Noninvasive Electrical Impedance Tomography Monitoring during Total Aortic Arch Replacement: incremental value for prediction of postoperative neurological deficit

Noninvasive Electrical Impedance Tomography Monitoring during Total Aortic Arch Replacement: incremental value for prediction of postoperative neurological deficit | 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 Article Noninvasive Electrical Impedance Tomography Monitoring during Total Aortic Arch Replacement: incremental value for prediction of postoperative neurological deficit Chen Yang, Yitong Guo, Wenjing Zhu, Weixun Duan, Chao Xue, Rong Zhao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7856033/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 Objectives Neurological deficit (ND) following total aortic arch replacement (TAAR) carries high morbidity and mortality, and effective treatments are lacking. Intraoperative neuromonitoring is crucial in preventing ND. This cohort study aims to investigate the prognostic value of noninvasive electrical impedance tomography (EIT) for detecting ND after TAAR. Methods In this study, a 16 electrode EIT system was applied to monitor patients' brain impedance during TAAR. Six EIT parameters regarding to the hypothermic circulatory arrest (HCA) phase were extracted. The correlation between changes in EIT parameters and ND were fully explored. Results The incidence of ND was 39.1% (59/151), of which stroke was 15.2% (19/125). The time integral of absolute value resistivity asymmetric index (TRAI HCA ), the maximum value of the absolute resistivity asymmetric ratio (MRAR HCA ), age, body mass index, supra-aortic branch vessel involvement and operation time were independent predictors of ND. The incorporation of EIT parameters into the clinical model resulted in a notable enhancement in area under the receiver operating characteristic curve from 0.752 to 0.817 (P = 0.027). Furthermore, the incremental value of EIT parameters was also reflected in the improvement of risk reclassification and discrimination for ND(net reclassification index = 0.510, P = 0.002; integrated discrimination improvement = 0.072, P < 0.001). Conclusions The EIT parameters have been demonstrated to be an effective means of predicting ND following TAAR. Cerebral EIT may represent a potential alternative for intraoperative noninvasive multimodal neuromonitoring options. The findings of this study require further validation in further research. Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Electrical impedance tomography Noninvasive multimodal neuromonitoring Total aortic arch replacement Cardiopulmonary bypass Postoperative neurological deficit Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stanford type A aortic dissection (TAAD) is a life-threatening cardiovascular disease, and the total aortic arch replacement (TAAR) is one of the most effective treatments for TAAD 1 . Despite an increase in survival rates following TAAR over the past two decades, the incidence of postoperative neurological deficit (ND) remains high 1 . Consequently, there has been a growing emphasis on the utilization of non-invasive multimodal neuromonitoring (NMN) as a means of guiding neuroprotective interventions and safeguarding patients from brain injury 3 . However, to our knowledge, current techniques provide indirect or post-insult recognition of irreversible brain injury, and devices to continuously monitor and prevent neurological injury in patients during TAAR are rather limited. Near-infrared spectroscopy (NIRS) 5 , electroencephalogram (EEG) and transcranial doppler (TCD) 7 , 8 are the most commonly used devices for neuromonitoring during cardiac surgery, but their correlation with postoperative ND remains controversial 9 – 11 . Compared with other neuromonitoring techniques, electrical impedance tomography (EIT) is capable of estimating the electrical properties at the interior of an object in a noninvasive, non-radiative, real-time and functional manner, which makes it have potential clinical application prospects 12 . In this study, we validated the feasibility of EIT for NMN during TAAR and assessed the correlation between EIT parameters extracted from HCA and incidence of postoperative ND. Results Patient demographic and clinical characteristics Out of 151 patients, the incidence of ND was 39.1%, the incidence of stroke was 15.2%. The age of patients in ND+ group was greater than that in ND- group, as was the body mass index (BMI). However, the rate of Syncope was higher in ND+ group. With regard to the imaging data, the rate of supra-aortic branch vessel (SABV) involvement was higher in ND+ group (Table 1). The ND+ group had more complex conditions such as long operation time and cardiopulmonary bypass (CPB) time. Furthermore, the prognosis of the ND group is worse than that without ND. The rate of long ventilation duration, continuous renal replacement therapy (CRRT), 30-day and 2-year mortality was higher in the ND+ group. The days of stay in intensive care unit were also longer in ND+ group. Classification of EIT data based on ND None of the EIT parameters conformed to normal distribution. The TRAI HCA was significantly higher in ND+ than that in ND- [2.06 (1.32,3.05) vs. 2.10 (1.62,4.14), P = 0.043) (Table 2). In addition, ACR HCA ( P = 0.081) and MRAR HCA ( P = 0.072) were also selected as EIT parameters for further multivariate analysis. However, there was no significant difference in other parameters (Table 3). In patients without ND, there was little difference between the left and right hemispheres (Figure 1a). In contrast, in patients with ND, there was a notable discrepancy in EIT between the two hemispheres of the brain was significant (Figure 1b). Potential prognostic factors associated with ND Univariable predictors of ND were listed in Table S2. Compared with ND- group, variables with independent adjusted associations with ND included greater TRAI HCA [OR = 1.29 (1.01,1.66); P = 0.019), higher MRAR HCA [OR = 1.23 (1.01,1.49); P = 0.041], greater age [OR = 1.06 (1.02,1.12]; P = 0.003), higher BMI [OR = 1.19 (1.05,1.34); P = 0.003), more SABV involvement [OR = 2.44 (1.04-5.71); P = 0.026], and longer operation time [OR = 1.11 (1.02,1.20); P = 0.025] (Figure 2a). Discriminative accuracy and reliability of EIT parameters for estimating risk for postoperative neurological deficits The ROC curves revealed that adding EIT parameters (TARI HCA , MRAR HCA and ACR HCA ) to the clinical model significantly improved the AUC from 0.752 to 0.817 ( P = 0.027) (Figure 2b). The incremental value of EIT parameters was also reflected in the improvement of risk reclassification and discrimination for postoperative ND [NRI = 0.510 (0.091,1.003), P = 0.002; IDI = 0.072(0.030, 0.113), P < 0.001] (Table 4). Risk stratification by postoperative ND The overall 30-day mortality was 11.3% (17/151), which was significantly modulated by ND (6.5% vs 18.6%; P = 0.021). During a median follow-up of 694 days, all-cause mortality was observed in 24 (15.9%) patients. Kaplan-Meier curves (Figure 3) indicated that all-cause mortality was significantly higher in patients with ND (9.8% vs. 25.4%; P = 0.003). Discussion To the best of our knowledge, this is the first study of its size to explore the neurological prognostic value of EIT in a large TAAD cohort underwent TAAR, with incidence of 39.1% (59/151) for ND. Multivariate analysis showed that TRAI HCA , MRAR HCA , age, BMI, SABV involvement and operation time were independent predictors of ND. The incremental value of EIT parameters was also proved in the improvement of risk reclassification and discrimination for postoperative ND. Importantly, noninvasive EIT neuromonitoring was safe and associated with no adverse effects. At present, the core strategies of intraoperative cerebral protection are hypothermia and selective cerebral perfusion 13 . However, the rate of ND after TAAR are still much higher than that of other types of cardiac surgery, reaching 20–50% 14 . The etiology of ND is multifactorial with proposed mechanisms including microembolism, cerebral hypoperfusion, and systemic inflammation 15 . Compared to other types of cardiac surgery, TAAR is more complex, which is reflected in the long operation time and the perfusion imbalance during the HCA phase. Therefore, the degree of injury in the three parts of the etiology of ND is relatively higher 15 . Particularly, in the presence of established embolism and neural tissue damage, any concomitant inadvertent hypoperfusion is even more deleterious 16 . It can therefore be argued that this warrants even closer monitoring. In addition, the neurological status of patients after TAAR is clinically difficult to evaluate due to multiple factors including preoperative emergency status, sedation, and pharmacological paralysis 17 . Therefore, intraoperative neuromonitoring and early warning are the top priorities of perioperative monitoring༎ The risk factors of ND after TAAR were divided into patient characteristics and surgical related factors. Bossone et al . presented the large cohort of patients with stroke of TAAD and their patients presenting with stroke were older, had more comorbidities, higher rates of malperfusion and syncope compared to patients without stroke after TAAR 18 . The involvement and stenosis of SABV are also recognized risk factors for ND 19–21 . In addition, numerous studies have addressed intraoperative cannulation techniques and proposed that the axillary artery may serve as a protective factor against for postoperative ND 22–24 . However, in this study, there was no significant difference in cannulation between two groups (Table S3). Andreas et al. presented the long-term follow-up results of the German registry for acute aortic dissection 22 – 24 . ND and greater age were the risk factors for 10-year mortality. This study also demonstrated a higher 2-year mortality of ND + group. Due to the multifactorial nature of the mechanism of ND, no neuromonitoring modality has been conclusively proved to be superior when used in aortic surgery. TCD, NIRS, EEG, somatosensory evoked potential and biomarkers are commonly used in the study of aortic surgery, but each has its own advantages and disadvantages 11 . TCD is a commonly used device in cerebral perfusion related researches 8 . However, TCD is subjected to the following constraints: it necessitates the involvement of trained professionals; it is constrained by the acoustic and doppler alignment of the patient; and it is only capable of detecting blood flow in the major cerebral arteries 2 . During TAAR, TCD is challenging to fulfil the criteria for continuous monitoring and to reflect the overall brain perfusion, which limits its clinical application to a relatively narrow scope. Additionally, NIRS is the most prevalent monitoring device in clinical practice due to its simplicity of use and cost-effectiveness 26 . Nevertheless, NIRS also has some disadvantages. These include the inability to accurately reflect oxygen saturation in the frontal region, the presence of a time delay 2 , and the potential for inaccuracy due to contamination from sources outside the cerebral cortex 27 . Meanwhile, with the increasing clinical application, some studies pointed out that currently recognized early warning standard of cerebral oxygen are not related to ND 8,14 . In addition, the EEG or quantitative EEG mainly records spontaneous electrical activity in the superficial layers of the cortex 2 , and, biomarkers can be easily influenced by hemolysis and intraoperative factors, and have limitations for real-time monitoring. In summary, there is an urgent need in clinical practice for a bedside neuromonitoring technique that can reflect the overall cerebral perfusion during surgery 28 . EIT is a non-invasive, non-radiation and functional imaging technique that measures the transfer impedance signals between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues 29 . The biophysical foundation of EIT is that different tissues have different resistivities, and that resistivities vary between healthy and diseased tissues 30 . In the context of respiratory function monitoring within intensive care units, the application of EIT has reached a relatively advanced stage of development 31 . Additionally, EIT is increasingly being utilized for neuromonitoring purposes 11 . Following animal validation 32 , 33 , EIT has been employed in clinical trials for epilepsy 34 , brain edema and mannitol dehydration treatment 36 , as well as subdural hematoma 37 . Our group studied the feasibility of EIT for neuromonitoring during TAAR in a relatively small sample size and demonstrated strong correlations between EIT parameters and neurological biomarkers 38 . EIT offers a number of advantages over other neuroimaging technologies, which has led to suggestions that it could be used in clinical practice 12 . The correlation coefficients for EIT parameters during the HCA phase were presented in Table S1 . The correlation coefficients between the three EIT parameters focusing on the change in whole-brain impedance (ΔARV HCA , k HCA , ACR HCA ) and between the three EIT parameters describing the impedance imbalance between the left and right hemispheres (MRAI HCA , TARI HCA , MRAR HCA ) were statistically significant, respectively. The observed changes in EIT parameters may be attributed to alterations in cerebral perfusion volume and cerebral edema. It was noted that the whole brain ARV increased as perfusion flow decreased and vice versa, since the impedance of the blood was smaller than that of the brain parenchyma 39 . Meanhwhile, hypoperfusion causes insufficient oxygen supply and adenosine triphosphate depletion, followed by intracellular sodium and calcium overload, cellular swelling, and cerebral edema 17 , which manifests as a gradual increase in resistivity. A higher level of TARI HCA indicated that prolonged brain perfusion imbalance during the HCA phase can predict postoperative ND. Moreover, the MRAR HCA suggested that the maximum degree of brain perfusion imbalance was related to ND. It was possible that EIT parameters to reflect some physiological indicators such as hypoperfusion, hyperperfusion or even cerebral edema which had changed during surgery and were therefore related to postoperative ND. The NMN protocol involves conducting serial neurological examinations at specified intervals during the perioperative period. Real-time NMN at the bedside allows for a direct evaluation of the physiological interplay between systemic disorders and intracranial processes, potentially enabling early detection of neurological deterioration prior to the onset of clinically apparent symptoms 3 . Our research has demonstrated the incremental value of EIT parameters over other predictors of ND after TAAR, suggesting that EIT may be one of the alternatives for real-time intraoperative bedside NMN. However, like other devices that rely on electricity, EIT has similar drawbacks, such as susceptibility to temperature changing, subject movements and interference from the electrocautery 2 . The focus of this study was mainly on the HCA phase, during which there was little change in temperature, little use of electrocautery and no subject movement. Nevertheless, it was reported that cerebral ischemia was also prone to occur during the cooling and the rewarming phase 2 , to which should also be paid attention. Actually, the study of EIT monitoring for the cerebral injury during both the cooling and rewarming phases using the influence of temperature on EIT has been undergoing, and the related results will be reported in the late future. The findings of this study should be approached with caution due to several inherent limitations. Nevertheless, this study represents the largest study of EIT neuromonitoring and its association with ND after TAAR. Firstly, there are individual variations in brain compliance and tolerance to hypoxia among patients. TND is also influenced by factors such as the patient's educational attainment, psychological condition, and preoperative education. Second, Second, the challenge in identifying the potential etiology of coma. Eleven comatose patients could not be transported to imaging facilities due to persistent hemodynamic instability. Furthermore, CT exhibit relatively low sensitivity in detecting acute ischemic brain injury. Finally, this study only analyzed the EIT parameters of the HCA phase, which is the most extensively studied and considered the most critical phase. Research on the correlation between EIT parameters throughout the procedure and ND is currently underway. Conclusion The EIT parameters have been demonstrated to be an effective means of predicting ND following TAAR. Cerebral EIT may represent a potential alternative for intraoperative noninvasive multimodal neuromonitoring options. The findings of this study require further validated in subsequent research. Methods Population and Recruitment This study was conducted in accordance with the Declaration of Helsinki, was approved by the ethics committee of Xijing Hospital [FMMU-E-III-001(1–7)] and written informed consent was obtained from all subjects or their legal guardian. The trial was registered prior to patient enrollment at Chinese Clinical Trial Registry ( https://www.chictr.org.cn ; ChiCTR-OOC-16007844). This manuscript adheres to the applicable STROBE guidelines. A total of 366 TAAD patients underwent TAAR from July 2020 to December 2021 were screened (Figure S1 ), and 163 patients included in the study satisfied the inclusion criteria included: a) stanford type A aortic dissection; b) aged from 24–75 years old both male and female; c) computed tomography angiography (CTA) images with value of diagnosis; and d) accepted for cerebral monitoring by EIT system (EH-300, China) during surgery; In addition, the exclusion criteria: a) early death (within 24 hours) after surgery make it difficult in assessing ND, n = 1; b) interference with EIT data, n = 6; c) incomplete EIT data, n = 2; d) change in surgical procedure, n = 3. According to the inclusion and exclusion criteria, 151 patients were ultimately included in this study. Neurological deficit Neurological deficit indicates if the patient developed a new abnormality in neurological function after TAAR. This includes the entire perioperative period up to post-operative day five. A ‘neurological deficit’ includes the following manifestations: new stroke, transient neurological deficit (TND), and coma 14 . The definitions are as follows: Stroke is a cerebrovascular accident that represents neurological loss caused by ischemic events. The diagnosis of stroke can be confirmed via computed tomography (CT) imaging (Figure S2). TND is defined as sensory or motor impairment resulting from vascular damage. The presence of TND is considered when patients combining postoperative neurological symptom recorded by at least two experienced nurses. Finally, coma was defined as complete or partial mental unresponsiveness (beyond that expected from anesthesia). Intraoperative Noninvasive Multimodal Neuromonitoring All patients enrolled underwent TAAR with HCA and anterograde cerebral perfusion (ACP) (detailed in the supplementary material). A multimodal approach using EIT (EH-300, UTRON Technology Co., Ltd., Hangzhou, China) combined with NIRS (INVOS 5100c, Covidien, Mansfield, MA, USA) was continuously used for intraoperative neuromonitoring (Figure S3), and were acted on by adjusting ACP settings (e.g. perfusion pressure) by a clinical perfusionist if deemed necessary and possible, for example in case of marked regional cerebral oxygen saturation drops or SctO 2 left-to-right asymmetry 10 . The specifications of the EIT system are as follows: range of the working frequency, 1 ~ 190 kHz; measurement accuracy, ± 0.01%; and common mode rejection ratio, over 80 dB. Sixteen body surface electrodes (EH-PET-16CL) were placed equidistantly on the cross-sectional surfaces above the frontal lobe, temporal lobe, and occipital tuberosity of the patient. The electrical impedance of the patient’s brain was monitored in real-time by the EIT system. The reference data were calculated by averaging the one-minute EIT data subsequent to the commencement of the HCA. In order to achieve the desired results, the damped least-squares reconstruction algorithm was employed for the purpose of EIT imaging. The biophysical foundation of EIT is based on the principle that different tissues exhibit varying degrees of resistivity, with notable differences observed between the healthy and diseased tissues. The impedance distribution of the scalp (ρscalp = 2.27 Ωm), skull (ρskull = 55.56 Ωm) and brain parenchyma (ρbrain = 4 Ωm) was incorporated into the EIT reconstruction process in order to enhance the quality of the resulting imaging 41 . In addition, a CT image of each patient at the EIT electrode layer was used as an anatomical reference. We have optimized imaging algorithm and implemented various data processing methods in the imaging process given the possible clinical interference in order to improve the signal-to-noise ratio. EIT system and EIT parameters Previous research showed that brain injury could appear in the HCA phase due to the sudden change of blood flow to the brain 8 . We calculated six parameters that characterize the changes in EIT from different perspectives. The first three parameters were designed to describe changes in whole-brain impedance, while the latter three aimed to capture impedance imbalances between the left and right hemispheres. The specifications of the EIT system are as follows: range of the working frequency, 1 ~ 190 kHz; measurement accuracy, ± 0.01%; and common mode rejection ratio, over 80 dB.The reference data were calculated by averaging the one-minute EIT data subsequent to the commencement of the HCA. In order to achieve the desired results, the damped least-squares reconstruction algorithm was employed for the purpose of EIT imaging. The biophysical foundation of EIT is based on the principle that different tissues exhibit varying degrees of resistivity, with notable differences observed between the healthy and diseased tissues. The impedance distribution of the scalp (ρscalp = 2.27 Ωm), skull (ρskull = 55.56 Ωm) and brain parenchyma (ρbrain = 4 Ωm) was incorporated into the EIT reconstruction process in order to enhance the quality of the resulting imaging. In addition, a CT image of each patient at the EIT electrode layer was used as an anatomical reference. We have optimized imaging algorithm and implemented various data processing methods in the imaging process given the possible clinical interference in order to improve the signal-to-noise ratio 42 . We calculated six parameters based on average resistivity value (ARV) which was defined as: $$\:\text{A}\text{R}\text{V}=\:\frac{1}{M}\sum\:{A}_{k}{x}_{k},\:k=1,\dots\:,\:M$$ 1 where M is the total units of brain dissection model, \(\:{\text{A}}_{\text{k}}\) is the area \(\:{\text{k}}^{\text{t}\text{h}}\) of unit and \(\:{\text{x}}_{\text{k}}\) is the relative variation of electrical resistivity of the unit number. ARV reflects the change of electrical impedance of the whole brain during surgery. Therefore, the first parameter was defined as the difference of average resistivity value before and after the HCA phase (ΔARV HCA ): $$\:\varDelta\:{\text{A}\text{R}\text{V}}_{\text{H}\text{C}\text{A}}={ARV}_{after\:HCA}-{ARV}_{before\:HCA}$$ 2 which reflects the range of change of electrical impedance during HCA. The second parameter k HCA was defined as: $$\:{k}_{\text{H}\text{C}\text{A}}=\frac{{\varDelta\:ARV}_{HCA}}{{t}_{HCA}}$$ 3 Where k stands for the slope of electrical impedance during the HCA phase, and t is the duration of the HCA phase. This parameter represents for the speed of variation of electrical impedance during the HCA phase. The third parameter was called the average change rate of average resistivity value (ACR HCA ) which was described as: $$\:\text{A}\text{C}\text{R}=abs\left[\frac{{\sum\:}_{{frame}_{end\:of\:HCA}}^{{frame}_{start\:of\:HCA}}（{ARV}_{i+1}-{ARV}_{i}）/（{t}_{i+1}-{t}_{i}）}{{frame}_{end\:of\:HCA}-{frame}_{start\:of\:HCA}}\right]$$ 4 Next, we determined resistivity asymmetric index (RAI) as the electrical impedance between left and right hemispheres, which was described as: $$\:\text{R}\text{A}\text{I}={ARV}_{L}-{ARV}_{R}$$ 5 Therefore, the fourth parameter was defined as the maximum value of the absolute resistivity asymmetric index (MRAI HCA ) 39 . The fifth parameter-the time integral of absolute value resistivity asymmetric index (TRAI HCA ) was described as: $$\:\text{T}\text{R}\text{A}\text{I}={\int\:}_{start\:of\:HCA}^{end\:of\:HCA}\:abs\left(RAI\right)\bullet\:dt$$ 6 Finally, we also calculated the resistivity asymmetric ratio (RAR) as the ratio of left and right hemispheres impedance to whole brain impedance: $$\:\text{R}\text{A}\text{R}=\frac{{ARV}_{L}-{ARV}_{R}}{ARV}$$ 7 Therefore, the sixth parameter was defined as the maximum value of the absolute resistivity asymmetric ratio (MRAR HCA ). Statistical Analysis All normally distributed continuous variables were described with means [± standard deviation (SD)] and compared across groups with Student’s t-test. Median [interquartile range (IQR)] and the Wilcoxon rank sum test were used for non-normally distributed continuous variables. Categorical variables were described with frequencies (%) and compared with chi-square test or Fisher’s exact test. After each variable was tested in univariate analysis ( P < 0.10), a multivariate regression analysis was performed by using stepwise forward selection to determine independent predictors of ND. Furthermore, the clinical models with and without EIT parameters were compared, and the difference between areas under the curve (AUC)-the receiver operating characteristic curve (ROC) was validated through the Delong test. Meanwhile, the ability of the EIT parameters to discriminate risk for ND and mortality based on the clinical model was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). To further understand the prognosis of ND, Kaplan-Meier curves were constructed for 2-year mortality, and the resulting data were subjected to a log-rank test for comparison (Fig. 4 ). P < 0.05 was considered statistically significant. Small amounts of missing values were imputated using multiple data imputation with predictive mean matching, after patterns of missing data were checked to follow the assumption that data were missing at random. All analyses were performed with SPSS 19.0 and R (version 4.0.0.0). Declarations Funding Declaration: This work was supported by Key Research and Development Projects of the Science and Technology Committee (2022YFC2404803), Key Basic Research Projects of the Basic Strengthening Plan of the Science and Technology Committee (2019-JCJQ-ZD-115-00-02), National Key Technology Research and Development Program (2023ZD0504402), National Natural Science Foundation of China-General Program (82370273), Shaanxi Key Science and Technology Innovation Team Project (2022ZDLSF02-01) and Cross-integration project of Xijing Hospital (XJZT24JC37). Author contributions: Z Jin, X Shi, S Yu: Conception and design of study, Approval of final version of manuscript; C Yang, Y Guo, W Zhu: Acquisition of data, Data analysis and/or interpretation, Drafting of manuscript and or critical revision; C Xue: Acquisition of data; W Duan, H Zhao, J Liu, R Zhao: Acquisition of data, Approval of final version of manuscript. Data availability statement: If the request is reasonable, the corresponding author can share the data from this study to the requestors. Acknowledgement: None. Conflicts of interest: The authors declare no conflicts of interest related to this study. Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki, was approved by the ethics committee of Xijing Hospital [FMMU-E-III-001(1-7)] and written informed consent was obtained from all participants/guardians in the trial. 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Guo, Y. et al., Electrical impedance tomography provides information of brain injury during total aortic arch replacement through its correlation with relative difference of neurological biomarkers. SCI REP-UK 14 14236 (2024). Li, Y. et al., Noninvasive Cerebral Imaging and Monitoring Using Electrical Impedance Tomography During Total Aortic Arch Replacement. J CARDIOTHOR VASC AN 32 2469 (2018). Grigore, A. M. et al., The Rewarming Rate and Increased Peak Temperature Alter Neurocognitive Outcome After Cardiac Surgery. Anesthesia & Analgesia 94 4 (2002). Bagshaw, A. P. et al., Electrical impedance tomography of human brain function using reconstruction algorithms based on the finite element method. NEUROIMAGE 20 752 (2003). Zhang, G. et al., Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography. BIOMED ENG ONLINE 16 7 (2017). Tables Table 1. Baseline and imaging characteristics of patients with and without neurological deficit after total aortic arch replacement. All, n=151 ND-, n=92 ND+, n=59 P -value Sex, male 126(83.4) 74(80.4) 52(88.1) 0.214 Age, years 49.7±10.0 48.4±9.7 51.8±10.2 0.043 BMI, kg/m 2 25.5±4.0 24.9±3.1 26.5±5.1 0.021 Acute aortic dissection 143(94.7) 85(92.4) 58(98.3) 0.150 Interval from symptom to surgery, hours 20.0(14.0,24.0) 20.0(14.0,24.0) 20.0(12.0,24.0) 0.351 Stroke history 16(10.6) 11(12.0) 5(8.5) 0.498 New stroke on admission 7(4.6) 3(3.3) 4(6.8) 0.433 Syncope 20(13.2) 7(7.6) 13(22.0) 0.011 Lower limb ischemic syndrome 32(21.2) 16(17.4) 16(27.1) 0.154 CAD 19(12.6) 11(12.0) 8(13.6) 0.772 Hypertension 99(65.6) 57(62.0) 42(71.2) 0.244 Diabetes 13(8.6) 5(5.4) 8(13.6) 0.082 Marfan syndrome 7(4.6) 2(2.2) 5(8.5) 0.111 Cardiac surgery history 8(5.3) 3(3.3) 5(8.5) 0.263 NYHA Ⅲ&Ⅳ 51(33.8) 32(34.8) 19(32.2) 0.744 Imaging data EF value, % 51.0±6.5 51.3±6.4 51.0±6.8 0.800 Aortic valve insufficiency 51(33.8) 32(34.8) 19(32.2) 0.744 SABV involvement 88(58.3) 44(47.8) 44(74.6) 0.002 ND: Neurological deficit; BMI: Body mass index; CAD: Coronary artery disease; NYHA: New York heart association; EF value: Ejection fraction value; SABV: Supra-aortic branch vessel. Table 2. Surgical details and outcomes of patients with and without neurological deficit after total aortic arch replacement. All, n=151 ND-, n=92 ND+, n=59 P -value Operation time, min 409.4±70.2 391.4±58.9 437.5±77.4 <0.001 CPB time, min 220.9±35.1 213.2±31.8 232.8±37.0 0.001 Cross-clamp time, min 104.9±24.7 103.8±25.6 106.7±23.4 0.520 HCA time, min 35.4±8.9 35.3±10.3 35.7±6.3 0.266 Lowest esophageal temperature, ℃ 25.3(25.0,25.8) 25.4(25.0,25.7) 25.3(25.0,26.0) 0.997 Cerebral perfusion (bilateral, antegrade) 84(55.6) 48(52.2) 36(61.0) 0.286 Postoperative complications Delayed awakening (>6h) 21(13.9) 11(12.0) 10(16.9) 0.387 Ventilation duration > 24h 87(57.6) 40(43.5) 47(79.7) <0.001 ICU stay, day 5.0(4.0,7.0) 4.0(3.0,5.0) 7.0(5.0,13.0) <0.001 CRRT therapy 23(15.2) 6(6.5) 17(28.8) <0.001 ECMO therapy 1(0.7) 0 1(1.7) 0.391 Re-exploration for bleeding 5(3.3) 2(2.2) 3(5.1) 0.379 Rescue 15(9.9) 6(6.5) 9(15.3) 0.080 30-day mortality 17(11.3) 6(6.5) 11(18.6) 0.021 2-year mortality 24(15.9) 9(9.8) 15(25.4) 0.010 ND: Neurological deficit; CPB: Cardiopulmonary bypass; ICU: Intensive care unit; CRRT: Continuous renal replacement therapy; HCA: Hypothermic circulatory arrest; ECMO: Extra-corporeal membrane oxygenation. Table 3. Electrical impedance tomography parameters of patients with and without postoperative neurological deficit. Variable All, n=151 ND-, n=92 ND+, n=59 P -value ΔARV HCA , 10 -3 AU 0.17(-6.45,5.65) -0.16(-8.05,5.00) 0.76(-6.16,6.37) 0.588 K HCA , 10 -3 AU/min 0.01(-0.20,0.18) -0.26(-0.26,0.17) 0.02(-0.17,0.22) 0.500 ACR HCA , 10 -6 AU/s 4.07(1.74,6.80) 3.90(1.76,6.65) 4.07(1.61,7.00) 0.081 MRAI HCA , 10 -3 AU 2.50(1.64,3.62) 2.44(1.62,3.57) 2.56(1.73,3.63) 0.723 TRAI HCA , AU 2.06(1.53,3.25) 2.06(1.32,3.05) 2.10(1.62,4.14) 0.043 MRAR HCA 0.026(0.014,0.044) 0.026(0.014,0.042) 0.027(0.014,0.059) 0.072 ND: Neurological deficit; ΔARV HCA : the difference of ARV before and after HCA phase; k HCA : the slope of ARV during HCA phase; ACR HCA : the average change rate of ARV during HCA phase; MRAI HCA : the maximum value of the absolute ARV index during HCA phase; TRAI HCA : the time integral of ARV asymmetric index during HCA phase; MRAR HCA : the maximum value of the absolute resistivity asymmetric ratio during HCA phase. Table 4. Prediction increment of the electrical impedance tomography parameters to postoperative neurological deficit. ND (95%CI) P -value 30-day mortality (95%CI) P -value 2-year mortality (95%CI) P -value AUC (95% CI) Clinical 0.752(0.675,0.819) - 0.755(0.678,0.821) - 0.742(0.664,0.810) - Clinical+EIT parameters 0.817(0.745,0.875) - 0.761(0.685,0.827) - 0.746(0.669,0.814) - ΔAUC 0.064 0.027 0.007 0.793 0.005 0.841 Continuous NRI (95% CI) All NRI 0.510(0.091,1.003) 0.002 0.135(-0.048,1.084) 0.793 0.283(-0.098,0.965) 0.103 NRI+ 0.119(-0.126,0.453) - -0.059(-0.294,0.714) - -0.077(-0.297,0.539) - NRI- 0.391(0.084,0.607) - 0.194(-0.185,0.662) - 0.360(-0.191,0.661) - IDI (95% CI) 0.072(0.030,0.113) <0.001 0.004(-0.003,0.011) 0.305 0.002(-0.006,0.009) 0.672 Clinical: Clinical model included age, body mass index, syncope, SABV involvement, operation time, CPB time. EIT parameters: ACR HCA , MRAR HCA , TRAI HCA . ND: Neurological deficit; CI: Confidence interval; AUC: Area under the curve; NRI: Net reclassification index; IDI: Integrated discrimination improvement. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7856033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":592106692,"identity":"92bf3492-ee38-4c01-a92e-fa0a6a9a5b10","order_by":0,"name":"Chen Yang","email":"","orcid":"","institution":"Tangdu Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Yang","suffix":""},{"id":592106693,"identity":"fda3fd69-19a8-413c-ba45-71c3c3d91462","order_by":1,"name":"Yitong Guo","email":"","orcid":"","institution":"Tangdu Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yitong","middleName":"","lastName":"Guo","suffix":""},{"id":592106694,"identity":"9b1b7530-c2ab-4845-ae1d-f2da2f89bf75","order_by":2,"name":"Wenjing Zhu","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Zhu","suffix":""},{"id":592106695,"identity":"56df418c-ebee-4804-adb7-326e67025e3f","order_by":3,"name":"Weixun Duan","email":"","orcid":"","institution":"Xijing Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weixun","middleName":"","lastName":"Duan","suffix":""},{"id":592106696,"identity":"3addd7de-b7ff-43c6-9b62-ba1a9adf6030","order_by":4,"name":"Chao Xue","email":"","orcid":"","institution":"Xijing Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Xue","suffix":""},{"id":592106697,"identity":"adb73be7-4b50-4432-bba0-114f152f57b3","order_by":5,"name":"Rong Zhao","email":"","orcid":"","institution":"Xijing Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhao","suffix":""},{"id":592106698,"identity":"7454b0f4-38a0-4e39-9b26-64d091caa6e4","order_by":6,"name":"Jincheng Liu","email":"","orcid":"","institution":"Xijing Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jincheng","middleName":"","lastName":"Liu","suffix":""},{"id":592106699,"identity":"458c5893-9c9f-4a6b-ae42-c045578ac365","order_by":7,"name":"Huadong Zhao","email":"","orcid":"","institution":"Tangdu Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huadong","middleName":"","lastName":"Zhao","suffix":""},{"id":592106700,"identity":"e15a8f61-554d-4b35-9048-0c157d9edb9f","order_by":8,"name":"Shiqiang Yu","email":"","orcid":"","institution":"Xijing Hospital of Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shiqiang","middleName":"","lastName":"Yu","suffix":""},{"id":592106701,"identity":"0dbb78dc-2009-47cc-9010-0354e5f9d51c","order_by":9,"name":"Xuetao Shi","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuetao","middleName":"","lastName":"Shi","suffix":""},{"id":592106702,"identity":"170386ce-164f-4e0b-8e69-82223233ffb6","order_by":10,"name":"Zhenxiao Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACCRBhwMDA2MzA+CDBwIaHn72BeC3MBg8q0mQkew4QowUC2CQfnDlsY3DDAb8O+dnNxx7zFNjJMbfzHpBIbDvPw3CDgfHDxxzcWhjnHEs3nGGQbMzYzJdgkNh2m4dxdgOz5MxtuLUwS+SYSXwwYE5sbOYxSABpYZY5wMbMi0cLm0T+N4kEg3qwlgOJbed42CQS8GvhkchhA9pyGKTFsCHhzAEeHkJaJCTSzCRnGBwH+oXHmCGhIplHgudgM16/yM9IfibN86dazrD/jPnPHwZ29vbHmw9++IhHCxwYNsCZjA04VaFaR5yyUTAKRsEoGIkAAEsNS7yxD9dzAAAAAElFTkSuQmCC","orcid":"","institution":"Xijing Hospital of Air Force Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhenxiao","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2025-10-14 08:23:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7856033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7856033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102892439,"identity":"99bbfc93-52a2-4a94-9f43-5ecfc5b704af","added_by":"auto","created_at":"2026-02-18 05:25:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":606484,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline of CPB in the surgery and representative cerebral electrical impedance tomography during HCA. a: A Representative data of ND- patients; b: A Representative data of ND+ patients. CPB: Cardiopulmonary bypass; HCA: Hypothermic circulatory arrest; ARV: Average resistivity value\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7856033/v1/f64d2e755397a75c01d478de.png"},{"id":102892433,"identity":"12807c22-17ce-4a27-ab48-aa0643549078","added_by":"auto","created_at":"2026-02-18 05:25:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":326632,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot based on multivariable logistic regression analysis (a) demonstrated incremental prognostic value of EIT parameters for postoperative neurological deficit (b). Clinical: Clinical model included age, BMI, syncope, SABV involvement, operation time, CPB time. EIT parameters: ACR\u003csub\u003eHCA\u003c/sub\u003e, MRAR\u003csub\u003eHCA\u003c/sub\u003e, TRAI\u003csub\u003eHCA\u003c/sub\u003e.\u003csub\u003e \u003c/sub\u003eEIT: Electrical impedance tomography; ACR\u003csub\u003eHCA\u003c/sub\u003e: the average change rate of ARV during HCA phase; MRAR\u003csub\u003eHCA\u003c/sub\u003e: the maximum value of the absolute resistivity asymmetric ratio during HCA phase; TRAI\u003csub\u003eHCA\u003c/sub\u003e: the time integral of absolute ARV asymmetric index during HCA phase; BMI: Body mass index; CPB: Cardiopumonary bypass; SABV: Supra-aortic branch vessels\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7856033/v1/2a18819d3d16f8dfc8ae0ddc.png"},{"id":102892388,"identity":"9791f43a-6947-422e-8d5c-335454092824","added_by":"auto","created_at":"2026-02-18 05:25:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":324293,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative survival curve of patients with and without postoperative neurological deficit. ND: neurological deficit; HR: hazard ratio; CI: confidence interval\u003c/p\u003e\n\u003cp\u003eGraphical Abstract. EIT: Electrical impedance tomography; ND: Neurological deficit; TAAR: Total aortic arch replacement;\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7856033/v1/245a7fac71b8251cff5e3b80.png"},{"id":102892436,"identity":"474339b8-2f98-4e3b-951b-ae5ff356e558","added_by":"auto","created_at":"2026-02-18 05:25:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1050183,"visible":true,"origin":"","legend":"\u003cp\u003eThe incremental value of noninvasive electrical impedance tomography monitoring during total aortic arch replacement for prediction of postoperative neurological deficit. EIT: Electrical impedance tomography; ND: Neurological deficit; TAAR: Total aortic arch replacement;\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7856033/v1/0429b34136c33e84f211f797.png"},{"id":109162191,"identity":"b2b99735-d36b-42aa-a8c2-f288e34aeb4b","added_by":"auto","created_at":"2026-05-13 07:46:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2553613,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7856033/v1/1a4f7211-ee67-4b32-bf1f-cbd944973bed.pdf"},{"id":102892441,"identity":"28df16ab-13b7-43d9-8aa8-ccace79faa13","added_by":"auto","created_at":"2026-02-18 05:25:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":669929,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7856033/v1/cb3aab00eee562db4edad4e1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eNoninvasive Electrical Impedance Tomography Monitoring during Total Aortic Arch Replacement: incremental value for prediction of postoperative neurological deficit\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStanford type A aortic dissection (TAAD) is a life-threatening cardiovascular disease, and the total aortic arch replacement (TAAR) is one of the most effective treatments for TAAD\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite an increase in survival rates following TAAR over the past two decades, the incidence of postoperative neurological deficit (ND) remains high\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsequently, there has been a growing emphasis on the utilization of non-invasive multimodal neuromonitoring (NMN) as a means of guiding neuroprotective interventions and safeguarding patients from brain injury\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, to our knowledge, current techniques provide indirect or post-insult recognition of irreversible brain injury, and devices to continuously monitor and prevent neurological injury in patients during TAAR are rather limited. Near-infrared spectroscopy (NIRS)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, electroencephalogram (EEG) and transcranial doppler (TCD)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e are the most commonly used devices for neuromonitoring during cardiac surgery, but their correlation with postoperative ND remains controversial\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Compared with other neuromonitoring techniques, electrical impedance tomography (EIT) is capable of estimating the electrical properties at the interior of an object in a noninvasive, non-radiative, real-time and functional manner, which makes it have potential clinical application prospects\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In this study, we validated the feasibility of EIT for NMN during TAAR and assessed the correlation between EIT parameters extracted from HCA and incidence of postoperative ND.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient demographic and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 151 patients, the incidence of ND was 39.1%, the incidence of stroke was 15.2%. The age of patients in ND+ group was greater than that in ND- group, as was the body mass index (BMI). However, the rate of Syncope was higher in ND+ group. With regard to the imaging data, the rate of supra-aortic branch vessel (SABV) involvement was higher in ND+ group (Table 1). The ND+ group had more complex conditions such as long operation time and cardiopulmonary bypass (CPB) time. Furthermore, the prognosis of the ND group is worse than that without ND. The rate of long ventilation duration, continuous renal replacement therapy (CRRT), 30-day and 2-year mortality was higher in the ND+ group. The days of stay in intensive care unit were also longer in ND+ group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification of EIT data based on ND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the EIT parameters conformed to normal distribution. The TRAI\u003csub\u003eHCA\u003c/sub\u003e was significantly higher in ND+ than that in ND- [2.06 (1.32,3.05) vs. 2.10 (1.62,4.14), \u003cem\u003eP\u003c/em\u003e= 0.043) (Table 2). In addition, ACR\u003csub\u003eHCA\u0026nbsp;\u003c/sub\u003e(\u003cem\u003eP\u003c/em\u003e = 0.081) and MRAR\u003csub\u003eHCA\u0026nbsp;\u003c/sub\u003e(\u003cem\u003eP\u003c/em\u003e = 0.072) were also selected as EIT parameters for further multivariate analysis. However, there was no significant difference in other parameters (Table 3).\u003c/p\u003e\n\u003cp\u003eIn patients without ND, there was little difference between the left and right hemispheres (Figure 1a). In contrast, in patients with ND, there was a notable discrepancy in EIT between the two hemispheres of the brain was significant (Figure 1b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential prognostic factors associated with ND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable predictors of ND were listed in Table S2. Compared with ND- group, variables with independent adjusted associations with ND included greater TRAI\u003csub\u003eHCA\u0026nbsp;\u003c/sub\u003e[OR = 1.29 (1.01,1.66); \u003cem\u003eP\u003c/em\u003e = 0.019), higher MRAR\u003csub\u003eHCA\u0026nbsp;\u003c/sub\u003e[OR = 1.23 (1.01,1.49); \u003cem\u003eP\u003c/em\u003e = 0.041], greater age [OR = 1.06 (1.02,1.12]; \u003cem\u003eP\u003c/em\u003e = 0.003), higher BMI [OR = 1.19 (1.05,1.34); \u003cem\u003eP\u003c/em\u003e = 0.003), more SABV involvement [OR = 2.44 (1.04-5.71); \u003cem\u003eP\u003c/em\u003e = 0.026], and longer operation time [OR = 1.11 (1.02,1.20); \u003cem\u003eP\u003c/em\u003e = 0.025] (Figure 2a).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscriminative accuracy and reliability of EIT parameters for estimating risk for postoperative neurological deficits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ROC curves revealed that adding EIT parameters (TARI\u003csub\u003eHCA\u003c/sub\u003e, MRAR\u003csub\u003eHCA\u003c/sub\u003e and ACR\u003csub\u003eHCA\u003c/sub\u003e) to the clinical model significantly improved the AUC from 0.752 to 0.817 (\u003cem\u003eP\u003c/em\u003e = 0.027) (Figure 2b). The incremental value of EIT parameters was also reflected in the improvement of risk reclassification and discrimination for postoperative ND [NRI = 0.510 (0.091,1.003), \u003cem\u003eP\u003c/em\u003e = 0.002; IDI = 0.072(0.030, 0.113), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001] (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk stratification by postoperative ND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall 30-day mortality was 11.3% (17/151), which was significantly modulated by ND (6.5% vs 18.6%; \u003cem\u003eP\u003c/em\u003e = 0.021). During a median follow-up of 694 days, all-cause mortality was observed in 24 (15.9%) patients. Kaplan-Meier curves (Figure 3) indicated that all-cause mortality was significantly higher in patients with ND (9.8% vs. 25.4%; \u003cem\u003eP\u003c/em\u003e = 0.003).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study of its size to explore the neurological prognostic value of EIT in a large TAAD cohort underwent TAAR, with incidence of 39.1% (59/151) for ND. Multivariate analysis showed that TRAI\u003csub\u003eHCA\u003c/sub\u003e, MRAR\u003csub\u003eHCA\u003c/sub\u003e, age, BMI, SABV involvement and operation time were independent predictors of ND. The incremental value of EIT parameters was also proved in the improvement of risk reclassification and discrimination for postoperative ND. Importantly, noninvasive EIT neuromonitoring was safe and associated with no adverse effects.\u003c/p\u003e \u003cp\u003eAt present, the core strategies of intraoperative cerebral protection are hypothermia and selective cerebral perfusion\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, the rate of ND after TAAR are still much higher than that of other types of cardiac surgery, reaching 20\u0026ndash;50%\u003csup\u003e14\u003c/sup\u003e. The etiology of ND is multifactorial with proposed mechanisms including microembolism, cerebral hypoperfusion, and systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Compared to other types of cardiac surgery, TAAR is more complex, which is reflected in the long operation time and the perfusion imbalance during the HCA phase. Therefore, the degree of injury in the three parts of the etiology of ND is relatively higher\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Particularly, in the presence of established embolism and neural tissue damage, any concomitant inadvertent hypoperfusion is even more deleterious\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. It can therefore be argued that this warrants even closer monitoring. In addition, the neurological status of patients after TAAR is clinically difficult to evaluate due to multiple factors including preoperative emergency status, sedation, and pharmacological paralysis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Therefore, intraoperative neuromonitoring and early warning are the top priorities of perioperative monitoring༎\u003c/p\u003e \u003cp\u003eThe risk factors of ND after TAAR were divided into patient characteristics and surgical related factors. Bossone \u003cem\u003eet al\u003c/em\u003e. presented the large cohort of patients with stroke of TAAD and their patients presenting with stroke were older, had more comorbidities, higher rates of malperfusion and syncope compared to patients without stroke after TAAR\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The involvement and stenosis of SABV are also recognized risk factors for ND\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e. In addition, numerous studies have addressed intraoperative cannulation techniques and proposed that the axillary artery may serve as a protective factor against for postoperative ND\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. However, in this study, there was no significant difference in cannulation between two groups (Table S3). Andreas \u003cem\u003eet al.\u003c/em\u003e presented the long-term follow-up results of the German registry for acute aortic dissection\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. ND and greater age were the risk factors for 10-year mortality. This study also demonstrated a higher 2-year mortality of ND\u0026thinsp;+\u0026thinsp;group.\u003c/p\u003e \u003cp\u003eDue to the multifactorial nature of the mechanism of ND, no neuromonitoring modality has been conclusively proved to be superior when used in aortic surgery. TCD, NIRS, EEG, somatosensory evoked potential and biomarkers are commonly used in the study of aortic surgery, but each has its own advantages and disadvantages\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. TCD is a commonly used device in cerebral perfusion related researches\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, TCD is subjected to the following constraints: it necessitates the involvement of trained professionals; it is constrained by the acoustic and doppler alignment of the patient; and it is only capable of detecting blood flow in the major cerebral arteries\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. During TAAR, TCD is challenging to fulfil the criteria for continuous monitoring and to reflect the overall brain perfusion, which limits its clinical application to a relatively narrow scope. Additionally, NIRS is the most prevalent monitoring device in clinical practice due to its simplicity of use and cost-effectiveness\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Nevertheless, NIRS also has some disadvantages. These include the inability to accurately reflect oxygen saturation in the frontal region, the presence of a time delay\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and the potential for inaccuracy due to contamination from sources outside the cerebral cortex\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Meanwhile, with the increasing clinical application, some studies pointed out that currently recognized early warning standard of cerebral oxygen are not related to ND\u003csup\u003e8,14\u003c/sup\u003e. In addition, the EEG or quantitative EEG mainly records spontaneous electrical activity in the superficial layers of the cortex\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and, biomarkers can be easily influenced by hemolysis and intraoperative factors, and have limitations for real-time monitoring. In summary, there is an urgent need in clinical practice for a bedside neuromonitoring technique that can reflect the overall cerebral perfusion during surgery\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEIT is a non-invasive, non-radiation and functional imaging technique that measures the transfer impedance signals between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The biophysical foundation of EIT is that different tissues have different resistivities, and that resistivities vary between healthy and diseased tissues\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In the context of respiratory function monitoring within intensive care units, the application of EIT has reached a relatively advanced stage of development\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, EIT is increasingly being utilized for neuromonitoring purposes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Following animal validation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, EIT has been employed in clinical trials for epilepsy\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, brain edema and mannitol dehydration treatment\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, as well as subdural hematoma\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Our group studied the feasibility of EIT for neuromonitoring during TAAR in a relatively small sample size and demonstrated strong correlations between EIT parameters and neurological biomarkers\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. EIT offers a number of advantages over other neuroimaging technologies, which has led to suggestions that it could be used in clinical practice\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe correlation coefficients for EIT parameters during the HCA phase were presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The correlation coefficients between the three EIT parameters focusing on the change in whole-brain impedance (ΔARV\u003csub\u003eHCA\u003c/sub\u003e, k\u003csub\u003eHCA\u003c/sub\u003e, ACR\u003csub\u003eHCA\u003c/sub\u003e) and between the three EIT parameters describing the impedance imbalance between the left and right hemispheres (MRAI\u003csub\u003eHCA\u003c/sub\u003e, TARI\u003csub\u003eHCA\u003c/sub\u003e, MRAR\u003csub\u003eHCA\u003c/sub\u003e) were statistically significant, respectively. The observed changes in EIT parameters may be attributed to alterations in cerebral perfusion volume and cerebral edema. It was noted that the whole brain ARV increased as perfusion flow decreased and vice versa, since the impedance of the blood was smaller than that of the brain parenchyma\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Meanhwhile, hypoperfusion causes insufficient oxygen supply and adenosine triphosphate depletion, followed by intracellular sodium and calcium overload, cellular swelling, and cerebral edema\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which manifests as a gradual increase in resistivity. A higher level of TARI\u003csub\u003eHCA\u003c/sub\u003e indicated that prolonged brain perfusion imbalance during the HCA phase can predict postoperative ND. Moreover, the MRAR\u003csub\u003eHCA\u003c/sub\u003e suggested that the maximum degree of brain perfusion imbalance was related to ND. It was possible that EIT parameters to reflect some physiological indicators such as hypoperfusion, hyperperfusion or even cerebral edema which had changed during surgery and were therefore related to postoperative ND.\u003c/p\u003e \u003cp\u003eThe NMN protocol involves conducting serial neurological examinations at specified intervals during the perioperative period. Real-time NMN at the bedside allows for a direct evaluation of the physiological interplay between systemic disorders and intracranial processes, potentially enabling early detection of neurological deterioration prior to the onset of clinically apparent symptoms\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Our research has demonstrated the incremental value of EIT parameters over other predictors of ND after TAAR, suggesting that EIT may be one of the alternatives for real-time intraoperative bedside NMN.\u003c/p\u003e \u003cp\u003eHowever, like other devices that rely on electricity, EIT has similar drawbacks, such as susceptibility to temperature changing, subject movements and interference from the electrocautery\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The focus of this study was mainly on the HCA phase, during which there was little change in temperature, little use of electrocautery and no subject movement. Nevertheless, it was reported that cerebral ischemia was also prone to occur during the cooling and the rewarming phase\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, to which should also be paid attention. Actually, the study of EIT monitoring for the cerebral injury during both the cooling and rewarming phases using the influence of temperature on EIT has been undergoing, and the related results will be reported in the late future.\u003c/p\u003e \u003cp\u003eThe findings of this study should be approached with caution due to several inherent limitations. Nevertheless, this study represents the largest study of EIT neuromonitoring and its association with ND after TAAR. Firstly, there are individual variations in brain compliance and tolerance to hypoxia among patients. TND is also influenced by factors such as the patient's educational attainment, psychological condition, and preoperative education. Second, Second, the challenge in identifying the potential etiology of coma. Eleven comatose patients could not be transported to imaging facilities due to persistent hemodynamic instability. Furthermore, CT exhibit relatively low sensitivity in detecting acute ischemic brain injury. Finally, this study only analyzed the EIT parameters of the HCA phase, which is the most extensively studied and considered the most critical phase. Research on the correlation between EIT parameters throughout the procedure and ND is currently underway.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe EIT parameters have been demonstrated to be an effective means of predicting ND following TAAR. Cerebral EIT may represent a potential alternative for intraoperative noninvasive multimodal neuromonitoring options. The findings of this study require further validated in subsequent research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and Recruitment\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki, was approved by the ethics committee of Xijing Hospital [FMMU-E-III-001(1\u0026ndash;7)] and written informed consent was obtained from all subjects or their legal guardian. The trial was registered prior to patient enrollment at Chinese Clinical Trial Registry (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chictr.org.cn\u003c/span\u003e\u003cspan address=\"https://www.chictr.org.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; ChiCTR-OOC-16007844). This manuscript adheres to the applicable STROBE guidelines. A total of 366 TAAD patients underwent TAAR from July 2020 to December 2021 were screened (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and 163 patients included in the study satisfied the inclusion criteria included: a) stanford type A aortic dissection; b) aged from 24\u0026ndash;75 years old both male and female; c) computed tomography angiography (CTA) images with value of diagnosis; and d) accepted for cerebral monitoring by EIT system (EH-300, China) during surgery; In addition, the exclusion criteria: a) early death (within 24 hours) after surgery make it difficult in assessing ND, n\u0026thinsp;=\u0026thinsp;1; b) interference with EIT data, n\u0026thinsp;=\u0026thinsp;6; c) incomplete EIT data, n\u0026thinsp;=\u0026thinsp;2; d) change in surgical procedure, n\u0026thinsp;=\u0026thinsp;3. According to the inclusion and exclusion criteria, 151 patients were ultimately included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNeurological deficit\u003c/h2\u003e \u003cp\u003eNeurological deficit indicates if the patient developed a new abnormality in neurological function after TAAR. This includes the entire perioperative period up to post-operative day five. A \u0026lsquo;neurological deficit\u0026rsquo; includes the following manifestations: new stroke, transient neurological deficit (TND), and coma\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The definitions are as follows: Stroke is a cerebrovascular accident that represents neurological loss caused by ischemic events. The diagnosis of stroke can be confirmed via computed tomography (CT) imaging (Figure S2). TND is defined as sensory or motor impairment resulting from vascular damage. The presence of TND is considered when patients combining postoperative neurological symptom recorded by at least two experienced nurses. Finally, coma was defined as complete or partial mental unresponsiveness (beyond that expected from anesthesia).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIntraoperative Noninvasive Multimodal Neuromonitoring\u003c/h2\u003e \u003cp\u003eAll patients enrolled underwent TAAR with HCA and anterograde cerebral perfusion (ACP) (detailed in the supplementary material). A multimodal approach using EIT (EH-300, UTRON Technology Co., Ltd., Hangzhou, China) combined with NIRS (INVOS 5100c, Covidien, Mansfield, MA, USA) was continuously used for intraoperative neuromonitoring (Figure S3), and were acted on by adjusting ACP settings (e.g. perfusion pressure) by a clinical perfusionist if deemed necessary and possible, for example in case of marked regional cerebral oxygen saturation drops or SctO\u003csub\u003e2\u003c/sub\u003e left-to-right asymmetry\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The specifications of the EIT system are as follows: range of the working frequency, 1\u0026thinsp;~\u0026thinsp;190 kHz; measurement accuracy, \u0026plusmn;\u0026thinsp;0.01%; and common mode rejection ratio, over 80 dB. Sixteen body surface electrodes (EH-PET-16CL) were placed equidistantly on the cross-sectional surfaces above the frontal lobe, temporal lobe, and occipital tuberosity of the patient. The electrical impedance of the patient\u0026rsquo;s brain was monitored in real-time by the EIT system.\u003c/p\u003e \u003cp\u003eThe reference data were calculated by averaging the one-minute EIT data subsequent to the commencement of the HCA. In order to achieve the desired results, the damped least-squares reconstruction algorithm was employed for the purpose of EIT imaging. The biophysical foundation of EIT is based on the principle that different tissues exhibit varying degrees of resistivity, with notable differences observed between the healthy and diseased tissues. The impedance distribution of the scalp (ρscalp\u0026thinsp;=\u0026thinsp;2.27 Ωm), skull (ρskull\u0026thinsp;=\u0026thinsp;55.56 Ωm) and brain parenchyma (ρbrain\u0026thinsp;=\u0026thinsp;4 Ωm) was incorporated into the EIT reconstruction process in order to enhance the quality of the resulting imaging\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In addition, a CT image of each patient at the EIT electrode layer was used as an anatomical reference. We have optimized imaging algorithm and implemented various data processing methods in the imaging process given the possible clinical interference in order to improve the signal-to-noise ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEIT system and EIT parameters\u003c/h2\u003e \u003cp\u003ePrevious research showed that brain injury could appear in the HCA phase due to the sudden change of blood flow to the brain\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. We calculated six parameters that characterize the changes in EIT from different perspectives. The first three parameters were designed to describe changes in whole-brain impedance, while the latter three aimed to capture impedance imbalances between the left and right hemispheres.\u003c/p\u003e \u003cp\u003eThe specifications of the EIT system are as follows: range of the working frequency, 1\u0026thinsp;~\u0026thinsp;190 kHz; measurement accuracy, \u0026plusmn;\u0026thinsp;0.01%; and common mode rejection ratio, over 80 dB.The reference data were calculated by averaging the one-minute EIT data subsequent to the commencement of the HCA. In order to achieve the desired results, the damped least-squares reconstruction algorithm was employed for the purpose of EIT imaging. The biophysical foundation of EIT is based on the principle that different tissues exhibit varying degrees of resistivity, with notable differences observed between the healthy and diseased tissues. The impedance distribution of the scalp (ρscalp\u0026thinsp;=\u0026thinsp;2.27 Ωm), skull (ρskull\u0026thinsp;=\u0026thinsp;55.56 Ωm) and brain parenchyma (ρbrain\u0026thinsp;=\u0026thinsp;4 Ωm) was incorporated into the EIT reconstruction process in order to enhance the quality of the resulting imaging. In addition, a CT image of each patient at the EIT electrode layer was used as an anatomical reference. We have optimized imaging algorithm and implemented various data processing methods in the imaging process given the possible clinical interference in order to improve the signal-to-noise ratio\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe calculated six parameters based on average resistivity value (ARV) which was defined as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{R}\\text{V}=\\:\\frac{1}{M}\\sum\\:{A}_{k}{x}_{k},\\:k=1,\\dots\\:,\\:M$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere M is the total units of brain dissection model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}}_{\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e is the area \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{k}}^{\\text{t}\\text{h}}\\)\u003c/span\u003e\u003c/span\u003e of unit and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{x}}_{\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e is the relative variation of electrical resistivity of the unit number. ARV reflects the change of electrical impedance of the whole brain during surgery.\u003c/p\u003e \u003cp\u003eTherefore, the first parameter was defined as the difference of average resistivity value before and after the HCA phase (ΔARV\u003csub\u003eHCA\u003c/sub\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:{\\text{A}\\text{R}\\text{V}}_{\\text{H}\\text{C}\\text{A}}={ARV}_{after\\:HCA}-{ARV}_{before\\:HCA}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhich reflects the range of change of electrical impedance during HCA.\u003c/p\u003e \u003cp\u003eThe second parameter k\u003csub\u003eHCA\u003c/sub\u003e was defined as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{k}_{\\text{H}\\text{C}\\text{A}}=\\frac{{\\varDelta\\:ARV}_{HCA}}{{t}_{HCA}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere k stands for the slope of electrical impedance during the HCA phase, and t is the duration of the HCA phase. This parameter represents for the speed of variation of electrical impedance during the HCA phase.\u003c/p\u003e \u003cp\u003eThe third parameter was called the average change rate of average resistivity value (ACR\u003csub\u003eHCA\u003c/sub\u003e) which was described as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{C}\\text{R}=abs\\left[\\frac{{\\sum\\:}_{{frame}_{end\\:of\\:HCA}}^{{frame}_{start\\:of\\:HCA}}（{ARV}_{i+1}-{ARV}_{i}）/（{t}_{i+1}-{t}_{i}）}{{frame}_{end\\:of\\:HCA}-{frame}_{start\\:of\\:HCA}}\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNext, we determined resistivity asymmetric index (RAI) as the electrical impedance between left and right hemispheres, which was described as:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{A}\\text{I}={ARV}_{L}-{ARV}_{R}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTherefore, the fourth parameter was defined as the maximum value of the absolute resistivity asymmetric index (MRAI\u003csub\u003eHCA\u003c/sub\u003e)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe fifth parameter-the time integral of absolute value resistivity asymmetric index (TRAI\u003csub\u003eHCA\u003c/sub\u003e) was described as:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{R}\\text{A}\\text{I}={\\int\\:}_{start\\:of\\:HCA}^{end\\:of\\:HCA}\\:abs\\left(RAI\\right)\\bullet\\:dt$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, we also calculated the resistivity asymmetric ratio (RAR) as the ratio of left and right hemispheres impedance to whole brain impedance:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{A}\\text{R}=\\frac{{ARV}_{L}-{ARV}_{R}}{ARV}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTherefore, the sixth parameter was defined as the maximum value of the absolute resistivity asymmetric ratio (MRAR\u003csub\u003eHCA\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll normally distributed continuous variables were described with means [\u0026plusmn;\u0026thinsp;standard deviation (SD)] and compared across groups with Student\u0026rsquo;s t-test. Median [interquartile range (IQR)] and the Wilcoxon rank sum test were used for non-normally distributed continuous variables. Categorical variables were described with frequencies (%) and compared with chi-square test or Fisher\u0026rsquo;s exact test. After each variable was tested in univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10), a multivariate regression analysis was performed by using stepwise forward selection to determine independent predictors of ND.\u003c/p\u003e \u003cp\u003eFurthermore, the clinical models with and without EIT parameters were compared, and the difference between areas under the curve (AUC)-the receiver operating characteristic curve (ROC) was validated through the Delong test. Meanwhile, the ability of the EIT parameters to discriminate risk for ND and mortality based on the clinical model was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). To further understand the prognosis of ND, Kaplan-Meier curves were constructed for 2-year mortality, and the resulting data were subjected to a log-rank test for comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Small amounts of missing values were imputated using multiple data imputation with predictive mean matching, after patterns of missing data were checked to follow the assumption that data were missing at random. All analyses were performed with SPSS 19.0 and R (version 4.0.0.0).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eThis work was supported by Key Research and Development Projects of the Science and Technology Committee (2022YFC2404803), Key Basic Research Projects of the Basic Strengthening Plan of the Science and Technology Committee (2019-JCJQ-ZD-115-00-02), National Key Technology Research and Development Program (2023ZD0504402), National Natural Science Foundation of China-General Program (82370273), Shaanxi Key Science and Technology Innovation Team Project (2022ZDLSF02-01) and Cross-integration project of Xijing Hospital (XJZT24JC37).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e Z Jin, X Shi, S Yu: Conception and design of study, Approval of final version of manuscript; C Yang, Y Guo, W Zhu: Acquisition of data, Data analysis and/or interpretation, Drafting of manuscript and or critical revision; C Xue: Acquisition of data; W Duan, H Zhao, J Liu, R Zhao: Acquisition of data, Approval of final version of manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eIf the request is reasonable, the corresponding author can share the data from this study to the requestors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest related to this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This study was conducted in accordance with the Declaration of Helsinki, was approved by the ethics committee of Xijing Hospital [FMMU-E-III-001(1-7)] and written informed consent was obtained from all participants/guardians in the trial. The trial was registered prior to patient enrollment at Chinese Clinical Trial Registry (https://www.chictr.org.cn; ChiCTR-OOC-16007844).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIsselbacher, E. M. et al., 2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. CIRCULATION 146 e334 (2022).\u003c/li\u003e\n\u003cli\u003eGaudino, M. et al., Considerations for Reduction of Risk of Perioperative Stroke in Adult Patients Undergoing Cardiac and Thoracic Aortic Operations: A Scientific Statement From the American Heart Association. CIRCULATION 142 e193 (2020).\u003c/li\u003e\n\u003cli\u003eOng, C. S. et al., Neuromonitoring detects brain injury in patients receiving extracorporeal membrane oxygenation support. The Journal of Thoracic and Cardiovascular Surgery 165 2104 (2023).\u003c/li\u003e\n\u003cli\u003eRajagopalan, S. \u0026amp; Sarwal, A., Neuromonitoring in Critically Ill Patients. CRIT CARE MED 51 525 (2023).\u003c/li\u003e\n\u003cli\u003eLewis, C., Parulkar, S. D., Bebawy, J., Sherwani, S. \u0026amp; Hogue, C. W., Cerebral Neuromonitoring During Cardiac Surgery: A Critical Appraisal With an Emphasis on Near-Infrared Spectroscopy. J CARDIOTHOR VASC AN 32 2313 (2018).\u003c/li\u003e\n\u003cli\u003eSongsangvorn, N. et al., Electrical impedance tomography-guided positive end-expiratory pressure titration in ARDS: a systematic review and meta-analysis. INTENS CARE MED 50 617 (2024).\u003c/li\u003e\n\u003cli\u003eStygall, J. et al., Cerebral microembolism detected by transcranial Doppler during cardiac procedures. STROKE 31 2508 (2000).\u003c/li\u003e\n\u003cli\u003eWeijs, R. W. J. et al., Perioperative cerebral perfusion in aortic arch surgery: a potential link with neurological outcome. EUR J CARDIO-THORAC 63 ezad144 (2023).\u003c/li\u003e\n\u003cli\u003eMontisci, A. et al., Cerebral Perfusion and Neuromonitoring during Complex Aortic Arch Surgery: A Narrative Review. J CLIN MED 12 3470 (2023).\u003c/li\u003e\n\u003cli\u003eRogers, C. A. et al., Randomized trial of near-infrared spectroscopy for personalized optimization of cerebral tissue oxygenation during cardiac surgery. BRIT J ANAESTH 119 384 (2017).\u003c/li\u003e\n\u003cli\u003eMilne, B., Gilbey, T., Gautel, L. \u0026amp; Kunst, G., Neuromonitoring and Neurocognitive Outcomes in Cardiac Surgery: A Narrative Review. J CARDIOTHOR VASC AN 36 2098 (2022).\u003c/li\u003e\n\u003cli\u003eKe, X. et al., Advances in electrical impedance tomography-based brain imaging. MILITARY MED RES 9 10 (2022).\u003c/li\u003e\n\u003cli\u003eHori, D. et al., Arterial pressure above the upper cerebral autoregulation limit during cardiopulmonary bypass is associated with postoperative delirium. BRIT J ANAESTH 113 1009 (2014).\u003c/li\u003e\n\u003cli\u003eSultan, I. et al., Surgery for type A aortic dissection in patients with cerebral malperfusion: Results from the International Registry of Acute Aortic Dissection. The Journal of Thoracic and Cardiovascular Surgery 161 1713 (2021).\u003c/li\u003e\n\u003cli\u003eStanley, M. E. \u0026amp; Sellke, F. W., Neurocognitive decline in cardiac surgery patients: What do we know? The Journal of Thoracic and Cardiovascular Surgery 166 543 (2023).\u003c/li\u003e\n\u003cli\u003eFukuhara, S. et al., Type A Aortic Dissection With Cerebral Malperfusion: New Insights. The Annals of Thoracic Surgery 112 501 (2021).\u003c/li\u003e\n\u003cli\u003eMcDonagh, D. L. et al., Neurological complications of cardiac surgery. The Lancet Neurology 13 490 (2014).\u003c/li\u003e\n\u003cli\u003eBossone, E. et al., Stroke and Outcomes in Patients With Acute Type A Aortic Dissection. CIRCULATION 128 S175 (2013).\u003c/li\u003e\n\u003cli\u003eJia, S. et al., Effect of Asymptomatic Common Carotid Artery Dissection on the Prognosis of Patients With Acute Type A Aortic Dissection. J AM HEART ASSOC 13 e31542 (2024).\u003c/li\u003e\n\u003cli\u003eZhao, H. et al., Neurological prognosis in surgically treated acute aortic dissection with brain computed tomography perfusion. EUR J CARDIO-THORAC 65 ezad437 (2024).\u003c/li\u003e\n\u003cli\u003eCho, T. et al., Brachiocephalic artery dissection is a marker of stroke after acute type A aortic dissection repair. J CARDIAC SURG 36 902 (2021).\u003c/li\u003e\n\u003cli\u003ePeterson, M. D. et al., A randomized trial comparing axillary versus innominate artery cannulation for aortic arch surgery. The Journal of Thoracic and Cardiovascular Surgery 164 1426 (2022).\u003c/li\u003e\n\u003cli\u003eKim, J. et al., Axillary artery cannulation reduces early embolic stroke and mortality after open arch repair with circulatory arrest. The Journal of Thoracic and Cardiovascular Surgery 159 772 (2020).\u003c/li\u003e\n\u003cli\u003eGhoreishi, M. et al., Factors associated with acute stroke after type A aortic dissection repair: An analysis of the Society of Thoracic Surgeons National Adult Cardiac Surgery Database. The Journal of Thoracic and Cardiovascular Surgery 159 2143 (2020).\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;ning, A. et al., Risk factors for long-term mortality after acute aortic dissection\u0026mdash;results of the German registry for acute aortic dissection type a long-term follow-up. EUR J CARDIO-THORAC 65 ezae116 (2024).\u003c/li\u003e\n\u003cli\u003eFriess, J. et al., Determination of selective antegrade perfusion flow rate in aortic arch surgery to restore baseline cerebral near-infrared spectroscopy values: a single-centre observational study. EUR J CARDIO-THORAC 63 ezad047 (2023).\u003c/li\u003e\n\u003cli\u003eEleveld, N. et al., The Influence of Extracerebral Tissue on Continuous Wave Near-Infrared Spectroscopy in Adults: A Systematic Review of In Vivo Studies. J CLIN MED 12 2776 (2023).\u003c/li\u003e\n\u003cli\u003eSo, V. C. \u0026amp; Poon, C. C. M., Intraoperative neuromonitoring in major vascular surgery. BRIT J ANAESTH 117 i13 (2016).\u003c/li\u003e\n\u003cli\u003eYan, X. et al., A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes. FRONT NEUROSCI-SWITZ 18 (2024).\u003c/li\u003e\n\u003cli\u003eBayford, R. H., BIOIMPEDANCE TOMOGRAPHY (ELECTRICAL IMPEDANCE TOMOGRAPHY). ANNU REV BIOMED ENG 8 63 (2006).\u003c/li\u003e\n\u003cli\u003eKaiser, H. A., Hight, D. \u0026amp; Avidan, M. S., A narrative review of electroencephalogram-based monitoring during cardiovascular surgery. Current Opinion in Anaesthesiology 33 92 (2020).\u003c/li\u003e\n\u003cli\u003eAristovich, K. Y. et al., Imaging fast electrical activity in the brain with electrical impedance tomography. NEUROIMAGE 124 204 (2016).\u003c/li\u003e\n\u003cli\u003eHannan, S. et al., In vivo imaging of deep neural activity from the cortical surface during hippocampal epileptiform events in the rat brain using electrical impedance tomography. NEUROIMAGE 209 116525 (2020).\u003c/li\u003e\n\u003cli\u003eWitkowska-Wrobel, A., Aristovich, K., Faulkner, M., Avery, J. \u0026amp; Holder, D., Feasibility of imaging epileptic seizure onset with EIT and depth electrodes. NEUROIMAGE 173 311 (2018).\u003c/li\u003e\n\u003cli\u003eLan, J., Wu, L., Tan, X., Xiang, L. \u0026amp; Guo, C., Application of the cerebral edema monitor on cardiopulmonary bypass in infants. BRAIN INJURY 33 1379 (2019).\u003c/li\u003e\n\u003cli\u003eYang, B. et al., Comparison of electrical impedance tomography and intracranial pressure during dehydration treatment of cerebral edema. NeuroImage: Clinical 23 101909 (2019).\u003c/li\u003e\n\u003cli\u003eDai, M. et al., In Vivo Imaging of Twist Drill Drainage for Subdural Hematoma: A Clinical Feasibility Study on Electrical Impedance Tomography for Measuring Intracranial Bleeding in Humans. PLOS ONE 8 e55020 (2013).\u003c/li\u003e\n\u003cli\u003eGuo, Y. et al., Electrical impedance tomography provides information of brain injury during total aortic arch replacement through its correlation with relative difference of neurological biomarkers. SCI REP-UK 14 14236 (2024).\u003c/li\u003e\n\u003cli\u003eLi, Y. et al., Noninvasive Cerebral Imaging and Monitoring Using Electrical Impedance Tomography During Total Aortic Arch Replacement. J CARDIOTHOR VASC AN 32 2469 (2018).\u003c/li\u003e\n\u003cli\u003eGrigore, A. M. et al., The Rewarming Rate and Increased Peak Temperature Alter Neurocognitive Outcome After Cardiac Surgery. Anesthesia \u0026amp; Analgesia 94 4 (2002).\u003c/li\u003e\n\u003cli\u003eBagshaw, A. P. et al., Electrical impedance tomography of human brain function using reconstruction algorithms based on the finite element method. NEUROIMAGE 20 752 (2003).\u003c/li\u003e\n\u003cli\u003eZhang, G. et al., Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography. BIOMED ENG ONLINE 16 7 (2017).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline and imaging characteristics of patients with and without neurological deficit after total aortic arch replacement.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eAll, n=151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eND-, n=92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eND+, n=59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eSex, male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e126(83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e74(80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e52(88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e49.7\u0026plusmn;10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e48.4\u0026plusmn;9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e51.8\u0026plusmn;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e25.5\u0026plusmn;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e24.9\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e26.5\u0026plusmn;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAcute aortic dissection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e143(94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e85(92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e58(98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eInterval from symptom to surgery, hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e20.0(14.0,24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e20.0(14.0,24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e20.0(12.0,24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eStroke history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e16(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e11(12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e5(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eNew stroke on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e7(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e3(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e4(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eSyncope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e20(13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e7(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e13(22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eLower limb ischemic syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e32(21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e16(17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e16(27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e19(12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e11(12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e8(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e99(65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e57(62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e42(71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e13(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e5(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e8(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMarfan syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e7(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e5(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eCardiac surgery history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e8(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e3(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e5(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eNYHA Ⅲ\u0026amp;Ⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e51(33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e32(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e19(32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eImaging data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eEF value, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e51.0\u0026plusmn;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e51.3\u0026plusmn;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e51.0\u0026plusmn;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAortic valve insufficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e51(33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e32(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e19(32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eSABV involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e88(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e44(47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e44(74.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eND: Neurological deficit; BMI: Body mass index; CAD: Coronary artery disease; NYHA: New York heart association; EF value: Ejection fraction value; SABV: Supra-aortic branch vessel.\u003c/p\u003e\n\u003cp\u003eTable 2. Surgical details and outcomes of patients with and without neurological deficit after total aortic arch replacement.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eAll, n=151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eND-, n=92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eND+, n=59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eOperation time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e409.4\u0026plusmn;70.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e391.4\u0026plusmn;58.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e437.5\u0026plusmn;77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eCPB time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e220.9\u0026plusmn;35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e213.2\u0026plusmn;31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e232.8\u0026plusmn;37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eCross-clamp time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e104.9\u0026plusmn;24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e103.8\u0026plusmn;25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e106.7\u0026plusmn;23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eHCA time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e35.4\u0026plusmn;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e35.3\u0026plusmn;10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e35.7\u0026plusmn;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eLowest esophageal temperature, ℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e25.3(25.0,25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e25.4(25.0,25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e25.3(25.0,26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eCerebral perfusion (bilateral, antegrade)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e84(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e48(52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e36(61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ePostoperative complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eDelayed awakening (\u0026gt;6h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e21(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e11(12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e10(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eVentilation duration \u0026gt; 24h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e87(57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e40(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e47(79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eICU stay, day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e5.0(4.0,7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4.0(3.0,5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e7.0(5.0,13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eCRRT therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e23(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e17(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eECMO therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eRe-exploration for bleeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e5(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eRescue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e15(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9(15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e30-day mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e17(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e11(18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e2-year mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e24(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e9(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e15(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eND: Neurological deficit; CPB: Cardiopulmonary bypass; ICU: Intensive care unit; CRRT: Continuous renal replacement therapy; HCA: Hypothermic circulatory arrest; ECMO: Extra-corporeal membrane oxygenation.\u003c/p\u003e\n\u003cp\u003eTable 3. Electrical impedance tomography parameters of patients with and without postoperative neurological deficit.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"669\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eAll, n=151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eND-, n=92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eND+, n=59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026Delta;ARV\u003csub\u003eHCA\u003c/sub\u003e, 10\u003csup\u003e-3\u003c/sup\u003e AU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.17(-6.45,5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e-0.16(-8.05,5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.76(-6.16,6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eK\u003csub\u003eHCA\u003c/sub\u003e, 10\u003csup\u003e-3\u003c/sup\u003e AU/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.01(-0.20,0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e-0.26(-0.26,0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.02(-0.17,0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eACR\u003csub\u003eHCA\u003c/sub\u003e, 10\u003csup\u003e-6\u003c/sup\u003e AU/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e4.07(1.74,6.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e3.90(1.76,6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e4.07(1.61,7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eMRAI\u003csub\u003eHCA\u003c/sub\u003e, 10\u003csup\u003e-3\u003c/sup\u003e AU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.50(1.64,3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e2.44(1.62,3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e2.56(1.73,3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eTRAI\u003csub\u003eHCA\u003c/sub\u003e, AU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.06(1.53,3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e2.06(1.32,3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e2.10(1.62,4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eMRAR\u003csub\u003eHCA\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.026(0.014,0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.026(0.014,0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.027(0.014,0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eND: Neurological deficit; \u0026Delta;ARV\u003csub\u003eHCA\u003c/sub\u003e: the difference of ARV before and after HCA phase; k\u003csub\u003eHCA\u003c/sub\u003e: the slope of ARV during HCA phase; ACR\u003csub\u003eHCA\u003c/sub\u003e: the average change rate of ARV during HCA phase; MRAI\u003csub\u003eHCA\u003c/sub\u003e: the maximum value of the absolute ARV index during HCA phase; TRAI\u003csub\u003eHCA\u003c/sub\u003e: the time integral of ARV asymmetric index during HCA phase; MRAR\u003csub\u003eHCA\u003c/sub\u003e: the maximum value of the absolute resistivity asymmetric ratio during HCA phase.\u003c/p\u003e\n\u003cp\u003eTable 4. Prediction increment of the electrical impedance tomography parameters to postoperative neurological deficit.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"836\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eND (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e30-day mortality (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e2-year mortality (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eClinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.752(0.675,0.819)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.755(0.678,0.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.742(0.664,0.810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eClinical+EIT parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.817(0.745,0.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.761(0.685,0.827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.746(0.669,0.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026Delta;AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eContinuous NRI (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAll NRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.510(0.091,1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.135(-0.048,1.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.283(-0.098,0.965)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNRI+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.119(-0.126,0.453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e-0.059(-0.294,0.714)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e-0.077(-0.297,0.539)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNRI-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.391(0.084,0.607)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.194(-0.185,0.662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.360(-0.191,0.661)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eIDI (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.072(0.030,0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.004(-0.003,0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e0.002(-0.006,0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eClinical: Clinical model included age, body mass index, syncope, SABV involvement, operation time, CPB time. EIT parameters: ACR\u003csub\u003eHCA\u003c/sub\u003e,\u003csub\u003e\u0026nbsp;\u003c/sub\u003eMRAR\u003csub\u003eHCA\u003c/sub\u003e,\u003csub\u003e\u0026nbsp;\u003c/sub\u003eTRAI\u003csub\u003eHCA\u003c/sub\u003e.\u003csub\u003e\u0026nbsp;\u003c/sub\u003eND: Neurological deficit; CI: Confidence interval; AUC: Area under the curve; NRI: Net reclassification index; IDI: Integrated discrimination improvement.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\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":"Electrical impedance tomography, Noninvasive multimodal neuromonitoring, Total aortic arch replacement, Cardiopulmonary bypass, Postoperative neurological deficit","lastPublishedDoi":"10.21203/rs.3.rs-7856033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7856033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eNeurological deficit (ND) following total aortic arch replacement (TAAR) carries high morbidity and mortality, and effective treatments are lacking. Intraoperative neuromonitoring is crucial in preventing ND. This cohort study aims to investigate the prognostic value of noninvasive electrical impedance tomography (EIT) for detecting ND after TAAR.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, a 16 electrode EIT system was applied to monitor patients' brain impedance during TAAR. Six EIT parameters regarding to the hypothermic circulatory arrest (HCA) phase were extracted. The correlation between changes in EIT parameters and ND were fully explored.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe incidence of ND was 39.1% (59/151), of which stroke was 15.2% (19/125). The time integral of absolute value resistivity asymmetric index (TRAI\u003csub\u003eHCA\u003c/sub\u003e), the maximum value of the absolute resistivity asymmetric ratio (MRAR\u003csub\u003eHCA\u003c/sub\u003e), age, body mass index, supra-aortic branch vessel involvement and operation time were independent predictors of ND. The incorporation of EIT parameters into the clinical model resulted in a notable enhancement in area under the receiver operating characteristic curve from 0.752 to 0.817 (P\u0026thinsp;=\u0026thinsp;0.027). Furthermore, the incremental value of EIT parameters was also reflected in the improvement of risk reclassification and discrimination for ND(net reclassification index\u0026thinsp;=\u0026thinsp;0.510, P\u0026thinsp;=\u0026thinsp;0.002; integrated discrimination improvement\u0026thinsp;=\u0026thinsp;0.072, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe EIT parameters have been demonstrated to be an effective means of predicting ND following TAAR. Cerebral EIT may represent a potential alternative for intraoperative noninvasive multimodal neuromonitoring options. The findings of this study require further validation in further research.\u003c/p\u003e","manuscriptTitle":"Noninvasive Electrical Impedance Tomography Monitoring during Total Aortic Arch Replacement: incremental value for prediction of postoperative neurological deficit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 05:23:32","doi":"10.21203/rs.3.rs-7856033/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":"34aff4a7-4ac7-4907-a03b-ba036de12180","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-13T07:30:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T21:28:50+00:00","index":185,"fulltext":""},{"type":"reviewerAgreed","content":"285071991394014000022047818198419381981","date":"2026-05-06T08:49:51+00:00","index":184,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63084626,"name":"Health sciences/Diseases"},{"id":63084627,"name":"Health sciences/Medical research"},{"id":63084628,"name":"Health sciences/Neurology"},{"id":63084629,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-13T07:44:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 05:23:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7856033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7856033","identity":"rs-7856033","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}