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Dong S, Wang Q, Wang S, Zhou C, Wang H. Hypotension prediction index for the prevention of hypotension during surgery and critical care: A narrative review. Comput Biol Med 2024; 170:107995. [PMID: 38325215 DOI: 10.1016/j.compbiomed.2024.107995] [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: 10/01/2023] [Revised: 12/17/2023] [Accepted: 01/13/2024] [Indexed: 02/09/2024]
Abstract
Surgeons and anesthesia clinicians commonly face a hemodynamic disturbance known as intraoperative hypotension (IOH), which has been linked to more severe postoperative outcomes and increases mortality rates. Increased occurrence of IOH has been positively associated with mortality and incidence of myocardial infarction, stroke, and organ dysfunction hypertension. Hence, early detection and recognition of IOH is meaningful for perioperative management. Currently, when hypotension occurs, clinicians use vasopressor or fluid therapy to intervene as IOH develops but interventions should be taken before hypotension occurs; therefore, the Hypotension Prediction Index (HPI) method can be used to help clinicians further react to the IOH process. This literature review evaluates the HPI method, which can reliably predict hypotension several minutes before a hypotensive event and is beneficial for patients' outcomes.
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Affiliation(s)
- Siwen Dong
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Qing Wang
- Department of Anesthesiology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
| | - Shuai Wang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Congcong Zhou
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China; Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Hongwei Wang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China; Department of Anesthesiology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China.
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The Effect of Intermittent versus Continuous Non-Invasive Blood Pressure Monitoring on the Detection of Intraoperative Hypotension, a Sub-Study. J Clin Med 2022; 11:jcm11144083. [PMID: 35887844 PMCID: PMC9321987 DOI: 10.3390/jcm11144083] [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: 05/30/2022] [Revised: 06/26/2022] [Accepted: 07/01/2022] [Indexed: 02/01/2023] Open
Abstract
Intraoperative hypotension is associated with postoperative complications. However, in the majority of surgical patients, blood pressure (BP) is measured intermittently with a non-invasive cuff around the upper arm (NIBP-arm). We hypothesized that NIBP-arm, compared with a non-invasive continuous alternative, would result in missed events and in delayed recognition of hypotensive events. This was a sub-study of a previously published cohort study in adult patients undergoing surgery. The detection of hypotension (mean arterial pressure below 65 mmHg) was compared using two non-invasive methods; intermittent oscillometric NIBP-arm versus continuous NIBP measured with a finger cuff (cNIBP-finger) (Nexfin, Edwards Lifesciences). cNIBP-finger was used as the reference standard. Out of 350 patients, 268 patients (77%) had one or more hypotensive events during surgery. Out of the 286 patients, 72 (27%) had one or more missed hypotensive events. The majority of hypotensive events (92%) were detected with NIBP-arm, but were recognized at a median of 1.2 (0.6–2.2) minutes later. Intermittent BP monitoring resulted in missed hypotensive events and the hypotensive events that were detected were recognized with a delay. This study highlights the advantage of continuous monitoring. Future studies are needed to understand the effect on patient outcomes.
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Siontis GCM, Sweda R, Noseworthy PA, Friedman PA, Siontis KC, Patel CJ. Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials. BMJ Health Care Inform 2022; 28:bmjhci-2021-100466. [PMID: 34969668 PMCID: PMC8718483 DOI: 10.1136/bmjhci-2021-100466] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 12/04/2021] [Indexed: 12/20/2022] Open
Abstract
Objective Given the complexities of testing the translational capability of new artificial intelligence (AI) tools, we aimed to map the pathways of training/validation/testing in development process and external validation of AI tools evaluated in dedicated randomised controlled trials (AI-RCTs). Methods We searched for peer-reviewed protocols and completed AI-RCTs evaluating the clinical effectiveness of AI tools and identified development and validation studies of AI tools. We collected detailed information, and evaluated patterns of development and external validation of AI tools. Results We found 23 AI-RCTs evaluating the clinical impact of 18 unique AI tools (2009–2021). Standard-of-care interventions were used in the control arms in all but one AI-RCT. Investigators did not provide access to the software code of the AI tool in any of the studies. Considering the primary outcome, the results were in favour of the AI intervention in 82% of the completed AI-RCTs (14 out of 17). We identified significant variation in the patterns of development, external validation and clinical evaluation approaches among different AI tools. A published development study was found only for 10 of the 18 AI tools. Median time from the publication of a development study to the respective AI-RCT was 1.4 years (IQR 0.2–2.2). Conclusions We found significant variation in the patterns of development and validation for AI tools before their evaluation in dedicated AI-RCTs. Published peer-reviewed protocols and completed AI-RCTs were also heterogeneous in design and reporting. Upcoming guidelines providing guidance for the development and clinical translation process aim to improve these aspects.
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Affiliation(s)
- George C M Siontis
- Department of Cardiology, Inselspital, University Hospital of Bern, Bern, Switzerland
| | - Romy Sweda
- Department of Cardiology, Inselspital, University Hospital of Bern, Bern, Switzerland
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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Birkhoff DC, van Dalen ASH, Schijven MP. A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov 2021; 28:611-619. [PMID: 33625307 PMCID: PMC8450995 DOI: 10.1177/1553350621996961] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background. Artificial intelligence (AI) is an era upcoming in medicine and, more recently, in the operating room (OR). Existing literature elaborates mainly on the future possibilities and expectations for AI in surgery. The aim of this study is to systematically provide an overview of the current actual AI applications used to support processes inside the OR. Methods. PubMed, Embase, Cochrane Library, and IEEE Xplore were searched using inclusion criteria for relevant articles up to August 25th, 2020. No study types were excluded beforehand. Articles describing current AI applications for surgical purposes inside the OR were reviewed. Results. Nine studies were included. An overview of the researched and described applications of AI in the OR is provided, including procedure duration prediction, gesture recognition, intraoperative cancer detection, intraoperative video analysis, workflow recognition, an endoscopic guidance system, knot-tying, and automatic registration and tracking of the bone in orthopedic surgery. These technologies are compared to their, often non-AI, baseline alternatives. Conclusions. Currently described applications of AI in the OR are limited to date. They may, however, have a promising future in improving surgical precision, reduce manpower, support intraoperative decision-making, and increase surgical safety. Nonetheless, the application and implementation of AI inside the OR still has several challenges to overcome. Clear regulatory, organizational, and clinical conditions are imperative for AI to redeem its promise. Future research on use of AI in the OR should therefore focus on clinical validation of AI applications, the legal and ethical considerations, and on evaluation of implementation trajectory.
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Affiliation(s)
- David C. Birkhoff
- Department of Surgery, Amsterdam UMC, University of Amsterdam, The Netherlands
| | | | - Marlies P. Schijven
- Department of Surgery, Amsterdam Gastroenterology and Metabolism, University of Amsterdam, The Netherlands
- institution-id-type="Ringgold" />Li Ka Shing Knowledge Institute, institution-id-type="Ringgold" />St Michaels Hospital, Toronto, Canada
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Schenk J, Wijnberge M, Maaskant JM, Hollmann MW, Hol L, Immink RV, Vlaar AP, van der Ster BJP, Geerts BF, Veelo DP. Effect of Hypotension Prediction Index-guided intraoperative haemodynamic care on depth and duration of postoperative hypotension: a sub-study of the Hypotension Prediction trial. Br J Anaesth 2021; 127:681-688. [PMID: 34303491 DOI: 10.1016/j.bja.2021.05.033] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/29/2021] [Accepted: 05/18/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Intraoperative and postoperative hypotension are associated with morbidity and mortality. The Hypotension Prediction (HYPE) trial showed that the Hypotension Prediction Index (HPI) reduced the depth and duration of intraoperative hypotension (IOH), without excess use of intravenous fluid, vasopressor, and/or inotropic therapies. Here, We hypothesised that intraoperative HPI-guided haemodynamic care would reduce the severity of postoperative hypotension in the PACU. METHODS This was a sub-study of the HYPE study, in which 60 adults undergoing elective noncardiac surgery were allocated randomly to intraoperative HPI-guided or standard haemodynamic care. Blood pressure was measured using a radial intra-arterial catheter, which was connected to a FloTracIQ sensor. Hypotension was defined as MAP <65 mm Hg, and a hypotensive event was defined as MAP <65 mm Hg for at least 1 min. The primary outcome was the time-weighted average (TWA) of postoperative hypotension. Secondary outcomes were absolute incidence, area under threshold for hypotension, and percentage of time spent with MAP <65 mm Hg. RESULTS Overall, 54/60 (90%) subjects (age 64 (8) yr; 44% female) completed the protocol, owing to failure of the FloTracIQ device in 6/60 (10%) patients. Intraoperative HPI-guided care was used in 28 subjects; 26 subjects were randomised to the control group. Postoperative hypotension occurred in 37/54 (68%) subjects. HPI-guided care did not reduce the median duration (TWA) of postoperative hypotension (adjusted median difference, vs standard of care: 0.118; 95% confidence interval [CI], 0-0.332; P=0.112). HPI-guidance reduced the percentage of time with MAP <65 mm Hg by 4.9% (adjusted median difference: -4.9; 95% CI, -11.7 to -0.01; P=0.046). CONCLUSIONS Intraoperative HPI-guided haemodynamic care did not reduce the TWA of postoperative hypotension.
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Affiliation(s)
- Jimmy Schenk
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
| | - Marije Wijnberge
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
| | - Jolanda M Maaskant
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, Location Academic Medical Centre and University of Amsterdam, Amsterdam, the Netherlands
| | - Markus W Hollmann
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
| | - Liselotte Hol
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
| | - Rogier V Immink
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
| | - Alexander P Vlaar
- Department of Intensive Care, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands.
| | - Björn J P van der Ster
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
| | - Bart F Geerts
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
| | - Denise P Veelo
- Department of Anaesthesiology, Amsterdam UMC, Location Academic Medical Centre, Amsterdam, the Netherlands
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Awad H, Alcodray G, Raza A, Boulos R, Essandoh M, Bhandary S, Dalton R. Intraoperative Hypotension-Physiologic Basis and Future Directions. J Cardiothorac Vasc Anesth 2021; 36:2154-2163. [PMID: 34218998 DOI: 10.1053/j.jvca.2021.05.057] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/26/2021] [Accepted: 05/30/2021] [Indexed: 02/05/2023]
Abstract
Intraoperative hypotension (IOH) is a major concern to the anesthesiologist. Its appropriate identification and management require an understanding of the physiology of blood pressure regulation, prudent blood pressure monitoring, and treatment. Even short durations of low mean arterial pressure have been associated with adverse postoperative clinical outcomes. The challenge is for the clinician to respond proactively, address the specific etiology of IOH, and keep in mind any changes to the patient's physiology. Predictive technology, such as the Hypotension Prediction Index, offers the clinician new insight into IOH. It has been shown to predict hypotension up to 15 minutes before occurrence. It also calculates stroke volume variation, dynamic arterial elastance, and left ventricular contractility, which can inform the anesthesiologist of the etiology of IOH to direct management. This new technology has the potential to reduce duration or even prevent IOH. In the authors' opinion, it is an example of how human-machine interaction will contribute to future advances in medicine. Additional studies should evaluate the effects of its use on postoperative outcomes.
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Affiliation(s)
- Hamdy Awad
- Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH.
| | | | - Arwa Raza
- Ohio State University College of Medicine, Columbus, OH
| | - Racha Boulos
- Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Michael Essandoh
- Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Sujatha Bhandary
- Department of Anesthesiology, Emory University School of Medicine, Atlanta, GA
| | - Ryan Dalton
- Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH
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Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: A cohort study. Eur J Anaesthesiol 2021; 38:609-615. [PMID: 33927105 DOI: 10.1097/eja.0000000000001521] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Intra-operative hypotension is associated with adverse postoperative outcomes. A machine-learning-derived algorithm developed to predict hypotension based on arterial blood pressure (ABP) waveforms significantly reduced intra-operative hypotension. The algorithm calculates the likelihood of hypotension occurring within minutes, expressed as the Hypotension Prediction Index (HPI) which ranges from 0 to 100. Currently, HPI is only available for patients monitored with invasive ABP, which is restricted to high-risk procedures and patients. In this study, the performance of HPI, employing noninvasive continuous ABP measurements, is assessed. OBJECTIVES The first aim was to compare the performance of the HPI algorithm, using noninvasive versus invasive ABP measurements, at a mathematically optimal HPI alarm threshold (Youden index). The second aim was to assess the performance of the algorithm using a HPI alarm threshold of 85 that is currently used in clinical trials. Hypotension was defined as a mean arterial pressure (MAP) below 65 mmHg for at least 1 min. The predictive performance of the algorithm at different HPI alarm thresholds (75 and 95) was studied. DESIGN Observational cohort study. SETTING Tertiary academic medical centre. PATIENTS Five hundred and seven adult patients undergoing general surgery. RESULTS The performance of the algorithm with invasive and noninvasive ABP input was similar. A HPI alarm threshold of 85 showed a median [IQR] time from alarm to hypotension of 2.7 [1.0 to 7.0] min with a sensitivity of 92.7 (95% confidence interval [CI], 91.2 to 94.3), specificity of 87.6 (95% CI, 86.2 to 89.0), positive predictive value of 79.9 (95% CI, 77.7 to 82.1) and negative predictive value of 95.8 (95% CI, 94.9 to 96.7). A HPI alarm threshold of 75 provided a lower positive predictive value but a prolonged time from prediction to actual hypotension. CONCLUSION This study demonstrated that the algorithm can be employed using continuous noninvasive ABP waveforms. This opens up the potential to predict and prevent hypotension in a larger patient population. TRIAL REGISTRATION Clinical trials registration number NCT03533205.
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Rozental O, Thalappillil R, White RS, Tam CW. To Swan or Not to Swan: Indications, Alternatives, and Future Directions. J Cardiothorac Vasc Anesth 2020; 35:600-615. [PMID: 32859489 DOI: 10.1053/j.jvca.2020.07.067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 07/21/2020] [Accepted: 07/22/2020] [Indexed: 01/10/2023]
Abstract
The pulmonary artery catheter (PAC) has revolutionized bedside assessment of preload, afterload, and contractility using measured pulmonary capillary wedge pressure, calculated systemic vascular resistance, and estimated cardiac output. It is placed percutaneously by a flow-directed balloon-tipped technique through the venous system and the right heart to the pulmonary artery. Interest in the hemodynamic variables obtained from PACs paved the way for the development of numerous less-invasive hemodynamic monitors over the past 3 decades. These devices estimate cardiac output using concepts such as pulse contour and pressure analysis, transpulmonary thermodilution, carbon dioxide rebreathing, impedance plethysmography, Doppler ultrasonography, and echocardiography. Herein, the authors review the conception, technologic advancements, and modern use of PACs, as well as the criticisms regarding the clinical utility, reliability, and safety of PACs. The authors comment on the current understanding of the benefits and limitations of alternative hemodynamic monitors, which is important for providers caring for critically ill patients. The authors also briefly discuss the use of hemodynamic monitoring in goal-directed fluid therapy algorithms in Enhanced Recovery After Surgery programs.
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Affiliation(s)
- Olga Rozental
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY; Department of Anesthesiology, NewYork-Presbyterian Hospital, New York, NY
| | - Richard Thalappillil
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY; Department of Anesthesiology, NewYork-Presbyterian Hospital, New York, NY
| | - Robert S White
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY; Department of Anesthesiology, NewYork-Presbyterian Hospital, New York, NY
| | - Christopher W Tam
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY; Department of Anesthesiology, NewYork-Presbyterian Hospital, New York, NY.
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Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, Schenk J, Terwindt LE, Hollmann MW, Vlaar AP, Veelo DP. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA 2020; 323:1052-1060. [PMID: 32065827 PMCID: PMC7078808 DOI: 10.1001/jama.2020.0592] [Citation(s) in RCA: 240] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
IMPORTANCE Intraoperative hypotension is associated with increased morbidity and mortality. A machine learning-derived early warning system to predict hypotension shortly before it occurs has been developed and validated. OBJECTIVE To test whether the clinical application of the early warning system in combination with a hemodynamic diagnostic guidance and treatment protocol reduces intraoperative hypotension. DESIGN, SETTING, AND PARTICIPANTS Preliminary unblinded randomized clinical trial performed in a tertiary center in Amsterdam, the Netherlands, among adult patients scheduled for elective noncardiac surgery under general anesthesia and an indication for continuous invasive blood pressure monitoring, who were enrolled between May 2018 and March 2019. Hypotension was defined as a mean arterial pressure (MAP) below 65 mm Hg for at least 1 minute. INTERVENTIONS Patients were randomly assigned to receive either the early warning system (n = 34) or standard care (n = 34), with a goal MAP of at least 65 mm Hg in both groups. MAIN OUTCOMES AND MEASURES The primary outcome was time-weighted average of hypotension during surgery, with a unit of measure of millimeters of mercury. This was calculated as the depth of hypotension below a MAP of 65 mm Hg (in millimeters of mercury) × time spent below a MAP of 65 mm Hg (in minutes) divided by total duration of operation (in minutes). RESULTS Among 68 randomized patients, 60 (88%) completed the trial (median age, 64 [interquartile range {IQR}, 57-70] years; 26 [43%] women). The median length of surgery was 256 minutes (IQR, 213-430 minutes). The median time-weighted average of hypotension was 0.10 mm Hg (IQR, 0.01-0.43 mm Hg) in the intervention group vs 0.44 mm Hg (IQR, 0.23-0.72 mm Hg) in the control group, for a median difference of 0.38 mm Hg (95% CI, 0.14-0.43 mm Hg; P = .001). The median time of hypotension per patient was 8.0 minutes (IQR, 1.33-26.00 minutes) in the intervention group vs 32.7 minutes (IQR, 11.5-59.7 minutes) in the control group, for a median difference of 16.7 minutes (95% CI, 7.7-31.0 minutes; P < .001). In the intervention group, 0 serious adverse events resulting in death occurred vs 2 (7%) in the control group. CONCLUSIONS AND RELEVANCE In this single-center preliminary study of patients undergoing elective noncardiac surgery, the use of a machine learning-derived early warning system compared with standard care resulted in less intraoperative hypotension. Further research with larger study populations in diverse settings is needed to understand the effect on additional patient outcomes and to fully assess safety and generalizability. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03376347.
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Affiliation(s)
- Marije Wijnberge
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
- Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Bart F. Geerts
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Liselotte Hol
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Nikki Lemmers
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Marijn P. Mulder
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
- Department of Technical Medicine, University of Twente, Enschede, the Netherlands
| | - Patrick Berge
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Jimmy Schenk
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Lotte E. Terwindt
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Markus W. Hollmann
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Alexander P. Vlaar
- Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
| | - Denise P. Veelo
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
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Kouz K, Hoppe P, Briesenick L, Saugel B. Intraoperative hypotension: Pathophysiology, clinical relevance, and therapeutic approaches. Indian J Anaesth 2020; 64:90-96. [PMID: 32139925 PMCID: PMC7017666 DOI: 10.4103/ija.ija_939_19] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 01/06/2020] [Accepted: 01/08/2020] [Indexed: 12/20/2022] Open
Abstract
Intraoperative hypotension (IOH) i.e., low arterial blood pressure (AP) during surgery is common in patients having non-cardiac surgery under general anaesthesia. It has a multifactorial aetiology, and is associated with major postoperative complications including acute kidney injury, myocardial injury and death. Therefore, IOH may be a modifiable risk factor for postoperative complications. However, there is no uniform definition for IOH. IOH not only occurs during surgery but also after the induction of general anaesthesia before surgical incision. However, the optimal therapeutic approach to IOH remains elusive. There is evidence from one small randomised controlled trial that individualising AP targets may reduce the risk of postoperative organ dysfunction compared with standard care. More research is needed to define individual AP harm thresholds, to develop therapeutic strategies to treat and avoid IOH, and to integrate new technologies for continuous AP monitoring.
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Affiliation(s)
- Karim Kouz
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Phillip Hoppe
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Luisa Briesenick
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bernd Saugel
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Outcomes Research Consortium, Cleveland, Ohio, USA
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