardiac surgery.  Anaesthesia. 2018;74(1):33-44. doi:10.1111/anae.14457PubMedGoogle ScholarCrossref
31.
Lee  KF, Wood  MD, Maslove  DM, Muscedere  JG, Boyd  JG.  Dysfunctional cerebral autoregulation is associated with delirium in critically ill adults.  [published online October 8, 2018].  J Cereb Blood Flow Metab. 2018;X18803081. doi:10.1177/0271678X18803081PubMedGoogle Scholar
32.
Hori  D, Brown  C, Ono  M,  et al.  Arterial pressure above the upper cerebral autoregulation limit during cardiopulmonary bypass is associated with postoperative delirium.  Br J Anaesth. 2014;113(6):1009-1017. doi:10.1093/bja/aeu319PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    May 22, 2019

    Effect of Targeting Mean Arterial Pressure During Cardiopulmonary Bypass by Monitoring Cerebral Autoregulation on Postsurgical Delirium Among Older Patients: A Nested Randomized Clinical Trial

    Author Affiliations
    • 1Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
    • 2Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
    • 3Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
    • 4School of Medicine, New York University, New York
    • 5Medical Student, School of Medicine, Tufts University, Medford Massachusetts
    • 6Department of Radiology, Massachusetts General Hospital, Boston
    • 7Department of Cardiovascular Surgery, Saitama Medical Center, Jichi Medical University, Saitama, Japan
    • 8Division of Cardiac Surgery, Department of Surgery, Penn State University Hershey Medical Center, Hershey, Pennsylvania
    • 9Department of Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
    • 10Bluhm Cardiovascular Institute, Department of Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
    JAMA Surg. 2019;154(9):819-826. doi:10.1001/jamasurg.2019.1163
    Key Points

    Question  Does targeting mean arterial pressure during cardiopulmonary bypass by monitoring cerebral autoregulation reduce the incidence of delirium compared with usual care?

    Findings  In this nested randomized clinical trial of 199 participants, the incidence of delirium (excluding coma) was significantly greater in the usual care group (53%) than in the group in which mean arterial pressure was targeted using cerebral autoregulation monitoring (38%).

    Meaning  Targeting mean arterial pressure during cardiopulmonary bypass using cerebral autoregulation monitoring may reduce the incidence of post–cardiac surgery delirium.

    Abstract

    Importance  Delirium occurs in up to 52% of patients after cardiac surgery and may result from changes in cerebral perfusion. Using intraoperative cerebral autoregulation monitoring to individualize and optimize cerebral perfusion may be a useful strategy to reduce the incidence of delirium after cardiac surgery.

    Objective  To determine whether targeting mean arterial pressure during cardiopulmonary bypass (CPB) using cerebral autoregulation monitoring reduces the incidence of delirium compared with usual care.

    Design, Setting, and Participants  This randomized clinical trial nested within a larger trial enrolled patients older than 55 years who underwent nonemergency cardiac surgery at a single US academic medical center between October 11, 2012, and May 10, 2016, and had a high risk for neurologic complications. Patients, physicians, and outcome assessors were masked to the assigned intervention. A total of 2764 patients were screened, and 199 were eligible for analysis in this study.

    Intervention  In the intervention group, the patient’s lower limit of cerebral autoregulation was identified during surgery before CPB. On CPB, the patient’s mean arterial pressure was targeted to be greater than that patient’s lower limit of autoregulation. In the control group, mean arterial pressure targets were determined according to institutional practice.

    Main Outcomes and Measures  The main outcome was any incidence of delirium on postoperative days 1 through 4, as adjudicated by a consensus expert panel.

    Results  Among the 199 participants in this study, mean (SD) age was 70.3 (7.5) years and 150 (75.4%) were male. One hundred sixty-two (81.4%) were white, 26 (13.1%) were black, and 11 (5.5%) were of other race. Of 103 patients randomized to usual care, 94 were analyzed, and of 102 patients randomized to the intervention 105 were analyzed. Excluding 5 patients with coma, delirium occurred in 48 of the 91 patients (53%) in the usual care group compared with 39 of the 103 patients (38%) in the intervention group (P = .04). The odds of delirium were reduced by 45% in patients randomized to the autoregulation group (odds ratio, 0.55; 95% CI, 0.31-0.97; P = .04).

    Conclusions and Relevance  The results of this study suggest that optimizing mean arterial pressure to be greater than the individual patient’s lower limit of cerebral autoregulation during CPB may reduce the incidence of delirium after cardiac surgery, but further study is needed.

    Trial Registration  ClinicalTrials.gov identifier: NCT00981474

    Introduction

    Delirium is common after cardiac surgery, affecting up to 52% of patients, depending on the method of assessment.1 The importance of this complication is increasingly recognized owing to its association with short-term morbidity,2 health resource utilization,3 cognitive decline,4 and long-term mortality.5 However, few intraoperative interventions have been shown to reduce the risk of postoperative delirium.

    In cardiac surgery, reduced cerebral perfusion may be important in the pathogenesis of delirium because extremes of blood pressure are common, with the potential for unrecognized cerebral ischemia especially in patients with clinically known or undiagnosed cerebrovascular disease. The current standard of care for managing blood pressure during cardiopulmonary bypass (CPB) is for mean arterial blood pressure (MAP) targets to be chosen empirically based mostly on historical practices. An alternative individualized method for targeting appropriate MAP has been proposed using real-time, individualized monitoring of cerebral autoregulation,6 a process that maintains cerebral blood flow during changes in MAP to protect the brain from both ischemia and hyperemia. A wide, interindividual range of MAPs at the lower limit of cerebral blood flow autoregulation has been found,7 supporting the hypothesis that hypotension is an individual and not population-based definition. These results further suggest that many patients may be exposed to the risk of hypoperfusion if empirical MAP targets are below the actual lower limit of autoregulation.8,9 Although the contribution of reduced cerebral perfusion to postoperative delirium has been investigated in several observational10,11 and interventional studies12,13 with somewhat conflicting results, earlier studies have used empirical cutoffs of MAP to define hypotension, leaving open the question of whether estimation of adequate cerebral perfusion in those studies was accurate.

    We conducted a nested study within an ongoing randomized clinical trial in which MAP during CPB was targeted to be greater than the lower limit of autoregulation for individual patients. Our primary hypothesis was that individualized targeting of MAP during CPB would reduce the incidence of delirium.

    Methods
    Study Design

    This study followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline. A patient flow diagram is shown in Figure 1. This was a nested prospective study (initiated by C. H. B.) within an ongoing parent clinical trial (primary investigator, C. W. H.). The primary aim of the parent trial was to determine whether targeting MAP during CPB to be greater than an individual’s lower limit of autoregulation would reduce the composite outcome of stroke, cognitive decline, and ischemic brain lesions on postoperative magnetic resonance imaging compared with standard of care (MAP based on usual clinical care). Enrollment for this nested study with delirium as a primary outcome began in 2012, year 4 of the parent study (when C. H. B. started joined the faculty of the Johns Hopkins University School of Medicine), and ended in 2016, when the senior author (C.W.H.) accepted a position at another institution, where the substantially different patient population and delirium assessment team would have made combining results at the 2 institutions difficult. Safety and conduct of the study was overseen by a data and safety monitoring board convened for the parent trial. The trial protocol is available in Supplement 1. This study was approved by the Johns Hopkins institutional review board. Written informed consent was obtained from patients before surgery by study personnel.

    Patients

    Patients at Johns Hopkins Medicine in Baltimore, Maryland, were enrolled between October 11, 2012, and May 10, 2016. Data were analyzed between July 2017 and March 2018.

    Inclusion criteria were age 55 years or older; undergoing primary or reoperative coronary artery bypass graft with or without valvular surgery, or ascending aorta surgery that required CPB; and high risk for neurologic complications (stroke or encephalopathy) as determined by a Johns Hopkins risk score,14 which includes of history of stroke, carotid artery bruit, hypertension, diabetes, and age and generally excluded patients in the lowest quartile of risk. Exclusion criteria were contraindications to magnetic resonance imaging; hepatic dysfunction (aspartate aminotransferase, alanine aminotransferase, or alkaline phosphatase elevated to twice the upper limit of the reference range); chronic renal failure, including requiring dialysis; inability to attend outpatient visits; non–English speaking; severe visual impairment; and emergency surgery. Patients without windows for transcranial Doppler analysis were excluded. Patients with delirium at baseline were also excluded from this substudy. Data on race, defined by patient report or medical record, were collected to examine treatment interactions.

    Randomization and Masking

    Patients were randomized 1:1 using computerized randomization. After enrollment, research assistants accessed the statistical automated website for treatment assignment. Patients, surgeons, anesthesiologists, outcome assessors, and the statistician were masked to the assigned intervention; the perfusionist was not masked.

    Procedures
    Cerebral Autoregulation Monitoring

    Transcranial Doppler monitoring of the middle cerebral arteries (Doppler Box, DWL; Compumedics) was performed using two 2.5-MHz transducers. Digitized arterial blood pressure and transcranial Doppler signals were processed using ICM software (University of Cambridge). Arterial blood pressure and Doppler signals were time-integrated and resampled as 10-second mean values, to remove pulse, rollerhead, and respiratory frequency variations and preserve low-frequency waveforms associated with autoregulatory vascular reactivity. Next, a continuous, moving Pearson correlation coefficient between 30 consecutive, paired MAP and cerebral blood flow velocity values was calculated to generate the mean velocity index (Mx). Mean velocity index was updated every 60 seconds from an overlapping, moving 300-second window and paired with the mean arterial pressure value from the same 300-second window.6 Mean velocity index values are plotted as a function of MAP in 5–mm Hg bins in a continuously updating graph at the bedside. Blood pressure in the autoregulation range is indicated by an Mx value that approaches zero (there is no correlation between flow velocity and MAP), whereas an Mx approaching 1 indicates dysregulated cerebral blood flow (flow velocity and MAP are correlated). The lower limit of autoregulation was determined by the senior author (C.W.H.) before CPB based on the highest MAP where Mx increased from less than 0.4 to 0.4 or greater.15 When Mx did not cross 0.4 clearly, the lower limit of autoregulation was defined as the blood pressure with the lowest Mx (the MAP with the best autoregulation). After the procedure, the product of the magnitude and duration of time that MAP was the lower limit of autoregulation or lower as determined by the senior author was calculated (mm Hg × h).9,16 Thus, information from autoregulation monitoring included MAP at the lower limit of autoregulation, and magnitude and duration of time of MAP less than the lower limit of autoregulation.

    Intervention and Comparison

    In the autoregulation group, the patient’s MAP at the lower limit of autoregulation was determined just before CPB, based on monitoring beginning at the start of the procedure. The patient’s MAP was maintained by perfusionists at above the specified MAP at the lower limit of autoregulation by using phenylephrine boluses (100 µg) and reductions in isoflurane concentrations as needed (generally maintained >0.5%). If MAP was not responsive to phenylephrine, 1-unit boluses of vasopressin could be given followed by infusion of a vasopressor. For high MAP, perfusionists increased isoflurane concentrations up to 1% and administered nitroglycerin. In the standard-care group, the patient’s MAP during CPB was maintained using usual MAP targets, typically greater than 60 mm Hg, using the same protocol. The intervention was discontinued after separation from CPB, because we hypothesized that the most variability in MAP would occur during CPB.

    Perioperative Management

    General anesthesia was induced and maintained with fentanyl (5-20 μg kg−1), propofol (0.5-2.0 mg kg−1), and isoflurane. Cardiopulmonary bypass was performed with a nonocclusive roller pump, a membrane oxygenator, and an arterial line filter 40 μm or less. Nonpulsatile flow was maintained between 2.0 and 2.4 L/min m−2, with α-stat pH management. Partial pressure of carbon dioxide was maintained between 35 and 45 mm Hg. Rewarming was based on institutional standards with a goal of maintaining pharyngeal temperature less than 37°C. Sedation after surgery was maintained with propofol until readiness for extubation or for 24 hours postoperatively.

    Primary Outcome

    The primary outcome of this study was predefined as any incidence of delirium after surgery. Originally, delirium was defined using assessment by research assistants, but the definition was changed to consensus panel diagnosis using Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)17 criteria when additional funding became available. Patients with coma at all assessments were censored. Secondary outcomes included maximum score on the Delirium Rating Scale-Revised-1998 (DRS-R-98)18 severity scale and number of delirium days. These analyses were exploratory, so there was no adjustment for multiple comparisons.

    Delirium was assessed daily on 3 of the first 4 postoperative days using the Confusion Assessment Method (CAM)19 and CAM-ICU,20 followed by consensus panel adjudication using DSM-5 criteria. The CAM assessment was performed by trained research assistants and included a structured cognitive examination and open-ended queries of patients, nurses, families, and medical records for evidence of delirium. For intubated patients in the intensive care unit, the CAM-ICU was used. For days (up to postoperative day 4) on which patients were not assessed in person, a validated medical record review was used.21 Delirium severity was assessed using the DRS-R-98.18 Coma was assessed using the Richmond Agitation Sedation Scale (RASS),22 with a score of –4 or –5 indicating coma. Patients who were comatose on all assessments were classified as having coma in this analysis. The once-daily delirium assessments were limited to the first 4 postoperative days because of evidence that more than 90% of delirium occurs within this time.23

    Delirium assessors underwent formal training by an expert psychiatrist (K.N.), with co-evaluation of patients every 2 weeks. During the study, κ statistics for agreement between assessors were from 0.7 to 0.8, consistent with substantial agreement. For adjudication, all delirium evaluation data were presented by research assistants to a panel with substantial clinical and research expertise in delirium, consisting of 4 consultation psychiatrists (including K.J.N. and O.J.B.) and 1 geriatric psychiatrist. The DSM-5 criteria for delirium were rated separately by each panel member using a standardized approach.24

    Statistical Analysis

    Baseline patient and surgical characteristics were compared using t tests, Wilcoxon rank sum tests, Fisher exact tests, and χ2 tests. The effect of the MAP intervention on the primary outcome was assessed using a χ2 test and logistic regression models. All analyses were based on intention-to-treat principles. Delirium severity was categorized into quintiles, because it was highly skewed. The effect of the intervention on quintile of delirium severity and number of days of delirium was assessed using ordinal logistic regression. Differences in subgroups defined post hoc were assessed using P values for interaction.

    The sample size was calculated before enrollment assuming a delirium incidence of 50%1,3 in the standard group and 30% in the autoregulation group, a difference that we thought to be clinically significant. With 87% power, 122 patients in each randomized group would be required to detect this difference in delirium frequency at a confidence level of 0.05, whereas with 80% power, 103 patients in each group would be required. Enrollment in this study was stopped with analyzable data on 199 patients.

    Sensitivity analyses included (1) adding patients who were comatose at all in-person assessments to the delirium group, because some argue that coma is a severe form of acute brain injury25; (2) adding medical record–review diagnosed delirium to account for days with no in-person assessment; and (3) using the CAM or CAM-ICU assessment of delirium, not the consensus panel adjudication.

    Analyses were conducted using SAS 9.2 (SAS Institute, Inc) with 2-sided hypotheses testing; P <.05 was considered significant.

    Results

    During the study period, 2764 patients were screened for eligibility in the parent trial, of whom 215 patients were enrolled and randomized between October 11, 2012, and May 10, 2016. Delirium assessments were available on 199 patients, of whom 94 patients were randomized into the standard group and 105 patients into the autoregulation group. Of the 199 patients included, mean (SD) age was 70.3 (7.5) years old and 150 (75.4%) were male. One hundred sixty-two (81.4%) were white, 26 (13.1%) were black, and 11 (5.5%) were of other race.

    Patient and surgical characteristics are listed in Table 1. Baseline variables appeared generally similar between the autoregulation and standard groups.

    All patients underwent CPB. As shown in Table 2, mean (SD) flow on CPB was similar between groups (standard care, 4.4 [0.6]; intervention, 4.4 [0.6]), but there was more phenylephrine administered in the intervention group (1.8 [0.5-3.6], vs 1.2 [0.3-2.3] mg). The mean (SD) MAP at the lower limit of autoregulation was 67.3 (11.1) mm Hg (range, 35.0-97.5) and was similar between the standard care (68.7 [11.3] mm Hg) and autoregulation-targeted groups (66.0 [10.9] mm Hg, P = .10). The autoregulation-based intervention was effective in reducing the duration and magnitude that a patient’s MAP was below the individual limit of autoregulation. Overall, 41 of 199 (20.6%) patients had evidence of dysautoregulation, with the average index of autoregulation greater than 0.4 across MAP, a frequency similar to previous observations.15

    The primary delirium outcome by treatment group is listed in Table 3 and shown in Figure 2. The incidence of delirium (excluding coma) was 52.7% (48 of 91) in the standard-treatment group compared with 37.9% (39 of 103) in the autoregulation group (P = .04). The odds of delirium were reduced by 45% in patients randomized to the autoregulation group (odds ratio [OR], 0.55; 95% CI, 0.31-0.97; P = .04).

    Delirium severity was assessed as a secondary exploratory outcome using the maximum score on the DRS-R-98.18 There was no statistically significant difference between groups in the median DRS-R-98 scores (Table 3), but the odds of being in a higher quintile of delirium severity were 41% lower for patients in the autoregulation group compared with patients in the standard group (Table 3). The number of days of delirium was greater in the standard group (median [IQR] 1 [0-2] days) compared with the autoregulation group (median [IQR] 0 [0-1] days) (P = .05). The odds of an additional day of delirium were 42% lower for patients in the autoregulation group compared with patients in the standard group. (OR, 0.58; 95% CI, 0.35-0.99).

    Sensitivity analyses were considered using different definitions of delirium. As shown in Table 3, the inferences were unchanged in each of these sensitivity analyses. In exploratory subgroup analyses, no significant interactions were observed between patient age, sex, Mini-Mental State Examination,26 or logistic EuroSCORE27 and the primary outcome of delirium.

    Discussion

    Our results suggest that optimizing MAP to be greater than an individual patient’s lower limit of cerebral autoregulation during CPB may reduce the incidence of postoperative delirium.

    There is currently no agreement on the appropriate MAP for individual patients during CPB. In practice, these targets are empirically chosen based on institutional and practitioner preferences. Fundamentally, a MAP as low as 50 mm Hg is believed adequate based on classic teaching that this represents the lower limit of cerebral blood flow autoregulation.28 Higher MAPs, though, may be necessary for many patients undergoing cardiac surgery given the high prevalence of hypertension and cerebrovascular disease in this population.7

    Monitoring cerebral blood flow autoregulation during surgery provides an alternative method to identify the MAP required for adequate cerebral perfusion in individual patients. Using such monitoring, it has been shown that the lower limits of cerebral blood flow autoregulation vary widely between individuals (40 to 90 mm Hg) during CPB.7 Thus, arbitrarily choosing MAP targets may result in hypoperfusion in patients with an elevated lower limit of autoregulation. In observational studies, we have demonstrated that the magnitude and duration that MAP is outside the limits of autoregulation is associated with acute kidney injury and major morbidity and mortality after cardiac surgery.9,16 However, these observational studies are limited by the potential for confounding. In the present study, we found that a targeted strategy to maintain the MAP above the lower limit of autoregulation reduced the incidence of postoperative delirium. Although the mean MAP was only slightly higher in the intervention group, the MAP below the lower limit of autoregulation was substantially reduced in the intervention group.

    It is well accepted that patient risk factors are paramount in susceptibility to delirium, but the interactions between these factors and superimposed perioperative insults are multifactorial.29 Several studies have implicated hypotension as a contributor to delirium. A randomized clinical trial of patients undergoing cardiac surgery found that MAP targets of 70 to 80 mm Hg compared with 50 to 60 mm Hg reduced the incidence of delirium.12 However, the study was small, and delirium was assessed by the Mini-Mental State Examination, which is not validated for this purpose. A pre-post study examining an intervention to optimize cerebral hemodynamics also demonstrated a reduction in delirium, although the study was not randomized and the delirium assessment was not standardized.13 Other studies in cardiac surgery have shown conflicting results.10,11 Two recent studies reported an association of delirium with impaired cerebral autoregulation after cardiac surgery30 and in patients with shock,31 but neither study assessed the lower limit of autoregulation.

    Strengths and Limitations

    Strengths of this study include a novel method of targeting adequate cerebral perfusion in individual patients using real-time cerebral autoregulation monitoring, which could be extended to various types of surgery and postoperative care. The delirium assessments were conducted by an experienced group, in accordance with DSM-5 criteria. However, there are several limitations to consider. This was a single-center nested study, and delirium was not the primary outcome for the parent trial. Thus, these results should be considered preliminary and need to be confirmed in a broad population. The delirium assessment was sensitive, although our methods and estimates of delirium incidence are similar to those of other prominent researchers in the field.1 We have also shown that patients in this study with delirium had greater postoperative cognitive decline, thus supporting the validity of our delirium assessment.4 There was some disagreement in delirium rating between the CAM criteria and the consensus panel. The reasons for and significance of these differences are unclear. This trial only focused on maintaining MAP above the lower limit of autoregulation, although cerebral hyperperfusion from MAP above the upper limit of autoregulation may also be associated with delirium.32 We used autoregulation indices derived from Doppler measurements, which are labor intensive. However, other indices derived from near infrared spectroscopy data are also validated and more feasible.6 We are unable to determine which intervention during CPB contributed to our findings of improved delirium outcomes, although administration of phenylephrine was most significant between groups. We provided guidance to perfusionists on strategies to maintain MAP greater than target MAP; thus the results of this study are pragmatic in nature and reflect different target MAPs rather than a proscriptive protocol on perfusion practice.

    Conclusions

    The findings of our trial suggest that individualizing MAP during CPB based on cerebral autoregulation monitoring may be effective in reducing the incidence of postoperative delirium. These results should be confirmed in a multicenter trial, and the benefit of extending this monitoring throughout surgery or in the intensive care unit requires further research.

    Back to top
    Article Information

    Accepted for Publication: March 2, 2019.

    Corresponding Author: Charles H. Brown IV, MD, MHS, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Zayed 6208, 1800 Orleans St, Baltimore, MD 21287 (cbrownv@jhmi.edu).

    Published Online: May 22, 2019. doi:10.1001/jamasurg.2019.1163

    Cerebral Autoregulation Study Group: Ashish Shah, MD; Kenton Zehr, MD; Duke Cameron, MD; John Conte, MD; O. Joseph Bienvenu, MD; Rebecca Gottesman, MD, PhD; Atsushi Yamaguchi, MD, PhD; Michael Kraut, MD, MS, PhD.

    Affiliations of Cerebral Autoregulation Study Group: Department of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, Tennessee (Shah); Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland (Zehr); Division of Cardiac Surgery, Department of Surgery, Massachusetts General Hospital, Boston (Cameron); Division of Cardiac Surgery, Department of Surgery, Penn State University Hershey Medical Center, Hershey, Pennsylvania (Conte); Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland (Bienvenu); Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland (Gottesman); Saitama Medical Center, Jichi Medical University, Saitama, Japan (Yamaguchi); Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland (Kraut).

    Author Contributions: Drs Brown and Hogue had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Brown, Neufeld, Max, Mandal, Conte, Yamaguchi, Brady, Hogue.

    Acquisition, analysis, or interpretation of data: Neufeld, Tian, Probert, LaFlam, Max, Hori, Nomura, Shah, Zehr, Cameron, Bienvenu, Gottesman, Kraut, Brady, Hogue.

    Drafting of the manuscript: Brown, Hogue.

    Critical revision of the manuscript for important intellectual content: Neufeld, Tian, Probert, LaFlam, Max, Hori, Nomura, Mandal, Shah, Zehr, Cameron, Conte, Bienvenu, Gottesman, Yamaguchi, Kraut, Brady, Hogue.

    Statistical analysis: Tian.

    Obtained funding: Brown, Hogue.

    Administrative, technical, or material support: Brown, LaFlam, Nomura, Shah, Zehr, Bienvenu, Yamaguchi, Kraut, Brady, Hogue.

    Supervision: Brown, Neufeld, LaFlam, Mandal, Cameron, Hogue.

    Conflict of Interest Disclosures: Dr Brown reported receiving grants from the National Institutes of Health (NIH) and grants from the International Anesthesia Research Society during the conduct of the study, consulting for and receiving grant funding from Medtronic; in addition, he reported receiving and an offer to participate in an advisory board for Medtronic, which had not been finalized at time of publication. Dr Neufeld reported receiving personal fees from Merck, Inc, and grant funding from Hitachi Inc, and Ornim, Inc, outside the submitted work. Dr Hori reported receiving a Japan Heart Foundation/Bayer Yakuhin Research Grant Abroad during the study period; a 2018 Jichi Medical University Young Investigators Award; and a 2018 KAKENHIII (Grants-in-Aid for Scientific Research) Grant-in-Aid for Young Scientists B. Dr Hogue reported receiving grants and personal fees being a consultant, and providing lectures for Medtronic/Covidien, Inc, being a consultant to Merck, Inc, and receiving grants from NIH outside the submitted work. Dr Conte reported serving on a scientific advisory board for Medtronic. Dr. Gottesman reported being Associate Editor of Neurology. Dr. Kraut reported receiving grants from NIH during the conduct of the study. No other disclosures were reported.

    Funding/Support: This study was funded by International Anesthesia Research Society grant NIH K76 AG057020 and Johns Hopkins Clinician Scientist Award (Dr Brown) and NIH RO1 HL092259 (Dr Hogue).

    Role of the Funder/Sponsor: The funders 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.

    Data Sharing Statement:Supplement 2.

    Additional Contributions: We acknowledge the efforts of Michelle Parish, Elizabeth White, and Mirinda Anderson of the Johns Hopkins Department of Anesthesiology and Critical Care Medicine Clinical Research Core; and Joseph G Hobelman, Peter Rabins, and Avi Gerstenblith of the delirium consensus panel. The Johns Hopkins Department of Anesthesiology and Critical Care Medicine Clinical Research Core received funding for research nurse support.

    References
    1.
    Rudolph  JL, Jones  RN, Levkoff  SE,  et al.  Derivation and validation of a preoperative prediction rule for delirium after cardiac surgery.  Circulation. 2009;119(2):229-236. doi:10.1161/CIRCULATIONAHA.108.795260PubMedGoogle ScholarCrossref
    2.
    Martin  BJ, Buth  KJ, Arora  RC, Baskett  RJ.  Delirium as a predictor of sepsis in post-coronary artery bypass grafting patients: a retrospective cohort study.  Crit Care. 2010;14(5):R171. doi:10.1186/cc9273PubMedGoogle ScholarCrossref
    3.
    Brown  CH  IV, Laflam  A, Max  L,  et al.  The impact of delirium after cardiac surgical procedures on postoperative resource use.  Ann Thorac Surg. 2016;101(5):1663-1669. doi:10.1016/j.athoracsur.2015.12.074PubMedGoogle ScholarCrossref
    4.
    Brown  CH  IV, Probert  J, Healy  R,  et al.  Cognitive decline after delirium in patients undergoing cardiac surgery.  Anesthesiology. 2018;129(3):406-416. doi:10.1097/ALN.0000000000002253PubMedGoogle ScholarCrossref
    5.
    Gottesman  RF, Grega  MA, Bailey  MM,  et al.  Delirium after coronary artery bypass graft surgery and late mortality.  Ann Neurol. 2010;67(3):338-344.PubMedGoogle Scholar
    6.
    Brady  K, Joshi  B, Zweifel  C,  et al.  Real-time continuous monitoring of cerebral blood flow autoregulation using near-infrared spectroscopy in patients undergoing cardiopulmonary bypass.  Stroke. 2010;41(9):1951-1956. doi:10.1161/STROKEAHA.109.575159PubMedGoogle ScholarCrossref
    7.
    Joshi  B, Ono  M, Brown  C,  et al.  Predicting the limits of cerebral autoregulation during cardiopulmonary bypass.  Anesth Analg. 2012;114(3):503-510. doi:10.1213/ANE.0b013e31823d292aPubMedGoogle ScholarCrossref
    8.
    Hori  D, Ono  M, Rappold  TE,  et al.  Hypotension after cardiac operations based on autoregulation monitoring leads to brain cellular injury.  Ann Thorac Surg. 2015;100(2):487-493. doi:10.1016/j.athoracsur.2015.03.036PubMedGoogle ScholarCrossref
    9.
    Ono  M, Brady  K, Easley  RB,  et al.  Duration and magnitude of blood pressure below cerebral autoregulation threshold during cardiopulmonary bypass is associated with major morbidity and operative mortality.  J Thorac Cardiovasc Surg. 2014;147(1):483-489. doi:10.1016/j.jtcvs.2013.07.069PubMedGoogle ScholarCrossref
    10.
    Wesselink  EM, Kappen  TH, van Klei  WA, Dieleman  JM, van Dijk  D, Slooter  AJ.  Intraoperative hypotension and delirium after on-pump cardiac surgery.  Br J Anaesth. 2015;115(3):427-433. doi:10.1093/bja/aev256PubMedGoogle ScholarCrossref
    11.
    Rudiger  A, Begdeda  H, Babic  D,  et al.  Intra-operative events during cardiac surgery are risk factors for the development of delirium in the ICU.  Crit Care. 2016;20:264. doi:10.1186/s13054-016-1445-8PubMedGoogle ScholarCrossref
    12.
    Siepe  M, Pfeiffer  T, Gieringer  A,  et al.  Increased systemic perfusion pressure during cardiopulmonary bypass is associated with less early postoperative cognitive dysfunction and delirium.  Eur J Cardiothorac Surg. 2011;40(1):200-207. doi:10.1016/j.ejcts.2010.11.024PubMedGoogle ScholarCrossref
    13.
    Palmbergen  WA, van Sonderen  A, Keyhan-Falsafi  AM, Keunen  RW, Wolterbeek  R.  Improved perioperative neurological monitoring of coronary artery bypass graft patients reduces the incidence of postoperative delirium: the Haga Brain Care Strategy.  Interact Cardiovasc Thorac Surg. 2012;15(4):671-677. doi:10.1093/icvts/ivs317PubMedGoogle ScholarCrossref
    14.
    McKhann  GM, Grega  MA, Borowicz  LM  Jr,  et al.  Encephalopathy and stroke after coronary artery bypass grafting: incidence, consequences, and prediction.  Arch Neurol. 2002;59(9):1422-1428. doi:10.1001/archneur.59.9.1422PubMedGoogle Scholar
    15.
    Ono  M, Joshi  B, Brady  K,  et al.  Risks for impaired cerebral autoregulation during cardiopulmonary bypass and postoperative stroke.  Br J Anaesth. 2012;109(3):391-398. doi:10.1093/bja/aes148PubMedGoogle ScholarCrossref
    16.
    Ono  M, Arnaoutakis  GJ, Fine  DM,  et al.  Blood pressure excursions below the cerebral autoregulation threshold during cardiac surgery are associated with acute kidney injury.  Crit Care Med. 2013;41(2):464-471. doi:10.1097/CCM.0b013e31826ab3a1PubMedGoogle ScholarCrossref
    17.
    American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA: American Psychiatric Association; 2013.
    18.
    Trzepacz  PT, Mittal  D, Torres  R, Kanary  K, Norton  J, Jimerson  N.  Validation of the Delirium Rating Scale-Revised-98: comparison with the delirium rating scale and the cognitive test for delirium.  J Neuropsychiatry Clin Neurosci. 2001;13(2):229-242. doi:10.1176/jnp.13.2.229PubMedGoogle ScholarCrossref
    19.
    Inouye  SK, van Dyck  CH, Alessi  CA, Balkin  S, Siegal  AP, Horwitz  RI.  Clarifying confusion: the confusion assessment method: a new method for detection of delirium.  Ann Intern Med. 1990;113(12):941-948. doi:10.7326/0003-4819-113-12-941PubMedGoogle ScholarCrossref
    20.
    Ely  EW, Margolin  R, Francis  J,  et al.  Evaluation of delirium in critically ill patients: validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU).  Crit Care Med. 2001;29(7):1370-1379. doi:10.1097/00003246-200107000-00012PubMedGoogle ScholarCrossref
    21.
    Inouye  SK, Leo-Summers  L, Zhang  Y, Bogardus  ST  Jr, Leslie  DL, Agostini  JV.  A chart-based method for identification of delirium: validation compared with interviewer ratings using the confusion assessment method.  J Am Geriatr Soc. 2005;53(2):312-318. doi:10.1111/j.1532-5415.2005.53120.xPubMedGoogle ScholarCrossref
    22.
    Sessler  CN, Gosnell  MS, Grap  MJ,  et al.  The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients.  Am J Respir Crit Care Med. 2002;166(10):1338-1344.PubMedGoogle ScholarCrossref
    23.
    Robinson  TN, Raeburn  CD, Tran  ZV, Angles  EM, Brenner  LA, Moss  M.  Postoperative delirium in the elderly: risk factors and outcomes.  Ann Surg. 2009;249(1):173-178. doi:10.1097/SLA.0b013e31818e4776PubMedGoogle ScholarCrossref
    24.
    Meagher  DJ, Morandi  A, Inouye  SK,  et al.  Concordance between DSM-IV and DSM-5 criteria for delirium diagnosis in a pooled database of 768 prospectively evaluated patients using the delirium rating scale-revised-98.  BMC Med. 2014;12:164. doi:10.1186/s12916-014-0164-8PubMedGoogle ScholarCrossref
    25.
    Pandharipande  PP, Pun  BT, Herr  DL,  et al.  Effect of sedation with dexmedetomidine vs lorazepam on acute brain dysfunction in mechanically ventilated patients: the MENDS randomized controlled trial.  JAMA. 2007;298(22):2644-2653. doi:10.1001/jama.298.22.2644PubMedGoogle ScholarCrossref
    26.
    Folstein  MF, Folstein  SE, McHugh  PR.  “Mini-Mental State”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198.PubMedGoogle ScholarCrossref
    27.
    Roques  F, Michel  P, Goldstone  AR, Nashef  SA.  The logistic EuroSCORE.  Eur Heart J. 2003;24(9):881-882.PubMedGoogle ScholarCrossref
    28.
    Lassen  NA.  Cerebral blood flow and oxygen consumption in man.  Physiol Rev. 1959;39(2):183-238. doi:10.1152/physrev.1959.39.2.183PubMedGoogle ScholarCrossref
    29.
    Inouye  SK, Westendorp  RG, Saczynski  JS.  Delirium in elderly people.  Lancet. 2014;383(9920):911-922. doi:10.1016/S0140-6736(13)60688-1PubMedGoogle ScholarCrossref
    30.
    Chan  B, Aneman  A.  A prospective, observational study of cerebrovascular autoregulation and its association with delirium following cardiac surgery.  Anaesthesia. 2018;74(1):33-44. doi:10.1111/anae.14457PubMedGoogle ScholarCrossref
    31.
    Lee  KF, Wood  MD, Maslove  DM, Muscedere  JG, Boyd  JG.  Dysfunctional cerebral autoregulation is associated with delirium in critically ill adults.  [published online October 8, 2018].  J Cereb Blood Flow Metab. 2018;X18803081. doi:10.1177/0271678X18803081PubMedGoogle Scholar
    32.
    Hori  D, Brown  C, Ono  M,  et al.  Arterial pressure above the upper cerebral autoregulation limit during cardiopulmonary bypass is associated with postoperative delirium.  Br J Anaesth. 2014;113(6):1009-1017. doi:10.1093/bja/aeu319PubMedGoogle ScholarCrossref
    ×