Safety Risk Analysis and Systematic Improvement Strategies for Intrahospital Transport of Patients Following General Anesthesia: A Retrospective Study Based on 255 Cases

Safety Risk Analysis and Systematic Improvement Strategies for Intrahospital Transport of Patients Following General Anesthesia: A Retrospective Study Based on 255 Cases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Safety Risk Analysis and Systematic Improvement Strategies for Intrahospital Transport of Patients Following General Anesthesia: A Retrospective Study Based on 255 Cases Ning Li, Xiu Jin, Yatao Liu, Yuchen Wu, Yujiang Yin, Huaping Wei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9166370/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To systematically evaluate the current safety status of intrahospital transport in patients recovering from general anesthesia, identify key risk factors, and propose data-driven, systematic improvement strategies. Methods A retrospective analysis was performed on transport monitoring records of 255 patients who underwent general anesthesia in a tertiary hospital. Transport duration, data integrity rate, and changes in oxygen saturation (SpO₂) and pulse rate (PR) were statistically analyzed. Results The mean transport duration was 580.85 ± 295.67 seconds (median: 564 seconds). The overall monitoring data integrity rate was 75.35%. The incidence of hypoxemia (SpO₂ < 90%) was 50.59% (129/255). The mean minimum SpO₂ was 89.23 ± 4.12%, and the mean maximum PR was 89.22 ± 16.46 beats per minute, indicating significant physiological fluctuations. Conclusion Despite meeting discharge criteria, patients recovering from general anesthesia experience physiological instability during intrahospital transport, characterized by a high incidence of hypoxemia and substantial gaps in monitoring data. Establishing a systematic safety framework—incorporating standardized procedures, enhanced team awareness, and technological support—is essential to mitigate transport-related risks and ensure patient safety. General anesthesia postoperative transport patient safety hypoxemia wearable devices 1. Introduction Intrahospital transport of patients following general anesthesia is a critical link connecting the operating room, postanesthesia care unit (PACU), and general wards or intensive care units (ICUs) [ 1 , 2 ] .This process is not merely a spatial transfer but represents an extension of anesthesia safety management in time and space, concentrating risks, and is equally crucial for the safety of surgical patients [ 3 , 4 ] . Due to the intraoperative use of analgesics, sedatives, and muscle relaxants, patients in the early recovery phase following general anesthesia may not have fully regained their protective reflexes, and the effects of muscle relaxants may not be completely reversed. These factors can lead to complications such as hypoventilation, airway obstruction, or respiratory depression [ 5 , 6 ] , Concurrently, vibrations and jolts during transport, positional changes, or painful stimuli can easily induce significant fluctuations in pluse rate and blood pressure, potentially endangering patient safety in severe cases [ 7 ] . Literature reports indicate that the incidence of intrahospital adverse events (ITAEs) during transport can be as high as 80%, with 4% to 9% of patients requiring medical intervention [ 8 ] . However, existing research has primarily focused on the transport of emergency or critically ill patients, with insufficient attention paid to the transport safety of routine general anesthesia patients, who account for over 80% of all surgical procedures [ 7 ] . In this context, this study analyzes transport data from 255 patients following general anesthesia to quantify transport risks. Based on these data, we aim to construct a safe transport framework encompassing standardized postoperative protocols, enhanced team safety awareness, and the application of continuous monitoring technologies, thereby providing evidence-based strategies to reduce transport risks and ensure patient safety. 2. Materials and Methods 2.1 Study Population This study retrospectively analyzed data from 255 patients who underwent general anesthesia and were subsequently transported to general wards between March 1, 2025, and June 30, 2025. Patients were transported from the PACU to the general ward by an anesthesia nurse after being assessed by an anesthesiologist as meeting PACU discharge criteria. Inclusion criteria were: (1) age 18–65 years; (2) hemodynamically stable upon PACU discharge with no delayed emergence from anesthesia. Exclusion criteria were: (1) preoperative upper respiratory tract infection; (2) unplanned significant hemodynamic fluctuations during surgery; (3) postoperative transfer directly from PACU to ICU due to clinical deterioration. 2.2 Data Collection The following data were extracted from portable wearable devices: (1)Transport Duration: Total time from donning the device upon leaving the PACU to doffing the device upon arrival at the surgical ward. (2)Monitoring Data: Pulse oximetry (SpO₂) and pulse rate (PR) recorded at corresponding time points throughout the transport. (3)Data Integrity: Percentage of total transport duration with valid, recorded data. 2.3 Statistical Analysis SPSS version 26.0 software was used for data processing. Measurement data are presented as mean ± standard deviation (mean ± SD) and median; count data are presented as numbers (percentages). Data integrity is described as a percentage. 3. Results 3.1 Transport Duration and Data Integrity The total transport time for the 255 patients was 148,117 seconds, with a mean transport duration of (580.85 ± 295.67) seconds and a median time of 562 seconds (range: 131–1,635 seconds). The total effective physiological parameter recording time was 111,604 seconds, with a mean recording time per patient of (437.66 ± 227.36) seconds, a median of 390 seconds, and a range of 50–1,509 seconds. The overall data integrity rate was 75.35%, indicating that over 24% of transport time occurred without effective monitoring(Table 1 ). 3.2 Changes in Oxygen Saturation (SpO₂) Patients' SpO₂ fluctuated significantly during transport. Individual minimum SpO₂ values ranged from 65% to 97%, with a mean of (88.96 ± 4.40)% and a median of 89%. The mean minimum SpO₂ was (89.23 ± 4.12)%, median 90%, range 65–97%; mean maximum SpO₂ was (95.67 ± 2.34)%, median 96%, range 89–100%; mean average SpO₂ was (92.44 ± 2.86)%, median 92.50%, range 86.37–99.44%. The incidence of hypoxemia (SpO₂<90%)was 50.59% (129/255). The lowest recorded value was 65%, indicating a risk of severe hypoxic events during transport. 3.3 Changes in Pulse Rate (PR) Patients' pulse rates also showed marked fluctuations during transport. The mean minimum PR was (68.45 ± 12.78) bpm, median 67 bpm, range 44–116 bpm; mean maximum PR was (89.23 ± 15.67) bpm, median 88 bpm, range 51–167 bpm; mean average PR was (78.34 ± 14.21) bpm, median 77.50 bpm, range 48.49–126.82 bpm. The individual minimum pulse rate was 44 bpm, and the individual maximum PR was 167 bpm, reflecting potential stress states during transport such as pain, agitation, hypovolemia, or compensatory responses to hypoxia. Table 1 Transport Indicators of 255 Patients Indicator Mean ± SD Median Range (Min-Max) Data Integrity Rate (%) 79.4 ± 21.80 86.20 14.30–100 Transport Duration (seconds) 580.85 ± 295.67 562.00 131.00-1635.00 Minimum SpO₂ (%) 89.23 ± 4.12 90.00 65.00–97.00 Maximum SpO₂ (%) 95.67 ± 2.34 96.00 89.00-100.00 Average SpO₂ (%) 92.44 ± 2.86 92.50 86.37–99.44 Minimum PR (bpm) 68.45 ± 12.78 67.00 44.00-116.00 Maximum PR (bpm) 89.23 ± 15.67 88.00 51.00-167.00 Average PR (bpm) 78.34 ± 14.21 77.50 48.49-126.82 Note: Data integrity rate = (Valid data points / Total potential time points) × 100%; SpO₂: Oxygen saturation; PR: Pulse Rate; SD: Standard Deviation. 4. Discussion This study identified a high incidence (50.59%) of hypoxemia during transport in patients following general anesthesia. The mean minimum SpO₂ (89.23 ± 4.12%) was already below the clinical alert threshold of 90%, and heart rate showed considerable fluctuation (maximum recorded 167 bpm). These findings align with domestic and international research: Bergman et al. [ 15 ] showed in a prospective observational study that 34% of transport adverse events were related to equipment or technical issues; in the multicenter study by Zirpe et al. [ 16 ] respiratory events accounted for 17.6% and cardiovascular events for 30.3% of 102 ITAEs. 4.1 Quantitative Analysis and Strategies for Shortening Transport Time In this study, the mean transport duration was 580.85 ± 295.67 seconds (approximately 10 minutes), with the longest transport time exceeding 27 minutes. Transport time, as a modifiable independent risk factor, is positively correlated with the incidence of adverse events. A meta-analysis by Murata et al. [ 9 ] showed that transport times exceeding 60 minutes in critically ill patients significantly increase the risk of adverse events; for routine postoperative patients, although the time threshold may be lower, the probability of exposure to risks such as hypoxia and tube displacement increases with each minute of transport. Factors contributing to prolonged transport time are multifaceted: (1) procedural factors: elevator waiting times, corridor obstacles, and inadequate preparation in the receiving unit are primary causes of delay [ 10 ] ; (2) personnel factors: unclear division of labor within the transport team and lack of coordination lead to inefficient patient handling and handover [ 11 ] ; (3) equipment factors: traditional transport equipment is often bulky, requiring multiple bed transfers and increasing operational time [ 12 ] . Evidence-based strategies to shorten transport time include: First, process management optimization. Zhang et al. [ 2 ] utilized quality control circle activities to reduce postoperative transport time for general anesthesia patients from 7.05 ± 0.88 minutes to 4.75 ± 0.54 minutes, decreasing the adverse event rate from 13.33% to 1.67%. Core improvements included reserving dedicated transport elevators, optimizing transport routes, and establishing interdepartmental coordination mechanisms. Second, multidisciplinary team (MDT) collaboration models. Liu et al. [ 13 ] implemented an MDT pathway management model for 171 patients, reducing transport time from 49.57 ± 8.29 minutes to 31.82 ± 6.26 minutes and decreasing the adverse event rate from 13.45% to 7.60%. This model integrates personnel from the operating room, transport center, and ward, enabling information pre-notification and seamless connection. Research from Bengbu Medical University also confirmed that MDT pathway management can reduce nursing defect rates from 27.99% to 22.58% [ 14 ] . Third, application of novel transport equipment. Wu et al. [ 12 ] developed a novel surgical transport sheet (polyurethane foam-nylon fiber composite), which significantly reduced bed transfer time and physical strain by lowering the friction coefficient and incorporating integrated handling design, while also decreasing the risk of tube dislodgement. Equipment innovation serves as a technical foundation for time reduction. 4.2 Current Safety Status and Research Gaps in Post-General Anesthesia Transport The root causes of risk can be categorized into three groups [ 17 , 18 ] :(1) patient factors: residual anesthetic effects depressing respiratory drive, incomplete reversal of muscle relaxation, and pain or positional changes affecting ventilation; (2) equipment factors: traditional pulse oximeters are prone to dislodgement or signal interruption during handling, bed transfers, and elevator transitions—the 24.65% data gap rate in this study originates from this issue; (3) system factors: insufficient staff safety awareness, unclear division of responsibilities, and non-standardized handovers. Current studies have the following limitations: (1) population focus: most literature concentrates on transport of ICU or critically ill emergency patients [ 9 , 19 ] , neglecting routine general anesthesia patients who constitute over 80% of surgeries;(2) heterogeneity of outcome measures: there is a lack of consensus on defining ITAEs; some use physiological thresholds (e.g., SBP < 100 mmHg), while others define events based on the need for clinical intervention, leading to poor comparability between studies [ 9 , 20 ] ;(3) fragmented interventions: existing interventions often focus on single elements (e.g., handover checklists), lacking a comprehensive, systematic approach covering assessment, preparation, intra-transport monitoring, and handover [ 21 ] ; (4) outdated monitoring technology: most studies still rely on intermittent recording, failing to capture dynamic changes during transport and thus underestimating actual risks [ 22 ] .Duffy et al.'s [ 23 ] systematic review pointed out that research on the implementation science aspects of handover interventions (e.g., sustainability, fidelity) is insufficient–a theoretical reflection of the data gap issue highlighted in our study. The 50.59% incidence of hypoxemia far exceeds routine expectations for resting ward patients, consistent with the high rate of respiratory adverse events reported in transport literature. The root cause lies in the combination of multiple factors: residual anesthetics depressing respiratory drive; pain and positioning affecting ventilation; and jolting/positional changes during transport exacerbating these effects. Simultaneously, the high data gap rate of 24.65% exposes a critical weakness in current transport monitoring models. First, as the final link in the peri-anesthesia chain, some staff may lack awareness of transport safety, failing to continuously monitor patients' vital signs and consciousness levels. Second, traditional monitoring equipment, such as portable pulse oximeters, is prone to disconnection or signal loss during handling, bed transfers, position changes, and elevator transitions, resulting in data gaps during transport. Clinical decisions are then based on incomplete information, preventing continuous assessment and early warning of patient status. The mean minimum SpO₂ approaching the 89% clinical alert threshold, combined with the high data gap rate, suggests that the actual hypoxic burden during transport is underestimated. 4.3 Comprehensive Strategy: Constructing a "Four-in-One" Systematic Safety System Based on the risk analysis above and identified research gaps, it is imperative to restructure the transport safety system from a systemic level, encompassing four pillars: standardized processes, specialized teams, safety culture, and technological empowerment. 4.3.1 Core Strategy: Mandatory Standard Operating Procedures (SOP) and Checklist Management Implementing SOPs covering pre-transport assessment, preparation, intra-transport monitoring, and handover is foundational for risk reduction. The American College of Critical Care Medicine guidelines emphasize four key components of transport: communication, personnel, equipment, and monitoring [ 24 ] .Structured checklists (e.g., based on a "pre-transport pause" and SBAR handover model) ensure critical steps are not missed. In the 11 studies reviewed by Duffy et al. [ 23 ] all interventions included structured pre-implementation planning and showed significant improvement in at least one implementation outcome (often acceptability), with 7 studies reporting significant improvements in clinical outcomes. A randomized controlled trial by Jaulin et al. [ 25 ] demonstrated that using a checklist reduced information omissions during PACU handover by 72%. 4.3.2 Foundational Element: Specialized Team Training, Clear Role Definition, and Interdepartmental Collaboration Transport must be executed by at least two trained healthcare professionals, with at least one possessing advanced life support qualifications. Team composition should allow the anesthesia provider to focus on patient monitoring rather than moving the bed, enabling timely intervention during adverse events. Feng et al [ 22 ] applied a modified early warning score (MEWS) for pre-transport assessment in general anesthesia patients, improving risk stratification accuracy. Team training should include emergency drill scenarios, division of labor and coordination, and communication techniques. The postoperative transport process involves collaboration among multiple departments, such as anesthesiology, operating room, surgical wards, and hospital logistics/support services. Errors or failures at any step during transport can prolong duration, increase risks, and potentially lead to unexpected incidents [ 10 ] .Hence, continuous optimization of transport processes [ 11 ] , and strengthening inter-departmental collaboration are essential for continuously improving transport quality and ensuring patient safety. Research by Guo Ya et al. [ 6 ] demonstrated that using a self-designed SBAR handover form effectively reduced the incidence of adverse events like tube dislodgement during postoperative transport, while also improving patient and staff satisfaction. However, as noted by Feng Haili et al. [ 22 ] transport duration and intra-transport monitoring require further in-depth investigation. Their work on applying a modified early warning score (MEWS) for more precise assessment of patients being transferred out of the PACU represents a valuable step toward enhancing transport safety through better risk stratification. 4.3.3 Systemic Support: Safety Culture and Closed-Loop Management Establish a non-punitive adverse event reporting system to facilitate systemic error correction through root cause analysis. The scoping review by Martins et al. [26] indicated that structured handover interventions promote safety culture, and evaluation metrics can guide quality improvement. Incorporate transport safety indicators (e.g., adverse event rates, checklist compliance, transport time) into departmental quality control, forming a Plan-Do-Check-Act (PDCA) cycle. 4.3.4 Technological Empowerment: Towards Continuous Smart Monitoring–The Role of Wearable Devices The data interruption problem highlighted in this study represents a core pain point that wearable devices can address. Wireless, lightweight wearable monitors (e.g., patch-type monitors) enable seamless continuous monitoring from operating room to ward, effectively eliminating "monitoring blind spots." Their advantages include: ① Early Warning: Continuous monitoring and trend analysis facilitate threshold alarms and trend alerts, enabling earlier detection of SpO₂ declines or pulse rate changes, providing decision support for transport personnel; ② Enhanced Compliance and Comfort: Cable-free design reduces monitoring interruptions caused by agitation or handling, improving data continuity; ③ Data Integration and AI Applications: Future integration of multiple parameters (respiratory rate, blood pressure trends, pulse rate variability) combined with AI algorithms could enable individualized risk prediction and automatically generate structured transport reports integrated into the electronic health record. 4.4 Limitations As a retrospective study, this research has several limitations: ① Inability to obtain data on potential confounding factors such as patients' preoperative comorbidities, surgical type, or intraoperative medications (e.g., muscle relaxant and opioid dosages), potentially introducing selection bias; ② Poor peripheral perfusion in some patients may have reduced SpO₂ monitoring sensitivity; thus, defining hypoxemia solely by peripheral oxygen saturation might deviate from arterial partial pressure of oxygen values; ③ Multiple perioperative factors can contribute to hypoxemia; this study did not include variables potentially affecting respiration and circulation, such as transport positioning, intraoperative fluid volume, or dosages of muscle relaxants and opioids. Future research should improve data collection, include multi-center samples, and account for the impact of preoperative comorbidities and surgical types on transport outcomes to minimize bias. 5. Conclusion This study, based on data from 255 cases, confirms that patients undergoing transport after general anesthesia are in a high-risk phase characterized by a high incidence of hypoxemia, significant physiological instability, and substantial gaps in monitoring data. As living standards rise and society rapidly ages, ensuring the safety of peri-anesthesia patient transport necessitates abandoning traditional, experience-dependent models. The focus must shift towards a systematic, structured model anchored by standardized processes, built upon specialized teams, supported by a safety culture, and propelled by smart monitoring technologies. Continuous wearable monitoring devices, as key technological enablers, hold the potential to fundamentally resolve the issue of monitoring continuity, facilitating a transition from "intermittent spot-checking" to "continuous comprehensive monitoring". This represents a crucial element in constructing a future-oriented, intelligent, and highly reliable perioperative patient safety system. Abbreviations PACU : postanesthesia care unit ICU : intensive care unit ITAEs : intrahospital adverse events SpO₂ : oxygen saturation PR : pulse rate SD : standard deviation MDT : multidisciplinary team SOP : standard operating procedures PDCA : plan-do-check-act Declarations Ethics approval and consent to participate: This retrospective study was approved by the Institutional Review Board of the Ethics Committee of the first hospital of Lanzhou University (Approval No.: LDYYLL2026-467). The study was conducted in accordance with the principles of the Declaration of Helsinki. The requirement for informed consent was waived by the Institutional Review Board of the Ethics Committee of the first hospital of Lanzhou University due to the retrospective design and the use of anonymized data. Consent for publication: Not applicable. (This manuscript does not contain any individual person’s data in any form.) Availability of data and materials: All raw data are available upon request. Competing Interests: All authors declare no conflicts of interest. Funding: This study was supported by a fund from the First Hospital of Lanzhou University, China (grant No. ldyyyn2025-268). Authors' contributions: Conceptualization: Ning Li; Data curation: Zhong Yang, Wangping Che, Hongjuan Shen; Formal analysis: Ning Li, Huaping Wei; Methodology: Yuchen Wu, Yujiang Yin; Writing–original draft: Ning Li; Writing–review & editing: Ning Li, Yatao Liu, Xiu Jin. All authors read and approved the final manuscript. References Ouyang XQ, Gao J. 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Standardised handover process with checklist improves quality and safety of care in the postanaesthesia care unit: the Postanaesthesia Team Handover trial [J]. Br J Anaesth, 2021, 127(6): 962-970. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Editor invited by journal 24 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 23 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9166370","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631078794,"identity":"402465b9-31e9-4db6-8e8a-30aa1f0bbed3","order_by":0,"name":"Ning Li","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Li","suffix":""},{"id":631078796,"identity":"def4cd12-b756-479b-853e-ca52fcf054ed","order_by":1,"name":"Xiu Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3QMQrCUAyA4YjwXKJ1TFHqCYSIoAhFr2IR6uLg2LGl8Fx6AE/i/LSgS4/g0CK4WnB1UFdB+twc3j/ngyQAJtMfZjXiNC8DF0UjiopSh9jJ0R9sM7/bwjQekg7h7Yo7TXlwHVrKNmoRmDNjplDYhQSCqdMPK8gY1Dyn4Iyi48l8DYvhSFWQSRQq5uz6JhsmUN6uinBaC8mT6WuxvSTUIsc60P5NqKZJ7ESIQZj5KNB7PZk1brF6t/vlEbiz3uZUFGUwdSrJ556/jZtMJpPpS0/L4kP/2RzISAAAAABJRU5ErkJggg==","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xiu","middleName":"","lastName":"Jin","suffix":""},{"id":631078798,"identity":"1ede2db4-d45f-40bb-8d06-7d9ce8ca5c03","order_by":2,"name":"Yatao Liu","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yatao","middleName":"","lastName":"Liu","suffix":""},{"id":631078800,"identity":"984c2ece-62f5-412e-9a1e-020f15b28671","order_by":3,"name":"Yuchen Wu","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Wu","suffix":""},{"id":631078801,"identity":"43414bc8-8ae3-402a-8963-3be9fd4970c2","order_by":4,"name":"Yujiang Yin","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yujiang","middleName":"","lastName":"Yin","suffix":""},{"id":631078803,"identity":"2d705ad9-9bdd-49a6-90ec-13ad3eeedc06","order_by":5,"name":"Huaping Wei","email":"","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Huaping","middleName":"","lastName":"Wei","suffix":""},{"id":631078804,"identity":"ba34afb9-cb6d-4b20-a035-030405566606","order_by":6,"name":"Zhong Yang","email":"","orcid":"","institution":"People's Hospital of tanchang county","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Yang","suffix":""},{"id":631078808,"identity":"a73049ea-2759-46e6-93e8-dc125bbce940","order_by":7,"name":"Wangping Che","email":"","orcid":"","institution":"People's Hospital of tanchang county","correspondingAuthor":false,"prefix":"","firstName":"Wangping","middleName":"","lastName":"Che","suffix":""},{"id":631078809,"identity":"403f144c-0cdb-41a8-ae9b-33e18a159ec9","order_by":8,"name":"Hongjuan Shen","email":"","orcid":"","institution":"People's Hospital of tanchang county","correspondingAuthor":false,"prefix":"","firstName":"Hongjuan","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2026-03-19 07:38:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9166370/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9166370/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108111260,"identity":"f7fabe5f-e1d2-40ef-b371-b6a182e8c687","added_by":"auto","created_at":"2026-04-29 12:55:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":216911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9166370/v1/a187abba-c363-4c49-b672-7852d3552fdf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Safety Risk Analysis and Systematic Improvement Strategies for Intrahospital Transport of Patients Following General Anesthesia: A Retrospective Study Based on 255 Cases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIntrahospital transport of patients following general anesthesia is a critical link connecting the operating room, postanesthesia care unit (PACU), and general wards or intensive care units (ICUs) \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.This process is not merely a spatial transfer but represents an extension of anesthesia safety management in time and space, concentrating risks, and is equally crucial for the safety of surgical patients\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Due to the intraoperative use of analgesics, sedatives, and muscle relaxants, patients in the early recovery phase following general anesthesia may not have fully regained their protective reflexes, and the effects of muscle relaxants may not be completely reversed. These factors can lead to complications such as hypoventilation, airway obstruction, or respiratory depression\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, Concurrently, vibrations and jolts during transport, positional changes, or painful stimuli can easily induce significant fluctuations in pluse rate and blood pressure, potentially endangering patient safety in severe cases\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLiterature reports indicate that the incidence of intrahospital adverse events (ITAEs) during transport can be as high as 80%, with 4% to 9% of patients requiring medical intervention\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, existing research has primarily focused on the transport of emergency or critically ill patients, with insufficient attention paid to the transport safety of routine general anesthesia patients, who account for over 80% of all surgical procedures\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In this context, this study analyzes transport data from 255 patients following general anesthesia to quantify transport risks. Based on these data, we aim to construct a safe transport framework encompassing standardized postoperative protocols, enhanced team safety awareness, and the application of continuous monitoring technologies, thereby providing evidence-based strategies to reduce transport risks and ensure patient safety.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThis study retrospectively analyzed data from 255 patients who underwent general anesthesia and were subsequently transported to general wards between March 1, 2025, and June 30, 2025. Patients were transported from the PACU to the general ward by an anesthesia nurse after being assessed by an anesthesiologist as meeting PACU discharge criteria.\u003c/p\u003e \u003cp\u003eInclusion criteria were: (1) age 18\u0026ndash;65 years; (2) hemodynamically stable upon PACU discharge with no delayed emergence from anesthesia. Exclusion criteria were: (1) preoperative upper respiratory tract infection; (2) unplanned significant hemodynamic fluctuations during surgery; (3) postoperative transfer directly from PACU to ICU due to clinical deterioration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cp\u003eThe following data were extracted from portable wearable devices:\u003c/p\u003e \u003cp\u003e(1)Transport Duration: Total time from donning the device upon leaving the PACU to doffing the device upon arrival at the surgical ward.\u003c/p\u003e \u003cp\u003e(2)Monitoring Data: Pulse oximetry (SpO₂) and pulse rate (PR) recorded at corresponding time points throughout the transport.\u003c/p\u003e \u003cp\u003e(3)Data Integrity: Percentage of total transport duration with valid, recorded data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eSPSS version 26.0 software was used for data processing. Measurement data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and median; count data are presented as numbers (percentages). Data integrity is described as a percentage.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Transport Duration and Data Integrity\u003c/h2\u003e \u003cp\u003eThe total transport time for the 255 patients was 148,117 seconds, with a mean transport duration of (580.85\u0026thinsp;\u0026plusmn;\u0026thinsp;295.67) seconds and a median time of 562 seconds (range: 131\u0026ndash;1,635 seconds). The total effective physiological parameter recording time was 111,604 seconds, with a mean recording time per patient of (437.66\u0026thinsp;\u0026plusmn;\u0026thinsp;227.36) seconds, a median of 390 seconds, and a range of 50\u0026ndash;1,509 seconds. The overall data integrity rate was 75.35%, indicating that over 24% of transport time occurred without effective monitoring(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Changes in Oxygen Saturation (SpO₂)\u003c/h2\u003e \u003cp\u003ePatients' SpO₂ fluctuated significantly during transport. Individual minimum SpO₂ values ranged from 65% to 97%, with a mean of (88.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.40)% and a median of 89%. The mean minimum SpO₂ was (89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12)%, median 90%, range 65\u0026ndash;97%; mean maximum SpO₂ was (95.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34)%, median 96%, range 89\u0026ndash;100%; mean average SpO₂ was (92.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86)%, median 92.50%, range 86.37\u0026ndash;99.44%. The incidence of hypoxemia (SpO₂\u0026lt;90%)was 50.59% (129/255). The lowest recorded value was 65%, indicating a risk of severe hypoxic events during transport.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Changes in Pulse Rate (PR)\u003c/h2\u003e \u003cp\u003ePatients' pulse rates also showed marked fluctuations during transport. The mean minimum PR was (68.45\u0026thinsp;\u0026plusmn;\u0026thinsp;12.78) bpm, median 67 bpm, range 44\u0026ndash;116 bpm; mean maximum PR was (89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;15.67) bpm, median 88 bpm, range 51\u0026ndash;167 bpm; mean average PR was (78.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14.21) bpm, median 77.50 bpm, range 48.49\u0026ndash;126.82 bpm. The individual minimum pulse rate was 44 bpm, and the individual maximum PR was 167 bpm, reflecting potential stress states during transport such as pain, agitation, hypovolemia, or compensatory responses to hypoxia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransport Indicators of 255 Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange (Min-Max)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Integrity Rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e79.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.30\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport Duration (seconds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e580.85\u0026thinsp;\u0026plusmn;\u0026thinsp;295.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e562.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131.00-1635.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum SpO₂ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.00\u0026ndash;97.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum SpO₂ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e95.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.00-100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage SpO₂ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e92.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.37\u0026ndash;99.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum PR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.45\u0026thinsp;\u0026plusmn;\u0026thinsp;12.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.00-116.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum PR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;15.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.00-167.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage PR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e78.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.49-126.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Data integrity rate = (Valid data points / Total potential time points) \u0026times; 100%; SpO₂: Oxygen saturation; PR: Pulse Rate; SD: Standard Deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study identified a high incidence (50.59%) of hypoxemia during transport in patients following general anesthesia. The mean minimum SpO₂ (89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12%) was already below the clinical alert threshold of 90%, and heart rate showed considerable fluctuation (maximum recorded 167 bpm). These findings align with domestic and international research: Bergman et al. \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e showed in a prospective observational study that 34% of transport adverse events were related to equipment or technical issues; in the multicenter study by Zirpe et al.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e respiratory events accounted for 17.6% and cardiovascular events for 30.3% of 102 ITAEs.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Quantitative Analysis and Strategies for Shortening Transport Time\u003c/h2\u003e \u003cp\u003eIn this study, the mean transport duration was 580.85\u0026thinsp;\u0026plusmn;\u0026thinsp;295.67 seconds (approximately 10 minutes), with the longest transport time exceeding 27 minutes. Transport time, as a modifiable independent risk factor, is positively correlated with the incidence of adverse events. A meta-analysis by Murata et al.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e showed that transport times exceeding 60 minutes in critically ill patients significantly increase the risk of adverse events; for routine postoperative patients, although the time threshold may be lower, the probability of exposure to risks such as hypoxia and tube displacement increases with each minute of transport.\u003c/p\u003e \u003cp\u003eFactors contributing to prolonged transport time are multifaceted: (1) procedural factors: elevator waiting times, corridor obstacles, and inadequate preparation in the receiving unit are primary causes of delay\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e; (2) personnel factors: unclear division of labor within the transport team and lack of coordination lead to inefficient patient handling and handover\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e; (3) equipment factors: traditional transport equipment is often bulky, requiring multiple bed transfers and increasing operational time\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEvidence-based strategies to shorten transport time include:\u003c/p\u003e \u003cp\u003eFirst, process management optimization. Zhang et al.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e utilized quality control circle activities to reduce postoperative transport time for general anesthesia patients from 7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88 minutes to 4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 minutes, decreasing the adverse event rate from 13.33% to 1.67%. Core improvements included reserving dedicated transport elevators, optimizing transport routes, and establishing interdepartmental coordination mechanisms.\u003c/p\u003e \u003cp\u003eSecond, multidisciplinary team (MDT) collaboration models. Liu et al.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e implemented an MDT pathway management model for 171 patients, reducing transport time from 49.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29 minutes to 31.82\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26 minutes and decreasing the adverse event rate from 13.45% to 7.60%. This model integrates personnel from the operating room, transport center, and ward, enabling information pre-notification and seamless connection. Research from Bengbu Medical University also confirmed that MDT pathway management can reduce nursing defect rates from 27.99% to 22.58%\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThird, application of novel transport equipment. Wu et al.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e developed a novel surgical transport sheet (polyurethane foam-nylon fiber composite), which significantly reduced bed transfer time and physical strain by lowering the friction coefficient and incorporating integrated handling design, while also decreasing the risk of tube dislodgement. Equipment innovation serves as a technical foundation for time reduction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Current Safety Status and Research Gaps in Post-General Anesthesia Transport\u003c/h2\u003e \u003cp\u003eThe root causes of risk can be categorized into three groups\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e:(1) patient factors: residual anesthetic effects depressing respiratory drive, incomplete reversal of muscle relaxation, and pain or positional changes affecting ventilation; (2) equipment factors: traditional pulse oximeters are prone to dislodgement or signal interruption during handling, bed transfers, and elevator transitions\u0026mdash;the 24.65% data gap rate in this study originates from this issue; (3) system factors: insufficient staff safety awareness, unclear division of responsibilities, and non-standardized handovers.\u003c/p\u003e \u003cp\u003eCurrent studies have the following limitations: (1) population focus: most literature concentrates on transport of ICU or critically ill emergency patients\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, neglecting routine general anesthesia patients who constitute over 80% of surgeries;(2) heterogeneity of outcome measures: there is a lack of consensus on defining ITAEs; some use physiological thresholds (e.g., SBP\u0026thinsp;\u0026lt;\u0026thinsp;100 mmHg), while others define events based on the need for clinical intervention, leading to poor comparability between studies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e;(3) fragmented interventions: existing interventions often focus on single elements (e.g., handover checklists), lacking a comprehensive, systematic approach covering assessment, preparation, intra-transport monitoring, and handover\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e; (4) outdated monitoring technology: most studies still rely on intermittent recording, failing to capture dynamic changes during transport and thus underestimating actual risks\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.Duffy et al.'s\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e systematic review pointed out that research on the implementation science aspects of handover interventions (e.g., sustainability, fidelity) is insufficient\u0026ndash;a theoretical reflection of the data gap issue highlighted in our study. The 50.59% incidence of hypoxemia far exceeds routine expectations for resting ward patients, consistent with the high rate of respiratory adverse events reported in transport literature. The root cause lies in the combination of multiple factors: residual anesthetics depressing respiratory drive; pain and positioning affecting ventilation; and jolting/positional changes during transport exacerbating these effects.\u003c/p\u003e \u003cp\u003eSimultaneously, the high data gap rate of 24.65% exposes a critical weakness in current transport monitoring models. First, as the final link in the peri-anesthesia chain, some staff may lack awareness of transport safety, failing to continuously monitor patients' vital signs and consciousness levels. Second, traditional monitoring equipment, such as portable pulse oximeters, is prone to disconnection or signal loss during handling, bed transfers, position changes, and elevator transitions, resulting in data gaps during transport. Clinical decisions are then based on incomplete information, preventing continuous assessment and early warning of patient status. The mean minimum SpO₂ approaching the 89% clinical alert threshold, combined with the high data gap rate, suggests that the actual hypoxic burden during transport is underestimated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Comprehensive Strategy: Constructing a \"Four-in-One\" Systematic Safety System\u003c/h2\u003e \u003cp\u003eBased on the risk analysis above and identified research gaps, it is imperative to restructure the transport safety system from a systemic level, encompassing four pillars: standardized processes, specialized teams, safety culture, and technological empowerment.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Core Strategy: Mandatory Standard Operating Procedures (SOP) and Checklist Management\u003c/h2\u003e \u003cp\u003eImplementing SOPs covering pre-transport assessment, preparation, intra-transport monitoring, and handover is foundational for risk reduction. The American College of Critical Care Medicine guidelines emphasize four key components of transport: communication, personnel, equipment, and monitoring\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.Structured checklists (e.g., based on a \"pre-transport pause\" and SBAR handover model) ensure critical steps are not missed. In the 11 studies reviewed by Duffy et al. \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e all interventions included structured pre-implementation planning and showed significant improvement in at least one implementation outcome (often acceptability), with 7 studies reporting significant improvements in clinical outcomes. A randomized controlled trial by Jaulin et al.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e demonstrated that using a checklist reduced information omissions during PACU handover by 72%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Foundational Element: Specialized Team Training, Clear Role Definition, and Interdepartmental Collaboration\u003c/h2\u003e \u003cp\u003eTransport must be executed by at least two trained healthcare professionals, with at least one possessing advanced life support qualifications. Team composition should allow the anesthesia provider to focus on patient monitoring rather than moving the bed, enabling timely intervention during adverse events. Feng et al\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e applied a modified early warning score (MEWS) for pre-transport assessment in general anesthesia patients, improving risk stratification accuracy. Team training should include emergency drill scenarios, division of labor and coordination, and communication techniques.\u003c/p\u003e \u003cp\u003eThe postoperative transport process involves collaboration among multiple departments, such as anesthesiology, operating room, surgical wards, and hospital logistics/support services. Errors or failures at any step during transport can prolong duration, increase risks, and potentially lead to unexpected incidents\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.Hence, continuous optimization of transport processes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and strengthening inter-departmental collaboration are essential for continuously improving transport quality and ensuring patient safety. Research by Guo Ya et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e demonstrated that using a self-designed SBAR handover form effectively reduced the incidence of adverse events like tube dislodgement during postoperative transport, while also improving patient and staff satisfaction. However, as noted by Feng Haili et al.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e transport duration and intra-transport monitoring require further in-depth investigation. Their work on applying a modified early warning score (MEWS) for more precise assessment of patients being transferred out of the PACU represents a valuable step toward enhancing transport safety through better risk stratification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Systemic Support: Safety Culture and Closed-Loop Management\u003c/h2\u003e \u003cp\u003eEstablish a non-punitive adverse event reporting system to facilitate systemic error correction through root cause analysis. The scoping review by Martins et al.\u003csup\u003e[26]\u003c/sup\u003e indicated that structured handover interventions promote safety culture, and evaluation metrics can guide quality improvement. Incorporate transport safety indicators (e.g., adverse event rates, checklist compliance, transport time) into departmental quality control, forming a Plan-Do-Check-Act (PDCA) cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Technological Empowerment: Towards Continuous Smart Monitoring\u0026ndash;The Role of Wearable Devices\u003c/h2\u003e \u003cp\u003eThe data interruption problem highlighted in this study represents a core pain point that wearable devices can address. Wireless, lightweight wearable monitors (e.g., patch-type monitors) enable seamless continuous monitoring from operating room to ward, effectively eliminating \"monitoring blind spots.\" Their advantages include: ① Early Warning: Continuous monitoring and trend analysis facilitate threshold alarms and trend alerts, enabling earlier detection of SpO₂ declines or pulse rate changes, providing decision support for transport personnel; ② Enhanced Compliance and Comfort: Cable-free design reduces monitoring interruptions caused by agitation or handling, improving data continuity; ③ Data Integration and AI Applications: Future integration of multiple parameters (respiratory rate, blood pressure trends, pulse rate variability) combined with AI algorithms could enable individualized risk prediction and automatically generate structured transport reports integrated into the electronic health record.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations\u003c/h2\u003e \u003cp\u003eAs a retrospective study, this research has several limitations: ① Inability to obtain data on potential confounding factors such as patients' preoperative comorbidities, surgical type, or intraoperative medications (e.g., muscle relaxant and opioid dosages), potentially introducing selection bias; ② Poor peripheral perfusion in some patients may have reduced SpO₂ monitoring sensitivity; thus, defining hypoxemia solely by peripheral oxygen saturation might deviate from arterial partial pressure of oxygen values; ③ Multiple perioperative factors can contribute to hypoxemia; this study did not include variables potentially affecting respiration and circulation, such as transport positioning, intraoperative fluid volume, or dosages of muscle relaxants and opioids. Future research should improve data collection, include multi-center samples, and account for the impact of preoperative comorbidities and surgical types on transport outcomes to minimize bias.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study, based on data from 255 cases, confirms that patients undergoing transport after general anesthesia are in a high-risk phase characterized by a high incidence of hypoxemia, significant physiological instability, and substantial gaps in monitoring data. As living standards rise and society rapidly ages, ensuring the safety of peri-anesthesia patient transport necessitates abandoning traditional, experience-dependent models. The focus must shift towards a systematic, structured model anchored by standardized processes, built upon specialized teams, supported by a safety culture, and propelled by smart monitoring technologies. Continuous wearable monitoring devices, as key technological enablers, hold the potential to fundamentally resolve the issue of monitoring continuity, facilitating a transition from \"intermittent spot-checking\" to \"continuous comprehensive monitoring\". This represents a crucial element in constructing a future-oriented, intelligent, and highly reliable perioperative patient safety system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePACU : postanesthesia care unit\u003c/p\u003e\n\u003cp\u003eICU : intensive care unit\u003c/p\u003e\n\u003cp\u003eITAEs : intrahospital adverse events\u003c/p\u003e\n\u003cp\u003eSpO₂ : oxygen saturation\u003c/p\u003e\n\u003cp\u003ePR : pulse rate\u003c/p\u003e\n\u003cp\u003eSD : standard deviation\u003c/p\u003e\n\u003cp\u003eMDT : multidisciplinary team\u003c/p\u003e\n\u003cp\u003eSOP : standard operating procedures\u003c/p\u003e\n\u003cp\u003ePDCA : plan-do-check-act\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This retrospective study was approved by the Institutional Review Board of the Ethics Committee of the first hospital of Lanzhou University (Approval No.: LDYYLL2026-467). The study was conducted in accordance with the principles of the Declaration of Helsinki.\u0026nbsp;The requirement for informed consent was waived by the Institutional Review Board of the Ethics Committee of\u0026nbsp;the first hospital of Lanzhou University\u0026nbsp;due to the retrospective design and the use of anonymized data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eNot applicable. (This manuscript does not contain any individual person\u0026rsquo;s data in any form.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e All raw data are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e All authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by a fund from the First Hospital of Lanzhou University, China (grant No. ldyyyn2025-268).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e Conceptualization: Ning Li; Data curation: Zhong Yang, Wangping Che, Hongjuan Shen; Formal analysis: Ning Li, Huaping Wei; Methodology: Yuchen Wu, Yujiang Yin; Writing\u0026ndash;original draft: Ning Li; Writing\u0026ndash;review \u0026amp; editing: Ning Li, Yatao Liu, Xiu Jin. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOuyang XQ, Gao J. Effect of PDCA nursing management mode on the quality of postoperative transport and handover in orthopedic patients under general anesthesia [J]. J Clin Med Res Pract, 2021, 6(15): 196-198.\u003c/li\u003e\n\u003cli\u003eZhang W, Wang XC, Long F, et al. Application of quality control circle activities in reducing the incidence of postoperative transport risk factors for general anesthesia patients in PACU [J]. Xinjiang Med J, 2021, 51(02): 230-232,179.\u003c/li\u003e\n\u003cli\u003eZhang W, Zheng H, Dong HL, et al. Expert consensus on perioperative patient transport (2014) [Z]. 2014. \u003c/li\u003e\n\u003cli\u003eSkoglund K, Bescher M, Ekwall S, et al. Intrahospital transport of critically ill patients: Nurse anaesthetists\u0026apos; and specialist ICU nurses\u0026apos; experiences [J]. Nurs Crit Care, 2024, 29(5): 1142-1150. \u003c/li\u003e\n\u003cli\u003eLi H, Zhang Y, Cai J, et al. Risk Factors of Hypoxemia in the Postanesthesia Care Unit After General Anesthesia in Children [J]. J Perianesth Nurs, 2023, 38(5): 799-803.\u003c/li\u003e\n\u003cli\u003eGuo Y, Lu H, Li J, et al. Application of a self-designed SBAR handover form in the transport and handover of patients undergoing general anesthesia surgery [J]. Nurs Sci, 2018, 35(04): 74-76.\u003c/li\u003e\n\u003cli\u003eScott M J. Perioperative Patients With Hemodynamic Instability: Consensus Recommendations of the Anesthesia Patient Safety Foundation [J]. Anesth Analg, 2024, 138(4): 713-724.\u003c/li\u003e\n\u003cli\u003eNonami S, Kawakami D, Ito J, et al. Incidence of Adverse Events Associated With the In-Hospital Transport of Critically Ill Patients [J]. Crit Care Explor, 2022, 4(3): e0657.\u003c/li\u003e\n\u003cli\u003eMurata M, Nakagawa N, Kawasaki T, et al. Adverse events during intrahospital transport of critically ill patients: A systematic review and meta-analysis [J]. Am J Emerg Med, 2022, 52: 13-19.\u003c/li\u003e\n\u003cli\u003eYao XH. Effect of PDCA nursing management mode on postoperative transport handover of orthopedic patients under general anesthesia [J]. China Health Ind, 2018, 15(26): 74-75.\u003c/li\u003e\n\u003cli\u003eGuo L. Evaluation of the effect of process management on improving the efficiency and safety of postoperative transport for general anesthesia patients [J]. J Pract Med Tech, 2020, 27(12): 1709-1710.\u003c/li\u003e\n\u003cli\u003eWu L, Lu Y, Gong R, et al. Design and application of a novel surgical transfer sheet: a randomized controlled trial [J]. BMC Nurs, 2025, 24(1): 1274.\u003c/li\u003e\n\u003cli\u003eLiu N, Shan DD, Zhang J. Effect of multidisciplinary team collaborative pathway management model on transport quality, nursing quality, and physical and mental stress of surgical patients in anesthesiology department [J]. Int J Nurs, 2023, 42(3): 545-549.\u003c/li\u003e\n\u003cli\u003eZhang Q, Miao SQ. Application of pathway management based on multidisciplinary team collaboration model in the transport of surgical patients in anesthesiology department [J]. J Bengbu Med Coll, 2019, 44(2): 253-256.\u003c/li\u003e\n\u003cli\u003eBergman L M, Pettersson M E, Chaboyer W P, et al. Safety Hazards During Intrahospital Transport: A Prospective Observational Study [J]. Crit Care Med, 2017, 45(10): e1043-e1049.\u003c/li\u003e\n\u003cli\u003eZirpe K G, Tiwari A M, Kulkarni A P, et al. Adverse Events during Intrahospital Transport of Critically Ill Patients: A Multicenter, Prospective, Observational Study (I-TOUCH Study) [J]. Indian J Crit Care Med, 2023, 27(9): 635-641.\u003c/li\u003e\n\u003cli\u003eBeckmann U, Gillies D M, Berenholtz S M, et al. Incidents relating to the intra-hospital transfer of critically ill patients. An analysis of the reports submitted to the Australian Incident Monitoring Study in Intensive Care [J]. Intensive Care Med, 2004, 30(8): 1579-1585.\u003c/li\u003e\n\u003cli\u003eWu LP, Gong RJ, Sun YH, et al. Relationship between the current safety status of post-general anesthesia patient transport and exercise-induced fatigue in clinical staff [J]. Anhui Med J, 2024, 45(01): 88-93. \u003c/li\u003e\n\u003cli\u003eLahner D, Nikolic A, Marhofer P, et al. Incidence of complications in intrahospital transport of critically ill patients--experience in an Austrian university hospital [J]. Wien Klin Wochenschr, 2007, 119(13-14): 412-416.\u003c/li\u003e\n\u003cli\u003eGillman L, Leslie G, Williams T, et al. Adverse events experienced while transferring the critically ill patient from the emergency department to the intensive care unit [J]. Emerg Med J, 2006, 23(11): 858-861.\u003c/li\u003e\n\u003cli\u003eZhuang L. Nursing care and preventive measures for the safety of intrahospital transport of patients in the postanesthesia care unit [J]. World Latest Med Inf Dig, 2019, 19(63): 343.\u003c/li\u003e\n\u003cli\u003eFeng HL, Liao CY, Qin RX, et al. Clinical study on the effect of applying a modified early warning score on the transport work of patients after general anesthesia surgery [J]. Chin Nurs Res, 2015, 29(34): 4291-4293.\u003c/li\u003e\n\u003cli\u003eDuffy C C, Lepore G, Bass G A, et al. A Systematic Review of Postoperative Care Transition Interventions: Examining the Implementation of Handoff Protocols and Checklists [J]. Anesth Analg, 2025.\u003c/li\u003e\n\u003cli\u003eWarren J, Fromm R E, Jr., Orr R A, et al. Guidelines for the inter- and intrahospital transport of critically ill patients [J]. Crit Care Med, 2004, 32(1): 256-262.\u003c/li\u003e\n\u003cli\u003eJaulin F, Lopes T, Martin F. Standardised handover process with checklist improves quality and safety of care in the postanaesthesia care unit: the Postanaesthesia Team Handover trial [J]. Br J Anaesth, 2021, 127(6): 962-970.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"General anesthesia, postoperative transport, patient safety, hypoxemia, wearable devices","lastPublishedDoi":"10.21203/rs.3.rs-9166370/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9166370/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo systematically evaluate the current safety status of intrahospital transport in patients recovering from general anesthesia, identify key risk factors, and propose data-driven, systematic improvement strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was performed on transport monitoring records of 255 patients who underwent general anesthesia in a tertiary hospital. Transport duration, data integrity rate, and changes in oxygen saturation (SpO₂) and pulse rate (PR) were statistically analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean transport duration was 580.85\u0026thinsp;\u0026plusmn;\u0026thinsp;295.67 seconds (median: 564 seconds). The overall monitoring data integrity rate was 75.35%. The incidence of hypoxemia (SpO₂ \u0026lt; 90%) was 50.59% (129/255). The mean minimum SpO₂ was 89.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12%, and the mean maximum PR was 89.22\u0026thinsp;\u0026plusmn;\u0026thinsp;16.46 beats per minute, indicating significant physiological fluctuations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDespite meeting discharge criteria, patients recovering from general anesthesia experience physiological instability during intrahospital transport, characterized by a high incidence of hypoxemia and substantial gaps in monitoring data. Establishing a systematic safety framework\u0026mdash;incorporating standardized procedures, enhanced team awareness, and technological support\u0026mdash;is essential to mitigate transport-related risks and ensure patient safety.\u003c/p\u003e","manuscriptTitle":"Safety Risk Analysis and Systematic Improvement Strategies for Intrahospital Transport of Patients Following General Anesthesia: A Retrospective Study Based on 255 Cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 12:54:01","doi":"10.21203/rs.3.rs-9166370/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-21T10:40:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T06:51:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T13:55:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T02:14:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2026-03-24T02:10:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47519506-05cc-4996-8c21-6417ecb45405","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T12:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 12:54:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9166370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9166370","identity":"rs-9166370","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}