1
|
Coeckelenbergh S, Soucy-Proulx M, Van der Linden P, Roullet S, Moussa M, Kato H, Toubal L, Naili S, Rinehart J, Grogan T, Cannesson M, Duranteau J, Joosten A. Restrictive versus Decision Support Guided Fluid Therapy during Major Hepatic Resection Surgery: A Randomized Controlled Trial. Anesthesiology 2024; 141:881-890. [PMID: 39052844 DOI: 10.1097/aln.0000000000005175] [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: 07/27/2024]
Abstract
BACKGROUND Fluid therapy during major hepatic resection aims at minimizing fluids during the dissection phase to reduce central venous pressure, retrograde liver blood flow, and venous bleeding. This strategy, however, may lead to hyperlactatemia. The Acumen assisted fluid management system uses novel decision support software, the algorithm of which helps clinicians optimize fluid therapy. The study tested the hypothesis that using this decision support system could decrease arterial lactate at the end of major hepatic resection when compared to a more restrictive fluid strategy. METHODS This two-arm, prospective, randomized controlled, assessor- and patient-blinded superiority study included consecutive patients undergoing major liver surgery equipped with an arterial catheter linked to an uncalibrated stroke volume monitor. In the decision support group, fluid therapy was guided throughout the entire procedure using the assisted fluid management software. In the restrictive fluid group, clinicians were recommended to restrict fluid infusion to 1 to 2 ml · kg-1 · h-1 until the completion of hepatectomy. They then administered fluids based on advanced hemodynamic variables. Noradrenaline was titrated in all patients to maintain a mean arterial pressure greater than 65 mmHg. The primary outcome was arterial lactate level upon completion of surgery (i.e., skin closure). RESULTS A total of 90 patients were enrolled over a 7-month period. The primary outcome was lower in the decision support group than in the restrictive group (median [quartile 1 to quartile 3], 2.5 [1.9 to 3.7] mmol · l-1vs. 4.6 [3.1 to 5.4] mmol · l-1; median difference, -2.1; 95% CI, -2.7 to -1.2; P < 0.001). Among secondary exploratory outcomes, there was no difference in blood loss (median [quartile 1 to quartile 3], 450 [300 to 600] ml vs. 500 [300 to 800] ml; P = 0.727), although central venous pressure was higher in the decision support group (mean ± SD of 7.7 ± 2.0 mmHg vs. 6.6 ± 1.1 mmHg; P < 0.002). CONCLUSIONS Patients managed using a clinical decision support system to guide fluid administration during major hepatic resection had a lower arterial lactate concentration at the end of surgery when compared to a more restrictive fluid strategy. Future trials are necessary to make conclusive recommendations that will change clinical practice. EDITOR’S PERSPECTIVE
Collapse
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; and Outcomes Research Consortium, Cleveland, Ohio; Department of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, California
| | - Maxim Soucy-Proulx
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France; and Department of Anesthesiology, Montreal University Hospital, Montreal, Canada
| | | | - Stéphanie Roullet
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France
| | - Maya Moussa
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France
| | - Hiromi Kato
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France
| | - Leila Toubal
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France
| | - Salima Naili
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France
| | - Joseph Rinehart
- Department of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, California
| | - Tristan Grogan
- Department of Medicine Statistics Core, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Jacques Duranteau
- Department of Anesthesiology and Intensive Care, Hôpitaux Universitaires Paris-Saclay, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France
| | - Alexandre Joosten
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| |
Collapse
|
2
|
Te R, Zhu B, Ma H, Zhang X, Chen S, Huang Y, Qi G. Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety. BMC Anesthesiol 2024; 24:136. [PMID: 38594630 PMCID: PMC11003123 DOI: 10.1186/s12871-024-02523-8] [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: 12/21/2023] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. METHODS In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). RESULTS The ICV values were proved to be consistent with the anesthetists' ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511-0.0513), RMSE (0.0792, 95% CI: 0.0790-0.0794), and MAPE (0.2086, 95% CI: 0.2077-0.2095) and the largest R2 (0.9047, 95% CI: 0.9043-0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. CONCLUSIONS Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.
Collapse
Affiliation(s)
- Rigele Te
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Bo Zhu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
| | - Haobo Ma
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Isreal Deaconess Medical Center, Boston, MA, 02215, USA
| | - Xiuhua Zhang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Shaohui Chen
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Yuguang Huang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Geqi Qi
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing, 100044, China
| |
Collapse
|
3
|
Vellinga R, Introna M, van Amsterdam K, Zhou XYT, De Smet T, Weber Jensen E, Struys MMRF, van den Berg JP. Implementation of a Bayesian based advisory tool for target-controlled infusion of propofol using qCON as control variable. J Clin Monit Comput 2024; 38:519-529. [PMID: 38112878 DOI: 10.1007/s10877-023-01106-1] [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: 06/29/2023] [Accepted: 11/09/2023] [Indexed: 12/21/2023]
Abstract
This single blinded randomized controlled trial aims to assess whether the application of a Bayesian-adjusted CePROP (effect-site of propofol) advisory tool leads towards a more stringent control of the cerebral drug effect during anaesthesia, using qCON as control variable. 100 patients scheduled for elective surgery were included and randomized into a control or intervention group (1:1 ratio). In the intervention group the advisory screen was made available to the clinician, whereas it was blinded in the control group. The settings of the target-controlled infusion pumps could be adjusted at any time by the clinician. Cerebral drug effect was quantified using processed EEG (CONOX monitor, Fresenius Kabi, Bad Homburg, Germany). The time of qCON between the desired range (35-55) during anaesthesia maintenance was defined as our primary end point. Induction parameters and recovery times were considered secondary end points and coefficient of variance of qCON and CePROP was calculated in order to survey the extent of control towards the mean of the population. The desired range of qCON between 35 and 55 was maintained in 84% vs. 90% (p = 0.15) of the case time in the control versus intervention group, respectively. Secondary endpoints showed similar results in both groups. The coefficient of variation for CePROP was higher in the intervention group. The application of the Bayesian-based CePROP advisory system in this trial did not result in a different time of qCON between 35 and 55 (84 [21] vs. 90 [18] percent of the case time). Significant differences between groups were hard to establish, most likely due to a very high performance level in the control group. More extensive control efforts were found in the intervention group. We believe that this advisory tool could be a useful educational tool for novices to titrate propofol effect-site concentrations.
Collapse
Affiliation(s)
- Remco Vellinga
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Michele Introna
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- NeuroAnesthesia and NeuroIntensive Care, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria, 11, 20133, Milan, Italy
| | - Kai van Amsterdam
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - X Y Tommy Zhou
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Erik Weber Jensen
- Centre for Biomedical Research (CREB), UPC-Barcelonatech, Barcelona, Spain
| | - Michel M R F Struys
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium
| | - Johannes P van den Berg
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Joosten A, Rinehart J, Cannesson M, Coeckelenbergh S, Pochard J, Vicaut E, Duranteau J. Control of mean arterial pressure using a closed-loop system for norepinephrine infusion in severe brain injury patients: the COMAT randomized controlled trial. J Clin Monit Comput 2024; 38:25-30. [PMID: 38310591 PMCID: PMC11330589 DOI: 10.1007/s10877-023-01119-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/15/2023] [Indexed: 02/06/2024]
Abstract
Brain injury patients require precise blood pressure (BP) management to maintain cerebral perfusion pressure (CPP) and avoid intracranial hypertension. Nurses have many tasks and norepinephrine titration has been shown to be suboptimal. This can lead to limited BP control in patients that are in critical need of cerebral perfusion optimization. We have designed a closed-loop vasopressor (CLV) system capable of maintaining mean arterial pressure (MAP) in a narrow range and we aimed to assess its performance when treating severe brain injury patients. Within the first 48 h of intensive care unit (ICU) admission, 18 patients with a severe brain injury underwent either CLV or manual norepinephrine titration. In both groups, the objective was to maintain MAP in target (within ± 5 mmHg of a predefined target MAP) to achieve optimal CPP. Fluid administration was standardized in the two groups. The primary objective was the percentage of time patients were in target. Secondary outcomes included time spent over and under target. Over the four-hour study period, the mean percentage of time with MAP in target was greater in the CLV group than in the control group (95.8 ± 2.2% vs. 42.5 ± 27.0%, p < 0.001). Severe undershooting, defined as MAP < 10 mmHg of target value was lower in the CLV group (0.2 ± 0.3% vs. 7.4 ± 14.2%, p < 0.001) as was severe overshooting defined as MAP > 10 mmHg of target (0.0 ± 0.0% vs. 22.0 ± 29.0%, p < 0.001). The CLV system can maintain MAP in target better than nurses caring for severe brain injury patients.
Collapse
Affiliation(s)
- Alexandre Joosten
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, Ronald Reagan Medical Center, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
| | - Joseph Rinehart
- Department of Anesthesiology & Perioperative Care, University of California Irvine, California, CA, 92868, USA
| | - Maxime Cannesson
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, Ronald Reagan Medical Center, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Sean Coeckelenbergh
- Department of Anesthesiology, Université Paris-Saclay, Hôpital Paul-Brousse, Assistance Publique Hôpitaux de Paris, Villejuif, France
- Outcomes Research Consortium, Cleveland, OH, USA
| | - Jonas Pochard
- Department of Intensive Care, Université Paris-Saclay, Hôpital Bicetre, Assistance Publique Hôpitaux de Paris, Le Kremlin Bicetre, France
| | - Eric Vicaut
- Unité de Recherche Clinique, Lariboisière University Hospital, Paris 7 Diderot University, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Jacques Duranteau
- Department of Intensive Care, Université Paris-Saclay, Hôpital Bicetre, Assistance Publique Hôpitaux de Paris, Le Kremlin Bicetre, France
| |
Collapse
|