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Mohammadi I, Firouzabadi SR, Hosseinpour M, Akhlaghpasand M, Hajikarimloo B, Tavanaei R, Izadi A, Zeraatian-Nejad S, Eghbali F. Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis. J Transl Med 2024; 22:725. [PMID: 39103852 DOI: 10.1186/s12967-024-05481-4] [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: 02/20/2024] [Accepted: 07/03/2024] [Indexed: 08/07/2024] Open
Abstract
INTRODUCTION Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings. METHOD A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI. RESULTS 43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83). CONCLUSION HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.
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Affiliation(s)
- Ida Mohammadi
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran
| | - Shahryar Rajai Firouzabadi
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran
| | - Melika Hosseinpour
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran
| | - Mohammadhosein Akhlaghpasand
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran.
- Department of Surgery, Surgery Research Center, School of Medicine, Rasool-E Akram Hospital, Iran University of Medical Sciences, Tehran, Iran.
| | - Bardia Hajikarimloo
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran
| | - Roozbeh Tavanaei
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran
| | - Amirreza Izadi
- Department of Surgery, Surgery Research Center, School of Medicine, Rasool-E Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Sam Zeraatian-Nejad
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran
- Department of Surgery, Surgery Research Center, School of Medicine, Rasool-E Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Foolad Eghbali
- Department of Surgery, Surgery Research Center, School of Medicine, Rasool-E Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
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Galouzis N, Khawam M, Alexander EV, Khreiss MR, Luu C, Mesropyan L, Riall TS, Kwass WK, Dull RO. Pilot Study to Optimize Goal-directed Hemodynamic Management During Pancreatectomy. J Surg Res 2024; 300:173-182. [PMID: 38815516 DOI: 10.1016/j.jss.2024.04.035] [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: 12/01/2023] [Revised: 04/15/2024] [Accepted: 04/24/2024] [Indexed: 06/01/2024]
Abstract
INTRODUCTION Intraoperative goal-directed hemodynamic therapy (GDHT) is a cornerstone of enhanced recovery protocols. We hypothesized that use of an advanced noninvasive intraoperative hemodynamic monitoring system to guide GDHT may decrease intraoperative hypotension (IOH) and improve perfusion during pancreatic resection. METHODS The monitor uses machine learning to produce the Hypotension Prediction Index to predict hypotensive episodes. A clinical decision-making algorithm uses the Hypotension Prediction Index and hemodynamic data to guide intraoperative fluid versus pressor management. Pre-implementation (PRE), patients were placed on the monitor and managed per usual. Post-implementation (POST), anesthesia teams were educated on the algorithm and asked to use the GDHT guidelines. Hemodynamic data points were collected every 20 s (8942 PRE and 26,638 POST measurements). We compared IOH (mean arterial pressure <65 mmHg), cardiac index >2, and stroke volume variation <12 between the two groups. RESULTS 10 patients were in the PRE and 24 in the POST groups. In the POST group, there were fewer minimally invasive resections (4.2% versus 30.0%, P = 0.07), more pancreaticoduodenectomies (75.0% versus 20.0%, P < 0.01), and longer operative times (329.0 + 108.2 min versus 225.1 + 92.8 min, P = 0.01). After implementation, hemodynamic parameters improved. There was a 33.3% reduction in IOH (5.2% ± 0.1% versus 7.8% ± 0.3%, P < 0.01, a 31.6% increase in cardiac index >2.0 (83.7% + 0.2% versus 63.6% + 0.5%, P < 0.01), and a 37.6% increase in stroke volume variation <12 (73.2% + 0.3% versus 53.2% + 0.5%, P < 0.01). CONCLUSIONS Advanced intraoperative hemodynamic monitoring to predict IOH combined with a clinical decision-making tree for GDHT may improve intraoperative hemodynamic parameters during pancreatectomy. This warrants further investigation in larger studies.
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Affiliation(s)
| | - Maria Khawam
- Department of Surgery, University of Arizona, Tucson, Arizona
| | | | | | - Carrie Luu
- Department of Surgery, University of Arizona, Tucson, Arizona
| | | | - Taylor S Riall
- Department of Surgery, University of Arizona, Tucson, Arizona.
| | - William K Kwass
- Department of Anesthesia, University of Arizona, Tucson, Arizona
| | - Randal O Dull
- Department of Anesthesia, University of Arizona, Tucson, Arizona
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Saugel B, Fletcher N, Gan TJ, Grocott MPW, Myles PS, Sessler DI. PeriOperative Quality Initiative (POQI) international consensus statement on perioperative arterial pressure management. Br J Anaesth 2024; 133:264-276. [PMID: 38839472 PMCID: PMC11282474 DOI: 10.1016/j.bja.2024.04.046] [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: 12/19/2023] [Revised: 03/09/2024] [Accepted: 04/05/2024] [Indexed: 06/07/2024] Open
Abstract
Arterial pressure monitoring and management are mainstays of haemodynamic therapy in patients having surgery. This article presents updated consensus statements and recommendations on perioperative arterial pressure management developed during the 11th POQI PeriOperative Quality Initiative (POQI) consensus conference held in London, UK, on June 4-6, 2023, which included a diverse group of international experts. Based on a modified Delphi approach, we recommend keeping intraoperative mean arterial pressure ≥60 mm Hg in at-risk patients. We further recommend increasing mean arterial pressure targets when venous or compartment pressures are elevated and treating hypotension based on presumed underlying causes. When intraoperative hypertension is treated, we recommend doing so carefully to avoid hypotension. Clinicians should consider continuous intraoperative arterial pressure monitoring as it can help reduce the severity and duration of hypotension compared to intermittent arterial pressure monitoring. Postoperative hypotension is often unrecognised and might be more important than intraoperative hypotension because it is often prolonged and untreated. Future research should focus on identifying patient-specific and organ-specific hypotension harm thresholds and optimal treatment strategies for intraoperative hypotension including choice of vasopressors. Research is also needed to guide monitoring and management strategies for recognising, preventing, and treating postoperative hypotension.
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Affiliation(s)
- Bernd Saugel
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Outcomes Research Consortium, Cleveland, OH, USA.
| | - Nick Fletcher
- Institute of Anesthesia and Critical Care, Cleveland Clinic London, London, UK
| | - Tong J Gan
- Division of Anesthesiology and Perioperative Medicine, Critical Care and Pain Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael P W Grocott
- Perioperative and Critical Care Theme, NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust/University of Southampton, Southampton, UK
| | - Paul S Myles
- Department of Anaesthesiology and Perioperative Medicine, Alfred Hospital and Monash University, Melbourne, VIC, Australia
| | - Daniel I Sessler
- Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, OH, USA
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Smith A, Turoczi Z, Al-Subaie N, Zilahi G. Postoperative Hypotension After Cardiac Surgery Is Associated With Acute Kidney Injury. J Cardiothorac Vasc Anesth 2024; 38:1683-1688. [PMID: 38879370 DOI: 10.1053/j.jvca.2024.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/30/2024] [Accepted: 04/17/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVES To describe the incidence of postoperative hypotension in patients undergoing cardiac surgery during the first 12 hours in the intensive care unit (ICU) and any relationship between hypotension and the development of acute kidney injury (AKI). DESIGN This was a retrospective, observational cohort study. SETTING The study took place in a single-center tertiary teaching hospital in London, UK. PARTICIPANTS Adult patients (n = 100) who underwent elective cardiac surgery requiring intraoperative cardiopulmonary bypass between May and November 2021 were enrolled. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS A hypotensive event was defined as mean arterial pressure <65 mmHg lasting at least 1 minute. Invasive blood pressure data was analyzed for the first 12 hours after surgery, and any association between postoperative hypotension and AKI was assessed. A total of 91% of patients experienced hypotension in the first 12 hours postprocedure. On average, patients experienced 9 hypotensive events, with events lasting an average of 5 minutes. A total of 16 patients (16%) developed at least stage 1 AKI. The average duration of hypotension was significantly higher in the AKI group (4.6 min [IQR 3.3, 8.0] v 8.1 min [IQR 5.2, 14.2], p = 0.029). Those suffering AKI had longer ICU and hospital stays. CONCLUSIONS This study demonstrated that hypotension in the first 12 hours following cardiac surgery is common and prolonged hypotensive events are associated with developing AKI. This emphasizes the importance of treating hypotension aggressively and highlights a target for further research and intervention.
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Affiliation(s)
- Alexander Smith
- Cardiothoracic Intensive Care Unit, St George's University Hospital NHS Foundation Trust, London, United Kingdom.
| | - Zsolt Turoczi
- Cardiothoracic Intensive Care Unit, St George's University Hospital NHS Foundation Trust, London, United Kingdom
| | - Nawaf Al-Subaie
- Cardiothoracic Intensive Care Unit, St George's University Hospital NHS Foundation Trust, London, United Kingdom
| | - Gabor Zilahi
- Cardiothoracic Intensive Care Unit, St George's University Hospital NHS Foundation Trust, London, United Kingdom
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Han L, Char DS, Aghaeepour N. Artificial Intelligence in Perioperative Care: Opportunities and Challenges. Anesthesiology 2024; 141:379-387. [PMID: 38980160 PMCID: PMC11239120 DOI: 10.1097/aln.0000000000005013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.
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Affiliation(s)
- Lichy Han
- Department of Anesthesiology, Perioperative, and Pain Medicine, School of Medicine, Stanford University, Stanford, California
| | - Danton S Char
- Department of Anesthesiology, Perioperative, and Pain Medicine, School of Medicine, Stanford University, Stanford, California
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, School of Medicine, Stanford University, Stanford, California
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Pardo E, Le Cam E, Verdonk F. Artificial intelligence and nonoperating room anesthesia. Curr Opin Anaesthesiol 2024; 37:413-420. [PMID: 38934202 DOI: 10.1097/aco.0000000000001388] [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: 06/28/2024]
Abstract
PURPOSE OF REVIEW The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. RECENT FINDINGS AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. SUMMARY The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.
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Affiliation(s)
- Emmanuel Pardo
- Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Critical Care, Saint-Antoine Hospital, Paris, France
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Abdallah AC, Song SH, Fleming NW. A retrospective study of the effects of a vasopressor bolus on systolic slope (dP/dt) and dynamic arterial elastance (Ea dyn). BMC Anesthesiol 2024; 24:257. [PMID: 39075354 PMCID: PMC11285466 DOI: 10.1186/s12871-024-02574-x] [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: 12/06/2023] [Accepted: 05/22/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND To enhance the utility of functional hemodynamic monitoring, the variables systolic slope (dP/dt) and dynamic arterial elastance (Eadyn) are calculated by the Hypotension Prediction Index (HPI) Acumen® Software. This study was designed to characterize the effects of phenylephrine and ephedrine on dP/dt and Eadyn. METHODS This was a retrospective, non-randomized analysis of data collected during two clinical studies. All patients required intra-operative controlled mechanical ventilation and had an indwelling radial artery catheter connected to an Acumen IQ sensor. Raw arterial pressure waveform data was downloaded from the patient monitor and all hemodynamic measurements were calculated off-line. The anesthetic record was reviewed for bolus administrations of either phenylephrine or ephedrine. Cardiovascular variables prior to drug administration were compared to those following vasopressor administrations. The primary outcome was the difference for dP/dt and Eadyn at baseline compared with the average after the bolus administration. All data sets demonstrated non-normal distributions so statistical analysis of paired and unpaired data followed the Wilcoxon matched pairs signed-rank test or Mann-Whitney U test, respectively. RESULTS 201 doses of phenylephrine and 100 doses of ephedrine were analyzed. All data sets are reported as median [95% CI]. Mean arterial pressure (MAP) increased from 62 [54,68] to 78 [76,80] mmHg following phenylephrine and from 59 [55,62] to 80 [77,83] mmHg following ephedrine. Stroke volume and cardiac output both increased. Stroke volume variation and pulse pressure variation decreased. Both drugs produced significant increases in dP/dt, from 571 [531, 645] to 767 [733, 811] mmHg/sec for phenylephrine and from 537 [509, 596] to 848 [779, 930] mmHg/sec for ephedrine. No significant changes in Eadyn were observed. CONCLUSION Bolus administration of phenylephrine or ephedrine increases dP/dt but does not change Eadyn. dP/dt demonstrates potential for predicting the inotropic response to phenylephrine or ephedrine, providing guidance for the most efficacious vasopressor when treating hypotension. TRIAL REGISTRATION Data was collected from two protocols. The first was deemed to not require written, informed consent by the Institutional Review Board (IRB). The second was IRB-approved (Effect of Diastolic Dysfunction on Dynamic Cardiac Monitors) and registered on ClinicalTrials.gov (NCT04177225).
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Affiliation(s)
- Alexa C Abdallah
- Department of Anesthesiology, University of California, San Diego, CA, USA
| | | | - Neal W Fleming
- Department of Anesthesiology & Pain Medicine, University of California, Davis, 4150 V Street Suite 1200 PSSB, Sacramento, CA, 95817, USA.
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Kittrell HD, Shaikh A, Adintori PA, McCarthy P, Kohli-Seth R, Nadkarni GN, Sakhuja A. Role of artificial intelligence in critical care nutrition support and research. Nutr Clin Pract 2024. [PMID: 39073166 DOI: 10.1002/ncp.11194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 06/06/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024] Open
Abstract
Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.
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Affiliation(s)
- Hannah D Kittrell
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ahmed Shaikh
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Peter A Adintori
- Food and Nutrition Services Department, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Program in Rehabilitation Sciences, New York University Steinhardt, New York, New York, USA
| | - Paul McCarthy
- Department of Cardiovascular and Thoracic Surgery, Division of Cardiovascular Critical Care, West Virginia University, Morgantown, West Virginia, USA
| | - Roopa Kohli-Seth
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ankit Sakhuja
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Pilakouta Depaskouale MA, Archonta SA, Katsaros DM, Paidakakos NA, Dimakopoulou AN, Matsota PK. Beyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis. J Clin Monit Comput 2024:10.1007/s10877-024-01202-w. [PMID: 39048785 DOI: 10.1007/s10877-024-01202-w] [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: 03/22/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
PURPOSE Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. METHODS A systematic search was performed in Pubmed and Scopus to retrieve articles published from January 2018 to February 2024 regarding the impact of the HPI software on reducing IOH in adult patients undergoing non-cardio/thoracic surgery. Excluded were case series, case reports, meta-analyses, systematic reviews, and studies using non-invasive arterial waveform analysis. The risk of bias was assessed by the Cochrane risk-of-bias tool (RoB 2) and the Risk Of Bias In Non-randomised Studies (ROBINS-I). A meta-analysis was undertaken solely for outcomes where sufficient data were available from the included studies. RESULTS 9 RCTs and 5 cohort studies were retrieved. The overall median differences between the HPI-guided and the control groups were - 0.21 (95% CI:-0.33, -0.09) - p < 0.001 for the Time-Weighted Average (TWA) of Mean Arterial Pressure (MAP) < 65mmHg, -3.71 (95% CI= -6.67, -0.74)-p = 0.014 for the incidence of hypotensive episodes per patient, and - 10.11 (95% CI= -15.82, -4.40)-p = 0.001 for the duration of hypotension. Notably a large amount of heterogeneity was detected among the studies. CONCLUSIONS While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.
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Affiliation(s)
- Myrto A Pilakouta Depaskouale
- 2nd Department of Anesthesiology, School of Medicine, National and Kapodistrian University of Athens, "Attikon" Hospital, 1 Rimini Street, Athens, 12462, Greece.
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece.
| | - Stela A Archonta
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Dimitrios M Katsaros
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Nikolaos A Paidakakos
- Department of Neurosurgery, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Antonia N Dimakopoulou
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Paraskevi K Matsota
- 2nd Department of Anesthesiology, School of Medicine, National and Kapodistrian University of Athens, "Attikon" Hospital, 1 Rimini Street, Athens, 12462, Greece
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Wang Z, Ma J, Liu X, Gao J. Development and validation of a predictive model for PACU hypotension in elderly patients undergoing sedated gastrointestinal endoscopy. Aging Clin Exp Res 2024; 36:149. [PMID: 39023685 PMCID: PMC11258065 DOI: 10.1007/s40520-024-02807-6] [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: 03/21/2024] [Accepted: 07/05/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND Hypotension, characterized by abnormally low blood pressure, is a frequently observed adverse event in sedated gastrointestinal endoscopy procedures. Although the examination time is typically short, hypotension during and after gastroscopy procedures is frequently overlooked or remains undetected. This study aimed to construct a risk nomogram for post-anesthesia care unit (PACU) hypotension in elderly patients undergoing sedated gastrointestinal endoscopy. METHODS This study involved 2919 elderly patients who underwent sedated gastrointestinal endoscopy. A preoperative questionnaire was used to collect data on patient characteristics; intraoperative medication use and adverse events were also recorded. The primary objective of the study was to evaluate the risk of PACU hypotension in these patients. To achieve this, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection, involving cyclic coordinate descent with tenfold cross-validation. Subsequently, multivariable logistic regression analysis was applied to build a predictive model using the selected predictors from the LASSO regression. A nomogram was visually developed based on these variables. To validate the model, a calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used. Additionally, external validation was conducted to further assess the model's performance. RESULTS The LASSO regression analysis identified predictors associated with an increased risk of adverse events during surgery: age, duration of preoperative water abstinence, intraoperative mean arterial pressure (MAP) <65 mmHg, decreased systolic blood pressure (SBP), and use of norepinephrine (NE). The constructed model based on these predictors demonstrated moderate predictive ability, with an area under the ROC curve of 0.710 in the training set and 0.778 in the validation set. The DCA indicated that the nomogram had clinical applicability when the risk threshold ranged between 20 and 82%, which was subsequently confirmed in the external validation with a range of 18-92%. CONCLUSION Incorporating factors such as age, duration of preoperative water abstinence, intraoperative MAP <65 mmHg, decreased SBP, and use of NE in the risk nomogram increased its usefulness for predicting PACU hypotension risk in elderly patient undergoing sedated gastrointestinal endoscopy.
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Affiliation(s)
- Zi Wang
- Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China
- Yangzhou University, Jiangsu, Yangzhou, 225001, China
| | - Juan Ma
- Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China
- Yangzhou University, Jiangsu, Yangzhou, 225001, China
| | - Xin Liu
- Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China
| | - Ju Gao
- Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China.
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Li J, Zhang Z. Establishment and validation of a predictive nomogram for polyuria during general anesthesia in thoracic surgery. J Cardiothorac Surg 2024; 19:414. [PMID: 38956694 PMCID: PMC11220976 DOI: 10.1186/s13019-024-02833-5] [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: 12/06/2023] [Accepted: 06/14/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery. METHODS A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance. RESULTS The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.
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Affiliation(s)
- Jiajie Li
- Department of Anesthesiology, Xinxiang Central Hospital, Xinxiang, Henan Province, 453000, China
| | - Zongwang Zhang
- Department of Anesthesiology, Liaocheng people's Hospital Affiliated to Shandong First Medical University, No. 67, Dongchang West Road, Dongchangfu District, Liaocheng, Shandong Province, 252004, China.
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12
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Cheng Y, Gao Y, Liu GY, Xue FS, Jin M. Short-term inhalation of xenon during anesthesia for prevention of postoperative delirium in elderly patients undergoing laparoscopic radical colectomy: study protocol for a randomized controlled clinical trial. Trials 2024; 25:434. [PMID: 38956691 PMCID: PMC11218218 DOI: 10.1186/s13063-024-08290-8] [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: 02/11/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Postoperative delirium (POD) is a common complication that is characterized by acute onset of impaired cognitive function and is associated with an increased mortality, a prolonged duration of hospital stay, and additional healthcare expenditures. The incidence of POD in elderly patients undergoing laparoscopic radical colectomy ranges from 8 to 54%. Xenon has been shown to provide neuroprotection in various neural injury models, but the clinical researches assessing the preventive effect of xenon inhalation on the occurrence of POD obtained controversial findings. This study aims to investigate the effects of a short xenon inhalation on the occurrence of POD in elderly patients undergoing laparoscopic radical colectomy. METHODS/DESIGN This is a prospective, randomized, controlled trial and 132 patients aged 65-80 years and scheduled for laparoscopic radical colectomy will be enrolled. The participants will be randomly assigned to either the control group or the xenon group (n = 66 in each group). The primary outcome will be the incidence of POD in the first 5 days after surgery. Secondary outcomes will include the subtype, severity, and duration of POD, postoperative pain score, Pittsburgh Sleep Quality Index (PQSI), perioperative non-delirium complications, and economic parameters. Additionally, the study will investigate the activation of microglial cells, expression of inflammatory factors in colon tissues, plasma inflammatory factors, and neurochemical markers. DISCUSSION Elderly patients undergoing laparoscopic radical colectomy are at a high risk of POD, with delayed postoperative recovery and increased healthcare costs. The primary objective of this study is to determine the preventive effect of a short xenon inhalation on the occurrence of POD in these patients. TRIAL REGISTRATION Chinese Clinical Trial Registry ChiCTR2300076666. Registered on October 16, 2023, http://www.chictr.org.cn .
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Affiliation(s)
- Yi Cheng
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Ying Gao
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Gu-Yue Liu
- Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Fu-Shan Xue
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China.
| | - Mu Jin
- Department of Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China.
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13
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González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. [Translated article] Introducing artificial intelligence to hospital pharmacy departments. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:TS35-TS44. [PMID: 39097375 DOI: 10.1016/j.farma.2024.04.001] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.
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Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, Spain.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, Spain
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14
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Mascagni P, Alapatt D, Sestini L, Yu T, Alfieri S, Morales-Conde S, Padoy N, Perretta S. Applications of artificial intelligence in surgery: clinical, technical, and governance considerations. Cir Esp 2024; 102 Suppl 1:S66-S71. [PMID: 38704146 DOI: 10.1016/j.cireng.2024.04.009] [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: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
Artificial intelligence (AI) will power many of the tools in the armamentarium of digital surgeons. AI methods and surgical proof-of-concept flourish, but we have yet to witness clinical translation and value. Here we exemplify the potential of AI in the care pathway of colorectal cancer patients and discuss clinical, technical, and governance considerations of major importance for the safe translation of surgical AI for the benefit of our patients and practices.
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Affiliation(s)
- Pietro Mascagni
- IHU Strasbourg, Strasbourg, France; Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy.
| | - Deepak Alapatt
- University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Luca Sestini
- University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Tong Yu
- University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Sergio Alfieri
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Nicolas Padoy
- IHU Strasbourg, Strasbourg, France; University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France
| | - Silvana Perretta
- IHU Strasbourg, Strasbourg, France; IRCAD, Research Institute Against Digestive Cancer, Strasbourg, France; Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
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15
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Madani A, Liu Y, Pryor A, Altieri M, Hashimoto DA, Feldman L. SAGES surgical data science task force: enhancing surgical innovation, education and quality improvement through data science. Surg Endosc 2024; 38:3489-3493. [PMID: 38831213 DOI: 10.1007/s00464-024-10921-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 05/05/2024] [Indexed: 06/05/2024]
Affiliation(s)
- Amin Madani
- Department of Surgery, University of Toronto, Toronto, ON, Canada.
| | - Yao Liu
- Department of Surgery, Brown University, Providence, RI, USA
| | - Aurora Pryor
- Department of Surgery, Northwell Health, New York, NY, USA
| | - Maria Altieri
- Department of Surgery, Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel A Hashimoto
- Department of Surgery, Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Liane Feldman
- Department of Surgery, McGill University, Montreal, QC, Canada
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16
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Horiguchi D, Shin S, Pepino JA, Peterson JT, Kehoe IE, Goldstein JN, Lee J, Kwon BK, Hahn JO, Reisner AT. Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis. J Intensive Care Med 2024; 39:683-692. [PMID: 38282376 DOI: 10.1177/08850666241226893] [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: 01/30/2024]
Abstract
Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.
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Affiliation(s)
- Daisuke Horiguchi
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
- Nihon Kohden Innovation Center, LLC, Cambridge, MA, USA
| | - Sungtae Shin
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Jeremy A Pepino
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey T Peterson
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Iain E Kehoe
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Joshua N Goldstein
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jarone Lee
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston MA, USA
| | - Brian K Kwon
- Department of Orthopaedics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Andrew T Reisner
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
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17
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González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. Approaching artificial intelligence to Hospital Pharmacy. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:S35-S44. [PMID: 39097366 DOI: 10.1016/j.farma.2024.02.007] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.
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Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, España.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, España
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18
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Wang XJ, Xuan XC, Sun ZC, Shen S, Yu F, Li NN, Chu XC, Yin H, Hu YL. Risk factors associated with intraoperative persistent hypotension in pancreaticoduodenectomy. World J Gastrointest Surg 2024; 16:1582-1591. [PMID: 38983354 PMCID: PMC11230017 DOI: 10.4240/wjgs.v16.i6.1582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/27/2024] [Accepted: 05/16/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Intraoperative persistent hypotension (IPH) during pancreaticoduodenectomy (PD) is linked to adverse postoperative outcomes, yet its risk factors remain unclear. AIM To clarify the risk factors associated with IPH during PD, ensuring patient safety in the perioperative period. METHODS A retrospective analysis of patient records from January 2018 to December 2022 at the First Affiliated Hospital of Nanjing Medical University identified factors associated with IPH in PD. These factors included age, gender, body mass index, American Society of Anesthesiologists classification, comorbidities, medication history, operation duration, fluid balance, blood loss, urine output, and blood gas parameters. IPH was defined as sustained mean arterial pressure < 65 mmHg, requiring prolonged deoxyepinephrine infusion for > 30 min despite additional deoxyepinephrine and fluid treatments. RESULTS Among 1596 PD patients, 661 (41.42%) experienced IPH. Multivariate logistic regression identified key risk factors: increased age [odds ratio (OR): 1.20 per decade, 95% confidence interval (CI): 1.08-1.33] (P < 0.001), longer surgery duration (OR: 1.15 per additional hour, 95%CI: 1.05-1.26) (P < 0.01), and greater blood loss (OR: 1.18 per 250-mL increment, 95%CI: 1.06-1.32) (P < 0.01). A novel finding was the association of arterial blood Ca2+ < 1.05 mmol/L with IPH (OR: 2.03, 95%CI: 1.65-2.50) (P < 0.001). CONCLUSION IPH during PD is independently associated with older age, prolonged surgery, increased blood loss, and lower plasma Ca2+.
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Affiliation(s)
- Xing-Jun Wang
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Xi-Chen Xuan
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Zhao-Chu Sun
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shi Shen
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Fan Yu
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Na-Na Li
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Xue-Chun Chu
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Hui Yin
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - You-Li Hu
- Department of Anesthesia and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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Lai CJ, Cheng YJ, Han YY, Hsiao PN, Lin PL, Chiu CT, Lee JM, Tien YW, Chien KL. Hypotension prediction index for prevention of intraoperative hypotension in patients undergoing general anesthesia: a randomized controlled trial. Perioper Med (Lond) 2024; 13:57. [PMID: 38879506 PMCID: PMC11180403 DOI: 10.1186/s13741-024-00414-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 06/07/2024] [Indexed: 06/19/2024] Open
Abstract
BACKGROUND Intraoperative hypotension is a common side effect of general anesthesia. Here we examined whether the Hypotension Prediction Index (HPI), a novel warning system, reduces the severity and duration of intraoperative hypotension during general anesthesia. METHODS This randomized controlled trial was conducted in a tertiary referral hospital. We enrolled patients undergoing general anesthesia with invasive arterial monitoring. Patients were randomized 1:1 either to receive hemodynamic management with HPI guidance (intervention) or standard of care (control) treatment. Intraoperative hypotension treatment was initiated at HPI > 85 (intervention) or mean arterial pressure (MAP) < 65 mmHg (control). The primary outcome was hypotension severity, defined as a time-weighted average (TWA) MAP < 65 mmHg. Secondary outcomes were TWA MAP < 60 and < 55 mmHg. RESULTS Of the 60 patients who completed the study, 30 were in the intervention group and 30 in the control group. The patients' median age was 62 years, and 48 of them were male. The median duration of surgery was 490 min. The median MAP before surgery presented no significant difference between the two groups. The intervention group showed significantly lower median TWA MAP < 65 mmHg than the control group (0.02 [0.003, 0.08] vs. 0.37 [0.20, 0.58], P < 0.001). Findings were similar for TWA MAP < 60 mmHg and < 55 mmHg. The median MAP during surgery was significantly higher in the intervention group than that in the control group (87.54 mmHg vs. 77.92 mmHg, P < 0.001). CONCLUSIONS HPI guidance appears to be effective in preventing intraoperative hypotension during general anesthesia. Further investigation is needed to assess the impact of HPI on patient outcomes. TRIAL REGISTRATION ClinicalTrials.gov (NCT04966364); 202105065RINA; Date of registration: July 19, 2021; The recruitment date of the first patient: July 22, 2021.
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Affiliation(s)
- Chih-Jun Lai
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Rd, Taipei, 10055, Taiwan
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ya-Jung Cheng
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan
| | - Yin-Yi Han
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Traumatology, National Taiwan University Hospital, Taipei, Taiwan
| | - Po-Ni Hsiao
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan
| | - Pei-Lin Lin
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ching-Tang Chiu
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan
| | - Jang-Ming Lee
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Wen Tien
- Division of General Surgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Kuo-Liong Chien
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Rd, Taipei, 10055, Taiwan.
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
- Population Health Research Center, National Taiwan University, Taipei, Taiwan.
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20
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Lee S, Islam N, Ladha KS, van Klei W, Wijeysundera DN. Intraoperative Hypotension in Patients Having Major Noncardiac Surgery Under General Anesthesia: A Systematic Review of Blood Pressure Optimization Strategies. Anesth Analg 2024:00000539-990000000-00845. [PMID: 38870081 DOI: 10.1213/ane.0000000000007074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
INTRODUCTION Intraoperative hypotension is associated with increased risks of postoperative complications. Consequently, a variety of blood pressure optimization strategies have been tested to prevent or promptly treat intraoperative hypotension. We performed a systematic review to summarize randomized controlled trials that evaluated the efficacy of blood pressure optimization interventions in either mitigating exposure to intraoperative hypotension or reducing risks of postoperative complications. METHODS Medline, Embase, PubMed, and Cochrane Controlled Register of Trials were searched from database inception to August 2, 2023, for randomized controlled trials (without language restriction) that evaluated the impact of any blood pressure optimization intervention on intraoperative hypotension and/or postoperative outcomes. RESULTS The review included 48 studies (N = 46,377), which evaluated 10 classes of blood pressure optimization interventions. Commonly assessed interventions included hemodynamic protocols using arterial waveform analysis, preoperative withholding of antihypertensive medications, continuous blood pressure monitoring, and adjuvant agents (vasopressors, anticholinergics, anticonvulsants). These same interventions reduced intraoperative exposure to hypotension. Conversely, low blood pressure alarms had an inconsistent impact on exposure to hypotension. Aside from limited evidence that higher prespecified intraoperative blood pressure targets led to a reduced risk of complications, there were few data suggesting that these interventions prevented postoperative complications. Heterogeneity in interventions and outcomes precluded meta-analysis. CONCLUSIONS Several different blood pressure optimization interventions show promise in reducing exposure to intraoperative hypotension. Nonetheless, the impact of these interventions on clinical outcomes remains unclear. Future trials should assess promising interventions in samples sufficiently large to identify clinically plausible treatment effects on important outcomes. KEY POINTS
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Affiliation(s)
- Sandra Lee
- From the Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Nehal Islam
- Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Karim S Ladha
- From the Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Anesthesia, St. Michael's Hospital - Unity Health Toronto, Toronto, Ontario, Canada
| | - Wilton van Klei
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Anesthesia and Pain Management, Toronto General Hospital - University Health Network, Toronto, Ontario, Canada
- Division of Anaesthesiology, Intensive Care, and Emergency Medicine, University Medical Center Utrecht, Utrecht, Netherlands
| | - Duminda N Wijeysundera
- From the Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Anesthesia, St. Michael's Hospital - Unity Health Toronto, Toronto, Ontario, Canada
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21
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Huyghebaert TA, Wallner C, Montemurro P. Implementation of a Machine Learning Approach Evaluating Risk Factors for Complications after Single-Stage Augmentation Mastopexy. Aesthetic Plast Surg 2024:10.1007/s00266-024-04142-7. [PMID: 38849552 DOI: 10.1007/s00266-024-04142-7] [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: 10/04/2023] [Accepted: 05/13/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Single-stage mastopexy augmentation is a much-debated intervention due to its complexity and the associated relatively high complication rates. This study aimed to reevaluate the risk factors for these complications using a novel approach based on artificial intelligence and to demonstrate its possible limitations. PATIENTS AND METHODS Complete datasets of patients who underwent single-staged augmentation mastopexy during 2014-2023 at one institution by a single surgeon were collected retrospectively. These were subsequently processed and analyzed by CART, RF and XGBoost algorithms. RESULTS A total of 342 patients were included in the study, of which 43 (12.57%) reported surgery-associated complications, whereby capsular contracture (n = 19) was the most common. BMI represented the most important variable for the development of complications (FIS = 0.44 in CART). 2.9% of the patients expressed the desire for implant change in the course, with absence of any complications. A statistically significant correlation between smoking and the desire for implant change (p < 0.001) was revealed. CONCLUSION The importance of implementing artificial intelligence into clinical research could be underpinned by this study, as risk variables can be reclassified based on factors previously considered less or even irrelevant. Thereby we encountered limitations using ML approaches. Further studies will be needed to investigate the association between smoking, BMI and the current implant size with the desire for implant change without any complications. Moreover, we could show that the procedure can be performed safely without high risk of developing major complications. LEVEL OF EVIDENCE IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
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Affiliation(s)
- Tom Alexander Huyghebaert
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, Bürkle-de-la-Camp Platz 1, 44789, Bochum, Germany.
| | - Christoph Wallner
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, Bürkle-de-la-Camp Platz 1, 44789, Bochum, Germany
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22
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Kim J, Lee S, Choi J, Ryu DK, Woo S, Park M. Effect of continuing angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers on the day of surgery on myocardial injury after non-cardiac surgery: A retrospective cohort study. J Clin Anesth 2024; 94:111401. [PMID: 38330844 DOI: 10.1016/j.jclinane.2024.111401] [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: 08/08/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024]
Abstract
STUDY OBJECTIVE To evaluate the effect of continuing of angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blocker (ARB) prescriptions 24 h before surgery on postoperative myocardial injury and blood pressure in patients undergoing non-cardiac surgery. DESIGN A single-center, retrospective study. SETTING Operating room and perioperative care area. PATIENTS 42,432 patients who had been taking chronic ACEI/ARB underwent non-cardiac surgery from January 2012 to June 2022. INTERVENTIONS Patients who discontinued ACEI/ARB 24 h before surgery (withheld group, n=31,055) and those who continued ACEI/ARB 24 h before surgery (continued group, n=11,377). MEASUREMENTS Primary outcome was myocardial injury after non-cardiac surgery (MINS) within 7 days postoperatively. MINS was defined as an elevated postoperative cardiac troponin measurement above the 99th percentile of the upper reference limit with a rise/fall pattern. Perioperative blood pressure and clinical outcomes were secondary outcomes. MAIN RESULTS Among 42,432 patients, MINS occurred in 2848 patients (6.7%) and was the all-cause of death within 30 days in 122 patients (0.3%). Incidence of MINS was significantly higher in the continued group than the withheld group (847/11,377 [7.4%] vs. 2001/31,055 [6.4%]; OR [95% CI] 1.17 [1.07-1.27]; P<0.001). After 1:1 propensity score matching, 11,373 patients were included in each group. There was still a significant difference for the occurrence of MINS between two groups in matched cohort (7.4% vs. 6.6%, OR [95% CI] 1.14 [1.03-1.26]; P=0.015). Time-average weight of mean arterial pressure <65 mmHg during surgery was significantly higher in the continued group (mean 0.11 vs. 0.09 [95% CI of mean difference] [0.01-0.03]; P<0.001). However, there was no significant difference in other clinical outcomes and mortality. CONCLUSIONS Withholding ACEI/ARB before surgery was associated with a reduced risk of intraoperative hypotension and postoperative myocardial injury, but it did not affect overall clinical outcomes in patients undergoing non-cardiac surgery.
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Affiliation(s)
- Jeayoun Kim
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seungwon Lee
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jisun Choi
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Dae Kyun Ryu
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seunghyeon Woo
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - MiHye Park
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Kouz K, Thiele R, Michard F, Saugel B. Haemodynamic monitoring during noncardiac surgery: past, present, and future. J Clin Monit Comput 2024; 38:565-580. [PMID: 38687416 PMCID: PMC11164815 DOI: 10.1007/s10877-024-01161-2] [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: 01/31/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024]
Abstract
During surgery, various haemodynamic variables are monitored and optimised to maintain organ perfusion pressure and oxygen delivery - and to eventually improve outcomes. Important haemodynamic variables that provide an understanding of most pathophysiologic haemodynamic conditions during surgery include heart rate, arterial pressure, central venous pressure, pulse pressure variation/stroke volume variation, stroke volume, and cardiac output. A basic physiologic and pathophysiologic understanding of these haemodynamic variables and the corresponding monitoring methods is essential. We therefore revisit the pathophysiologic rationale for intraoperative monitoring of haemodynamic variables, describe the history, current use, and future technological developments of monitoring methods, and finally briefly summarise the evidence that haemodynamic management can improve patient-centred outcomes.
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Affiliation(s)
- Karim Kouz
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg, 20246, Germany
- Outcomes Research Consortium, Cleveland, OH, USA
| | - Robert Thiele
- Department of Anesthesiology, University of Virginia, Charlottesville, VA, USA
| | | | - Bernd Saugel
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg, 20246, Germany.
- Outcomes Research Consortium, Cleveland, OH, USA.
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24
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Gunaratne C, Ison R, Price CC, Modave F, Tighe P. Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 249:108143. [PMID: 38552333 DOI: 10.1016/j.cmpb.2024.108143] [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: 12/03/2023] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/21/2024]
Abstract
BACKGROUND Blood pressure is a vital sign for organ perfusion that anesthesiologists measure and modulate during surgery. However, current decision-making processes rely heavily on clinicians' experience, which can lead to variability in treatment across surgeries. With the advent of machine learning, we can now create models to predict the outcomes of interventions and guide perioperative decision-making. The first step in this process involves translating the clinical decision-making process into a framework understood by an algorithm. Probabilistic Boolean networks (PBNs) provide an information-rich approach to this problem. A PBN trends toward a steady state, and its decisions are easily understood via its Boolean predictor functions. We hypothesize that a PBN can be developed that corrects hemodynamic instability in patients by selecting clinical interventions to maintain blood pressure within a given range. METHODS Data on patients over the age of 65 undergoing surgery with general anesthesia from 2018 to 2020 were drawn from the UF Health PRECEDE data set with IRB approval (IRB201700747). Parameters examined included heart rate, blood pressure, and frequency of medications given 15 min after anesthetic induction and 15 min before awakening. The medication frequency data were truncated into a 66/33 split for the training and validation set used in the PBN. The model was coded using Python 3 and evaluated by comparing the frequency of medications chosen by the program to the values in the testing set via linear regression analysis. RESULTS The network developed successfully models a hemodynamically unstable patient and corrects the imbalance by administering medications. This is evidenced by the model achieving a stable, steady state matrix in all iterations. However, the model's ability to emulate clinical drug selection was variable. It was successful with its use of vasodilator selection but struggled with the appropriate selection of vasopressors. CONCLUSIONS The PBN has demonstrated the ability to choose appropriate interventions based on a patient's current vitals. Additional work must be done to have the network emulate the frequency at which drugs are selected from in clinical practice. In its current state, the model provides an understanding of how a PBN behaves in the context of correcting hemodynamic instability and can aid in developing more robust models in the future.
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Affiliation(s)
- Chamara Gunaratne
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA.
| | - Ron Ison
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Catherine C Price
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA; Department of Clinical and Health Psychology, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL 32610, USA
| | - Francois Modave
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Patrick Tighe
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
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25
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Lv G, Wang Y. Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis. Technol Health Care 2024:THC240119. [PMID: 38875058 DOI: 10.3233/thc-240119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
BACKGROUND The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments. OBJECTIVE To comprehensively assess the research progress of antibiotic resistance prediction models based on machine learning (ML) algorithms, providing the latest quantitative analysis and methodological evaluation. METHODS Relevant literature was systematically retrieved from databases, including PubMed, Embase and the Cochrane Library, from inception up to December 2023. Studies meeting predefined criteria were selected for inclusion. The prediction model risk of bias assessment tool was employed for methodological quality assessment, and a random-effects model was utilised for meta-analysis. RESULTS The systematic review included a total of 22 studies with a combined sample size of 43,628; 10 studies were ultimately included in the meta-analysis. Commonly used ML algorithms included random forest, decision trees and neural networks. Frequently utilised predictive variables encompassed demographics, drug use history and underlying diseases. The overall sensitivity was 0.57 (95% CI: 0.42-0.70; p< 0.001; I2= 99.7%), the specificity was 0.95 (95% CI: 0.79-0.99; p< 0.001; I2 = 99.9%), the positive likelihood ratio was 10.7 (95% CI: 2.9-39.5), the negative likelihood ratio was 0.46 (95% CI: 0.34-0.61), the diagnostic odds ratio was 23 (95% CI: 7-81) and the area under the receiver operating characteristic curve was 0.78 (95% CI: 0.74-0.81; p< 0.001), indicating a good discriminative ability of ML models for antibiotic resistance. However, methodological assessment and funnel plots suggested a high risk of bias and publication bias in the included studies. CONCLUSION This meta-analysis provides a current and comprehensive evaluation of ML models for predicting antibiotic resistance, emphasising their potential application in clinical practice. Nevertheless, stringent research design and reporting are warranted to enhance the quality and credibility of future studies. Future research should focus on methodological innovation and incorporate more high-quality studies to further advance this field.
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Affiliation(s)
- Guodong Lv
- Department of STD and AIDS Prevention and Control, Langfang Center for Disease Prevention and Control, Langfang, Hebei, China
| | - Yuntao Wang
- Department of Pharmacy, Langfang Health Vocational College, Langfang, Hebei, China
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26
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Fritz BA, King CR, Abdelhack M, Chen Y, Kronzer A, Abraham J, Tripathi S, Abdallah AB, Kannampallil T, Budelier TP, Helsten D, Montes de Oca A, Mehta D, Sontha P, Higo O, Kerby P, Gregory SH, Wildes TS, Avidan MS. Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.22.24307754. [PMID: 38826471 PMCID: PMC11142290 DOI: 10.1101/2024.05.22.24307754] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration ClinicalTrials.gov NCT05042804.
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Affiliation(s)
- Bradley A Fritz
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Christopher R King
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Mohamed Abdelhack
- Department of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, USA
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, USA
| | - Alex Kronzer
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, USA
| | - Sandhya Tripathi
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, USA
| | - Thaddeus P Budelier
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Daniel Helsten
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Arianna Montes de Oca
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Divya Mehta
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Pratyush Sontha
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Omokhaye Higo
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Paul Kerby
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Stephen H. Gregory
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Troy S. Wildes
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
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27
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Alves MRD, Saturnino SF, Zen AB, de Albuquerque DGS, Diegoli H. Goal-directed therapy guided by the FloTrac sensor in major surgery: a systematic review and meta-analysis. CRITICAL CARE SCIENCE 2024; 36:e20240196en. [PMID: 38775544 PMCID: PMC11098079 DOI: 10.62675/2965-2774.20240196-en] [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: 08/04/2023] [Accepted: 12/08/2023] [Indexed: 05/25/2024]
Abstract
OBJECTIVE To provide insights into the potential benefits of goal-directed therapy guided by FloTrac in reducing postoperative complications and improving outcomes. METHODS We performed a systematic review and meta-analysis of randomized controlled trials to evaluate goal-directed therapy guided by FloTrac in major surgery, comparing goal-directed therapy with usual care or invasive monitoring in cardiac and noncardiac surgery subgroups. The quality of the articles and evidence were evaluated with a risk of bias tool and GRADE. RESULTS We included 29 randomized controlled trials with 3,468 patients. Goal-directed therapy significantly reduced the duration of hospital stay (mean difference -1.43 days; 95%CI 2.07 to -0.79; I2 81%), intensive care unit stay (mean difference -0.77 days; 95%CI -1.18 to -0.36; I2 93%), and mechanical ventilation (mean difference -2.48 hours, 95%CI -4.10 to -0.86, I2 63%). There was no statistically significant difference in mortality, myocardial infarction, acute kidney injury or hypotension, but goal-directed therapy significantly reduced the risk of heart failure or pulmonary edema (RR 0.46; 95%CI 0.23 - 0.92; I2 0%). CONCLUSION Goal-directed therapy guided by the FloTrac sensor improved clinical outcomes and shortened the length of stay in the hospital and intensive care unit in patients undergoing major surgery. Further research can validate these results using specific protocols and better understand the potential benefits of FloTrac beyond these outcomes.
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Affiliation(s)
| | - Saulo Fernandes Saturnino
- Universidade Federal de Minas GeraisBelo HorizonteMGBrazilUniversidade Federal de Minas Gerais - Belo Horizonte (MG), Brazil.
| | - Ana Beatriz Zen
- Academia VBHC Educação e ConsultoriaSão PauloSPBrazilAcademia VBHC Educação e Consultoria - São Paulo (SP), Brazil.
| | | | - Henrique Diegoli
- Academia VBHC Educação e ConsultoriaSão PauloSPBrazilAcademia VBHC Educação e Consultoria - São Paulo (SP), Brazil.
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Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health 2024; 6:e367-e373. [PMID: 38670745 PMCID: PMC11068159 DOI: 10.1016/s2589-7500(24)00047-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 04/28/2024]
Abstract
This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.
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Affiliation(s)
- Ryan Han
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA; University of California Los Angeles-Caltech Medical Scientist Training Program, Los Angeles, CA, USA
| | - Julián N Acosta
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Rad AI, San Francisco, CA, USA
| | - Zahra Shakeri
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - John P A Ioannidis
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA; Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA.
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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29
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Yoshikawa Y, Maeda M, Kunigo T, Sato T, Takahashi K, Ohno S, Hirahata T, Yamakage M. Effect of using hypotension prediction index versus conventional goal-directed haemodynamic management to reduce intraoperative hypotension in non-cardiac surgery: A randomised controlled trial. J Clin Anesth 2024; 93:111348. [PMID: 38039629 DOI: 10.1016/j.jclinane.2023.111348] [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: 09/05/2023] [Revised: 11/08/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023]
Abstract
STUDY OBJECTIVE It remains unclear whether it is the hypotension prediction index itself or goal-directed haemodynamic therapy that mitigates intraoperative hypotension. DESIGN A single centre randomised controlled trial. SETTING Sapporo Medical University Hospital. PATIENTS A total of 64 adults patients undergoing major non-cardiac surgery under general anaesthesia. INTERVENTIONS Patients were randomly assigned to either group receiving conventional goal-directed therapy (FloTrac group) or combination of the hypotension prediction index and conventional goal-directed therapy (HPI group). To investigate the independent utility of the index, the peak rates of arterial pressure and dynamic arterial elastance were not included in the treatment algorithm for the HPI group. MEASUREMENTS The primary outcome was the time-weighted average of the areas under the threshold. Secondary outcomes were area under the threshold, the number of hypotension events, total duration of hypotension events, mean mean arterial pressure during the hypotension period, number of hypotension events with mean arterial pressure < 50 mmHg, amounts of fluids, blood products, blood loss, and urine output, frequency and amount of vasoactive agents, concentration of haemoglobin during the monitoring period, and 30-day mortality. MAIN RESULTS The time-weighted average of the area below the threshold was lower in the HPI group than in the control group; 0.19 mmHg (interquartile range, 0.06-0.80 mmHg) vs. 0.66 mmHg (0.28-1.67 mmHg), with a median difference of -0.41 mmHg (95% confidence interval, -0.69 to -0.10 mmHg), p = 0.005. Norepinephrine was administered to 12 (40%) and 5 (17%) patients in the HPI and FloTrac groups, respectively (p = 0.045). No significant differences were observed in the volumes of fluid and blood products between the study groups. CONCLUSIONS The current randomised controlled trial results suggest that using the hypotension prediction index independently lowered the cumulative amount of intraoperative hypotension during major non-cardiac surgery.
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Affiliation(s)
- Yusuke Yoshikawa
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan.
| | - Makishi Maeda
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan
| | - Tatsuya Kunigo
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan
| | - Tomoe Sato
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan
| | - Kanako Takahashi
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan
| | - Sho Ohno
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan
| | - Tomoki Hirahata
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan
| | - Michiaki Yamakage
- Department of Anaesthesiology, Sapporo Medical University School of Medicine, South1 West16 291, Chuoku, Sapporo 060-8543, Japan
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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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31
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Ripollés-Melchor J, Carrasco-Sánchez L, Tomé-Roca JL, Aldecoa C, Zorrilla-Vaca A, Lorente-Olazábal JV, Colomina MJ, Pérez A, Jiménez-López JI, Navarro-Pérez R, Abad-Gurumeta A, Monge-García MI. Hypotension prediction index guided goal-directed therapy to reduce postoperative acute kidney injury during major abdominal surgery: study protocol for a multicenter randomized controlled clinical trial. Trials 2024; 25:288. [PMID: 38685032 PMCID: PMC11057064 DOI: 10.1186/s13063-024-08113-w] [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: 11/07/2023] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a significant postoperative complication associated with increased mortality and hospital costs. Hemodynamic strategies, such as goal-directed therapy, might reduce AKI risk. Predicting and proactively managing intraoperative hypotension may be helpful. This trial aims to investigate if a preemptive hemodynamic strategy guided by the hypotension prediction index (HPI) can decrease the incidence of moderate-to-severe AKI within 30 days following major elective abdominal surgery. METHODS This is an open-label, controlled, multicenter, randomized clinical trial that involves daily patient follow-up until hospital discharge. Inclusion criteria are patients aged over 65 and/or categorized as ASA III or IV physical status, undergoing major elective abdominal surgery (general, urological, or gynecological procedures) via laparoscopic or open approach under general or combined anesthesia. INTERVENTION In the intervention group, hemodynamic management will be based on the HPI and the advanced functional hemodynamic variables provided by the Hemosphere platform and the AcumenIQ® sensor (Edwards Lifesciences). The primary outcome is the incidence of moderate-to-severe AKI within 7 days post-surgery. Secondary outcomes include postoperative complications and 30-day mortality. DISCUSSION This study explores the potential of HPI-guided hemodynamic management in reducing AKI after major elective abdominal surgery, with implications for postoperative outcomes and patient care. TRIAL REGISTRATION ClinicalTrials.gov NCT05569265. Registered on October 6, 2022.
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Affiliation(s)
- Javier Ripollés-Melchor
- Infanta Leonor University Hospital, Madrid, Spain.
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain.
- Universidad Complutense de Madrid, Madrid, Spain.
| | - Laura Carrasco-Sánchez
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Althaia Xarxa Assistencial Universitària de Manresa, Manresa, Spain
- Doctoral Program in Medicine and Biomedical Sciences, University of Vic-Central University of Catalonia (UVic-UCC), Vic, Spain
- Institut de Recerca I Innovació en Ciències de La Vida I de La Salut a La Catalunya Central (IRIS-CC), Vic, Spain
| | - José L Tomé-Roca
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Virgen de las Nieves University Hospital, Granada, Spain
| | - César Aldecoa
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Río Hortega University Hospital, Valladolid, Spain
| | - Andres Zorrilla-Vaca
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Brigham and Women's Hospital, Boston, USA
| | - Juan V Lorente-Olazábal
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Juan Ramón Jiménez University Hospital, Huelva, Spain
| | - María J Colomina
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Bellvitge University Hospital, Barcelona, Spain
| | - Ana Pérez
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Elche University Hospital, Elche, Spain
| | - Juan I Jiménez-López
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Virgen del Rocío University Hospital, Seville, Spain
| | - Rosalía Navarro-Pérez
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Clinico San Carlos University Hospital, Madrid, Spain
| | - Alfredo Abad-Gurumeta
- Infanta Leonor University Hospital, Madrid, Spain
- Universidad Complutense de Madrid, Madrid, Spain
| | - Manuel I Monge-García
- Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care (SEDAR), Madrid, Spain
- Jerez de La Frontera University Hospital, Jerez de la Frontera, Spain
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Bsisu I, Alqassieh R, Aloweidi A, Abu-Humdan A, Subuh A, Masarweh D. Attitudes of Jordanian Anesthesiologists and Anesthesia Residents towards Artificial Intelligence: A Cross-Sectional Study. J Pers Med 2024; 14:447. [PMID: 38793029 PMCID: PMC11121815 DOI: 10.3390/jpm14050447] [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: 03/02/2024] [Revised: 03/29/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Success in integrating artificial intelligence (AI) in anesthesia depends on collaboration with anesthesiologists, respecting their expertise, and understanding their opinions. The aim of this study was to illustrate the confidence in AI integration in perioperative anesthetic care among Jordanian anesthesiologists and anesthesia residents working at tertiary teaching hospitals. This cross-sectional study was conducted via self-administered online questionnaire and includes 118 responses from 44 anesthesiologists and 74 anesthesia residents. We used a five-point Likert scale to investigate the confidence in AI's role in different aspects of the perioperative period. A significant difference was found between anesthesiologists and anesthesia residents in confidence in the role of AI in operating room logistics and management, with an average score of 3.6 ± 1.3 among residents compared to 2.9 ± 1.4 among specialists (p = 0.012). The role of AI in event prediction under anesthesia scored 3.5 ± 1.4 among residents compared to 2.9 ± 1.4 among specialists (p = 0.032) and the role of AI in decision-making in anesthetic complications 3.3 ± 1.4 among residents and 2.8 ± 1.4 among specialists (p = 0.034). Also, 65 (55.1%) were concerned that the integration of AI will lead to less human-human interaction, while 81 (68.6%) believed that AI-based technology will lead to more adherence to guidelines. In conclusion, AI has the potential to be a revolutionary tool in anesthesia, and hesitancy towards increased dependency on this technology is decreasing with newer generations of practitioners.
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Affiliation(s)
- Isam Bsisu
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
- UCSF Center for Health Equity in Surgery and Anesthesia, San Francisco, CA 94158, USA
- Department of Anesthesia and Intensive Care, Arab Medical Center, Amman 11181, Jordan
| | - Rami Alqassieh
- Department of General Surgery and Anesthesia and Urology, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan;
| | - Abdelkarim Aloweidi
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
| | - Abdulrahman Abu-Humdan
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
| | - Aseel Subuh
- Department of Internal Medicine, School of Medicine, The University of Jordan, Amman 11942, Jordan;
| | - Deema Masarweh
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
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Fritz BA, Pugazenthi S, Budelier TP, Tellor Pennington BR, King CR, Avidan MS, Abraham J. User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications. Anesth Analg 2024; 138:804-813. [PMID: 37339083 PMCID: PMC10730770 DOI: 10.1213/ane.0000000000006577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
BACKGROUND Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.
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Affiliation(s)
| | | | | | | | | | | | - Joanna Abraham
- From the Department of Anesthesiology
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri
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de Keijzer IN, Vos JJ, Yates D, Reynolds C, Moore S, Lawton RJ, Scheeren TWL, Davies SJ. Impact of clinicians' behavior, an educational intervention with mandated blood pressure and the hypotension prediction index software on intraoperative hypotension: a mixed methods study. J Clin Monit Comput 2024; 38:325-335. [PMID: 38112879 PMCID: PMC10995090 DOI: 10.1007/s10877-023-01097-z] [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: 08/18/2023] [Accepted: 10/21/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE Intraoperative hypotension (IOH) is associated with adverse outcomes. We therefore explored beliefs regarding IOH and barriers to its treatment. Secondarily, we assessed if an educational intervention and mandated mean arterial pressure (MAP), or the implementation of the Hypotension Prediction Index-software (HPI) were associated with a reduction in IOH. METHODS Structured interviews (n = 27) and questionnaires (n = 84) were conducted to explore clinicians' beliefs and barriers to IOH treatment, in addition to usefulness of HPI questionnaires (n = 14). 150 elective major surgical patients who required invasive blood pressure monitoring were included in three cohorts to assess incidence and time-weighted average (TWA) of hypotension (MAP < 65 mmHg). Cohort one received standard care (baseline), the clinicians of cohort two had a training on hypotension and a mandated MAP > 65 mmHg, and patients of the third cohort received protocolized care using the HPI. RESULTS Clinicians felt challenged to manage IOH in some patients, yet they reported sufficient knowledge and skills. HPI-software was considered useful and beneficial. No difference was found in incidence of IOH between cohorts. TWA was comparable between baseline and education cohort (0.15 mmHg [0.05-0.41] vs. 0.11 mmHg [0.02-0.37]), but was significantly lower in the HPI cohort (0.04 mmHg [0.00 to 0.11], p < 0.05 compared to both). CONCLUSIONS Clinicians believed they had sufficient knowledge and skills, which could explain why no difference was found after the educational intervention. In the HPI cohort, IOH was significantly reduced compared to baseline, therefore HPI-software may help prevent IOH. TRIAL REGISTRATION ISRCTN 17,085,700 on May 9th, 2019.
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Affiliation(s)
- Ilonka N de Keijzer
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, 9700 RB, The Netherlands.
| | - Jaap Jan Vos
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, 9700 RB, The Netherlands
| | - David Yates
- Department of Anesthesia, Critical Care and Perioperative Medicine York Teaching Hospitals NHS Foundation Trust, Centre for Health and Population Sciences, Hull York Medical School, York, UK
| | - Caroline Reynolds
- Bradford Institute for Health Research, Bradford Teaching Hospitals Foundation Trust, Bradford, UK
| | - Sally Moore
- Bradford Institute for Health Research, Bradford Teaching Hospitals Foundation Trust, Bradford, UK
| | | | - Thomas W L Scheeren
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, 9700 RB, The Netherlands
| | - Simon J Davies
- Department of Anesthesia, Critical Care and Perioperative Medicine York Teaching Hospitals NHS Foundation Trust, Centre for Health and Population Sciences, Hull York Medical School, York, UK
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Kovacheva VP, Nagle B. Opportunities of AI-powered applications in anesthesiology to enhance patient safety. Int Anesthesiol Clin 2024; 62:26-33. [PMID: 38348838 PMCID: PMC11185868 DOI: 10.1097/aia.0000000000000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Affiliation(s)
- Vesela P. Kovacheva
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Baily Nagle
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Lopes S, Rocha G, Guimarães-Pereira L. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. J Clin Monit Comput 2024; 38:247-259. [PMID: 37864754 PMCID: PMC10995017 DOI: 10.1007/s10877-023-01088-0] [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: 06/11/2023] [Accepted: 10/04/2023] [Indexed: 10/23/2023]
Abstract
PURPOSE Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context. METHODS A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted. RESULTS A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods. CONCLUSION AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.
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Affiliation(s)
- Sara Lopes
- Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal.
| | - Gonçalo Rocha
- Surgery and Physiology Department, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Luís Guimarães-Pereira
- Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal
- Surgery and Physiology Department, Faculty of Medicine, University of Porto, Porto, Portugal
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Davies SJ, Sessler DI, Jian Z, Fleming NW, Mythen M, Maheshwari K, Veelo DP, Vlaar AP, Settels J, Scheeren T, van der Ster B, Sander M, Cannesson M, Hatib F. Comparison of differences in cohort (forwards) and case control (backwards) methodological approaches for validation of the Hypotension Prediction Index. Anesthesiology 2024:139987. [PMID: 38557791 DOI: 10.1097/aln.0000000000004989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiological changes that may lead to hypotension. The original validation used a case control (backwards) analysis that has been suggested to be biased. We therefore conducted a cohort (forwards) analysis and compared this to the original validation technique. METHODS We conducted a retrospective analysis of data from previously reported studies. All data were analysed identically with 2 different methodologies and receiver operating characteristic curves (ROC) constructed. Both backwards and forwards analyses were performed to examine differences in area under the ROC for HPI and other haemodynamic variables to predict a MAP < 65mmHg for at least 1 minute 5, 10 and 15 minutes in advance. RESULTS Two thousand and twenty-two patients were included in the analysis, yielding 4,152,124 measurements taken at 20 second intervals. The area-under-the-curve for the index predicting hypotension analysed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947-0.964) vs 0.923 (95% CI, 0.912-0.933) 5 minutes in advance, 0.933 (95% CI, 0.924-0.942) vs 0.923 (95% CI, 0.911-0.933) 10 minutes in advance , and 0.929 (95% CI, 0.918-0.938) vs. 0.926 (95% CI, 0.914-0.937) 15 minutes in advance. No other variable had an area-under-the-curve > 0.7 except for MAP. Area-under-the-curve using forward analysis for MAP predicting hypotension 5, 10, and 15 minutes in advance was 0.932 (95% CI, 0.920-0.940), 0.929 (95% CI, 0.918-0.938), and 0.932 (95% CI, 0.921-0.940). The R 2 for the variation in the index due to MAP was 0.77. CONCLUSION Using an updated methodology, we found the utility of the HPI index to predict future hypotensive events is high, with an area under the receiver-operating-characteristics curve similar to that of the original validation method.
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Affiliation(s)
- Simon J Davies
- Department of Anaesthesia, Critical Care and Perioperative Medicine, York and Scarborough Teaching Hospitals NHS foundation Trust, York, UK
- Centre for Health and Population Science, Hull York Medical School, York, UK
| | | | | | | | - Monty Mythen
- Edwards Lifesciences, Irvine, California, USA
- UCL/UCLH National Institute of Health Research Biomedical Research Centre, London, UK
| | | | - Denise P Veelo
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Alexander Pj Vlaar
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jos Settels
- Edwards Lifesciences, Irvine, California, USA
| | - Thomas Scheeren
- Edwards Lifesciences, Irvine, California, USA
- Department of Anesthesiology, University Medical Centre Groningen, The Netherlands
| | - Bjp van der Ster
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
- ErasmusMc, Rotterdam, The Netherlands
| | - Michael Sander
- Department of Anaesthesiology, Intensive Care Medicine and Pain Medicine, University Hospital Giessen, Giessen, Germany
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, UCLA, California, USA
| | - Feras Hatib
- Edwards Lifesciences, Irvine, California, USA
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Chen Q, Wu B, Deng M, Wei K. Effect of different targets of goal-directed fluid therapy on intraoperative hypotension and fluid infusion in robot-assisted laparoscopic gynecological surgery: a randomized non-inferiority trial. J Robot Surg 2024; 18:127. [PMID: 38492125 DOI: 10.1007/s11701-024-01875-0] [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: 12/23/2023] [Accepted: 02/17/2024] [Indexed: 03/18/2024]
Abstract
Carotid corrected flow time (FTc) and tidal volume challenge pulse pressure variation (VtPPV) are useful clinical parameters for assessing volume status and fluid responsiveness in robot-assisted surgery, but their usefulness as goal-directed fluid therapy (GDFT) targets is unclear. We investigated whether FTc or VtPPV as targets are inferior to PPV in GDFT. This single-center, prospective, randomized, non-inferiority study included 133 women undergoing robot-assisted laparoscopic gynecological surgery in the modified head-down lithotomy position. Patients were equally divided into three groups, and the GDFT protocol was guided by FTc, VtPPV, or PPV during surgery. Primary outcomes were non-inferiority of the time-weighted average of hypotension, intraoperative fluid volume, and urine output. Secondary outcomes were optic nerve sheath diameter (ONSD) pre- and post-operatively and creatinine and blood urea nitrogen preoperatively and on day 1 post-operatively. No significant differences were observed in intraoperative hypotension index, infusion and urine volumes, and ONSD post-operatively between the FTc and VtPPV groups and the PPV group. No differences in serum creatinine and urea nitrogen levels were identified between the FTc and VtPPV groups preoperatively, but on day 1 post-operatively, the urea nitrogen level in the FTc group was higher than that in the PPV group (4.09 ± 1.28 vs. 3.0 ± 1.1 mmol/L, 1.08 [0.59, 1.58], p < 0.0001), and the difference from the preoperative value was smaller than that in the PPV group (- 2 [- 2.97, 1.43] vs. - 1.34 [- 1.9, - 0.67], p = 0.004). FTc- or VtPPV-guided protocols are not inferior to that of PPV in GDFT during robot-assisted laparoscopic surgery in the modified head-down lithotomy position.Trial registration: Chinese Clinical Trial Registry (ChiCTR2200064419).
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Affiliation(s)
- Qi Chen
- Department of Anesthesiology, The First Affiliated of Chongqing Medical University, Chongqing, China
- Department of Anesthesiology, Chongqing University Cancer Hospital, Chongqing, China
| | - Bin Wu
- Department of Anesthesiology, The First Affiliated of Chongqing Medical University, Chongqing, China
| | - Meiling Deng
- Department of Anesthesiology, The First Affiliated of Chongqing Medical University, Chongqing, China
| | - Ke Wei
- Department of Anesthesiology, The First Affiliated of Chongqing Medical University, Chongqing, China.
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Bao X, Kumar SS, Shah NJ, Penning D, Weinstein M, Malhotra G, Rose S, Drover D, Pennington MW, Domino K, Meng L, Treggiari M, Clavijo C, Wagener G, Chitilian H, Maheshwari K. AcumenTM hypotension prediction index guidance for prevention and treatment of hypotension in noncardiac surgery: a prospective, single-arm, multicenter trial. Perioper Med (Lond) 2024; 13:13. [PMID: 38439069 PMCID: PMC10913612 DOI: 10.1186/s13741-024-00369-9] [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: 08/06/2023] [Accepted: 02/25/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND Intraoperative hypotension is common during noncardiac surgery and is associated with postoperative myocardial infarction, acute kidney injury, stroke, and severe infection. The Hypotension Prediction Index software is an algorithm based on arterial waveform analysis that alerts clinicians of the patient's likelihood of experiencing a future hypotensive event, defined as mean arterial pressure < 65 mmHg for at least 1 min. METHODS Two analyses included (1) a prospective, single-arm trial, with continuous blood pressure measurements from study monitors, compared to a historical comparison cohort. (2) A post hoc analysis of a subset of trial participants versus a propensity score-weighted contemporaneous comparison group, using external data from the Multicenter Perioperative Outcomes Group (MPOG). The trial included 485 subjects in 11 sites; 406 were in the final effectiveness analysis. The post hoc analysis included 457 trial participants and 15,796 comparison patients. Patients were eligible if aged 18 years or older, American Society of Anesthesiologists (ASA) physical status 3 or 4, and scheduled for moderate- to high-risk noncardiac surgery expected to last at least 3 h. MEASUREMENTS minutes of mean arterial pressure (MAP) below 65 mmHg and area under MAP < 65 mmHg. RESULTS Analysis 1: Trial subjects (n = 406) experienced a mean of 9 ± 13 min of MAP below 65 mmHg, compared with the MPOG historical control mean of 25 ± 41 min, a 65% reduction (p < 0.001). Subjects with at least one episode of hypotension (n = 293) had a mean of 12 ± 14 min of MAP below 65 mmHg compared with the MPOG historical control mean of 28 ± 43 min, a 58% reduction (p< 0.001). Analysis 2: In the post hoc inverse probability treatment weighting model, patients in the trial demonstrated a 35% reduction in minutes of hypotension compared to a contemporaneous comparison group [exponentiated coefficient: - 0.35 (95%CI - 0.43, - 0.27); p < 0.001]. CONCLUSIONS The use of prediction software for blood pressure management was associated with a clinically meaningful reduction in the duration of intraoperative hypotension. Further studies must investigate whether predictive algorithms to prevent hypotension can reduce adverse outcomes. TRIAL REGISTRATION Clinical trial number: NCT03805217. Registry URL: https://clinicaltrials.gov/ct2/show/NCT03805217 . Principal investigator: Xiaodong Bao, MD, PhD. Date of registration: January 15, 2019.
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Affiliation(s)
- Xiaodong Bao
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
| | - Sathish S Kumar
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Nirav J Shah
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Donald Penning
- Department of Anesthesiology, Henry Ford Health System, Detroit, MI, USA
| | - Mitchell Weinstein
- Department of Anesthesiology and Critical Care, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Gaurav Malhotra
- Department of Anesthesiology and Critical Care, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Rose
- Department of Anesthesiology and Perioperative Medicine, Oregon Health & Science University, Portland, OR, USA
| | - David Drover
- Department of Anesthesia, Stanford University, Stanford, CA, USA
| | - Matthew W Pennington
- Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Karen Domino
- Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Lingzhong Meng
- Department of Anesthesiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mariam Treggiari
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USA
| | - Claudia Clavijo
- Department of Anesthesiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Gebhard Wagener
- Department of Anesthesiology, College of Physicians & Surgeons of Columbia University, New York, NY, USA
| | - Hovig Chitilian
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kamal Maheshwari
- Department of General Anesthesiology, Cleveland Clinic, Cleveland, OH, USA
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Pražetina M, Šribar A, Sokolović Jurinjak I, Matošević J, Peršec J. Effect of machine learning-guided haemodynamic optimization on postoperative free flap perfusion in reconstructive maxillofacial surgery: A study protocol. Br J Clin Pharmacol 2024; 90:684-690. [PMID: 37876305 DOI: 10.1111/bcp.15942] [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: 08/04/2023] [Revised: 10/14/2023] [Accepted: 10/15/2023] [Indexed: 10/26/2023] Open
Abstract
AIMS Intraoperative hypotension and liberal fluid haemodynamic therapy are associated with postoperative medical and surgical complications in maxillofacial free flap surgery. The novel haemodynamic parameter hypotension prediction index (HPI) has shown good performance in predicting hypotension by analysing arterial pressure waveform in various types of surgery. HPI-based haemodynamic protocols were able to reduce the duration and depth of hypotension. We will try to determine whether haemodynamic therapy based on HPI can improve postoperative flap perfusion and tissue oxygenation by improving intraoperative mean arterial pressure and reducing fluid infusion. METHODS We present here a study protocol for a single centre, randomized, controlled trial (n = 42) in maxillofacial patients undergoing free flap surgery. Patients will be randomized into an intervention or a control group. In the intervention, group haemodynamic optimization will be guided by machine learning algorithm and functional haemodynamic parameters presented by the HemoSphere platform (Edwards Lifesciences, Irvine, CA, USA), most importantly, HPI. Tissue oxygen saturation of the free flap will be monitored noninvasively by near-infrared spectroscopy during the first 24 h postoperatively. The primary outcome will be the average value of tissue oxygen saturation in the first 24 h postoperatively.
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Affiliation(s)
- Marko Pražetina
- Clinical Department of Anesthesiology, Reanimatology and Intensive Care Medicine, University Hospital Dubrava, Zagreb, Croatia
| | - Andrej Šribar
- Clinical Department of Anesthesiology, Reanimatology and Intensive Care Medicine, University Hospital Dubrava, Zagreb, Croatia
- School of Dental Medicine, Zagreb University, Zagreb, Croatia
| | - Irena Sokolović Jurinjak
- Clinical Department of Anesthesiology, Reanimatology and Intensive Care Medicine, University Hospital Dubrava, Zagreb, Croatia
| | - Jelena Matošević
- Clinical Department of Anesthesiology, Reanimatology and Intensive Care Medicine, University Hospital Dubrava, Zagreb, Croatia
| | - Jasminka Peršec
- Clinical Department of Anesthesiology, Reanimatology and Intensive Care Medicine, University Hospital Dubrava, Zagreb, Croatia
- School of Dental Medicine, Zagreb University, Zagreb, Croatia
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Cylwik J, Celińska-Spodar M, Dudzic M. Individualized Perioperative Hemodynamic Management Using Hypotension Prediction Index Software and the Dynamics of Troponin and NTproBNP Concentration Changes in Patients Undergoing Oncological Abdominal Surgery. J Pers Med 2024; 14:211. [PMID: 38392644 PMCID: PMC10890224 DOI: 10.3390/jpm14020211] [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: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
INTRODUCTION Abdominal oncologic surgeries pose significant risks due to the complexity of the surgery and patients' often weakened health, multiple comorbidities, and increased perioperative hazards. Hypotension is a major risk factor for perioperative cardiovascular complications, necessitating individualized management in modern anesthesiology. AIM This study aimed to determine the dynamics of changes in troponin and NTproBNP levels during the first two postoperative days in patients undergoing major cancer abdominal surgery with advanced hemodynamic monitoring including The AcumenTM Hypotension Prediction Index software (HPI) (Edwards Lifesciences, Irvine, CA, USA) and their association with the occurrence of postoperative cardiovascular complications. METHODS A prospective study was conducted, including 50 patients scheduled for abdominal cancer surgery who, due to the overall risk of perioperative complications (ASA class 3 or 4), were monitored using the HPI software. Hypotension was qualified as at least one ≥ 1 min episode of a MAP < 65 mm Hg. Preoperatively and 24 and 48 h after the procedure, the levels of NTproBNP and troponin were measured, and an ECG was performed. RESULTS We analyzed data from 46 patients and found that 82% experienced at least one episode of low blood pressure (MAP < 65 mmHg). However, the quality indices of hypotension were low, with a median time-weighted average MAP < 65 mmHg of 0.085 (0.03-0.19) mmHg and a median of 2 (2-1.17) minutes spent below MAP < 65 mmHg. Although the incidence of perioperative myocardial injury was 10%, there was no evidence to suggest a relationship with hypotension. Acute kidney injury was seen in 23.9% of patients, and it was significantly associated with a number of episodes of MAP < 50 mmHg. Levels of NTproBNP were significantly higher on the first postoperative day compared to preoperative values (285.8 [IQR: 679.8] vs. 183.9 [IQR: 428.1] pg/mL, p < 0.001). However, they decreased on the second day (276.65 [IQR: 609.4] pg/mL, p = 0.154). The dynamics of NTproBNP were similar for patients with and without heart failure, although those with heart failure had significantly higher preoperative concentrations (435.9 [IQR: 711.15] vs. 87 [IQR: 232.2] pg/mL, p < 0.001). Patients undergoing laparoscopic surgery showed a statistically significant increase in NTproBNP. CONCLUSIONS This study suggests that advanced HPI monitoring in abdominal cancer surgery effectively minimizes intraoperative hypotension with no significant NTproBNP or troponin perioperative dynamics, irrespective of preoperative heart failure.
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Affiliation(s)
- Jolanta Cylwik
- Anesthesiology and Intensive Care Unit, Mazovia Regional Hospital, 08-110 Siedlce, Poland
| | - Małgorzata Celińska-Spodar
- Anesthesiology and Intensive Care Unit, Mazovia Regional Hospital, 08-110 Siedlce, Poland
- Anesthesiology and Intensive Care Unit, The National Institute of Cardiology, 04-628 Warsaw, Poland
| | - Mariusz Dudzic
- Critical Care, Edwards Lifesciences, 00-807 Warsaw, Poland
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Tol JTM, Terwindt LE, Rellum SR, Wijnberge M, van der Ster BJP, Kho E, Hollmann MW, Vlaar APJ, Veelo DP, Schenk J. Performance of a Machine Learning Algorithm to Predict Hypotension in Spontaneously Breathing Non-Ventilated Post-Anesthesia and ICU Patients. J Pers Med 2024; 14:210. [PMID: 38392643 PMCID: PMC10890176 DOI: 10.3390/jpm14020210] [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: 01/21/2024] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
Background: Hypotension is common in the post-anesthesia care unit (PACU) and intensive care unit (ICU), and is associated with adverse patient outcomes. The Hypotension Prediction Index (HPI) algorithm has been shown to accurately predict hypotension in mechanically ventilated patients in the OR and ICU and to reduce intraoperative hypotension (IOH). Since positive pressure ventilation significantly affects patient hemodynamics, we performed this validation study to examine the performance of the HPI algorithm in a non-ventilated PACU and ICU population. Materials & Methods: The performance of the HPI algorithm was assessed using prospectively collected blood pressure (BP) and HPI data from a PACU and a mixed ICU population. Recordings with sufficient time (≥3 h) spent without mechanical ventilation were selected using data from the electronic medical record. All HPI values were evaluated for sensitivity, specificity, predictive value, and time-to-event, and a receiver operating characteristic (ROC) curve was constructed. Results: BP and HPI data from 282 patients were eligible for analysis, of which 242 (86%) were ICU patients. The mean age (standard deviation) was 63 (13.5) years, and 186 (66%) of the patients were male. Overall, the HPI predicted hypotension accurately, with an area under the ROC curve of 0.94. The most used HPI threshold cutoff in research and clinical use, 85, showed a sensitivity of 1.00, specificity of 0.79, median time-to-event of 160 s [60-380], PPV of 0.85, and NPV of 1.00. Conclusion: The absence of positive pressure ventilation and the influence thereof on patient hemodynamics does not negatively affect the performance of the HPI algorithm in predicting hypotension in the PACU and ICU. Future research should evaluate the feasibility and influence on hypotension and outcomes following HPI implementation in non-ventilated patients at risk of hypotension.
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Affiliation(s)
- Johan T M Tol
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Lotte E Terwindt
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Santino R Rellum
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
- Department of Intensive Care Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Marije Wijnberge
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Björn J P van der Ster
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Eline Kho
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
- Department of Intensive Care Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Markus W Hollmann
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
- Laboratory of Experimental Intensive Care and Anesthesiology, Academic Medical Center Location, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Alexander P J Vlaar
- Department of Intensive Care Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
- Laboratory of Experimental Intensive Care and Anesthesiology, Academic Medical Center Location, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Denise P Veelo
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Jimmy Schenk
- Department of Anesthesiology, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
- Department of Intensive Care Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
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Huo M, Zhang Q, Zheng X, Wang H, Bai N, Xu R, Zhao Z. Consistency analysis of consciousness index and bispectral index in monitoring the depth of sevoflurane anesthesia in laparoscopic surgery. PeerJ 2024; 12:e16848. [PMID: 38371374 PMCID: PMC10874172 DOI: 10.7717/peerj.16848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/07/2024] [Indexed: 02/20/2024] Open
Abstract
Background The Index of Consciousness (IoC) is a new monitoring index of anesthesia depth reflecting the state of consciousness of the brain independently developed by China. The research on monitoring the depth of anesthesia mainly focuses on propofol, and bispectral index (BIS) is a sensitive and accurate objective index to evaluate the state of consciousness at home and abroad. This study mainly analyzed the effect of IoC on monitoring the depth of sevoflurane anesthesia and the consistency and accuracy with BIS when monitoring sevoflurane maintenance anesthesia. Objective To investigate the monitoring value of the Index of Consciousness (IoC) for the depth of sevoflurane anesthesia in laparoscopic surgery. Methods The study population consisted of 108 patients who experienced elective whole-body anesthesia procedures within the timeframe of April 2020 to June 2023 at our hospital. Throughout the anesthesia process, which encompassed induction and maintenance using inhaled sevoflurane, all patients were diligently monitored for both the Bispectral Index (BIS) and the Index of Consciousness (IoC). We conducted an analysis to assess the correlation between IoC and BIS throughout the anesthesia induction process and from the maintenance phase to the regaining of consciousness. To evaluate the predictive accuracy of IoC and BIS for the onset of unconsciousness during induction and the return of consciousness during emergence, we employed receiver operating characteristic (ROC) curve analysis. Results The mean difference between BIS and IoC, spanning from the pre-anesthesia induction phase to the completion of propofol induction, was 1.3 (95% Limits of Agreement [-53.4 to 56.0]). Similarly, during the interval from the initiation of sevoflurane inhalation to the point of consciousness restoration, the average difference between BIS and IoC was 0.3 (95% LOA [-10.8 to 11.4]). No statistically significant disparities were observed in the data acquired from the two measurement methodologies during both the anesthesia induction process and the journey from maintenance to the regaining of consciousness (P > 0.05). The outcomes of the ROC curve analysis disclosed that the areas under the curve (AUC) for prognosticating the occurrence of loss of consciousness were 0.967 (95% CI [0.935-0.999]) for BIS and 0.959 (95% CI [0.924-0.993]) for IoC, with optimal threshold values set at 81 (sensitivity: 88.10%, specificity: 92.16%) and 77 (sensitivity: 79.55%, specificity: 95.45%) correspondingly. For the prediction of recovery of consciousness, the AUCs were 0.995 (95% CI [0.987-1.000]) for BIS and 0.963 (95% CI [0.916-1.000]) for IoC, each associated with optimal cutoff values of 76 (sensitivity: 92.86%, specificity: 100.00%) and 72 (sensitivity: 86.36%, specificity: 100.00%) respectively. Conclusion The monitoring of sevoflurane anesthesia maintenance using IoC demonstrates a level of comparability to BIS, and its alignment with BIS during the maintenance phase of sevoflurane anesthesia is robust. IoC displays promising potential for effectively monitoring the depth of anesthesia.
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Affiliation(s)
- Miao Huo
- Department of Anesthesiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Qian Zhang
- Department of Burn and Plastic Surgery, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xingxing Zheng
- Department of Anesthesiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Hui Wang
- Department of Anesthesiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Ning Bai
- Department of Anesthesiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Ruifen Xu
- Department of Anesthesiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Ziyu Zhao
- Department of Anesthesiology, Shaanxi Provincial People’s Hospital, Xi’an, China
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Szrama J, Gradys A, Bartkowiak T, Woźniak A, Nowak Z, Zwoliński K, Lohani A, Jawień N, Smuszkiewicz P, Kusza K. The Incidence of Perioperative Hypotension in Patients Undergoing Major Abdominal Surgery with the Use of Arterial Waveform Analysis and the Hypotension Prediction Index Hemodynamic Monitoring-A Retrospective Analysis. J Pers Med 2024; 14:174. [PMID: 38392607 PMCID: PMC10889918 DOI: 10.3390/jpm14020174] [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: 01/07/2024] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/24/2024] Open
Abstract
Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and alerts the clinician of an impending hypotension event. The purpose of the study was to compare the frequency of perioperative hypotension in patients undergoing major abdominal surgery with different types of hemodynamic monitoring. The study included 61 patients who were monitored with the arterial pressure-based cardiac output (APCO) technology (FloTrac group) and 62 patients with the Hypotension Prediction Index algorithm (HPI group). Our primary outcome was the time-weighted average (TWA) of hypotension below < 65 mmHg. The median TWA of hypotension in the FloTrac group was 0.31 mmHg versus 0.09 mmHg in the HPI group (p = 0.000009). In the FloTrac group, the average time of hypotension was 27.9 min vs. 8.1 min in the HPI group (p = 0.000023). By applying the HPI algorithm in addition to an arterial waveform analysis alone, we were able to significantly decrease the frequency and duration of perioperative hypotension events in patients who underwent major abdominal surgery.
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Affiliation(s)
- Jakub Szrama
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Agata Gradys
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Tomasz Bartkowiak
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Amadeusz Woźniak
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Zuzanna Nowak
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Krzysztof Zwoliński
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Ashish Lohani
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Natalia Jawień
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Piotr Smuszkiewicz
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Krzysztof Kusza
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
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Vistisen ST, Enevoldsen J. CON: The hypotension prediction index is not a validated predictor of hypotension. Eur J Anaesthesiol 2024; 41:118-121. [PMID: 38085015 DOI: 10.1097/eja.0000000000001939] [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: 01/03/2024]
Abstract
The Hypotension Prediction Index (HPI) algorithm is a commercial prediction algorithm developed to predict hypotension, a mean arterial pressure (MAP) below 65 mmHg. Although HPI has been investigated in several studies, recent concerns of have been raised regarding HPI's predictive abilities, which may have been overstated. A selection bias may have forced the HPI algorithm to learn almost exclusively from MAP. This CON position paper describes the selection bias further and summarises the scientific status of HPI's predictive abilities, including the meaning of a recent erratum retracting the primary conclusion of a published HPI validation study. We argue that the HPI algorithm needs re-validation or complete re-development to achieve a clinically relevant 'added value' in comparison with the predictive performance of a simple and costless MAP alarm threshold in the range of 70 to 75 mmHg.
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Affiliation(s)
- Simon Tilma Vistisen
- From the Institute of Clinical Medicine, Aarhus University (STV, JE) and Department of Anaesthesiology & Intensive Care, Aarhus University Hospital, Aarhus, Denmark (STV, JE)
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46
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Ali JT, Yang G, Green CA, Reed BL, Madani A, Ponsky TA, Hazey J, Rothenberg SS, Schlachta CM, Oleynikov D, Szoka N. Defining digital surgery: a SAGES white paper. Surg Endosc 2024; 38:475-487. [PMID: 38180541 DOI: 10.1007/s00464-023-10551-7] [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] [Accepted: 10/17/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND Digital surgery is a new paradigm within the surgical innovation space that is rapidly advancing and encompasses multiple areas. METHODS This white paper from the SAGES Digital Surgery Working Group outlines the scope of digital surgery, defines key terms, and analyzes the challenges and opportunities surrounding this disruptive technology. RESULTS In its simplest form, digital surgery inserts a computer interface between surgeon and patient. We divide the digital surgery space into the following elements: advanced visualization, enhanced instrumentation, data capture, data analytics with artificial intelligence/machine learning, connectivity via telepresence, and robotic surgical platforms. We will define each area, describe specific terminology, review current advances as well as discuss limitations and opportunities for future growth. CONCLUSION Digital Surgery will continue to evolve and has great potential to bring value to all levels of the healthcare system. The surgical community has an essential role in understanding, developing, and guiding this emerging field.
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Affiliation(s)
- Jawad T Ali
- University of Texas at Austin, Austin, TX, USA
| | - Gene Yang
- University at Buffalo, Buffalo, NY, USA
| | | | | | - Amin Madani
- University of Toronto, Toronto, ON, Canada
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada
| | - Todd A Ponsky
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | | | | | - Dmitry Oleynikov
- Monmouth Medical Center, Robert Wood Johnson Barnabas Health, Rutgers School of Medicine, Long Branch, NJ, USA
| | - Nova Szoka
- Department of Surgery, West Virginia University, Suite 7500 HSS, PO Box 9238, Morgantown, WV, 26506-9238, USA.
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Hurley NC, Gupta RK, Schroeder KM, Hess AS. Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia? Reg Anesth Pain Med 2024:rapm-2023-104868. [PMID: 38253610 DOI: 10.1136/rapm-2023-104868] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/18/2023] [Indexed: 01/24/2024]
Abstract
INTRODUCTION Artificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in performing guideline-based evaluation of neuraxial bleeding risk in hypothetical patients on anticoagulation medication. The study also explored the impact of structured prompt guidance on the LLM's performance. METHODS A dataset of 10 hypothetical patient stems and 26 anticoagulation profiles (260 unique combinations) was developed based on American Society of Regional Anesthesia and Pain Medicine guidelines. Five prompts were created for the LLM, ranging from minimal guidance to explicit instructions. The model's responses were compared with a "truth table" based on the guidelines. Performance metrics, including accuracy and area under the receiver operating curve (AUC), were used. RESULTS Baseline performance of GPT-3.5 was slightly above chance. With detailed prompts and explicit guidelines, performance improved significantly (AUC 0.70, 95% CI (0.64 to 0.77)). Performance varied among medication classes. DISCUSSION LLMs show potential for assisting in clinical decision making but rely on accurate and relevant prompts. Integration of LLMs should consider safety and privacy concerns. Further research is needed to optimize LLM performance and address complex scenarios. The tested LLM demonstrates potential in assessing neuraxial bleeding risk but relies on precise prompts. LLM integration should be approached cautiously, considering limitations. Future research should focus on optimization and understanding LLM capabilities and limitations in healthcare.
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Affiliation(s)
- Nathan C Hurley
- Department of Anesthesiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Rajnish K Gupta
- Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Aaron S Hess
- Department of Anesthesiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 PMCID: PMC11146190 DOI: 10.1097/aln.0000000000004764] [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: 06/22/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R. Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A. Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Langeron O, Castoldi N, Rognon N, Baillard C, Samama CM. How anesthesiology can deal with innovation and new technologies? Minerva Anestesiol 2024; 90:68-76. [PMID: 37526467 DOI: 10.23736/s0375-9393.23.17464-5] [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: 08/02/2023]
Abstract
Innovation and new technologies have always impacted significantly the anesthesiology practice all along the perioperative course, as it is recognized as one of the most transformative medical specialties specifically regarding patient's safety. Beside a number of major changes in procedures, equipment, training, and organization that aggregated to establish a strong safety culture with effective practices, anesthesiology is also a stakeholder in disruptive innovation. The present review is not exhaustive and aims to provide an overview on how innovation could change and improve anesthesiology practices through some examples as telemedicine (TM), machine learning and artificial intelligence (AI). For example, postoperative complications can be accurately predicted by AI from automated real-time electronic health record data, matching physicians' predictive accuracy. Clinical workflow could be facilitated and accelerated with mobile devices and applications, assuming that these tools should remain at the service of patients and care providers. Care providers and patients connections have improved, thanks to these digital and innovative transformations, without replacing existing relationships between them. It also should give time back to physicians and nurses to better spend it in the perioperative care, and to provide "personalized" medicine keeping a high level of standard of care.
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Affiliation(s)
- Olivier Langeron
- Department of Anesthesia and Intensive Care, Cochin University Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France -
- Paris-Est Créteil University (UPEC), Paris, France -
- Innovation Department, Hotel Dieu de Paris Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France -
| | - Nicolas Castoldi
- Innovation Department, Hotel Dieu de Paris Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Nina Rognon
- Innovation Department, Hotel Dieu de Paris Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Christophe Baillard
- Department of Anesthesia and Intensive Care, Cochin University Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
- Paris Cité University, Paris, France
| | - Charles M Samama
- Department of Anesthesia and Intensive Care, Cochin University Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
- Paris Cité University, Paris, France
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50
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Craig AD, Piras SE. Advanced Variables to Optimize Hemodynamic Monitoring. AACN Adv Crit Care 2023; 34:287-296. [PMID: 38033220 DOI: 10.4037/aacnacc2023903] [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: 12/02/2023]
Abstract
Measuring hemodynamic parameters has become safer and more precise than in the past. Accurately monitoring and evaluating the effectiveness of fluid, inotrope, and vasoactive medication administration can improve patient outcomes. Arbitrary fluid administration without stroke volume measurement can be detrimental to patient outcomes. Early detection and prompt treatment of shock states is essential to combat deleterious effects on critically ill patients. In addition to measuring traditional hemodynamic variables, the use of advanced variables such as hypotension prediction index, dynamic arterial elastance, and systolic slope can improve the precision of treat ment for critically ill patients. Using predictive analytics can help the bedside critical care nurse provide patient care that is proactive rather than reactive.
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Affiliation(s)
- Angela D Craig
- Angela D. Craig is Clinical Nurse Specialist, Intensive Care Unit, Cookeville Regional Medical Center, Cookeville, Tennessee
| | - Susan E Piras
- Susan E. Piras is Associate Professor, Whitson-Hester School of Nursing, Tennessee Tech University, PO Box 5001, Cookeville, TN 38505-0001
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