Association of different combination doses of remifentanil-propofol with transcranial motor-evoked potentials during skull base surgery

Association of different combination doses of remifentanil-propofol with transcranial motor-evoked potentials during skull base surgery | 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 Association of different combination doses of remifentanil-propofol with transcranial motor-evoked potentials during skull base surgery Ruixue Hou, Wei Xiao, Fangfang Miao, Cheng Yin, Di Jin, Qingfang Duan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4166426/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Monitoring of transcranial electrical motor evoked potentials (TceMEPs) is widely used in neurosurgery. The association of different combination doses of remifentanil-propofol in total intravenous anesthesia (TIVA) with TceMEPs during surgery remains uncertain. Methods In this prospective observational study, consecutive patients (aged 18–65 years) who underwent skull base surgery under general anesthesia at our clinical center between April 2021 and April 2023 were included. All patients were anesthetised with propofol-remifentanil TIVA and maintained at a Bispectral Index of 40–60. The association between different combination doses of remifentanil-propofol and the change in TceMEP amplitude from baseline was assessed using a multivariable model adjusted for confounders and a response surface model. Besides, anesthetic dose, extubation time, pain score at 24h postoperatively and unexpected body movements during surgery were recorded. Results A total of 240 patients (mean age, 49.3 [SD, 12.1] years; 107 [44.6%] women) who underwent skull base surgery were included in this study. Our study showed that an increase in propofol dose was significantly associated with a constant increase in the Log of the change in TceMEP amplitude (β = 0.29 [95%CI: 0.01 to 0.58], p = 0.046) after adjustment. Increasing remifentanil was not associated with Log (change in TceMEP amplitude) (β = 0.33 [95%CI: -2.1 to 2.76], p = 0.79) after adjustment. In addition, through the response surface analysis, we found that when the propofol infusion was less than 2.96 mg/kg/h and the remifentanil infusion was greater than 0.24 µg/kg/min, the more the remifentanil infusion was, the less impact it had on the changes in TceMEP. When the propofol infusion was greater than 2.96 mg/kg/h, as the propofol infusion increased, it had a greater impact on the changes in TceMEP. Pearson’s test showed a correlation between propofol and remifentanil dose at BIS 40–60 (γ= -0.4637, p < 0.001). Conclusions In this study, propofol reduced TceMEP amplitude in a dose-dependent manner. Due to the synergistic interactions between propofol and remifentanil, remifentanil could reduce the amount of propofol at the same anesthesia depth, a propofol infusion rate less than 2.96mg/kg/h and a remifentanil rate greater than 0.24 µg/kg/min is recommended in neurosurgery requiring TceMEP monitoring. Transcranial motor-evoked potentials (TceMEPs) General anesthesia Propofol Remifentanil BIS Figures Figure 1 Figure 2 Figure 3 Introduction Intraoperative neurophysiological monitoring (IONM) using TceMEPs is increasingly being used to detect and reduce the risk of neural injury in neurosurgery [ 1 – 2 ] . TceMEPs are based on a train of 4 to 8 short, sequential electrical pulses delivered over the scalp, resulting in the firing of skeletal muscle motor neurons through subdermal needles placed in peripheral muscles [ 3 ] . Many factors affect the amplitude and latency of TceMEPs, including intraoperative hypotension, hypothermia, electrode failure and anesthetic agents [ 4 – 6 ] . A number of studies have shown that a variety of anesthetic agents depress TceMEPs in a dose-dependent manner. [ 3 , 7 – 8 ] . In particular, neuromuscular blockade (NMB) can lead to an apparent suppression of TceMEPs [ 9 – 10 ] . As the incidence of monitoring failure and false-positive results was increased with NMB, the use of NMB after intubation is generally avoided in most cases [ 10 – 12 ] . Total intravenous anesthesia (TIVA) using propofol together with remifentanil is cited as the ideal anesthetic regimen for MEP monitoring as it has minimal impact on IONM methods [ 13 – 14 ] . Hiroki [ 15 ] findings regarding the effect of total propofol dose on TceMEPs suggested that total intraoperative propofol dose is independently associated with false-positive alarms during spinal surgery. Controversially, remifentanil has minimal suppression of MEP signal amplitudes [ 16 ] . Therefore, we hypothesise that decreasing the concentration of propofol and increasing the concentration of remifentanil may help to restore TceMEPs amplitude in clinical practice. In this prospective observational study, our main objective was to evaluate the association between different combination doses of remifentanil-propofol in total intravenous anesthesia (TIVA) using the Bispectral Index (BIS) to monitor depth of anesthesia (BIS 40–60) and tceMEPs during skull base surgery. Methods The ethical review board of Capital Medical University XuanWu Hospital approved this study (protocol No. [2021]078). In this prospective observational study, the infusion rate of propofol and remifentanil was at the discretion of the anesthesiologist on duty based on clinical experience. The authors of this observational study did not interfere with the anesthesiologist's dosing regimen. In addition, patient data were anonymous, so written informed consent was not required. From April 2021 to April 2023, 240 patients (ASA I-II, aged 18–65 years) who underwent skull base surgery at the Capital Medical University Xuanwu Hospital were included in this study. Exclusion criteria were: pre-existing sensory or motor neurological deficits, neuromuscular disorder(s), significant organ system disease (e.g. cardiovascular and/or pulmonary dysfunction, liver and/or kidney disease). All patients were continuously monitored with electrocardiogram, heart rate, pulse oxygen saturation, invasive blood pressure, core temperature, and BIS on arrival to the operating room. Anesthesia was induced with 3 µg/kg sufentanil, 2 mg/kg etomidate, 0.6 mg/kg rocuronium. Propofol-remifentanil TIVA was used to maintain anesthesia and neuromuscular blocking agents were discontinued after tracheal intubation. We collected and recorded the total dose of anesthetic and the duration of surgery, and then calculated the average dose of two anesthetics during surgery. During surgery, mean arterial blood pressure was maintained within ± 10% of baseline, core temperature between 36° and 37°C, BIS between 40 and 60, and end-tidal carbon dioxide between 35 and 45 mmHg. The anesthesiologist and the neurophysiological technologist who monitored the TceMEP were blinded to the protocol of the study. Standard TceMEP monitoring was used routinely with primary motor cortex stimulation at C1 and C2 according to international guidelines [ 17 ] . Neuromonitoring subdermal needle electrodes were placed and secured to peripheral muscles after intubation by a neurophysiologist. TceMEPs were elicited by a series of pulses (4–8) at 100–400 volts. TceMEPs were recorded bilaterally from the patients' upper and lower extremities, including the abductor pollicis brevis, tibialis anterior, and extensor digitorum brevis. The success of TceMEPs was determined by a professionally trained neurophysiologist. We chose the TceMEP amplitude at the moment of dural opening as the baseline. The primary outcome of the study was the TceMEP amplitude in the abductor pollicis brevis ipsilateral to the surgical site relative to baseline at tumor incision. A numerical rating scale (NRS) (0 [no pain] to 10 [worst pain]) was used to measure pain in all patients 24 h after surgery. Moreover, extubation time, the dosage of propofol and remifentanil, and unexpected body movements during surgery were recorded in the surgery procedure. Statistical analysis The change in TceMEP amplitude was highly skewed and was transformed to the logarithmic scale for analysis. Linear univariate regression was used to assess the association of age, sex, BMI, baseline TceMEP and operative TceMEP with change in TceMEP. Multivariate logistic regression analysis was used to estimate the independent association between remifentanil-propofol and log (TceMEP change), adjusting for potential confounders. Pearson's test was used to analyze the correlation between propofol and remifentanil dose. Response surface methodology was used to analyze the relationship between different combination doses of remifentanil-propofol and TceMEP. All statistical tests were 2-sided, and p < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS, Empower(R) and R software. Results Between April 2021 and April 2023, 260 patients were screened for participation in the study, of which 20 patients were excluded from the final analysis due to unavailability of tceMEP (n = 3), failure to receive the anesthetic dose during surgery (n = 15), or occurrence of surgery related complications (n = 2). The demographic characteristics of the participants, the TceMEP amplitudes at the tested points, and some perioperative anesthetic data are summarized in Table 1. The change in TceMEP amplitudes was 117.49 ± 118.64 µV in males and 121.56 ± 144.92 µV in females. Log TceMEP change was 4.19 ± 1.34 µV and 4.26 ± 1.16 µV for both men and women during surgery.. The log (change in TceMEP amplitude) was significantly reduced by increasing propofol concentrations during surgery (β = 0.29 [95%CI: 0.01 to 0.58], p = 0.046). However, remifentanil dose was not associated with the log (change in TceMEP amplitude) (β = 0.33 [95%CI: -2.1 to 2.76], p = 0.79) after adjustment ( Table 2). Propofol and remifentanil doses during surgery were correlated (γ= -0.6437, p < 0.001) (Fig. 1 ). When the propofol infusion rate was less than 2.96 mg/kg/h and the remifentanil infusion rate was greater than 0.24 µg/kg/min, the more the remifentanil infusion rate was, the less impact it had on the changes in TceMEP. When the propofol infusion rate was greater than 2.96 mg/kg/h, as the propofol infusion rate increased, it had a greater impact on the changes in TceMEP. When the remifentanil infusion was less than 0.24 µg/kg/min, as the infusion rate of remifentanil decreased, the impact on changes in TceMEP decreased (Fig. 2 ). The MEP % change with different doses of propofol/remifentanil is shown in Figure − 3. During surgery, mean core temperature was 36.63 ± 1.03°C, BIS was 49.12 ± 1.46, and end-tidal carbon dioxide was 41.45 ± 2.35 mmHg. The mean time of extubation was 5.6 ± 2.1 min among patients. Inceasing remifentanil dose can reduce the extubation time (β= -5.45 [95%CI: -9.91, -0.99], p = 0.02). The intensity of pain using the NRS at 24h postoperatively was also no significant difference among patients receiving different doses of remifentanil ( p = 0.13). Five out of 240 patients experienced unexpected body movements during surgery. The remifentanil infusion was less than 0.25 ± 0.08µg/kg/min in the five patients. Discussion The main finding of this study is that a higher dose of propofol is independently associated with greater suppression of TceMEP. In contrast, remifentanil has only a minor effect on TceMEP. Due to the synergy between propofol and remifentanil, remifentanil can lower the amount of propofol at the same depth of anesthesia (BIS 40–60), a propofol infusion rate less than 2.96 mg/kg/h and a remifentanil rate greater than 0.24 µg/kg/min is recommended in neurosurgery, which may not only reduce the impact on the TceMEPs but also reduce the occurrence of body movements. To the best of our knowledge, this is the first study exploring an association between different combination doses of remifentanil-propofol and transcranial motor-evoked potentials. The effect of anesthetics on TceMEP is a critical consideration in intraoperative neurological monitoring during surgery [ 17 ] . Nathan [ 18 ] et al. evaluated the effect of propofol dose on TceMEP and concluded that higher doses of propofol decreased TceMEP amplitude. Therefore, it is often necessary to reduce the rate of propofol infusion during surgery. However, a lower dose of propofol may cause intraoperative awareness. Our results suggest that remifentanil can reduce the dose of propofol while maintaining the same depth of anesthesia. This finding is consistent with other studies reporting anesthesia-sparing effects [ 19 – 21 ] . At the same time, remifentanil provides deep analgesia during surgery, reduces the stress caused by the trauma of surgery and attenuates somatic movements [ 22 – 24 ] . Previous studies have investigated the relationship between single anesthetic (propofol or remifentanil) and TceMEP. We used response surface methodology to analyze the relationship between different combination doses of remifentanil-propofol and TceMEP. (Fig. 2 ). The equation of the response surface model in our study is: y = 6.77–2.96*X1 + 13.31*X2 + 2.41*X1*X2 + 0.40*X1^2-42.99*X2^2 (y: Log (TceMEP change), X1: propofol infusion, X2: remifentanil infusion). Due to the synergistic effect of propofol and remifentanil, under the same depth of anesthesia, the total amount of propofol and remifentanil within the same time period is different in different patients, triggering different degrees of TceMEPs decrease. In our study, we did not re-dose patients with relaxant after intubation. There was no occurrence of body movements in the patients who received remifentanil infusion more than 0.25 ± 0.08 µg/kg/min during surgery. In the current study, a propofol infusion rate less than 2.96 mg/kg/h and a remifentanil rate greater than 0.24 µg/kg/min is recommended in neurosurgical cases requiring TceMEP monitoring, which may not only reduce the impact on the TceMEPs but also reduce the occurrence of body movements. Several studies have suggested that acute and chronic exposure to high-dose remifentanil may be associated with the development of hyperalgesia after surgery [ 25 – 27 ] . None of the patients in our study experienced hyperalgesia after surgery. This was due to the gradual withdrawal of the remifentanil infusion at the end of surgery. We also administered non-steroidal anti-inflammatory drugs (NSAIDs) and low-dose sufentanyl to counteract the rapid elimination of remifentanil after the infusion was stopped. The anesthesiologist and neurophysiologist monitoring the TceMEP were blinded to the study protocol. This may reduce observational bias. In addition, we chose the change in TceMEP in the abductor pollicis brevis ipsilateral to the surgical site as our outcome. This may reduce the effect of the surgical stimulus on our main outcome. Ugawa R [ 28 ] found that TceMEP amplitude was significantly decreased in the upper limb at 5 and 6 h after the initial infusion of propofol. In our study, the mean duration of surgery was 258.3 ± 52.9 min in males and 253.3 ± 52.5 min in females. The present study has several limitations that should be recognized. It usually took about 1 hour after induction to reach the time when the dural opens. We supposed that it can be almost completely metabolized at the moment of dural opening after induction of the nuromuscular dose, taking into account the pharmacokinetic profile of nuromuscular. It should be better to have TOF monitoring during surgery. Warning criteria for T-Ce MEP amplitude changes have not been universally established and include 50%, 60%, 70%, 80%, and complete loss [ 29 ] . There were no patients in our study whose TceMEP amplitude decreased by more than 50% from baseline. Because the anesthetic doses in this observational study were decided by the anesthesiologist. We didn't randomly assign patients to a specific group that maintained an unchanged propofol/remifentanil infusion during surgery. Our conclusion is based on the current data we collected in this observational study, well-designed prospective RCT studies are needed to answer these questions. Conclusions This study showed that propofol dose-dependently reduced TceMEP amplitude and that remifentanil was not associated with the change in TceMEP. Due to the synergistic interactions between propofol and remifentanil, remifentanil could reduce the amount of propofol at the same depth of anesthesia (BIS 40–60), a propofol infusion rate less than 2.96 mg/kg/h and a remifentanil rate greater than 0.24 µg/kg/min is recommended in neurosurgical cases requiring TceMEP monitoring. Declarations Ethics approval and Consent to Participate Consent for Publication: The ethical review board of Capital Medical University XuanWu Hospital approved this study (protocol No. [2021]078). The need for written informed consent was waived by the Capital Medical University XuanWu Hospital ethics committee due to observational nature of the study. Availability of Data and Materials: Data can be provided within supplementary information files. Funding: No funding. Competing Interests: No Competing Interests. Authors' Contribution: Ruixue Hou wrote the main manuscript. Wei Xiao and Fangfang Miao performed the statistical analysis. Cheng yin, Di jin, and Qingfang duan collected data. Tianlong wang is corresponding author, had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript. Consent to publish statement Not Applicable as there is no any identifying image/figure in our study. References See RB, Awosika OO, Cambria RP, Conrad MF, Lancaster RT, Patel VI, Chitilian HV, Kumar S, Simon MV. 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Tables Table 1 Demographic data and anaesthetic variables of study participants Table 2 Relationship between propofol-remifentanil dose and log (TceMEP change) Univariable Analysis,β (95% CI) P Value Multivariable Analysis,β (95% CI) P Value Propofol dose (mg/kg/h) 0.75 (0.51, 0.98) <0.0001 0.29 (0.01, 0.58) 0.046 Propofol dose (mg/kg/h) Low 0 0 Medium 0.41 (0.04, 0.77) 0.03 0.06 (-0.30, 0.42) 0.74 High 1.14 (0.78, 1.51) <0.0001 0.54 (0.12, 0.95) 0.01 Remifentanil dose ( μg/kg/min) -3.13 (-5.18, -1.07) 0.003 0.33 (-2.10, 2.76) 0.7899 0.79 Remifentanil dose ( μg/kg/min) Low 0 0 Medium 0.06 (-0.33, 0.46) 0.76 0.19 (-0.18, 0.56) 0.32 High -0.46 (-0.85, -0.08) 0.02 0.15 (-0.30, 0.59) 0.52 Multivariable Analysis adjusted for: sex, age, BMI, ASA, hypertension, diabetes, coronary artery disease, operative time; baseline TceMEP, operative TceMEP, pain scores 24h postoperatively and body movements. Additional Declarations No competing interests reported. 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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-4166426","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288947381,"identity":"9f2e7442-b81f-4456-9bf9-418423e43b59","order_by":0,"name":"Ruixue Hou","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruixue","middleName":"","lastName":"Hou","suffix":""},{"id":288947382,"identity":"5061bf47-c241-4862-9e00-c0dba9e27611","order_by":1,"name":"Wei Xiao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xiao","suffix":""},{"id":288947383,"identity":"90da8e41-69ed-4d5f-9fb7-d5af78a6aa36","order_by":2,"name":"Fangfang Miao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Miao","suffix":""},{"id":288947384,"identity":"df92b467-2af4-4df2-a0cf-78b015565961","order_by":3,"name":"Cheng Yin","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Yin","suffix":""},{"id":288947385,"identity":"196fe13c-980a-485e-b6b4-749a7ea0d134","order_by":4,"name":"Di Jin","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Jin","suffix":""},{"id":288947386,"identity":"7c4dab45-4b29-4948-89ba-309d4a3f6bd1","order_by":5,"name":"Qingfang Duan","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingfang","middleName":"","lastName":"Duan","suffix":""},{"id":288947387,"identity":"8518caa0-3871-4343-a290-f44b1285bdfc","order_by":6,"name":"Tianlong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYHACxsM//9jU80uAORIyROhgZjjM2JCWIDmDgbEBqIWHWC2HEwxugLUwENZicID/wOHCHYfzjG83H390o8aCh4H98NEN+LUAbZl5Jr3Y7M6xxOacY0CH8aSl3cCnxQyo5QAPmzXjths5hs05bEAtEjxmxGhhZtw8A6TlH5FaDvO2OSdukABqyW0jQov9AWaDgzPOpBlL3EhLnJ3bJ8HDRsgvkg2MDx98qLCR45+RfOBzzrc6OX72w8fwamGQf4AmwIZX+SgYBaNgFIwCogAApQ9K3GcaBqEAAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Tianlong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-26 02:31:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4166426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4166426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54370604,"identity":"73a722bd-a399-488f-9984-e70010f11537","added_by":"auto","created_at":"2024-04-09 13:08:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":275752,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between propofol and remifentanil dose during surgery\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4166426/v1/cf23240f8f82b231ae629187.jpeg"},{"id":54370605,"identity":"3cb34cb2-fa5a-4de0-a84b-86845d3137d6","added_by":"auto","created_at":"2024-04-09 13:08:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":383223,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface for the relationship between different combination doses of remifentanil-propofol and\u003c/p\u003e\n\u003cp\u003elog (TceMEP change)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4166426/v1/f0581a067d752ca7e38bb2e6.jpeg"},{"id":54370603,"identity":"0656203b-3119-40a5-9269-ca4f42eee948","added_by":"auto","created_at":"2024-04-09 13:08:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24874,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between propofol-remifentanil dose and TceMEP change\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4166426/v1/6fcc71a282978620f70cb79c.png"},{"id":60637547,"identity":"9dd6fbe8-4de0-4bcc-a156-75890f314c8c","added_by":"auto","created_at":"2024-07-19 02:44:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1092508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4166426/v1/428df880-276c-4e84-a245-d80870477cc4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of different combination doses of remifentanil-propofol with transcranial motor-evoked potentials during skull base surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIntraoperative neurophysiological monitoring (IONM) using TceMEPs is increasingly being used to detect and reduce the risk of neural injury in neurosurgery\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. TceMEPs are based on a train of 4 to 8 short, sequential electrical pulses delivered over the scalp, resulting in the firing of skeletal muscle motor neurons through subdermal needles placed in peripheral muscles\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Many factors affect the amplitude and latency of TceMEPs, including intraoperative hypotension, hypothermia, electrode failure and anesthetic agents\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA number of studies have shown that a variety of anesthetic agents depress TceMEPs in a dose-dependent manner.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In particular, neuromuscular blockade (NMB) can lead to an apparent suppression of TceMEPs\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. As the incidence of monitoring failure and false-positive results was increased with NMB, the use of NMB after intubation is generally avoided in most cases\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Total intravenous anesthesia (TIVA) using propofol together with remifentanil is cited as the ideal anesthetic regimen for MEP monitoring as it has minimal impact on IONM methods\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Hiroki\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e findings regarding the effect of total propofol dose on TceMEPs suggested that total intraoperative propofol dose is independently associated with false-positive alarms during spinal surgery. Controversially, remifentanil has minimal suppression of MEP signal amplitudes\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Therefore, we hypothesise that decreasing the concentration of propofol and increasing the concentration of remifentanil may help to restore TceMEPs amplitude in clinical practice.\u003c/p\u003e \u003cp\u003eIn this prospective observational study, our main objective was to evaluate the association between different combination doses of remifentanil-propofol in total intravenous anesthesia (TIVA) using the Bispectral Index (BIS) to monitor depth of anesthesia (BIS 40\u0026ndash;60) and tceMEPs during skull base surgery.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e The ethical review board of Capital Medical University XuanWu Hospital approved this study (protocol No. [2021]078). In this prospective observational study, the infusion rate of propofol and remifentanil was at the discretion of the anesthesiologist on duty based on clinical experience. The authors of this observational study did not interfere with the anesthesiologist's dosing regimen. In addition, patient data were anonymous, so written informed consent was not required. From April 2021 to April 2023, 240 patients (ASA I-II, aged 18\u0026ndash;65 years) who underwent skull base surgery at the Capital Medical University Xuanwu Hospital were included in this study. Exclusion criteria were: pre-existing sensory or motor neurological deficits, neuromuscular disorder(s), significant organ system disease (e.g. cardiovascular and/or pulmonary dysfunction, liver and/or kidney disease). All patients were continuously monitored with electrocardiogram, heart rate, pulse oxygen saturation, invasive blood pressure, core temperature, and BIS on arrival to the operating room. Anesthesia was induced with 3 \u0026micro;g/kg sufentanil, 2 mg/kg etomidate, 0.6 mg/kg rocuronium. Propofol-remifentanil TIVA was used to maintain anesthesia and neuromuscular blocking agents were discontinued after tracheal intubation. We collected and recorded the total dose of anesthetic and the duration of surgery, and then calculated the average dose of two anesthetics during surgery. During surgery, mean arterial blood pressure was maintained within \u0026plusmn;\u0026thinsp;10% of baseline, core temperature between 36\u0026deg; and 37\u0026deg;C, BIS between 40 and 60, and end-tidal carbon dioxide between 35 and 45 mmHg. The anesthesiologist and the neurophysiological technologist who monitored the TceMEP were blinded to the protocol of the study.\u003c/p\u003e \u003cp\u003eStandard TceMEP monitoring was used routinely with primary motor cortex stimulation at C1 and C2 according to international guidelines\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Neuromonitoring subdermal needle electrodes were placed and secured to peripheral muscles after intubation by a neurophysiologist. TceMEPs were elicited by a series of pulses (4\u0026ndash;8) at 100\u0026ndash;400 volts. TceMEPs were recorded bilaterally from the patients' upper and lower extremities, including the abductor pollicis brevis, tibialis anterior, and extensor digitorum brevis. The success of TceMEPs was determined by a professionally trained neurophysiologist. We chose the TceMEP amplitude at the moment of dural opening as the baseline. The primary outcome of the study was the TceMEP amplitude in the abductor pollicis brevis ipsilateral to the surgical site relative to baseline at tumor incision. A numerical rating scale (NRS) (0 [no pain] to 10 [worst pain]) was used to measure pain in all patients 24 h after surgery. Moreover, extubation time, the dosage of propofol and remifentanil, and unexpected body movements during surgery were recorded in the surgery procedure.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe change in TceMEP amplitude was highly skewed and was transformed to the logarithmic scale for analysis. Linear univariate regression was used to assess the association of age, sex, BMI, baseline TceMEP and operative TceMEP with change in TceMEP. Multivariate logistic regression analysis was used to estimate the independent association between remifentanil-propofol and log (TceMEP change), adjusting for potential confounders. Pearson's test was used to analyze the correlation between propofol and remifentanil dose. Response surface methodology was used to analyze the relationship between different combination doses of remifentanil-propofol and TceMEP. All statistical tests were 2-sided, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using SPSS, Empower(R) and R software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBetween April 2021 and April 2023, 260 patients were screened for participation in the study, of which 20 patients were excluded from the final analysis due to unavailability of tceMEP (n\u0026thinsp;=\u0026thinsp;3), failure to receive the anesthetic dose during surgery (n\u0026thinsp;=\u0026thinsp;15), or occurrence of surgery related complications (n\u0026thinsp;=\u0026thinsp;2). The demographic characteristics of the participants, the TceMEP amplitudes at the tested points, and some perioperative anesthetic data are summarized in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe change in TceMEP amplitudes was 117.49\u0026thinsp;\u0026plusmn;\u0026thinsp;118.64 \u0026micro;V in males and 121.56\u0026thinsp;\u0026plusmn;\u0026thinsp;144.92 \u0026micro;V in females. Log TceMEP change was 4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34 \u0026micro;V and 4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 \u0026micro;V for both men and women during surgery.. The log (change in TceMEP amplitude) was significantly reduced by increasing propofol concentrations during surgery (β\u0026thinsp;=\u0026thinsp;0.29 [95%CI: 0.01 to 0.58], \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.046). However, remifentanil dose was not associated with the log (change in TceMEP amplitude) (β\u0026thinsp;=\u0026thinsp;0.33 [95%CI: -2.1 to 2.76], \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.79) after adjustment ( Table\u0026nbsp;2). Propofol and remifentanil doses during surgery were correlated (γ= -0.6437, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When the propofol infusion rate was less than 2.96 mg/kg/h and the remifentanil infusion rate was greater than 0.24 \u0026micro;g/kg/min, the more the remifentanil infusion rate was, the less impact it had on the changes in TceMEP. When the propofol infusion rate was greater than 2.96 mg/kg/h, as the propofol infusion rate increased, it had a greater impact on the changes in TceMEP. When the remifentanil infusion was less than 0.24 \u0026micro;g/kg/min, as the infusion rate of remifentanil decreased, the impact on changes in TceMEP decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The MEP % change with different doses of propofol/remifentanil is shown in Figure \u0026minus;\u0026thinsp;3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring surgery, mean core temperature was 36.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u0026deg;C, BIS was 49.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46, and end-tidal carbon dioxide was 41.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35 mmHg. The mean time of extubation was 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 min among patients. Inceasing remifentanil dose can reduce the extubation time (β= -5.45 [95%CI: -9.91, -0.99], \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.02). The intensity of pain using the NRS at 24h postoperatively was also no significant difference among patients receiving different doses of remifentanil (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13). Five out of 240 patients experienced unexpected body movements during surgery. The remifentanil infusion was less than 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u0026micro;g/kg/min in the five patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main finding of this study is that a higher dose of propofol is independently associated with greater suppression of TceMEP. In contrast, remifentanil has only a minor effect on TceMEP. Due to the synergy between propofol and remifentanil, remifentanil can lower the amount of propofol at the same depth of anesthesia (BIS 40\u0026ndash;60), a propofol infusion rate less than 2.96 mg/kg/h and a remifentanil rate greater than 0.24 \u0026micro;g/kg/min is recommended in neurosurgery, which may not only reduce the impact on the TceMEPs but also reduce the occurrence of body movements. To the best of our knowledge, this is the first study exploring an association between different combination doses of remifentanil-propofol and transcranial motor-evoked potentials.\u003c/p\u003e \u003cp\u003eThe effect of anesthetics on TceMEP is a critical consideration in intraoperative neurological monitoring during surgery\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNathan\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e et al. evaluated the effect of propofol dose on TceMEP and concluded that higher doses of propofol decreased TceMEP amplitude. Therefore, it is often necessary to reduce the rate of propofol infusion during surgery. However, a lower dose of propofol may cause intraoperative awareness. Our results suggest that remifentanil can reduce the dose of propofol while maintaining the same depth of anesthesia. This finding is consistent with other studies reporting anesthesia-sparing effects\u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. At the same time, remifentanil provides deep analgesia during surgery, reduces the stress caused by the trauma of surgery and attenuates somatic movements\u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Previous studies have investigated the relationship between single anesthetic (propofol or remifentanil) and TceMEP. We used response surface methodology to analyze the relationship between different combination doses of remifentanil-propofol and TceMEP. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The equation of the response surface model in our study is: y\u0026thinsp;=\u0026thinsp;6.77\u0026ndash;2.96*X1\u0026thinsp;+\u0026thinsp;13.31*X2\u0026thinsp;+\u0026thinsp;2.41*X1*X2\u0026thinsp;+\u0026thinsp;0.40*X1^2-42.99*X2^2 (y: Log (TceMEP change), X1: propofol infusion, X2: remifentanil infusion). Due to the synergistic effect of propofol and remifentanil, under the same depth of anesthesia, the total amount of propofol and remifentanil within the same time period is different in different patients, triggering different degrees of TceMEPs decrease. In our study, we did not re-dose patients with relaxant after intubation. There was no occurrence of body movements in the patients who received remifentanil infusion more than 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 \u0026micro;g/kg/min during surgery. In the current study, a propofol infusion rate less than 2.96 mg/kg/h and a remifentanil rate greater than 0.24 \u0026micro;g/kg/min is recommended in neurosurgical cases requiring TceMEP monitoring, which may not only reduce the impact on the TceMEPs but also reduce the occurrence of body movements.\u003c/p\u003e \u003cp\u003eSeveral studies have suggested that acute and chronic exposure to high-dose remifentanil may be associated with the development of hyperalgesia after surgery\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. None of the patients in our study experienced hyperalgesia after surgery. This was due to the gradual withdrawal of the remifentanil infusion at the end of surgery. We also administered non-steroidal anti-inflammatory drugs (NSAIDs) and low-dose sufentanyl to counteract the rapid elimination of remifentanil after the infusion was stopped.\u003c/p\u003e \u003cp\u003eThe anesthesiologist and neurophysiologist monitoring the TceMEP were blinded to the study protocol. This may reduce observational bias. In addition, we chose the change in TceMEP in the abductor pollicis brevis ipsilateral to the surgical site as our outcome. This may reduce the effect of the surgical stimulus on our main outcome. Ugawa R\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e found that TceMEP amplitude was significantly decreased in the upper limb at 5 and 6 h after the initial infusion of propofol. In our study, the mean duration of surgery was 258.3\u0026thinsp;\u0026plusmn;\u0026thinsp;52.9 min in males and 253.3\u0026thinsp;\u0026plusmn;\u0026thinsp;52.5 min in females.\u003c/p\u003e \u003cp\u003eThe present study has several limitations that should be recognized. It usually took about 1 hour after induction to reach the time when the dural opens. We supposed that it can be almost completely metabolized at the moment of dural opening after induction of the nuromuscular dose, taking into account the pharmacokinetic profile of nuromuscular. It should be better to have TOF monitoring during surgery. Warning criteria for T-Ce MEP amplitude changes have not been universally established and include 50%, 60%, 70%, 80%, and complete loss\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. There were no patients in our study whose TceMEP amplitude decreased by more than 50% from baseline. Because the anesthetic doses in this observational study were decided by the anesthesiologist. We didn't randomly assign patients to a specific group that maintained an unchanged propofol/remifentanil infusion during surgery. Our conclusion is based on the current data we collected in this observational study, well-designed prospective RCT studies are needed to answer these questions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study showed that propofol dose-dependently reduced TceMEP amplitude and that remifentanil was not associated with the change in TceMEP. Due to the synergistic interactions between propofol and remifentanil, remifentanil could reduce the amount of propofol at the same depth of anesthesia (BIS 40\u0026ndash;60), a propofol infusion rate less than 2.96 mg/kg/h and a remifentanil rate greater than 0.24 \u0026micro;g/kg/min is recommended in neurosurgical cases requiring TceMEP monitoring.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to Participate Consent for Publication:\u003c/strong\u003e The ethical review board of Capital Medical University XuanWu Hospital approved this study (protocol No. [2021]078). The need for written informed consent was waived by the Capital Medical University XuanWu Hospital ethics committee due to observational nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u0026nbsp;\u003c/strong\u003eData can be provided within supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eNo Competing Interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contribution:\u0026nbsp;\u003c/strong\u003eRuixue Hou wrote the main manuscript. Wei Xiao and Fangfang Miao performed the statistical analysis. Cheng yin, Di jin, and Qingfang duan collected data. Tianlong wang is corresponding author, had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable as there is no any identifying image/figure in our study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSee RB, Awosika OO, Cambria RP, Conrad MF, Lancaster RT, Patel VI, Chitilian HV, Kumar S, Simon MV. Extended Motor Evoked Potentials Monitoring Helps Prevent Delayed Paraplegia After Aortic Surgery. Ann Neurol. 2016 Apr;79(4):636-45.\u003c/li\u003e\n \u003cli\u003eSilverstein JW, Shah HA, Unadkat P, Vilaysom S, Boockvar JA, Langer DJ, Ellis JA, D\u0026apos;Amico RS. Short and long-term prognostic value of intraoperative motor evoked potentials in brain tumor patients: a case series of 121 brain tumor patients. J Neurooncol. 2023 Jan;161(1):127-133.\u003c/li\u003e\n \u003cli\u003eGlover CD, Carling NP. Neuromonitoring for scoliosis surgery. 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J Neurosurg Spine. 2014 Jan;20(1):102-7. \u0026nbsp; \u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u0026nbsp;Demographic data and anaesthetic variables of study participants\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"809\" height=\"891\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Relationship between propofol-remifentanil dose and log (TceMEP change)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" align=\"\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003eUnivariable Analysis,\u0026beta; (95% CI)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u003cem\u003eP Value\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003eMultivariable Analysis,\u0026beta;\u0026nbsp;(95% CI)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\u003cem\u003eP Value\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003ePropofol dose\u0026nbsp;(mg/kg/h)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e0.75 (0.51, 0.98)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u0026lt;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0.29 (0.01, 0.58)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e0.046\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003ePropofol dose (mg/kg/h)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\u0026nbsp; \u0026nbsp;Low\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\u0026nbsp; \u0026nbsp;Medium\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e0.41 (0.04, 0.77)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0.06 (-0.30, 0.42)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e0.74\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\u0026nbsp; \u0026nbsp;High\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e1.14 (0.78, 1.51)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u0026lt;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0.54 (0.12, 0.95)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003eRemifentanil dose\u003cbr\u003e( \u0026mu;g/kg/min)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e-3.13 (-5.18, -1.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u0026nbsp;0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0.33 (-2.10, 2.76) 0.7899\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e0.79\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003eRemifentanil dose\u003cbr\u003e( \u0026mu;g/kg/min)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\u0026nbsp; \u0026nbsp;Low\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\u0026nbsp; \u0026nbsp;Medium\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e0.06 (-0.33, 0.46)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e0.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0.19 (-0.18, 0.56)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\u0026nbsp; \u0026nbsp;High\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e-0.46 (-0.85, -0.08)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e0.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25%\"\u003e0.15 (-0.30, 0.59)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e0.52\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMultivariable Analysis adjusted for: sex, age, BMI, ASA, hypertension, diabetes, coronary artery disease, operative time; baseline TceMEP, operative TceMEP, pain scores 24h postoperatively and body movements.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transcranial motor-evoked potentials (TceMEPs), General anesthesia, Propofol, Remifentanil, BIS","lastPublishedDoi":"10.21203/rs.3.rs-4166426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4166426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMonitoring of transcranial electrical motor evoked potentials (TceMEPs) is widely used in neurosurgery. The association of different combination doses of remifentanil-propofol in total intravenous anesthesia (TIVA) with TceMEPs during surgery remains uncertain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective observational study, consecutive patients (aged 18\u0026ndash;65 years) who underwent skull base surgery under general anesthesia at our clinical center between April 2021 and April 2023 were included. All patients were anesthetised with propofol-remifentanil TIVA and maintained at a Bispectral Index of 40\u0026ndash;60. The association between different combination doses of remifentanil-propofol and the change in TceMEP amplitude from baseline was assessed using a multivariable model adjusted for confounders and a response surface model. Besides, anesthetic dose, extubation time, pain score at 24h postoperatively and unexpected body movements during surgery were recorded.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 240 patients (mean age, 49.3 [SD, 12.1] years; 107 [44.6%] women) who underwent skull base surgery were included in this study. Our study showed that an increase in propofol dose was significantly associated with a constant increase in the Log of the change in TceMEP amplitude (β\u0026thinsp;=\u0026thinsp;0.29 [95%CI: 0.01 to 0.58], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) after adjustment. Increasing remifentanil was not associated with Log (change in TceMEP amplitude) (β\u0026thinsp;=\u0026thinsp;0.33 [95%CI: -2.1 to 2.76], \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.79) after adjustment. In addition, through the response surface analysis, we found that when the propofol infusion was less than 2.96 mg/kg/h and the remifentanil infusion was greater than 0.24 \u0026micro;g/kg/min, the more the remifentanil infusion was, the less impact it had on the changes in TceMEP. When the propofol infusion was greater than 2.96 mg/kg/h, as the propofol infusion increased, it had a greater impact on the changes in TceMEP. Pearson\u0026rsquo;s test showed a correlation between propofol and remifentanil dose at BIS 40\u0026ndash;60 (γ= -0.4637, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this study, propofol reduced TceMEP amplitude in a dose-dependent manner. Due to the synergistic interactions between propofol and remifentanil, remifentanil could reduce the amount of propofol at the same anesthesia depth, a propofol infusion rate less than 2.96mg/kg/h and a remifentanil rate greater than 0.24 \u0026micro;g/kg/min is recommended in neurosurgery requiring TceMEP monitoring.\u003c/p\u003e","manuscriptTitle":"Association of different combination doses of remifentanil-propofol with transcranial motor-evoked potentials during skull base surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 13:08:44","doi":"10.21203/rs.3.rs-4166426/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd483e75-cdd7-4ecc-a38b-b0eb2baf6be8","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-19T02:36:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 13:08:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4166426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4166426","identity":"rs-4166426","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}