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Sethi N, Dutta A, Puri GD, Sood J, Choudhary PK, Gupta M, Panday BC, Malhotra S. Evaluation of Quality of Recovery With Quality of Recovery-15 Score After Closed-Loop Anesthesia Delivery System-Guided Propofol Versus Desflurane General Anesthesia in Patients Undergoing Transabdominal Robotic Surgery: A Randomized Controlled Study. Anesth Analg 2024; 138:1052-1062. [PMID: 38416594 DOI: 10.1213/ane.0000000000006849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
BACKGROUND Robotic technique of surgery allows surgeons to perform complex procedures in difficult-to-access areas of the abdominal/pelvic cavity (eg, radical prostatectomy and radical hysterectomy) with improved access and precision approach. At the same time, automated techniques efficiently deliver propofol total intravenous anesthesia (TIVA) with lower anesthetic consumption. As both above are likely to bring benefit to the patients, it is imperative to explore their effect on postanesthesia recovery. Quality of Recovery-15 (QoR-15) is a comprehensive patient-reported measure of the quality of postanesthesia recovery and assesses compendious patients' experiences (physical and mental well-being). This randomized study assessed the effect of automated propofol TIVA versus inhaled desflurane anesthesia on postoperative quality of recovery using the QoR-15 questionnaire in patients undergoing elective robotic surgery. METHODS One hundred twenty patients undergoing robotic abdominal surgery under general anesthesia (GA) were randomly allocated to receive propofol TIVA administered by closed-loop anesthesia delivery system (CLADS) (CLADS group) or desflurane GA (desflurane group). Postoperative QoR-15 score on postoperative day 1 (POD-1) and postoperative day 2 (POD-2) (primary outcome variables), individual QoR-15 item scores (15 nos.), intraoperative hemodynamics (heart rate, mean blood pressure), anesthesia depth consistency, anesthesia delivery system performance, early recovery from anesthesia (time-to-eye-opening, and time to tracheal extubation), and postoperative adverse events (sedation, postoperative nausea and vomiting [PONV], pain, intraoperative awareness recall) (secondary outcome variables) were analyzed. RESULTS On POD-1, the CLADS group scored significantly higher than the desflurane group in terms of "overall" QoR-15 score (QoR-15 score: 114.5 ± 13 vs 102.1 ± 20.4; P = .001) and 3 individual QoR-15 "items" scores ("feeling rested" 7.5 ± 1.9 vs 6.4 ± 2.2, P = .007; "good sleep" 7.8 ± 1.9 vs 6.6 ± 2.7, P = .027; and "feeling comfortable and in control" 8.1 ± 1.7 vs 6.9 ± 2.4, P = .006). On the POD-2, the CLADS group significantly outscored the desflurane group with respect to the "overall" QoR-15 score (126.0 ± 13.6 vs 116.3 ± 20.3; P = .011) and on "5" individual QoR-15 items ("feeling rested" 8.1 ± 1.4 vs 7.0 ± 2.0, P = .003; "able to return to work or usual home activities" 6.0 ± 2.2 vs 4.6 ± 2.6, P = .008; "feeling comfortable and in control" 8.6 ± 1.2 vs 7.7 ± 1.9, P = .004; "feeling of general well-being" 7.8 ± 1.6 vs 6.9 ± 2.0, P = .042; and "severe pain" 9.0 ± 1.9 vs 8.1 ± 2.5, P = .042). CONCLUSIONS Automated propofol TIVA administered by CLADS is superior to desflurane inhalation GA with respect to early postoperative recovery as comprehensively assessed on the QoR-15 scoring system. The effect of combined automated precision anesthesia and surgery (robotics) techniques on postoperative recovery may be explored further.
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Affiliation(s)
- Nitin Sethi
- From the Department of Anaesthesiology, Pain, & Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Amitabh Dutta
- From the Department of Anaesthesiology, Pain, & Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Goverdhan D Puri
- Department of Anesthesia and Intensive Care, Post Graduate Institute of Medical, Education and Research, Chandigarh, India
| | - Jayashree Sood
- From the Department of Anaesthesiology, Pain, & Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Prabhat K Choudhary
- From the Department of Anaesthesiology, Pain, & Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Manish Gupta
- From the Department of Anaesthesiology, Pain, & Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Bhuwan C Panday
- From the Department of Anaesthesiology, Pain, & Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Savitar Malhotra
- From the Department of Anaesthesiology, Pain, & Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
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Coeckelenbergh S, Boelefahr S, Alexander B, Perrin L, Rinehart J, Joosten A, Barvais L. Closed-loop anesthesia: foundations and applications in contemporary perioperative medicine. J Clin Monit Comput 2024; 38:487-504. [PMID: 38184504 DOI: 10.1007/s10877-023-01111-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/21/2023] [Indexed: 01/08/2024]
Abstract
A closed-loop automatically controls a variable using the principle of feedback. Automation within anesthesia typically aims to improve the stability of a controlled variable and reduce workload associated with simple repetitive tasks. This approach attempts to limit errors due to distractions or fatigue while simultaneously increasing compliance to evidence based perioperative protocols. The ultimate goal is to use these advantages over manual care to improve patient outcome. For more than twenty years, clinical studies in anesthesia have demonstrated the superiority of closed-loop systems compared to manual control for stabilizing a single variable, reducing practitioner workload, and safely administering therapies. This research has focused on various closed-loops that coupled inputs and outputs such as the processed electroencephalogram with propofol, blood pressure with vasopressors, and dynamic predictors of fluid responsiveness with fluid therapy. Recently, multiple simultaneous independent closed-loop systems have been tested in practice and one study has demonstrated a clinical benefit on postoperative cognitive dysfunction. Despite their advantages, these tools still require that a well-trained practitioner maintains situation awareness, understands how closed-loop systems react to each variable, and is ready to retake control if the closed-loop systems fail. In the future, multiple input multiple output closed-loop systems will control anesthetic, fluid and vasopressor titration and may perhaps integrate other key systems, such as the anesthesia machine. Human supervision will nonetheless always be indispensable as situation awareness, communication, and prediction of events remain irreplaceable human factors.
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Affiliation(s)
- Sean Coeckelenbergh
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France.
- Outcomes Research Consortium, Cleveland, OH, USA.
| | - Sebastian Boelefahr
- Department of Anesthesiology and Intensive Care, Klinikum Aschaffenburg-Alzenau, Frankfurt University and Wuerzburg University Affiliated Academic Training Hospital, Aschaffenburg, Germany
| | - Brenton Alexander
- Department of Anesthesiology & Perioperative Care, University of California San Diego, San Diego, CA, USA
| | - Laurent Perrin
- Department of Anaesthesia and Resuscitation, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Joseph Rinehart
- Outcomes Research Consortium, Cleveland, OH, USA
- Department of Anesthesiology & Perioperative Care, University of California Irvine, Irvine, CA, USA
| | - Alexandre Joosten
- Department of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Luc Barvais
- Department of Anaesthesia and Resuscitation, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium
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Xie T, Wang Y, Liu Y, Li J, Li W, Xu H. Accuracy of closed-loop and open-loop propofol delivery systems by bispectral index monitoring in breast surgery patients: a prospective randomized trial. BRAZILIAN JOURNAL OF ANESTHESIOLOGY (ELSEVIER) 2024; 74:744438. [PMID: 37247817 DOI: 10.1016/j.bjane.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 05/14/2023] [Accepted: 05/18/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND This randomized and controlled prospective study tested the hypothesis that closed-loop Target-Controlled Infusion (TCI) of propofol would be associated with better system performance when compared with open-loop controlled delivery of propofol. METHODS Patients scheduled for elective breast surgery were randomly assigned to two groups: a closed-loop group, in which propofol infusion was performed by a closed-loop TCI system that used the Bispectral Index (BIS) as a feedback parameter to titrate the rate of propofol infusion, and an open-loop group, in which propofol infusion was performed manually and guided by the bispectral index. RESULTS A total of 156 patients were recruited for this study (closed-loop group n = 79; open-loop group n = 77). The Global Score (GS) of the closed-loop group was lower than that of the open-loop group (34.3 and 42.2) (p = 0.044). The proportions of time with a BIS value between 40 and 60 were almost identical in the closed-loop group and the open-loop group (68.7 ± 10.6% and 66.7 ± 13.3%) (p = 0.318). The individuals in the closed-loop group consumed more propofol compared with those in the open-loop group (7.20 ± 1.65 mg.kg-1.h-1 vs. 6.03 ± 1.31 mg.kg-1.h-1, p < 0.001). No intraoperative recall, somatic events or adverse events occurred. No significant difference in heart rate was observed between the two groups (p = 0.169). CONCLUSION The closed-loop protocol was associated with lower BIS variability and lower out-of-range BIS values, at the cost of a greater consumption of propofol when compared to the open loop group. REGISTER NUMBER ChiCTR-INR-17010399.
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Affiliation(s)
- Tian Xie
- Forth Hospital of Hebei Medical University, Department of Anesthesiology, Hebei, China
| | - Yong Wang
- Forth Hospital of Hebei Medical University, Department of Anesthesiology, Hebei, China
| | - Yuhua Liu
- Forth Hospital of Hebei Medical University, Department of Anesthesiology, Hebei, China
| | - Junjie Li
- Forth Hospital of Hebei Medical University, Department of Anesthesiology, Hebei, China
| | - Weijing Li
- Forth Hospital of Hebei Medical University, Department of Anesthesiology, Hebei, China
| | - Hongmeng Xu
- Forth Hospital of Hebei Medical University, Department of Anesthesiology, Hebei, China.
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Spataru A, Eiben P, Pluddemann A. Performance of closed-loop systems for intravenous drug administration: a systematic review and meta-analysis of randomised controlled trials. J Clin Monit Comput 2024; 38:5-18. [PMID: 37695449 DOI: 10.1007/s10877-023-01069-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 08/13/2023] [Indexed: 09/12/2023]
Abstract
Closed-loop drug delivery systems are autonomous computers able to administer medication in response to changes in physiological parameters (controlled variables). While limited evidence suggested that closed-loop systems can perform better than manual drug administration in certain settings, this technology remains a research tool with an uncertain risk/benefit profile. Our aim was comparing the performance of closed-loop systems with manual intravenous drug administration in adults. We searched MEDLINE, CENTRAL, and Embase from inception until November 2022, without restriction to language. We assessed for inclusion randomised controlled trials comparing closed-loop and manual administration of intravenous drugs in adults, intraoperatively or in the Intensive Care Unit. We identified 32 studies on closed-loop administration of propofol, noradrenaline, phenylephrine, insulin, neuromuscular blockers, and vasodilators. Most studies were at moderate or high risk of bias. The results showed that closed-loop systems reduced the duration of blood pressure outside prespecified targets during noradrenaline (MD 14.9%, 95% CI 9.6-20.2%, I2 = 66.6%) and vasodilators administration (MD 7.4%, 95% CI 5.2-9.7%, I2 = 62.3%). Closed-loop systems also decreased the duration of recovery after propofol (MD 1.3 min, 95% CI 0.4-2.1 min, I2 = 58.6%) and neuromuscular blockers (MD 9.0 min, 95% CI 7.9-10.0 min, I2 = 0%). The certainty of the evidence was low or very low for most outcomes. Automatic technology may be used to improve the hemodynamic profile during noradrenaline and vasodilators administration and reduce the duration of postanaesthetic recovery.Registration: This systematic review was registered with PROSPERO (CRD42022336950) on the 7th of June 2022.
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Affiliation(s)
- Ana Spataru
- Department of Neurocritical Care, Southampton General Hospital, Hampshire, SO164YO, UK.
- Centre for Evidence Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
| | - Paola Eiben
- Department of Anaesthesia, St. Bartholomew's Hospital, Barts Health NHS Trust, London, EC1A7BE, UK
| | - Annette Pluddemann
- Centre for Evidence Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
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Cochand L, Filipovic MG, Huber M, Luedi MM, Urman RD, Bello C. Systems Anesthesiology: Systems of Care Delivery and Optimization in the Operating Room. Anesthesiol Clin 2023; 41:847-861. [PMID: 37838388 DOI: 10.1016/j.anclin.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2023]
Abstract
Anesthesiology presents a challenge to a traditional simplifying approach given the ever-increasing amount of medical data and a more demanding environment. Systems anesthesiology is a modern approach to perioperative care, integrating the complexity of multifactorial knowledge and data to achieve a more adequate representation of reality, while including both patient-related medical aspects as well as economic and organizational challenges. We discuss the value of some innovative technologies such as the emergence of anesthesia information systems, the use of tele-medicine, predictive monitoring, or closed-loop systems as it pertains to the changes in the current standards of care in anesthesiology. Furthermore, we highlight the importance of systems anesthesiology in operating room planning, anesthesia research, and education.
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Affiliation(s)
- Laure Cochand
- Department of Anesthesiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mark G Filipovic
- Department of Anesthesiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus Huber
- Department of Anesthesiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus M Luedi
- Department of Anesthesiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Richard D Urman
- Department of Anesthesiology, The Ohio State University College of Medicine, OH, USA.
| | - Corina Bello
- Department of Anesthesiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Karer G, Škrjanc I. Improved Individualized Patient-Oriented Depth-of-Hypnosis Measurement Based on Bispectral Index. SENSORS (BASEL, SWITZERLAND) 2022; 23:293. [PMID: 36616891 PMCID: PMC9824030 DOI: 10.3390/s23010293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Total intravenous anesthesia is an anesthesiologic technique where all substances are injected intravenously. The main task of the anesthesiologist is to assess the depth of anesthesia, or, more specifically, the depth of hypnosis (DoH), and accordingly adjust the dose of intravenous anesthetic agents. However, it is not possible to directly measure the anesthetic agent concentrations or the DoH, so the anesthesiologist must rely on various vital signs and EEG-based measurements, such as the bispectral (BIS) index. The ability to better measure DoH is directly applicable in clinical practice-it improves the anesthesiologist's assessment of the patient state regarding anesthetic agent concentrations and, consequently, the effects, as well as provides the basis for closed-loop control algorithms. This article introduces a novel structure for modeling DoH, which employs a residual dynamic model. The improved model can take into account the patient's individual sensitivity to the anesthetic agent, which is not the case when using the available population-data-based models. The improved model was tested using real clinical data. The results show that the predictions of the BIS-index trajectory were improved considerably. The proposed model thus seems to provide a good basis for a more patient-oriented individualized assessment of DoH, which should lead to better administration methods that will relieve the anesthesiologist's workload and will benefit the patient by providing improved safety, individualized treatment, and, thus, alleviation of possible adverse effects during and after surgery.
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Kumar S, Puri GD, Mathew PJ, Mandal B. Evaluation of indigenously developed closed-loop automated blood pressure control system (claps): a preliminary study. J Clin Monit Comput 2022; 36:1657-1665. [PMID: 35589874 DOI: 10.1007/s10877-022-00810-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/09/2022] [Indexed: 11/30/2022]
Abstract
Closed-loop systems have been designed to assist anesthetists in controlling anesthetic drugs and also maintaining the stability of various physiological variables in the normal range. In the present study, we describe and clinically evaluated a novel closed-loop automated blood pressure control system (CLAPS) in patients undergoing cardiac surgery under cardiopulmonary bypass. Forty ASA II-IV adult patients undergoing elective cardiac surgery were randomly allocated to receive adrenaline, noradrenaline, phenylephrine and nitroglycerine (NTG) adjusted either through CLAPS (CLAPS group) or manually (Manual group). The desired target mean arterial blood pressure (MAP) for each patient in both groups was set by the attending anesthesiologist. The hemodynamic performance was assessed based on the percentage duration of time the MAP remained within 20% of the set target. Automated controller performances were compared using performance error criteria of Varvel (MDPE, MDAPE, Wobble) and Global Score. MAP was maintained a significantly longer proportion of time within 20% of the target in the CLAPS group (79.4% vs. 65.5% p < 0.001, 't' test) as compared to the manual group. Median absolute performance error, wobble, and Global score was significantly lower in the CLAPS group. Hemodynamic stability was achieved with a significantly lower dose of Phenyepherine in the CLAPS group (1870 μg vs. 5400 μg, p < 0.05, 't' test). The dose of NTG was significantly higher in the CLAPS group (3070 μg vs. 1600 μg, p-value < 0.05, 't' test). The cardiac index and left ventricular end-diastolic area were comparable between the groups. Automated infusion of vasoactive drugs using CLAPS is feasible and also better than manual control for controlling hemodynamics during cardiac surgery. Trial registration number and date This trial was registered in the Clinical Trial Registry of India under Registration Number CTRI/2018/01/011487 (Retrospective; registration date; January 23, 2018).
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Affiliation(s)
- Sumit Kumar
- Department of Anaesthesia & Critical Care, Postgraduate Institute of Medical Education & Research, Chandigarh, India. .,Nehru Hospital, Postgraduate Institute of Medical Education & Research, , Anaesthesia Office 4th Floor, Chandigarh, 160012, India.
| | - Goverdhan Dutt Puri
- Department of Anaesthesia & Critical Care, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Preethy J Mathew
- Department of Anaesthesia & Critical Care, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Banashree Mandal
- Department of Anaesthesia & Critical Care, Postgraduate Institute of Medical Education & Research, Chandigarh, India
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Vanhonacker D, Verdonck M, Nogueira Carvalho H. Impact of Closed-Loop Technology, Machine Learning, and Artificial Intelligence on Patient Safety and the Future of Anesthesia. CURRENT ANESTHESIOLOGY REPORTS 2022. [DOI: 10.1007/s40140-022-00539-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Schiavo M, Padula F, Latronico N, Paltenghi M, Visioli A. A modified PID-based control scheme for depth-of-hypnosis control: Design and experimental results. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106763. [PMID: 35349908 DOI: 10.1016/j.cmpb.2022.106763] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/05/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Many methodologies have been proposed for the control of total intravenous anesthesia in general surgery, as this yields a reduced stress for the anesthesiologist and an increased safety for the patient. The objective of this work is to design a PID-based control system for the regulation of the depth of hypnosis by propofol and remifentanil coadministration that takes into account the clinical practice. METHODS With respect to a standard PID control system, additional functionalities have been implemented in order to consider specific requirements related to the clinical practice. In particular, suitable boluses are determined and used in the induction phase and a nonzero baseline infusion is used in the maintenance phase when the predicted effect-site concentration drops below a safety threshold. RESULTS The modified controller has been experimentally assessed on a group of 10 patients receiving general anesthesia for elective plastic surgery. The control system has been able to induce and maintain adequate anesthesia without any manual intervention from the anesthesiologist. CONCLUSIONS Results confirm the effectiveness of the overall design approach and, in particular, highlight that the new version of the control system, with respect to a standard PID controller, provides significant advantages from a clinical standpoint.
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Affiliation(s)
- Michele Schiavo
- Dipartimento di Ingegneria dell'Informazione, University of Brescia, Brescia, Italy.
| | - Fabrizio Padula
- Curtin Centre for Optimisation and Decision Science, Curtin University, Perth, Australia.
| | - Nicola Latronico
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy; Department of Anesthesiology, Critical Care and Emergency Spedali Civili di Brescia, Brescia, Italy.
| | - Massimiliano Paltenghi
- Department of Anesthesiology, Critical Care and Emergency Spedali Civili di Brescia, Brescia, Italy.
| | - Antonio Visioli
- Dipartimento di Ingegneria Meccanica e Industriale University of Brescia, Brescia, Italy.
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Singh M, Nath G. Artificial intelligence and anesthesia: A narrative review. Saudi J Anaesth 2022; 16:86-93. [PMID: 35261595 PMCID: PMC8846233 DOI: 10.4103/sja.sja_669_21] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 11/04/2022] Open
Abstract
Rapid advances in Artificial Intelligence (AI) have led to diagnostic, therapeutic, and intervention-based applications in the field of medicine. Today, there is a deep chasm between AI-based research articles and their translation to clinical anesthesia, which needs to be addressed. Machine learning (ML), the most widely applied arm of AI in medicine, confers the ability to analyze large volumes of data, find associations, and predict outcomes with ongoing learning by the computer. It involves algorithm creation, testing and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes. AI-supported closed loops have been designed for pharmacological maintenance of anesthesia and hemodynamic management. Mechanical robots can perform dexterity and skill-based tasks such as intubation and regional blocks with precision, whereas clinical-decision support systems in crisis situations may augment the role of the clinician. The possibilities are boundless, yet widespread adoption of AI is still far from the ground reality. Patient-related “Big Data” collection, validation, transfer, and testing are under ethical scrutiny. For this narrative review, we conducted a PubMed search in 2020-21 and retrieved articles related to AI and anesthesia. After careful consideration of the content, we prepared the review to highlight the growing importance of AI in anesthesia. Awareness and understanding of the basics of AI are the first steps to be undertaken by clinicians. In this narrative review, we have discussed salient features of ongoing AI research related to anesthesia and perioperative care.
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Ma X, Pan B, Song T, Sun Y, Fu Y. Development of a Novel Anesthesia Airway Management Robot. SENSORS 2021; 21:s21238144. [PMID: 34884149 PMCID: PMC8662423 DOI: 10.3390/s21238144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/21/2021] [Accepted: 12/01/2021] [Indexed: 11/16/2022]
Abstract
Non-invasive positive pressure ventilation has attracted increasing attention for air management in general anesthesia. This work proposes a novel robot equipped with two snake arms and a mask-fastening mechanism to facilitate trachea airway management for anesthesia as well as deep sedation and to improve surgical outcomes. The two snake arms with supporting terminals have been designed to lift a patient's jaw with design optimization, and the mask-fastening mechanism has been utilized to fasten the mask onto a patient's face. The control unit has been developed to implement lifting and fastening force control with safety and robustness. Loading experiments on the snake arm and tension experiments on the mask-fastening mechanism have been performed to investigate and validate the performances of the proposed anesthesia airway management robot. Experiments on a mock person have also been employed to further verify the effectiveness and reliability of the developed robot system. As an early study of an anesthesia airway management robot, it was verified as a valid attempt to perform mask non-invasive positive pressure ventilation technology by taking advantage of a robotic system.
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Affiliation(s)
- Xuesong Ma
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China;
- The Fourth Clinical Medical School, Harbin Medical University, Harbin 150001, China
| | - Bo Pan
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China; (B.P.); (T.S.); (Y.S.)
| | - Tao Song
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China; (B.P.); (T.S.); (Y.S.)
| | - Yanwen Sun
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China; (B.P.); (T.S.); (Y.S.)
| | - Yili Fu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China;
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China; (B.P.); (T.S.); (Y.S.)
- Correspondence:
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Wingert T, Lee C, Cannesson M. Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery. Anesthesiol Clin 2021; 39:565-581. [PMID: 34392886 PMCID: PMC9847584 DOI: 10.1016/j.anclin.2021.03.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.
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Affiliation(s)
- Theodora Wingert
- University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA; Department of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Suite 3325, Los Angeles, CA 90095-7403, USA.
| | - Christine Lee
- Edwards Lifesciences, Irvine, CA, USA; Critical Care R&D, 1 Edwards Way, Irvine, CA 92614, USA
| | - Maxime Cannesson
- University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA; Department of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Suite 3325, Los Angeles, CA 90095-7403, USA
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Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J Gastroenterol 2021; 27:2758-2770. [PMID: 34135552 PMCID: PMC8173379 DOI: 10.3748/wjg.v27.i21.2758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 04/06/2021] [Accepted: 04/28/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called "big data" to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.
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Affiliation(s)
- Sohan Lal Solanki
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Saneya Pandrowala
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Abhirup Nayak
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Manish Bhandare
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Reshma P Ambulkar
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Shailesh V Shrikhande
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
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Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10369-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Sethi N, Dutta A, Puri GD, Panday BC, Sood J, Gupta M, Choudhary PK, Sharma S. Evaluation of Automated Delivery of Propofol Using a Closed-Loop Anesthesia Delivery System in Patients Undergoing Thoracic Surgery: A Randomized Controlled Study. J Cardiothorac Vasc Anesth 2020; 35:1089-1095. [PMID: 33036887 DOI: 10.1053/j.jvca.2020.09.101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Automated propofol total intravenous anesthesia (TIVA) administered by a closed-loop anesthesia delivery system (CLADS) exhibits greater efficiency than conventional manual methods, but its use in major thoracic surgery is limited. DESIGN Prospective, single-blind, randomized controlled study. SETTING Single-center tertiary care hospital. PARTICIPANTS Patients undergoing thoracic surgery. INTERVENTIONS Patients were randomly allocated to receive CLADS-driven (CLADS group) or manually controlled (manual group) propofol TIVA. MEASUREMENTS AND MAIN RESULTS Anesthesia depth consistency (primary objective) and anesthesia delivery performance, propofol usage, work ergonomics, intraoperative hemodynamics, and recovery profile (secondary objectives) were analyzed. No differences were found for anesthesia depth consistency (percentage of time the bispectral index was within ± 10 of target) (CLADS group: 82.5% [78.5%-87.2%] v manual group: 86.5% [74.2%-92.5%]; p = 0.581) and delivery performance, including median performance error (CLADS group: 3 [-4 to 6] v manual group: 1 [-2.5 to 6]); median absolute performance error (CLADS group: 10 [10-12] v manual group:10 [8-12]); wobble (CLADS group: 10 [8-12] v manual group: 9 [6-10.5]); and global score (CLADS group: 24.2 [21.2-29.3] v manual group: 22.1 [17.3-32.3]) (p > 0.05). However, propofol requirements were significantly lower in the CLADS group for induction (CLADS group: 1.27 ± 0.21] mg/kg v manual group: 1.78 ± 0.51 mg/kg; p = 0.014) and maintenance (CLADS group: 4.02 ± 0.99 mg/kg/h v manual group: 5.11 ± 1.40 mg/kg/h; p = 0.025) of TIVA. Ergonomically, CLADS-driven TIVA was found to be significantly superior to manual control (infusion adjustment frequency/h) (manual infusion: 9.6 [7.8-14.9] v CLADS delivery [none]). CONCLUSIONS In thoracic surgery patients, CLADS-automated propofol TIVA confers significant ergonomic advantage along with lower propofol usage.
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Affiliation(s)
- Nitin Sethi
- Department of Anaesthesiology, Pain, and Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Amitabh Dutta
- Department of Anaesthesiology, Pain, and Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India.
| | - Goverdhan D Puri
- Department of Anaesthesia and Intensive Care, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Bhuwan C Panday
- Department of Anaesthesiology, Pain, and Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Jayashree Sood
- Department of Anaesthesiology, Pain, and Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Manish Gupta
- Department of Anaesthesiology, Pain, and Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Prabhat K Choudhary
- Department of Anaesthesiology, Pain, and Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
| | - Shikha Sharma
- Department of Anaesthesiology, Pain, and Perioperative Medicine, Sir Ganga Ram Hospital, New Delhi, India
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Zaouter C, Joosten A, Rinehart J, Struys MMRF, Hemmerling TM. Autonomous Systems in Anesthesia. Anesth Analg 2020; 130:1120-1132. [DOI: 10.1213/ane.0000000000004646] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Kong E, Nicolaou N, Vizcaychipi MP. Hemodynamic stability of closed-loop anesthesia systems: a systematic review. Minerva Anestesiol 2020; 86:76-87. [DOI: 10.23736/s0375-9393.19.13927-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Al-Mufti F, Kim M, Dodson V, Sursal T, Bowers C, Cole C, Scurlock C, Becker C, Gandhi C, Mayer SA. Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success. Curr Neurol Neurosci Rep 2019; 19:89. [PMID: 31720867 DOI: 10.1007/s11910-019-0998-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
PURPOSE OF REVIEW Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury. Artificial intelligence (AI) has garnered interest in the basic management of these complicated patients as data collection becomes increasingly automated. RECENT FINDINGS In this opinion article, we highlight the potential AI has in aiding the clinician in several aspects of neurocritical care, particularly in monitoring and managing intracranial pressure, seizures, hemodynamics, and ventilation. The model-based method and data-driven method are currently the two major AI methods for analyzing critical care data. Both are able to analyze the vast quantities of patient data that are accumulated in the neurocritical care unit. AI has the potential to reduce healthcare costs, minimize delays in patient management, and reduce medical errors. However, these systems are an aid to, not a replacement for, the clinician's judgment.
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Affiliation(s)
- Fawaz Al-Mufti
- Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA.
- Departments of Neurology, Westchester Medical Center at New York Medical College, Valhalla, NY, USA.
- Neuroendovascular Surgery and Neurocritical Care Attending, Westchester Medical Center at New York Medical College, 100 Woods Road, Macy Pavilion 1331, Valhalla, NY, 10595, USA.
| | - Michael Kim
- Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Vincent Dodson
- Department of Neurosurgery, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Tolga Sursal
- Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Christian Bowers
- Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Chad Cole
- Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Corey Scurlock
- eHealth Center, Westchester Medical Center Health Network, Valhalla, NY, USA
- Departments of Anesthesiology, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Christian Becker
- eHealth Center, Westchester Medical Center Health Network, Valhalla, NY, USA
- Departments of Internal Medicine, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Chirag Gandhi
- Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Stephan A Mayer
- Department of Neurology, Henry Ford Health System, Detroit, MI, USA
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Dutta A, Sethi N, Sood J, Panday BC, Gupta M, Choudhary P, Puri GD. The Effect of Dexmedetomidine on Propofol Requirements During Anesthesia Administered by Bispectral Index-Guided Closed-Loop Anesthesia Delivery System. Anesth Analg 2019; 129:84-91. [DOI: 10.1213/ane.0000000000003470] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Al-Mufti F, Dodson V, Lee J, Wajswol E, Gandhi C, Scurlock C, Cole C, Lee K, Mayer SA. Artificial intelligence in neurocritical care. J Neurol Sci 2019; 404:1-4. [PMID: 31302258 DOI: 10.1016/j.jns.2019.06.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 06/16/2019] [Accepted: 06/22/2019] [Indexed: 01/31/2023]
Abstract
BACKGROUND Neurocritical care combines the management of extremely complex disease states with the inherent limitations of clinically assessing patients with brain injury. As the management of neurocritical care patients can be immensely complicated, the automation of data-collection and basic management by artificial intelligence systems have garnered interest. METHODS In this opinion article, we highlight the potential artificial intelligence has in monitoring and managing several aspects of neurocritical care, specifically intracranial pressure, seizure monitoring, blood pressure, and ventilation. RESULTS The two major AI methods of analytical technique currently exist for analyzing critical care data: the model-based method and data driven method. Both of these methods have demonstrated an ability to analyze vast quantities of patient data, and we highlight the ways in which these modalities of artificial intelligence might one day play a role in neurocritical care. CONCLUSIONS While none of these artificial intelligence systems are meant to replace the clinician's judgment, these systems have the potential to reduce healthcare costs and errors or delays in medical management.
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Affiliation(s)
- Fawaz Al-Mufti
- Departments of Neurosurgery, Westchester Medical Center, New York Medical College, Valhalla, NY, United States of America; Departments of Neurology, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America.
| | - Vincent Dodson
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America
| | - James Lee
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America; Department of Neurology, Rutgers University, Robert Wood Johnson Medical School, New Brunswick, NJ, United States of America
| | - Ethan Wajswol
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America
| | - Chirag Gandhi
- Departments of Neurosurgery, Westchester Medical Center, New York Medical College, Valhalla, NY, United States of America; Departments of Neurology, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America
| | - Corey Scurlock
- Departments of Anesthesiology, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America; Departments of Internal Medicine, Westchester Medical Center at New York Medical College, Valhalla, NY, United States of America
| | - Chad Cole
- Departments of Neurosurgery, Westchester Medical Center, New York Medical College, Valhalla, NY, United States of America
| | - Kiwon Lee
- Department of Neurosurgery, Rutgers University, New Jersey Medical School, Newark, NJ, United States of America; Department of Neurology, Rutgers University, Robert Wood Johnson Medical School, New Brunswick, NJ, United States of America
| | - Stephan A Mayer
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States of America
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Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia. ALGORITHMS 2018. [DOI: 10.3390/a11070106] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control.
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Merigo L, Padula F, Pawlowski A, Dormido S, Guzmán Sánchez JL, Latronico N, Paltenghi M, Visioli A. A model-based control scheme for depth of hypnosis in anesthesia. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.01.023] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Merigo L, Beschi M, Padula F, Latronico N, Paltenghi M, Visioli A. Event-Based control of depth of hypnosis in anesthesia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 147:63-83. [PMID: 28734531 DOI: 10.1016/j.cmpb.2017.06.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 06/10/2017] [Accepted: 06/20/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE In this paper, we propose the use of an event-based control strategy for the closed-loop control of the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. METHODS A new event generator with high noise-filtering properties is employed in addition to a PIDPlus controller. The tuning of the parameters is performed off-line by using genetic algorithms by considering a given data set of patients. RESULTS The effectiveness and robustness of the method is verified in simulation by implementing a Monte Carlo method to address the intra-patient and inter-patient variability. A comparison with a standard PID control structure shows that the event-based control system achieves a reduction of the total variation of the manipulated variable of 93% in the induction phase and of 95% in the maintenance phase. CONCLUSIONS The use of event based automatic control in anesthesia yields a fast induction phase with bounded overshoot and an acceptable disturbance rejection. A comparison with a standard PID control structure shows that the technique effectively mimics the behavior of the anesthesiologist by providing a significant decrement of the total variation of the manipulated variable.
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Affiliation(s)
- Luca Merigo
- Dipartimento di Ingegneria dell'Informazione, University of Brescia, Italy.
| | - Manuel Beschi
- Istituto di Tecnologie Industriali e Automazione, National Research Council Milan, Italy.
| | - Fabrizio Padula
- Department of Mathematics and Statistics, Curtin University, Australia.
| | - Nicola Latronico
- Department of Surgery, Radiology, and Public Health,University of Brescia, Italy.
| | | | - Antonio Visioli
- Dipartimento di Ingegneria Meccanica e Industriale, University of Brescia, Italy.
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Pasin L, Nardelli P, Pintaudi M, Greco M, Zambon M, Cabrini L, Zangrillo A. Closed-Loop Delivery Systems Versus Manually Controlled Administration of Total IV Anesthesia: A Meta-analysis of Randomized Clinical Trials. Anesth Analg 2017; 124:456-464. [PMID: 28099320 DOI: 10.1213/ane.0000000000001394] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Bispectral Index Scale (BIS)-guided closed-loop delivery of anesthetics has been extensively studied. We performed a meta-analysis of all the randomized clinical trials comparing efficacy and performance between BIS-guided closed-loop delivery and manually controlled administration of total IV anesthesia. Scopus, PubMed, EMBASE, and the Cochrane Central Register of clinical trials were searched for pertinent studies. Inclusion criteria were random allocation to treatment and closed-loop delivery systems versus manually controlled administration of total IV anesthesia in any surgical setting. Exclusion criteria were duplicate publications and nonadult studies. Twelve studies were included, randomly allocating 1284 patients. Use of closed-loop anesthetic delivery systems was associated with a significant reduction in the dose of propofol administered for induction of anesthesia (mean difference [MD] = 0.37 [0.17-0.57], P for effect <0.00001, P for heterogeneity = 0.001, I = 74%) and a significant reduction in recovery time (MD = 1.62 [0.60-2.64], P for effect <0.0001, P for heterogeneity = 0.06, I = 47%). The target depth of anesthesia was preserved more frequently with closed-loop anesthetic delivery than with manual control (MD = -15.17 [-23.11 to -7.24], P for effect <0.00001, P for heterogeneity <0.00001, I = 83%). There were no differences in the time required to induce anesthesia and the total propofol dose. Closed-loop anesthetic delivery performed better than manual-control delivery. Both median absolute performance error and wobble index were significantly lower in closed-loop anesthetic delivery systems group (MD = 5.82 [3.17-8.46], P for effect <0.00001, P for heterogeneity <0.00001, I = 90% and MD = 0.92 [0.13-1.72], P for effect = 0.003, P for heterogeneity = 0.07, I = 45%). When compared with manual control, BIS-guided anesthetic delivery of total IV anesthesia reduces propofol requirements during induction, better maintains a target depth of anesthesia, and reduces recovery time.
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Affiliation(s)
- Laura Pasin
- From the Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Padula F, Ionescu C, Latronico N, Paltenghi M, Visioli A, Vivacqua G. Optimized PID control of depth of hypnosis in anesthesia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 144:21-35. [PMID: 28495004 DOI: 10.1016/j.cmpb.2017.03.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 03/03/2017] [Accepted: 03/15/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing. METHODS In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case. RESULTS Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable. CONCLUSIONS Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed.
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Affiliation(s)
- Fabrizio Padula
- Department of Mathematics and Statistics, Curtin University, Australia.
| | - Clara Ionescu
- Department of Electrical Energy, Metals, Mechanical Constructions and Systems, Ghent University, Belgium.
| | - Nicola Latronico
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Italy; Department of Anesthesiology, Critical Care and Emergency Spedali Civili University Hospital, Brescia, Italy.
| | - Massimiliano Paltenghi
- Department of Anesthesiology, Critical Care and Emergency Spedali Civili University Hospital, Brescia, Italy.
| | - Antonio Visioli
- Dipartimento di Ingegneria Meccanica e Industriale, University of Brescia, Italy.
| | - Giulio Vivacqua
- Dipartimento di Ingegneria Meccanica e Industriale, University of Brescia, Italy.
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Ramos-Luengo A, Asensio-Merino F. Hypnosis closed loop TCI systems in outpatient surgery. ACTA ACUST UNITED AC 2017; 64:323-327. [PMID: 28057334 DOI: 10.1016/j.redar.2016.10.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Revised: 10/25/2016] [Accepted: 10/26/2016] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Determine the influence of general anaesthesia with closed-loop systems in the results of outpatient varicose vein surgery. PATIENTS AND METHODS Retrospective observational study including data from 270 outpatients between 2014 and 2015. The patients were divided into 2 groups according to the type of general anaesthesia used. The CL Group included patients who received propofol in closed-loop guided by BIS and remifentanil using TCI, and the C Group received non-closed-loop anaesthesia. Age, sex, surgical time, discharge time and failure of outpatient surgery were recorded. Quantitative data were checked for normal distribution by the method of Kolmogorov-Smirnov-Lilliefors. Differences between groups were analysed by a Student-t-test or Mann-Whitney-Wilcoxon test, depending on their distribution. Categorical data were analysed by a Chi-squared test. We used Kaplan-Meier estimator and the effect size (calculated by Cohen's d) to study the discharge time. Statistical analysis was performed using R 3.2.3 binary for Mac OS X 10.9. RESULTS There were no significant differences in age, sex and surgical time and failure of outpatient surgery. Discharge time was different in both groups: 200 (100) vs. 180 (82.5) minutes, C Group and CL Group, respectively (data are median and interquartile rank); P=.005. CONCLUSION The use of closed-loop devices for the hypnotic component of anaesthesia hastens discharge time. However, for this effect to be clinically significant, some improvements still need to be made in our outpatient surgery units.
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Affiliation(s)
- A Ramos-Luengo
- Servicio de Anestesia y Reanimación, Hospital Universitario Severo Ochoa, Leganés, Madrid, España.
| | - F Asensio-Merino
- Servicio de Anestesia y Reanimación, Hospital Universitario Severo Ochoa, Leganés, Madrid, España
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Moldovan M, Calin A, Kumaraswamy VM, Braver D, Simon MV. Burst-Suppression Ratio on Electrocorticography Depends on Interelectrode Distance. J Clin Neurophysiol 2017; 33:127-32. [PMID: 26690549 DOI: 10.1097/wnp.0000000000000248] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION With deepening of anesthesia-induced comatose states, the EEG becomes fragmented by increasing periods of suppression. When measured from conventional EEG recordings, the binary burst-suppression signal (BS) appears similar across the scalp. As such, the BS ratio (BSR), quantifying the fraction of time spent in suppression, is clinically considered a global index of brain function in sedation monitoring. Recent studies indicate that BS may be considerably asynchronous when measured with higher spatial resolution such as on electrocorticography. The authors investigated the magnitude of BSR changes with cortical recording interelectrode distance. METHODS The authors selected fronto-parietal electrocorticography recordings showing propofol-induced BS recorded via 8-electrode strips (1-cm interelectrode distance) during cortical motor mapping in 31 patients. For 1-minute epochs, bipolar recordings were computed between each electrode pair. The median BSR, burst duration (BD), and bursting frequency were derived for each interelectrode distance. RESULTS At 1-cm interelectrode distance, with increasing BSR, BD decreased exponentially. For a BSR between 50% and 80%, BD reached a plateau of 2.1 seconds while the bursting frequency decreased from 14 to 6 bursts per minute. With increasing interelectrode distance, BD increased at a rate of 0.2 seconds per cm. This correlated with a decrease in BSR with distance that reached the rate of -4.4 percentage per centimeters during deepest anesthesia. CONCLUSIONS With increasing cortical interelectrode recording distance, burst summation leads to an increasing BD associated with a reduction in BSR. Standardization of interelectrode distance is important for cortical BSR measurements.
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Affiliation(s)
- Mihai Moldovan
- *Division of Physiology and Neuroscience, Department of Functional Sciences, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; †Department of Neuroscience and Pharmacology, University of Copenhagen, Copenhagen, Denmark; ‡Intraoperative Neurophysiology Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; §Department of Pharmacology, University of Oxford, Oxford, United Kingdom; and ‖Department of Neurology, University Hospitals Case Medical Center, Cleveland, Ohio
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Benefício da anestesia geral com monitoração do índice bispectral em comparação com o monitoramento guiado apenas por parâmetros clínicos. Revisão sistemática e metanálise. Braz J Anesthesiol 2017; 67:72-84. [PMID: 28017174 DOI: 10.1016/j.bjan.2016.10.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 09/22/2015] [Indexed: 11/21/2022] Open
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Total Intravenous Anaesthesia (TIVA) for Ambulatory Surgery: An Update. CURRENT ANESTHESIOLOGY REPORTS 2016. [DOI: 10.1007/s40140-016-0179-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Yang Y, Shanechi MM. An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia. J Neural Eng 2016; 13:066019. [DOI: 10.1088/1741-2560/13/6/066019] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Messina AG, Wang M, Ward MJ, Wilker CC, Smith BB, Vezina DP, Pace NL, Cochrane Anaesthesia Group. Anaesthetic interventions for prevention of awareness during surgery. Cochrane Database Syst Rev 2016; 10:CD007272. [PMID: 27755648 PMCID: PMC6461159 DOI: 10.1002/14651858.cd007272.pub2] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND General anaesthesia is usually associated with unconsciousness. 'Awareness' is when patients have postoperative recall of events or experiences during surgery. 'Wakefulness' is when patients become conscious during surgery, but have no postoperative recollection of the period of consciousness. OBJECTIVES To evaluate the efficacy of two types of anaesthetic interventions in reducing clinically significant awareness:- anaesthetic drug regimens; and- intraoperative anaesthetic depth monitors. SEARCH METHODS We searched the Cochrane Central Register of Controlled Trials (CENTRAL, ISSUE 4 2016); PubMed from 1950 to April 2016; MEDLINE from 1950 to April 2016; and Embase from 1980 to April 2016. We contacted experts to identify additional studies. We performed a handsearch of the citations in the review. We did not search trial registries. SELECTION CRITERIA We included randomized controlled trials (RCTs) of either anaesthetic regimens or anaesthetic depth monitors. We excluded volunteer studies, studies of patients prior to skin incision, intensive care unit studies, and studies that only randomized different word presentations for memory tests (not anaesthetic interventions).Anaesthetic drug regimens included studies of induction or maintenance, or both. Anaesthetic depth monitors included the Bispectral Index monitor, M-Entropy, Narcotrend monitor, cerebral function monitor, cerebral state monitor, patient state index, and lower oesophageal contractility monitor. The use of anaesthetic depth monitors allows the titration of anaesthetic drugs to maintain unconsciousness. DATA COLLECTION AND ANALYSIS At least two authors independently scanned abstracts, extracted data from the studies, and evaluated studies for risk of bias. We made attempts to contact all authors for additional clarification. We performed meta-analysis statistics in packages of the R language. MAIN RESULTS We included 160 studies with 54,109 enrolled participants; 53,713 participants started the studies and 50,034 completed the studies or data analysis (or both). We could not use 115 RCTs in meta-analytic comparisons because they had zero awareness events. We did not merge 27 of the remaining 45 studies because they had excessive clinical and methodological heterogeneity. We pooled the remaining 18 eligible RCTs in meta-analysis. There are 10 studies awaiting classification which we will process when we update the review.The meta-analyses included 18 trials with 36,034 participants. In the analysis of anaesthetic depth monitoring (either Bispectral Index or M-entropy) versus standard clinical and electronic monitoring, there were nine trials with 34,744 participants. The overall event rate was 0.5%. The effect favoured neither anaesthetic depth monitoring nor standard clinical and electronic monitoring, with little precision in the odds ratio (OR) estimate (OR 0.98, 95% confidence interval (CI) 0.59 to 1.62).In a five-study subset of Bispectral Index monitoring versus standard clinical and electronic monitoring, with 34,181 participants, 503 participants gave awareness reports to a blinded, expert panel who adjudicated or judged the outcome for each patient after reviewing the questionnaires: no awareness, possible awareness, or definite awareness. Experts judged 351 patient awareness reports to have no awareness, 87 to have possible awareness, and 65 to have definite awareness. The effect size favoured neither Bispectral Index monitoring nor standard clinical and electronic monitoring, with little precision in the OR estimate for the combination of definite and possible awareness (OR 0.96, 95% CI 0.35 to 2.65). The effect size favoured Bispectral Index monitoring for definite awareness, but with little precision in the OR estimate (OR 0.60, 95% CI 0.13 to 2.75).We performed three smaller meta-analyses of anaesthetic drugs. There were nine studies with 1290 participants. Wakefulness was reduced by ketamine and etomidate compared to thiopental. Wakefulness was more frequent than awareness. Benzodiazepines reduces awareness compared to thiopental, ketamine, and placebo., Also, higher doses of inhaled anaesthetics versus lower doses reduced the risk of awareness.We graded the quality of the evidence as low or very low in the 'Summary of findings' tables for the five comparisons.Most of the secondary outcomes in this review were not reported in the included RCTs. AUTHORS' CONCLUSIONS Anaesthetic depth monitors may have similar effects to standard clinical and electrical monitoring on the risk of awareness during surgery. In older studies comparing anaesthetics in a smaller portion of the patient sample, wakefulness occurred more frequently than awareness. Use of etomidate and ketamine lowered the risk of wakefulness compared to thiopental. Benzodiazepines compared to thiopental and ketamine, or higher doses of inhaled anaesthetics versus lower doses, reduced the risk of awareness.
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Affiliation(s)
- Anthony G Messina
- School of Management, University of Texas at DallasThe Alliance for Medical Management EducationBox 2331920 N. Coit RoadRichardsonTXUSA75080
| | - Michael Wang
- University of LeicesterClinical Psychology UnitLancaster RoadLeicesterUKLE1 7HA
| | - Marshall J Ward
- Dartmouth‐Hitchcock Medical Center1 Medical Center DrLebanonNHUSA03766
| | - Chase C Wilker
- ARUP LaboratoriesClinical Toxicology IIISalt Lake CityUTUSA
| | - Brett B Smith
- University of UtahUniversity of Utah School of MedicineSalt Lake CityUTUSA84112
| | - Daniel P Vezina
- University of UtahDepartment of Anesthesiology, Department of Internal Medicine, Division of CardiologySalt Lake CityUTUSA
- Veteran's AdministrationEchocardiography LaboratorySalt Lake CityUTUSA
| | - Nathan Leon Pace
- University of UtahDepartment of Anesthesiology3C444 SOM30 North 1900 EastSalt Lake CityUTUSA84132‐2304
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Zaouter C, Hemmerling TM, Lanchon R, Valoti E, Remy A, Leuillet S, Ouattara A. The Feasibility of a Completely Automated Total IV Anesthesia Drug Delivery System for Cardiac Surgery. Anesth Analg 2016; 123:885-93. [DOI: 10.1213/ane.0000000000001152] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Khaqan A, Bilal M, Ilyas M, Ijaz B, Ali Riaz R. Control Law Design for Propofol Infusion to Regulate Depth of Hypnosis: A Nonlinear Control Strategy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2015:1810303. [PMID: 27293475 PMCID: PMC4863132 DOI: 10.1155/2016/1810303] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 03/07/2016] [Accepted: 03/28/2016] [Indexed: 11/28/2022]
Abstract
Maintaining the depth of hypnosis (DOH) during surgery is one of the major objectives of anesthesia infusion system. Continuous administration of Propofol infusion during surgical procedures is essential but increases the undue load of an anesthetist in operating room working in a multitasking setup. Manual and target controlled infusion (TCI) systems are not good at handling instabilities like blood pressure changes and heart rate variability arising due to interpatient variability. Patient safety, large interindividual variability, and less postoperative effects are the main factors to motivate automation in anesthesia. The idea of automated system for Propofol infusion excites the control engineers to come up with a more sophisticated and safe system that handles optimum delivery of drug during surgery and avoids postoperative effects. In contrast to most of the investigations with linear control strategies, the originality of this research work lies in employing a nonlinear control technique, backstepping, to track the desired hypnosis level of patients during surgery. This effort is envisioned to unleash the true capabilities of this nonlinear control technique for anesthesia systems used today in biomedical field. The working of the designed controller is studied on the real dataset of five patients undergoing surgery. The controller tracks the desired hypnosis level within the acceptable range for surgery.
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Affiliation(s)
- Ali Khaqan
- Department of Electrical Engineering, COMSATS Institute of Information Technology, Chak Shahzad, Park Road, Islamabad 44000, Pakistan
| | - Muhammad Bilal
- Department of Electrical Engineering, COMSATS Institute of Information Technology, Chak Shahzad, Park Road, Islamabad 44000, Pakistan
| | - Muhammad Ilyas
- Department of Electrical Engineering, COMSATS Institute of Information Technology, Chak Shahzad, Park Road, Islamabad 44000, Pakistan
| | - Bilal Ijaz
- Department of Electrical Engineering, COMSATS Institute of Information Technology, Chak Shahzad, Park Road, Islamabad 44000, Pakistan
| | - Raja Ali Riaz
- Department of Electrical Engineering, COMSATS Institute of Information Technology, Chak Shahzad, Park Road, Islamabad 44000, Pakistan
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Oliveira CRD, Bernardo WM, Nunes VM. Benefit of general anesthesia monitored by bispectral index compared with monitoring guided only by clinical parameters. Systematic review and meta-analysis. Braz J Anesthesiol 2016; 67:72-84. [PMID: 28017174 DOI: 10.1016/j.bjane.2015.09.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 09/22/2015] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The bispectral index parameter is used to guide the titration of general anesthesia; however, many studies have shown conflicting results regarding the benefits of bispectral index monitoring. The objective of this systematic review with meta-analysis is to evaluate the clinical impact of monitoring with the bispectral index parameter. METHODS The search for evidence in scientific information sources was conducted during December 2013 to January 2015, the following primary databases: Medline/PubMed, LILACS, Cochrane, CINAHL, Ovid, SCOPUS and TESES. The criteria for inclusion in the study were randomized controlled trials, comparing general anesthesia monitored, with bispectral index parameter with anesthesia guided solely by clinical parameters, and patients aged over 18 years. The criteria for exclusion were studies involving anesthesia or sedation for diagnostic procedures, and intraoperative wake-up test for surgery of the spine. RESULTS The use of monitoring with the bispectral index has shown benefits reducing time to extubation, orientation in time and place, and discharge from both the operating room and post anesthetic care unit. The risk of nausea and vomiting after surgery was reduced by 12% in patients monitored with bispectral index. Occurred a reduction of 3% in the risk of cognitive impairment postoperatively at 3 months postoperatively and 6% reduction in the risk of postoperative delirium in patients monitored with bispectral index. Furthermore, the risk of intraoperative memory has been reduced by 1%. CONCLUSION Clinically, anesthesia monitoring with the BIS can be justified because it allows advantages from reducing the recovery time after waking, mainly by reducing the administration of general anesthetics as well as the risk of adverse events.
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Affiliation(s)
- Carlos Rogério Degrandi Oliveira
- Hospital Guilherme Alvaro, Departamento de Anestesiologia, Santos, SP, Brazil; Hospital Ana Costa, Departamento de Anestesiologia, Santos, SP, Brazil.
| | - Wanderley Marques Bernardo
- Universidade de São Paulo, Faculdade de Medicina, Medicina Baseada em Evidência, São Paulo, SP, Brazil; Centro Universitário Lusíada, Faculdade de Medicina de Santos, Santos, SP, Brazil; Programa Diretrizes da Associação Médica Brasileira, Santos, SP, Brazil
| | - Victor Moisés Nunes
- Centro Universitário Lusíada, Faculdade de Medicina de Santos, Santos, SP, Brazil
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Puri GD, Mathew PJ, Biswas I, Dutta A, Sood J, Gombar S, Palta S, Tsering M, Gautam PL, Jayant A, Arora I, Bajaj V, Punia TS, Singh G. A Multicenter Evaluation of a Closed-Loop Anesthesia Delivery System: A Randomized Controlled Trial. Anesth Analg 2016; 122:106-14. [PMID: 25902324 DOI: 10.1213/ane.0000000000000769] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Closed-loop systems for anesthesia delivery have been shown to outperform traditional manual control in different clinical settings. The present trial was aimed at evaluating the feasibility and efficacy of Bispectral Index (BIS)-guided closed-loop anesthesia delivery system (CLADS) in comparison with manual control across multiple centers in India. METHODS Adult patients scheduled for major surgical procedures of an expected duration of 1 to 3 hours were randomized across 6 sites into 2 groups: a CLADS group and a manual group. In the manual control group, propofol infusion was titrated manually by the attending anesthesiologist to a BIS of 50 during induction and maintenance. Analgesia was maintained with fentanyl infusion and nitrous oxide in both groups. In the CLADS group, both induction and maintenance of anesthesia were performed automatically using CLADS. The primary outcome measure was the performance of the system as assessed by the percentage of total anesthesia time BIS remained ±10 of target BIS. The secondary outcome measures were a percentage of anesthesia-time heart rate and mean arterial pressure within 25% of the baseline, median absolute performance error, wobble, and global score. Wobble indicates intraindividual variability in the control of BIS, and global score reflects the overall performance; lower values indicate superior performance for both parameters. The performance parameters of the system also were compared among the participating sites. RESULTS Two hundred forty-two patients were randomized. BIS was maintained within ±10 of target for significantly longer time in the CLADS group (81.4% ± 8.9 % of anesthesia duration) than in the manual group (55.34% ± 25%, P < 0.0001). The indices that assess performance were significantly better in the CLADS group than the manual group as follows: median absolute performance error was 10 (10, 12) (median [interquartile range]) in the CLADS group versus 18 (14, 24) in the manual group, P < 0.0001; wobble was 9 (8, 10) in CLADS group versus 10 (8, 14) in the manual group, P = 0.0009; and Global score, which reflects overall performance, was 24 (19, 30) in the CLADS group versus 51 (31, 99) in the manual group, P < 0.0001. The percentage of time heart rate was within 25% of the baseline was significantly greater in the CLADS group (heart rate of 95 [87, 99], median [interquartile range], in the CLADS group versus 90 [75, 98] in the manual group P = 0.0031). On comparison of data between the centers, the performance parameters did not differ significantly among the centers in the CLADS group (P = 0.94), but the parameters differed significantly among the centers in the manual group (P < 0.001). CONCLUSIONS Our study in a multicenter setting proves the consistently better performance of automated anesthesia drug delivery compared with conventional manual control. This highlights an important advantage of an automated system for delivering standardized anesthesia, thereby overcoming differences in practices among anesthesiologists.
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Affiliation(s)
- Goverdhan D Puri
- From the *Department of Anesthesia and Intensive Care, Post Graduate Institute of Medical Education and Research, Chandigarh, India; †Department of Anesthesia, Sir Ganga Ram Hospital, New Delhi, India; ‡Department of Anesthesia, Government Medical College and Hospital, Chandigarh, India; §Department of Anesthesia, Sonam Norbu M Hospital, Leh, Jammu & Kashmir, India; ∥Department of Anesthesia, Dayanand Medical College, Ludhiana, India; ¶Department of Anesthesia, Government Medical College and Hospital, Patiala, Punjab, India; and #National Institute of Electronics and Information Technology (NIELIT), Itanagar, Arunachal Pradesh, India
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Abstract
OBJECTIVE Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by burst suppression. Feedback control can be used to regulate burst suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma. APPROACH We developed a robust CLAD system to control the burst suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment. MAIN RESULTS In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]%. Under steady state conditions the CLAD system exhibited a median error of 0.1 [-0.5, 0.9]%; inaccuracy of 1.8 [0.9, 3.4]%; oscillation index of 1.8 [0.9, 3.4]%; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg(-1). The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg(-1) h(-1). Performance fell within clinically acceptable limits for all measures. SIGNIFICANCE A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.
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Affiliation(s)
- M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Seong-Eun Kim
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Emery N Brown
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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Shanechi MM. A generalizable adaptive brain-machine interface design for control of anesthesia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:1099-1102. [PMID: 26736457 DOI: 10.1109/embc.2015.7318557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Brain-machine interfaces (BMIs) for closed-loop control of anesthesia have the potential to automatically monitor and control brain states under anesthesia. Since a variety of anesthetic states are needed in different clinical scenarios, designing a generalizable BMI architecture that can control a wide range of anesthetic states is essential. In addition, drug dynamics are non-stationary over time and could change with the depth of anesthesia. Hence for precise control, a BMI needs to track these non-stationarities online. Here we design a BMI architecture that generalizes to control of various anesthetic states and their associated neural signatures, and is adaptive to time-varying drug dynamics. We provide a systematic approach to build general parametric models that quantify the anesthetic state and describe the drug dynamics. Based on these models, we develop an adaptive closed-loop controller within the framework of stochastic optimal feedback control. This controller tracks the non-stationarities in drug dynamics, achieves tight control in a time-varying environment, and removes the need for an offline system identification session. For robustness, the BMI also ensures small drug infusion rate variations at steady state. We test the BMI architecture for control of two common anesthetic states, i.e., burst suppression in medically-induced coma and unconsciousness in general anesthesia. Using numerical experiments, we find that the BMI generalizes to control of both these anesthetic states; in a time-varying environment, even without initial knowledge of model parameters, the BMI accurately controls these two different anesthetic states, reducing bias and error more than 70 times and 9 times, respectively, compared with a non-adaptive system.
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Closed-loop control better than open-loop control of profofol TCI guided by BIS: a randomized, controlled, multicenter clinical trial to evaluate the CONCERT-CL closed-loop system. PLoS One 2015; 10:e0123862. [PMID: 25886041 PMCID: PMC4401751 DOI: 10.1371/journal.pone.0123862] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 02/18/2015] [Indexed: 11/18/2022] Open
Abstract
Background The CONCERT-CL closed-loop infusion system designed by VERYARK Technology Co., Ltd. (Guangxi, China) is an innovation using TCI combined with closed-loop controlled intravenous anesthesia under the guide of BIS. In this study we performed a randomized, controlled, multicenter study to compare closed-loop control and open-loop control of propofol by using the CONCERT-CL closed-loop infusion system. Methods 180 surgical patients from three medical centers undergone TCI intravenous anesthesia with propofol and remifentanil were randomly assigned to propofol closed-loop group and propofol opened-loop groups. Primary outcome was global score (GS, GS = (MDAPE+Wobble)/% of time of bispectral index (BIS) 40-60). Secondary outcomes were doses of the anesthetics and emergence time from anesthesia, such as, time to tracheal extubation. Results There were 89 and 86 patients in the closed-loop and opened-loop groups, respectively. GS in the closed-loop groups (22.21±8.50) were lower than that in the opened-loop group (27.19±15.26) (p=0.009). The higher proportion of time of BIS between 40 and 60 was also observed in the closed-loop group (84.11±9.50%), while that was 79.92±13.17% in the opened-loop group, (p=0.016). No significant differences in propofol dose and time of tracheal extubation were observed. The frequency of propofol regulation in the closed-loop group (31.55±9.46 times/hr) was obverse higher than that in the opened-loop group (6.84±6.21 times/hr) (p=0.000). Conclusion The CONCERT-CL closed-loop infusion system can automatically regulate the TCI of propofol, maintain the BIS value in an adequate range and reduce the workload of anesthesiologists better than open-loop system. Trial Registration ChiCTR ChiCTR-OOR-14005551
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Feasibility of Closed-loop Titration of Propofol and Remifentanil Guided by the Bispectral Monitor in Pediatric and Adolescent Patients. Anesthesiology 2015; 122:759-67. [DOI: 10.1097/aln.0000000000000577] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Abstract
Background:
This study was designed to assess the feasibility of dual closed-loop titration of propofol and remifentanil guided solely by the Bispectral Index (BIS) monitor in pediatric and adolescent patients during anesthesia.
Methods:
Children undergoing elective surgery in this single-blind randomized study were allocated into the closed-loop (auto) or manual (manual) group. Primary outcome was the percentage of time with the BIS in the range 40 to 60 (BIS40–60). Secondary outcomes were the percentage of deep (BIS<40) anesthesia and drug consumption. Data are presented as median (interquartile range) or number (%).
Results:
Twenty-three patients (12 [10 to 14] yr) were assigned to the auto group and 19 (14 [7 to 14] yr) to the manual group. The closed-loop controller was able to provide induction and maintenance for all patients. The percentage of time with BIS40–60 was greater in the auto group (87% [75 to 96] vs. 72% [48 to 79]; P = 0.002), with a decrease in the percentage of BIS<40 (7% [2 to 17] vs. 21% [11 to 38]; P = 0.002). Propofol (2.4 [1.9 to 3.3] vs. 1.7 [1.2 to 2.8] mg/kg) and remifentanil (2.3 [2.0 to 3.0] vs. 2.5 [1.2 to 4.3] μg/kg) consumptions were similar in auto versus manual groups during induction, respectively. During maintenance, propofol consumption (8.2 [6.0 to 10.2] vs. 7.9 [7.2 to 9.1] mg kg−1 h−1; P = 0.89) was similar between the two groups, but remifentanil consumption was greater in the auto group (0.39 [0.22 to 0.60] vs. 0.22 [0.17 to 0.32] μg kg−1 min−1; P = 0.003). Perioperative adverse events and length of stay in the postanesthesia care unit were similar.
Conclusion:
Intraoperative automated control of hypnosis and analgesia guided by the BIS is clinically feasible in pediatric and adolescent patients and outperformed skilled manual control.
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Liu N, Lory C, Assenzo V, Cocard V, Chazot T, Le Guen M, Sessler D, Journois D, Fischler M. Feasibility of closed-loop co-administration of propofol and remifentanil guided by the bispectral index in obese patients: a prospective cohort comparison †. Br J Anaesth 2015; 114:605-14. [DOI: 10.1093/bja/aeu401] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
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Zhang CH, Ma WQ, Yang YL, Wang HM, Dong FT, Huang ZX. Median effective effect-site concentration of sufentanil for wake-up test in adolescents undergoing surgery: a randomized trial. BMC Anesthesiol 2015; 15:27. [PMID: 25774090 PMCID: PMC4359582 DOI: 10.1186/s12871-015-0003-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Accepted: 02/11/2015] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND To determine the median effective concentration of sufentanil as an analgesic during wake-up tests after sevoflurane anesthesia during surgery for adolescent idiopathic scoliosis (AIS). METHODS This is a randomised controlled trial. Sixty patients aged 13-18 years scheduled for AIS surgery were randomized into six groups of 10 patients each to receive target effect-site concentrations of sufentanil of 0.19, 0.1809, 0.1723, 0.1641, 0.1563, and 0.1489 ng/ml (target concentration ratio, 1.05). Wake-up time was recorded. Median EC50 and 95% confidence interval (CI) for sufentanil target-controlled infusion (TCI) were determined using Kärber's method. The primary outcome was median EC50 for sufentanil TCI as an analgesic during the wake-up test after sevoflurane anesthesia during surgery for AIS. RESULTS The EC50 and 95% CI of sufentanil TCI were 0.1682 ng/ml and 0.1641 ~ 0.1724 ng/ml, respectively. CONCLUSIONS The EC50 of sufentanil TCI was 0.1682 ng/ml (95% CI: 0.1641 ~ 0.1724 ng/ml) during sevoflurane anesthesia in adolescents undergoing surgery for idiopathic scoliosis with intraoperative wake-up tests. TRIAL REGISTRATION Clinicaltrials.gov identifier: ChiCTR-TTRCC-12002696.
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Affiliation(s)
- Cheng-Hua Zhang
- Department of Anesthesiology, Kunming General Hospital of Chengdu Military Area, Kunming, 650032 China
| | - Wei-Qing Ma
- Department of Anesthesiology, Kunming General Hospital of Chengdu Military Area, Kunming, 650032 China
| | - Yun-Li Yang
- Department of Anesthesiology, Kunming General Hospital of Chengdu Military Area, Kunming, 650032 China
| | - Hui-Ming Wang
- Department of Anesthesiology, Kunming General Hospital of Chengdu Military Area, Kunming, 650032 China
| | - Fa-Tuan Dong
- Department of Anesthesiology, Kunming General Hospital of Chengdu Military Area, Kunming, 650032 China
| | - Zhang-Xiang Huang
- Department of Anesthesiology, Kunming General Hospital of Chengdu Military Area, Kunming, 650032 China
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Affiliation(s)
- Timothy E Miller
- From the Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina
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Biswas I, Mathew PJ, Singh RS, Puri GD. Evaluation of closed-loop anesthesia delivery for propofol anesthesia in pediatric cardiac surgery. Paediatr Anaesth 2013; 23:1145-52. [PMID: 24118468 DOI: 10.1111/pan.12265] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/19/2013] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The objective of this study was to compare the feasibility of closed-loop anesthesia delivery with manual control of propofol in pediatric patients during cardiac surgery. METHODS Forty ASA II-III children, undergoing elective cardiac surgery under cardiopulmonary bypass (CPB) in a tertiary care hospital, were randomized to receive propofol either through a closed-loop anesthesia delivery system (CL group) or through traditional manual control (manual group) to achieve a target BIS of 50. Patients were induced and subsequently maintained with a propofol infusion. The propofol usage and the efficacy of closed-loop system in controlling BIS within ±10 of the target were compared with that of manual control. RESULTS The maintenance of BIS within ±10 of target and intraoperative hemodynamic stability were similar between the two groups. However, induction dose of propofol was less in the CL group (2.06 ± 0.79 mg·kg(-1) ) than the manual group (2.95 ± 1.03 mg·kg(-1) ) (P = 0.006) with less overshoot of BIS during induction in the closed-loop group (P = 0.007). Total propofol used in the off-CPB period was less in the CL group (6.29 ± 2.48 mg·kg(-1) h(-1) vs 7.82 ± 2.1 mg·kg(-1) h(-1) ) (P = 0.037). Phenylephrine use in the pre-CPB period was more in the manual group (16.92 ± 10.92 μg·kg(-1) vs 5.79 ± 5.98 μg·kg(-1) ) (P = 0.014). Manual group required a median of 18 (range 8-29) dose adjustments per hour, while the CL group required none. CONCLUSION This study demonstrated the feasibility of closed-loop controlled propofol anesthesia in children, even in challenging procedures such as cardiac surgery. Closed-loop system needs further and larger evaluation to establish its safety and efficacy.
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Affiliation(s)
- Indranil Biswas
- Department of Anaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Shanechi MM, Chemali JJ, Liberman M, Solt K, Brown EN. A brain-machine interface for control of medically-induced coma. PLoS Comput Biol 2013; 9:e1003284. [PMID: 24204231 PMCID: PMC3814408 DOI: 10.1371/journal.pcbi.1003284] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Accepted: 08/07/2013] [Indexed: 11/19/2022] Open
Abstract
Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care. Brain-machine interfaces (BMI) for closed-loop control of anesthesia have the potential to enable fully automated and precise control of brain states in patients requiring anesthesia care. Medically-induced coma is one such drug-induced state in which the brain is profoundly inactivated and unconscious and the electroencephalogram (EEG) pattern consists of bursts of electrical activity alternating with periods of suppression, termed burst suppression. Medical coma is induced to treat refractory intracranial hypertension and uncontrollable seizures. The state of coma is often required for days, making accurate manual control infeasible. We develop a BMI that can automatically and precisely control the level of burst suppression in real time in individual rodents. The BMI consists of novel estimation and control algorithms that take as input the EEG activity, estimate the burst suppression level based on this activity, and use this estimate as feedback to control the drug infusion rate in real time. The BMI maintains precise control and promptly changes the level of burst suppression while avoiding overshoot or undershoot. Our work demonstrates the feasibility of automatic reliable and accurate control of medical coma that can provide considerable therapeutic benefits.
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Affiliation(s)
- Maryam M. Shanechi
- School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, United States of America
- * E-mail: (MMS); (ENB)
| | - Jessica J. Chemali
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Max Liberman
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Ken Solt
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Emery N. Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- * E-mail: (MMS); (ENB)
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Ching S, Liberman MY, Chemali JJ, Westover MB, Kenny J, Solt K, Purdon PL, Brown EN. Real-time closed-loop control in a rodent model of medically induced coma using burst suppression. Anesthesiology 2013; 119:848-60. [PMID: 23770601 PMCID: PMC3857134 DOI: 10.1097/aln.0b013e31829d4ab4] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND A medically induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection after traumatic brain injuries. The authors hypothesized that a closed-loop anesthetic delivery system could automatically and precisely control the electroencephalogram state of burst suppression and efficiently maintain a medically induced coma. METHODS In six rats, the authors implemented a closed-loop anesthetic delivery system for propofol consisting of: a computer-controlled pump infusion, a two-compartment pharmacokinetics model defining propofol's electroencephalogram effects, the burst-suppression probability algorithm to compute in real time from the electroencephalogram the brain's burst-suppression state, an online parameter-estimation procedure and a proportional-integral controller. In the control experiment each rat was randomly assigned to one of the six burst-suppression probability target trajectories constructed by permuting the burst-suppression probability levels of 0.4, 0.65, and 0.9 with linear transitions between levels. RESULTS In each animal the controller maintained approximately 60 min of tight, real-time control of burst suppression by tracking each burst-suppression probability target level for 15 min and two between-level transitions for 5-10 min. The posterior probability that the closed-loop anesthetic delivery system was reliable across all levels was 0.94 (95% CI, 0.77-1.00; n = 18) and that the system was accurate across all levels was 1.00 (95% CI, 0.84-1.00; n = 18). CONCLUSION The findings of this study establish the feasibility of using a closed-loop anesthetic delivery systems to achieve in real time reliable and accurate control of burst suppression in rodents and suggest a paradigm to precisely control medically induced coma in patients.
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Affiliation(s)
- ShiNung Ching
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Anesthesia, Harvard Medical School, Boston, Massachusetts
- Research Fellow, Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts; Research Fellow, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Research Affiliate, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Max Y. Liberman
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Research Assistant, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Jessica J. Chemali
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Research Assistant, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts
- Instructor, Department of Neurology, Harvard Medical School, Boston, Massachusetts; Assistant in Neurology, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jonathan Kenny
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Research Assistant, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Ken Solt
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Anesthesia, Harvard Medical School, Boston, Massachusetts
- Assistant Professor, Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts; Assistant Anesthetist, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Research Affiliate, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Anesthesia, Harvard Medical School, Boston, Massachusetts
- Instructor, Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts; Instructor, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Research Affiliate, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Emery N. Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Anesthesia, Harvard Medical School, Boston, Massachusetts
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Warren M. Zapol Professor of Anaesthesia, Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts; Anesthetist, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital Boston, Massachusetts; Professor of Computational Neuroscience, Professor of Health Sciences and Technology, Institute for Medical Engineering and Sciences, Department of Brain and Cognitive Sciences, Harvard-MIT Health Sciences and Technology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Liberman MY, Ching S, Chemali J, Brown EN. A closed-loop anesthetic delivery system for real-time control of burst suppression. J Neural Eng 2013; 10:046004. [PMID: 23744607 PMCID: PMC3746775 DOI: 10.1088/1741-2560/10/4/046004] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst suppression, in which bursts of electrical activity alternate with periods of quiescence or suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for burst suppression control to automate management of medically-induced coma. APPROACH We establish a CLAD system to control burst suppression consisting of: a two-dimensional linear system model relating the anesthetic brain level to the EEG dynamics; a new control signal, the burst suppression probability (BSP) defining the instantaneous probability of suppression; the BSP filter, a state-space algorithm to estimate the BSP from EEG recordings; a proportional-integral controller; and a system identification procedure to estimate the model and controller parameters. MAIN RESULTS We demonstrate reliable performance of our system in simulation studies of burst suppression control using both propofol and etomidate in rodent experiments based on Vijn and Sneyd, and in human experiments based on the Schnider pharmacokinetic model for propofol. Using propofol, we further demonstrate that our control system reliably tracks changing target levels of burst suppression in simulated human subjects across different epidemiological profiles. SIGNIFICANCE Our results give new insights into CLAD system design and suggest a control-theory framework to automate second-to-second control of burst suppression for management of medically-induced coma.
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Affiliation(s)
- Max Y. Liberman
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - ShiNung Ching
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jessica Chemali
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Emery N. Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medicine, Engineering, and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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A technical description of a novel pharmacological anesthesia robot. J Clin Monit Comput 2013; 28:27-34. [DOI: 10.1007/s10877-013-9451-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2012] [Accepted: 03/02/2013] [Indexed: 10/26/2022]
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