Perioperative Multimodal General Anesthesia Focusing on Specific CNS Targets in Patients Undergoing Cardiac Surgeries: The Pathfinder Feasibility Trial

Akshay Shanker, John H Abel, Shilpa Narayanan, Pooja Mathur, Erin Work, Gabriel Schamberg, Aidan Sharkey, Ruma Bose, Valluvan Rangasamy, Venkatachalam Senthilnathan, Emery N Brown, Balachundhar Subramaniam, Akshay Shanker, John H Abel, Shilpa Narayanan, Pooja Mathur, Erin Work, Gabriel Schamberg, Aidan Sharkey, Ruma Bose, Valluvan Rangasamy, Venkatachalam Senthilnathan, Emery N Brown, Balachundhar Subramaniam

Abstract

Multimodal general anesthesia (MMGA) is a strategy that utilizes the well-known neuroanatomy and neurophysiology of nociception and arousal control in designing a rational and clinical practical paradigm to regulate the levels of unconsciousness and antinociception during general anesthesia while mitigating side effects of any individual anesthetic. We sought to test the feasibility of implementing MMGA for seniors undergoing cardiac surgery, a high-risk cohort for hemodynamic instability, delirium, and post-operative cognitive dysfunction. Twenty patients aged 60 or older undergoing on-pump coronary artery bypass graft (CABG) surgery or combined CABG/valve surgeries were enrolled in this non-randomized prospective observational feasibility trial, wherein we developed MMGA specifically for cardiac surgeries. Antinociception was achieved by a combination of intravenous remifentanil, ketamine, dexmedetomidine, and magnesium together with bupivacaine administered as a pecto-intercostal fascial block. Unconsciousness was achieved by using electroencephalogram (EEG)-guided administration of propofol along with the sedative effects of the antinociceptive agents. EEG-guided MMGA anesthesia was safe and feasible for cardiac surgeries, and exploratory analyses found hemodynamic stability and vasopressor usage comparable to a previously collected cohort. Intraoperative EEG suppression events and postoperative delirium were found to be rare. We report successful use of a total intravenous anesthesia (TIVA)-based MMGA strategy for cardiac surgery and establish safety and feasibility for studying MMGA in a full clinical trial. Clinical Trial Number: www.clinicaltrials.gov; identifier NCT04016740 (https://ichgcp.net/clinical-trials-registry/NCT04016740).

Keywords: EEG; analgesia; cardiac; multimodal; neuroanesthesia; nociception; regional; suppression.

Conflict of interest statement

Masimo Corporation has licensed and paid royalties on intellectual property to Massachusetts General Hospital created by EB. He is also a cofounder of PASCALL, a company developing closed loop physiological control systems for anesthesiology. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Shanker, Abel, Narayanan, Mathur, Work, Schamberg, Sharkey, Bose, Rangasamy, Senthilnathan, Brown and Subramaniam.

Figures

Figure 1
Figure 1
Schematic depictions of multimodal general anesthesia protocol split into individual components for anesthetic plan. (A) Nociceptive stimuli are managed with local and systemic anitinociceptives. This strategy leverages complementary mechanisms of each antinociceptive drug to achieve inhibition of nociceptive responses locally, at sites within the spinal cord, and at sites in the brain stem. (B) Decreased arousal (unconsciousness) is generated primarily via propofol potentiation of GABAergic inhibitory neurotransmission. The antinociceptive agents we selected have secondary effects in cortical or brain stem regions and contribute to decreased arousal. (C) Cardiopulmonary bypass (CPB) and anesthetic agents affect hemodynamic stability during cardiac surgery. Administration of propofol causes bradycardia and hypotension, and the anesthesiologist will guide administration of vasopressors to compensate for these changes. Importantly, CPB eliminates heart rate and pulsatile blood flow, causing the anesthesiologist to rely on mean arterial blood pressure to guide vasopressor dosing. This also eliminates the ability to use heart rate as a signal of patient response to painful stimuli.
Figure 2
Figure 2
Ultrasound image of the chest wall showing anterior chest wall structures at the level of 4th Intercostal space. PMM, Pectoralis major muscle, ICM, Intercostal Muscles, R4, Fourth rib, R5, Fifth rib, Shows the position of needle, pleura and lung parenchyma.
Figure 3
Figure 3
Quantification of hemodynamic stability and vasopressor usage during MMGA in comparison to a historical standard-of-care. Little difference was observed between hypotensive AUC (median, [IQR] min'mmHg; MMGA 921.0, [793.1, 1287.2]; historical 995.1, [653.7, 1292.7]; P = 0.72 Mann-Whitney U test), CV (median, [IQR]; MMGA 0.32, [0.28 0.36]; historical 0.34, [0.31, 0.38]; P = 0.14 Mann-Whitney U test), and norepinephrine equivalent dose (median, [IQR] mcg; MMGA 734.5, [526.1, 856.1]; historical 704.77, [336.4 1065.0], P = 0.90 Mann-Whitney U test).
Figure 4
Figure 4
Annotated recordings from one cardiac surgery using MMGA. (A) Computed EEG spectrogram for a selected MMGA cohort patient during cardiac surgery with labeled timepoints of surgical incision (S), sternotomy (ST), heparin bolus (H), bypass begin (BB), bypass end (BE), and sternal closure (CS) before ICU transfer. Suppression events are shown below spectrogram. (B) EEG spectrogram for a control cohort patient labeled as in (A). (C) Selected 60s EEG epochs comparing a waveform during general anesthesia with a waveform during burst suppression, a coma-like deeper state of anesthesia. (D) Distribution of the of time spent in suppression from induction to anesthesia end for the MMGA (n = 18, median suppression time = 4.24 min, IQR [1.87, 7.33]) and control (n = 2, suppression times 13.56 min and 8.35 min) cohorts.

References

    1. Brown EN, Lydic R, Schiff ND. General anesthesia, sleep, and coma. Schwartz RS, ed N Engl J Med. (2010) 363:2638–50. 10.1056/NEJMra0808281
    1. Berger M, Terrando N, Smith SK, Browndyke JN, Newman MF, Mathew JP. Neurocognitive function after cardiac surgery: from phenotypes to mechanisms. Anesthesiology. (2018) 129:829–51. 10.1097/ALN.0000000000002194
    1. Hendrickx JFA, Eger EI, Sonner JM, Shafer SL. Is Synergy the Rule? A review of anesthetic interactions producing hypnosis and immobility. Anesth Analg. (2008) 107:494–506. 10.1213/ane.0b013e31817b859e
    1. Brown EN, Pavone KJ, Naranjo M. Multimodal general anesthesia: theory and practice. Anesth Analg. (2018) 127:1246–58. 10.1213/ANE.0000000000003668
    1. McNicol E, Horowicz-Mehler N, Fisk RA, Bennett K, Gialeli-Goudas M, Chew PW, et al. . Management of opioid side effects in cancer-related and chronic noncancer pain: a systematic review. J Pain. (2003) 4:231–56. 10.1016/S1526-5900(03)00556-X
    1. Volkow ND, Collins FS. The role of science in addressing the opioid crisis. N Engl J Med. (2017) 377:1798. 10.1056/NEJMc1711494
    1. Furukawa H, Tanemoto K. Frailty in cardiothoracic surgery: systematic review of the literature. Gen Thorac Cardiovasc. (2015) 63:425–33. 10.1007/s11748-015-0553-8
    1. Bentov I, Kaplan SJ, Pham TN, Reed MJ. Frailty assessment: from clinical to radiological tools. Br J Anaesth. (2019) 123:37–50. 10.1016/j.bja.2019.03.034
    1. Panayi AC, Orkaby AR, Sakthivel D, Endo Y, Varon D, Roh D, et al. . Impact of frailty on outcomes in surgical patients: a systematic review and meta-analysis. Am J Surg. (2019) 218:393–400. 10.1016/j.amjsurg.2018.11.020
    1. Nicolini F, Agostinelli A, Vezzani A, Manca T, Benassi F, Molardi A, et al. . The evolution of cardiovascular surgery in elderly patients: a review of current options and outcomes. Biomed Res Int. (2014) 2014:1–10. 10.1155/2014/736298
    1. Mahanna-Gabrielli E, Schenning KJ, Eriksson LI, Browndyke JN, Wright CB, Culley DJ, et al. . State of the clinical science of perioperative brain health: report from the american society of anesthesiologists brain health initiative summit 2018. Br J Anaesth. (2019) 123:464–78. 10.1016/j.bja.2019.07.004
    1. Maheshwari K, Ahuja S, Khanna AK, Guangmei M, Perez-Protto S, Farag E, et al. . Association between perioperative hypotension and delirium in postoperative critically ill patients: a retrospective cohort analysis. Anesth Analg. (2019) 130:636–43. 10.1213/ANE.0000000000004517
    1. Radtke FM, Franck M, Lendner J, Krüger S, Wernecke KD, Spies CD. Monitoring depth of anaesthesia in a randomized trial decreases the rate of postoperative delirium but not postoperative cognitive dysfunction. Br J Anaesth. (2013) 110:i98–i105. 10.1093/bja/aet055
    1. Soehle M, Dittmann A, Ellerkmann RK, Baumgarten G, Putensen C, Guenther U. Intraoperative burst suppression is associated with postoperative delirium following cardiac surgery: a prospective, observational study. BMC Anesthesiol. (2015) 15:61. 10.1186/s12871-015-0051-7
    1. Sessler DI, Manberg PJ. Hospital stay and mortality are increased in patients having a “triple low” of low blood pressure, low bispectral index, and low minimum alveolar concentration of volatile anesthesia. Anesthesiology. (2012) 116:1195–203. 10.1097/ALN.0b013e31825683dc
    1. Wildes TS, Mickle AM, Ben Abdallah A, Maybrier HR, Oberhaus J, Budelier TP, et al. . Effect of electroencephalography-guided anesthetic administration on postoperative delirium among older adults undergoing major surgery: the ENGAGES randomized clinical trial. JAMA. (2019) 321:473. 10.1001/jama.2018.22005
    1. Ni K, Cooter M, Gupta DK, Thomas J, Hopkins TJ, Miller TE, et al. . Paradox of age: older patients receive higher age-adjusted minimum alveolar concentration fractions of volatile anaesthetics yet display higher bispectral index values. Br J Anaesth. (2019) 123:288–97. 10.1016/j.bja.2019.05.040
    1. Hesse S, Kreuzer M, Hight D, Gaskell A, Devari P, Singh D, et al. . Association of electroencephalogram trajectories during emergence from anaesthesia with delirium in the postanaesthesia care unit: an early sign of postoperative complications. Br J Anaesth. (2019) 122:622–34. 10.1016/j.bja.2018.09.016
    1. Jacobs JR, Reves JG, Marty J, White WD, Bai SA, Smith LR. Aging increases pharmacodynamic sensitivity to the hypnotic effects of midazolam. Anesth Analg. (1995) 80:143–8. 10.1097/00000539-199501000-00024
    1. Purdon PL, Pavone KJ, Akeju O, Smith AC, Sampson AL, Lee J, et al. . The ageing brain: age-dependent changes in the electroencephalogram during propofol and sevoflurane general anaesthesia. Br J Anaesth. (2015) 115:i46–57. 10.1093/bja/aev213
    1. Akeju O, Westover MB, Pavone KJ, Sampson AL, Hartnack KE, Brown EN, et al. . Effects of sevoflurane and propofol on frontal electroencephalogram power and coherence. Anesthesiology. (2014) 121:990–8. 10.1097/ALN.0000000000000436
    1. Landoni G, Lomivorotov VV, Nigro Neto C, Monaco F, Pasyuga VV, Bradic N, et al. . Volatile anesthetics versus total intravenous anesthesia for cardiac surgery. N Engl J Med. (2019) 380:1214–25. 10.1056/NEJMoa1816476
    1. Robinson BJ, Ebert TJ, O'brien TJ, Colinco MD, Muzi M. Mechanisms whereby propofol mediates peripheral vasolidation in humans: sympathoinhibition or direct vascular relaxation? Anesthesiology. (1997) 86:64–72. 10.1097/00000542-199701000-00010
    1. Ebert TJ, Hall JE, Barney JA, Uhrich TD, Colinco MD. The Effects of Increasing Plasma Concentrations of Dexmedetomidine in Humans. Anesthesiology. (2000) 93:382–94. 10.1097/00000542-200008000-00016
    1. Plummer GS, Ibala R, Hahm E, An J, Gitlin J, Deng H, et al. . Electroencephalogram Dynamics During General Anesthesia Predict the Later Incidence and Duration of Burst-suppression During Cardiopulmonary Bypass. Clin Neurophysiol. (2019) 130:55–60. 10.1016/j.clinph.2018.11.003
    1. Subramaniam B, Shankar P, Shaefi S, Mueller A, O'Gara B, Banner-Goodspeed V, et al. . Effect of intravenous acetaminophen vs placebo combined with propofol or dexmedetomidine on postoperative delirium among older patients following cardiac surgery: the DEXACET randomized clinical trial. JAMA. (2019) 321:686. 10.1001/jama.2019.0234
    1. Brandon Westover M, Shafi MM, Ching S, Chemali JJ, Purdon PL, Cash SS, et al. . Real-time segmentation of burst suppression patterns in critical care EEG monitoring. J Neurosci Methods. (2013) 219:131–41. 10.1016/j.jneumeth.2013.07.003
    1. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. . SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. (2020) 17:261–72. 10.1038/s41592-020-0772-5
    1. Mancl EE, Muzevich KM. Tolerability and safety of enteral nutrition in critically ill patients receiving intravenous vasopressor therapy. JPEN. (2013) 37:641–51. 10.1177/0148607112470460
    1. Russell JA, Walley KR, Singer J, Gordon AC, Hebert PC, James Cooper D, et al. . Vasopressin versus norepinephrine infusion in patients with septic shock. NEJM. (2008) 358:877–87. 10.1056/NEJMoa067373
    1. Ensor CR, Sabo RT, Voils SA. Impact of early postoperative hydrocortisone administration in cardiac surgical patients after cardiopulmonary bypass. Ann Pharmacother. (2011) 45:189–94. 10.1345/aph.1P468
    1. Wei LA, Fearing MA, Sternberg EJ, Inouye SK. The confusion assessment method: a systematic review of current usage. J Am Geriatr Soc. (2008) 56:823–30. 10.1111/j.1532-5415.2008.01674.x
    1. Ely EW, Inouye SK, Bernard GR, Gordon S, Francis J, May L, et al. . Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. (2001) 286:2703. 10.1001/jama.286.21.2703
    1. Engelman DT, Ben Ali W, Williams JB, Perrault LP, Reddy VS, Arora RC, et al. . Guidelines for perioperative care in cardiac surgery: enhanced recovery after surgery society recommendations. JAMA Surg. (2019) 154:755. 10.1001/jamasurg.2019.1153

Source: PubMed

3
S'abonner