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Ghita M, Birs IR, Copot D, Muresan CI, Neckebroek M, Ionescu CM. Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia. IEEE Trans Biomed Eng 2023; 70:2991-3002. [PMID: 37527300 DOI: 10.1109/tbme.2023.3274541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
OBJECTIVE The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only. METHODS This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS. RESULTS The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN. CONCLUSION We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information. SIGNIFICANCE Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.
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Malagutti N, McGinness G, Nithyanandam DA. Real-Time Personalised Pharmacokinetic-Pharmacodynamic Modelling in Propofol Anesthesia through Bayesian Inference. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38082840 DOI: 10.1109/embc40787.2023.10339991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Pharmacological models describe a patient's response to the administration of a medicinal drug based on parameters derived from population studies. However, considerable inter-patient variability exists, such that population models may underperform when used to predict the actual response of a specific individual. In applications which demand predictive accuracy-such as target-controlled infusion of anesthetic agents-modeling uncertainty may reduce system dependability and introduce clinical risk. Our work investigates the use of Bayesian inference, implemented through a particle filter algorithm, to refine a prior model of propofol pharmacokinetics-pharmacodynamics and estimate patient-specific parameters in real-time. We report here on an observational clinical study conducted on 40 adults undergoing general anesthesia, where we evaluated the performance of Bayesian inference-personalized models in forecasting forward trends of depth of anesthesia (Bispectral Index) measurements and compared it with that of a traditional population-based pharmacological model. Our results show a significant reduction in prediction error metrics for the patient-specific models. Our study demonstrates the viability and practical implementability of Bayesian inference as a tool for real-time intra-operative estimation of personalized pharmacological models in anesthesia applications.
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Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications. SURGERIES 2023; 4:264-274. [DOI: 10.3390/surgeries4020027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023] Open
Abstract
The field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes, such as machine learning, has the potential to improve efficiency, reduce costs, and ameliorate patient outcomes. AI can assist with decision making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. The potential uses of AI in anesthesia can be schematically grouped into clinical decision support and pharmacologic and mechanical robotic applications. Tele-anesthesia includes strategies of telemedicine, as well as device networking, for improving logistics in the operating room, and augmented reality approaches for training and assistance. Despite the growing scientific interest, further research and validation are needed to fully understand the benefits and limitations of these applications in clinical practice. Moreover, the ethical implications of AI in anesthesia must also be considered to ensure that patient safety and privacy are not compromised. This paper aims to provide a comprehensive overview of AI in anesthesia, including its current and potential applications, and the ethical considerations that must be considered to ensure the safe and effective use of the technology.
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Affiliation(s)
- Marco Cascella
- Pain Unit and Research, Istituto Nazionale Tumori IRCCS Fondazione Pascale, 80100 Napoli, Italy
| | - Maura C. Tracey
- Rehabilitation Medicine Unit, Strategic Health Services Department, Istituto Nazionale Tumori-IRCCS-Fondazione Pascale, 80100 Naples, Italy
| | - Emiliano Petrucci
- Department of Anesthesia and Intensive Care Unit, San Salvatore Academic Hospital of L’Aquila, 67100 L’Aquila, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
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Ghita M, Birs IR, Copot D, Muresan CI, Ionescu CM. Bioelectrical impedance analysis of thermal-induced cutaneous nociception. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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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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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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Analysis of Fast-Track Surgery with Pain Care on Postoperative Pain Improvement and Complication Prevention in Perioperative Spine Surgery Patients. Emerg Med Int 2022; 2022:9291583. [PMID: 36034483 PMCID: PMC9410989 DOI: 10.1155/2022/9291583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/28/2022] [Indexed: 11/26/2022] Open
Abstract
Objective The study aimed to analyze the effect of fast-track surgery with pain care on the improvement of postoperative pain and the prevention of postoperative complications in perioperative spinal surgery patients. Methods A total of 126 patients undergoing spinal surgery from January 2021 to September 2021 were chosen as the study population, and the patients were classified into the regular group, the FTS group, and the combined group by random grouping, with 42 cases in each group. Patients in the regular group used routine perioperative care in spine surgery, patients in the FTS group used the FTS care model, and patients in the combined group combined special pain care on the basis of the FTS group. We compared the numeric rating scale (NRS) and pain severity of patients in the three groups post-op, 30 min, 1 h, 3 h, 6 h, and 24 h after surgery; we compared the time to get out of bed, length of stay, and occurrence of postoperative adverse effects in the three groups, compared the incidence of complications in the three groups, and compared the satisfaction of care in the three groups. Results The NRS scores at 12 h, 24 h, 48 h, and 72 h post-op in the combined group and FTS group were lower than those in the regular group, and the NRS scores at 12 h and 24 h post-op in the combined group were lower than those in the FTS group (all P < 0.05); the post-op bed activity time, post-op hospitalization time, post-op adverse reaction rate, and post-op complication rate in the combined group and FTS group were shorter or lower than those of the regular group. Nursing satisfaction was higher than that of the regular group, the post-op time to bed activity in the combined group was shorter than that of the FTS group, and nursing satisfaction was higher than that of the FTS group (all P < 0.05). Conclusion The use of FTS with pain care interventions helps relieve postoperative pain in perioperative patients in spine surgery, reduce the incidence of post-op adverse effects and complications in patients, accelerate their postoperative recovery, and improve nursing satisfaction.
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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: 0] [Impact Index Per Article: 0] [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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Introna M, van den Berg JP, Eleveld DJ, Struys MMRF. Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists. J Anesth 2022; 36:294-302. [PMID: 35147768 PMCID: PMC8967750 DOI: 10.1007/s00540-022-03044-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 01/25/2022] [Indexed: 11/20/2022]
Abstract
This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we can update our initial belief about data with the evidence obtained from any kind of acquired data. It provides a theoretical framework and a statistical method to use pre-existing information within the context of new evidence. Several authors have described the Bayesian approach as capable of dealing with uncertainty in medical decision-making. This review describes the Bayes theorem and how it is used in clinical studies in anesthesia and critical care. It starts with a general introduction to the theorem and its related concepts of prior and posterior probabilities. Second, there is an explanation of the basic concepts of the Bayesian statistical inference. Last, a summary of the applicability of some of the Bayesian statistics in current literature is provided, such as Bayesian analysis of clinical trials and PKPD modeling.
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Affiliation(s)
- Michele Introna
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.,Department of Anesthesiology and Intensive Care Medicine, Cremona Hospital, Cremona, Italy
| | - Johannes P van den Berg
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
| | - Douglas J Eleveld
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Michel M R F Struys
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.,Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium
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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: 15] [Impact Index Per Article: 7.5] [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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Abstract
PURPOSE OF REVIEW To give an overview of cerebral monitoring techniques for surgical ICU patients. RECENT FINDINGS As the burden of postsurgical neurological and neurocognitive complications becomes increasingly recognized, cerebral monitoring in the surgical ICU might gain a relevant role in detecting and possibly preventing adverse outcomes. However, identifying neurological alterations in surgical ICU patients, who are often sedated and mechanically ventilated, can be challenging. Various noninvasive and invasive techniques are available for cerebral monitoring, providing an assessment of cortical electrical activity, cerebral oxygenation, blood flow autoregulation, intracranial pressure, and cerebral metabolism. These techniques can be used for the diagnosis of subclinical seizures, the assessment of sedation depth and delirium, the detection of an impaired cerebral blood flow, and the diagnosis of neurosurgical complications. SUMMARY Cerebral monitoring can be a valuable tool in the early detection of adverse outcomes in surgical ICU patients, but the evidence is limited, and clear clinical indications are still lacking.
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Patel B, Patel H, Shah D, Sarvaia A. Control strategy with multivariable fault tolerance module for automatic intravenous anesthesia. Biomed Eng Lett 2020; 10:555-578. [PMID: 33194248 DOI: 10.1007/s13534-020-00169-2] [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: 02/23/2020] [Revised: 07/23/2020] [Accepted: 07/31/2020] [Indexed: 11/30/2022] Open
Abstract
In the anesthesia automation, an automatic propofol infusion system uses Bi-spectral Index Signal (BIS) as a primary feedback signal to manipulate propofol dose. However, the BIS signal may be suspended for some time due to poor EEG signal quality, noise, and many other factors. Therefore, BIS signal failure may be the main cause of inadequate propofol infusion. This fact motivates the need for integration of multivariable fault tolerance module (MFTM) and fractional-order Smith predictor controller to avoid adverse reactions of inadequate propofol dosing during BIS failure. Smith Predictor control strategy is sufficiently robust to predict feedback BIS during BIS failure via patient pharmacological modeled BIS. However, modeled BIS may not provide a guarantee of adequate propofol infusion during BIS failure and especially in the presence of hypotension and hypertension. Thus, the proposed control strategy is designed with MFTM to detect BIS sensor fault and to estimate feedback BIS during BIS failure. Further, the proposed control strategy is designed with a multivariable pharmacological patient model to analyze the cross effect of propofol infusion on BIS and hemodynamic variables. The robustness of the proposed control strategy is tested in the presence of noxious surgical stimulation, BIS sensor fault and heavy hemodynamic disturbance. The pharmacological parameters and recorded signals of 30 patients during various surgeries have been used to validate simulated results. The performance of the proposed control strategy assures optimization and smooth propofol infusion during BIS failure. The proposed system provides stability for a wide range of physiological parameters range. The proposed scheme maintains smooth BIS and MAP signal despite the delay, BIS sensor fault, and surgical disturbances.
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Affiliation(s)
- Bhavina Patel
- Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India
| | - Hirenkumar Patel
- Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India
| | - Divyang Shah
- Surat Municipal Institute of Medical Education and Research (SMIMER), Surat, India
| | - Alpesh Sarvaia
- U. N. Mehta Institute of Cardiology and Research, Ahmedabad, 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: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Neckebroek M, Ghita M, Ghita M, Copot D, Ionescu CM. Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial. J Clin Med 2020; 9:E684. [PMID: 32143327 PMCID: PMC7141233 DOI: 10.3390/jcm9030684] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 02/13/2020] [Accepted: 02/29/2020] [Indexed: 12/21/2022] Open
Abstract
Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0-10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r2 = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions.
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Affiliation(s)
- Martine Neckebroek
- Department of Anesthesia, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium;
| | - Mihaela Ghita
- Research group of Dynamical Systems and Control, Ghent University, Tech Lane Science Park 125, 9052 Ghent, Belgium (D.C.); (C.M.I.)
- EEDT—Core Lab on Decision and Control, Flanders Make Consortium, Tech Lane Science Park 131, 9052 Ghent, Belgium
| | - Maria Ghita
- Research group of Dynamical Systems and Control, Ghent University, Tech Lane Science Park 125, 9052 Ghent, Belgium (D.C.); (C.M.I.)
- EEDT—Core Lab on Decision and Control, Flanders Make Consortium, Tech Lane Science Park 131, 9052 Ghent, Belgium
| | - Dana Copot
- Research group of Dynamical Systems and Control, Ghent University, Tech Lane Science Park 125, 9052 Ghent, Belgium (D.C.); (C.M.I.)
- EEDT—Core Lab on Decision and Control, Flanders Make Consortium, Tech Lane Science Park 131, 9052 Ghent, Belgium
| | - Clara M. Ionescu
- Research group of Dynamical Systems and Control, Ghent University, Tech Lane Science Park 125, 9052 Ghent, Belgium (D.C.); (C.M.I.)
- EEDT—Core Lab on Decision and Control, Flanders Make Consortium, Tech Lane Science Park 131, 9052 Ghent, Belgium
- Department of Automatic Control, Technical University of Cluj Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
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Hendrickx JFA, Van Zundert T, De Wolf AM. End of year summary 2019: anaesthesia and airway management. J Clin Monit Comput 2020; 34:1-5. [PMID: 31898149 DOI: 10.1007/s10877-019-00453-2] [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] [Received: 12/14/2019] [Accepted: 12/14/2019] [Indexed: 11/26/2022]
Abstract
This end of the year summary reviews anesthesia related manuscripts that have been published in the Journal of Clinical Monitoring and Computing in 2019. Anesthesia is currently defined as being composed of unconsciousness, immobility, and autonomic nervous system (ANS) control (Br J Anaesth;122:e127-e135135, Egan 2019). Pain is a postoperative issue, because by definition unconsciousness implies pain cannot be experienced. We first review work related to these aspect of the profession: unconsciousness (EEG, target control), immobility (muscle relaxants), and ANS control. Regaining consciousness has to be accompanied by pain control, and it is important to ensure that the patient regains baseline cognitive function. Anesthesia machine equipment, drug administration, and airway related topics make up the rest of published manuscripts.
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Affiliation(s)
| | - Tom Van Zundert
- Department of Anesthesiology/CCM, OLV Hospital, Aalst, Belgium
| | - Andre M De Wolf
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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