Association between preoperative blood–brain barrier dysfunction and postoperative delirium in older patients undergoing cardiac surgery: a prospective cohort study

Association between preoperative blood–brain barrier dysfunction and postoperative delirium in older patients undergoing cardiac surgery: a prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between preoperative blood–brain barrier dysfunction and postoperative delirium in older patients undergoing cardiac surgery: a prospective cohort study Lichao Di, Peiying Huang, Yeju He, Jie Li, Yu Liu, Liwei Chi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4986382/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Previous research indicates that the breakdown of the blood-brain barrier (BBB) is an early biomarker of cognitive dysfunction in humans, and it deteriorates with age. Patients with coronary heart disease may have concomitant impairment of the BBB. The off-pump coronary artery bypass grafting (OPCABG) is an effective surgical strategy for myocardial revascularization. However, cardiac surgery leads to a high incidence of postoperative delirium (POD), which can seriously affect clinical recovery. Therefore, it is important to explore whether preoperative BBB dysfunction is associated with POD in older patients undergoing OPCABG. Methods A prospective observational study was performed on OPCABG patients. Fifty older patients with coronary heart disease were recruited. Before surgery, patients underwent Gadolinium-enhanced magnetic resonance imaging. BBB was assessed using GE AW4.7 workstation GEN IQ module. The physiological parameter volume transfer constant (K trans ) is the most common and classical method for assessing BBB in the neuroimaging. All patients underwent standardized anesthetic management. Participants were assessed for POD twice daily for 5 days using the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) in non-intubated patients or the CAM for the Intensive Care Unit in intubated patients. Results 19 patients (38%) were diagnosed with POD. The preoperative median hippocampus K trans of the POD and NPOD patients were 5.36 (IQR, 3.99,8.39) ×10 -3 min -1 , and 3.89 (IQR, 3.40,4.68) ×10 -3 min -1 . The preoperative median thalamus K trans of the POD and NPOD patients were 4.80 (IQR, 3.60,6.62) ×10 -3 min -1 , and 3.55 (IQR, 3.05,4.57) ×10 -3 min -1 . Hippocampal and thalamic K trans were statistically higher in the POD group compared to the NPOD group ( P = 0.012 and P = 0.017). Univariable logistic regression analysis revealed that higher hippocampus K trans (OR, 1.350; 95%CI, 1.048–1.740; P = 0.020) and thalamus K trans (OR, 1.466; 95%CI, 1.017–2.113; P = 0.040) were significantly associated with higher odds of POD. Multivariable logistic regression analysis, adjustment variables were age, interleukin-6. The adjusted models revealed that preoperative hippocampus K trans (OR, 1.250; 95%CI, 0.859–1.817; P = 0.244) and thalamus K trans (OR, 1.164; 95% CI, 0.648–2.090; P = 0.611) were not associated with higher odds of POD. Conclusion POD patients have higher preoperative hippocampal and thalamic BBB permeability, but this was not an independent risk factor for POD. Postoperative delirium Blood-brain barrier Hippocampus Thalamus Off-pump coronary artery bypass grafting Magnetic resonance imaging Figures Figure 1 Figure 2 Introduction Postoperative delirium (POD) is an acute, transient, fluctuating neuropsychiatric syndrome in attention, consciousness, and cognition [ 1 ] . POD is common in older patients following cardiac surgery [ 2 ] and is associated with longer hospital stay [ 3 ] , increased healthcare costs [ 4 ] , significant mortality and long-term dementia risk [ 1 , 5 ] .To date, there are no highly efficacious medicines for the prevention and treatment of POD, mainly due to the limited understanding of its underlying pathophysiology. The blood-brain barrier (BBB) is a physiological structure that separates the central nervous system from the periphery. It plays a critical role in protecting the brain from potentially harmful substances of peripheral origin, preventing the entry of toxins, pathogens and inflammatory agents, thereby maintaining brain homeostasis. It has been reported that the BBB breakdown is an early biomarker of human cognitive dysfunction [ 6 ] , including Alzheimer’s disease and mild cognitive impairment [ 7 , 8 ] . The pathogenesis of POD is considered to be related to BBB dysfunction [ 9 – 13 ] , and BBB plays a key protective role. Animal models of postoperative delirium-like behavior exhibit significant BBB [ 11 – 13 ] . The BBB dysfunction resulting from the inflammatory response to anesthesia and surgery is generally dynamic, transient and reversible [ 9 , 10 ] . It is unclear whether POD patients have preoperative BBB dysfunction. The common method for evaluating the BBB permeability is cerebrospinal fluid/plasma albumin ratio [ 14 ] , which is invasive and difficult to perform routinely during the perioperative period or episodes of POD. However, It is important to assess the patient's preoperative BBB permeability to identify potential etiology of POD [ 15 ] . Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is currently the most widely used non-invasive imaging technique to assess BBB breakdown [ 16 , 17 ] . It offers new possibilities to understand how the brain functions in health and disease and allows the detection of subtle regional changes in the BBB integrity [ 18 ] . The physiological parameter volume transfer constant (K trans ) is the rate at which contrast agent is delivered to the extravascular extracellular space per volume of tissue and contrast agent concentration in the arterial blood plasma. The K trans is the most common and classical method for assessing BBB in the neuroimaging [ 16 , 19 , 20 ] . Patients with coronary artery disease may have concomitant impairment of the BBB because they often share common causative factors, such as ApoE4 [ 21 , 22 ] . The off-pump coronary artery bypass grafting (OPCABG) may be an effective surgical strategy for myocardial revascularization [ 23 ] . Compared to non-cardiac surgery, OPCABG usually leads to a higher incidence of POD, which can seriously affect clinical recovery [ 24 ] . Therefore, it is important to explore whether preoperative BBB dysfunction associated with POD in older patients with coronary heart disease undergoing OPCABG, especially in different brain regions. Accordingly, this study aimed to elucidate the association of the BBB dysfunction with the odds of developing POD. We hypothesized that patients with preoperative higher BBB would have an increased odds of developing POD. Materials and methods Study design and participants This prospective observational study was approved by the Research Ethics Committee of The Second Hospital of Hebei Medical University (No.2022-R707), registered in the Chinese Clinical Trial Registry (No. ChiCTR2200063774), and conducted at The Second Hospital of Hebei Medical University from September 2022 to June 2023. Written informed consent for study participation was obtained from all patients or their legal representatives. After obtaining written informed consent, patients aged ≥ 65 years, with an American Society of Anesthesiologists (ASA) physical status of Ⅲ–Ⅳ, and scheduled for OPCABG were recruited from October 2022 to June 2023 (Fig. 1 for STROBE diagram). The exclusion criteria were any of the following: (1) education < 6 years; (2) Montreal Cognitive Assessment Scale-Basic (MoCA-B) < 18; (3) pre-existing psychological and/or mental illness; (4) Parkinson's disease or Epileptic disease; (5) history of previous brain surgery; (6) taking sedatives or antidepressants in the last year; (7) severe hepatic insufficiency, acute kidney injury or renal insufficiency (eGFR < 30 ml/min/1.73m 2 ), aortic arch atherosclerosis thicker than 4 mm; (8) alcoholism or drug abuse; (9) audition, vision or language troubles impeding communication; (10) situations unsuitable for an MRI scan (claustrophobia); (11) contraindications to gadolinium contrast agents. The eliminate criteria were: (1) emergency intraoperative extracorporeal circulation; (2) secondary postoperative thoracotomy; or (3) postoperative stroke. Anesthesia and perioperative management All patients underwent standardized anesthetic management. All preoperative cardiac medications were continued until the morning of surgery. In the operating room, all patients were monitored using electrocardiography, pulse oximetry, end-tidal carbon dioxide, bispectral index (BIS), body temperature, invasive blood pressure, and central venous pressure and received general anesthesia, with or without a parasternal nerve block at the discretion of the attending anesthesiologist. Anesthesia was induced with midazolam (0.03–0.05 mg/kg), etomidate (0.2–0.3 mg/kg), sufentanil (0.4–0.6 µg/kg) and rocuronium (0.9 mg/kg). Anesthesia was maintained with continuous intravenous infusion of ciprofol (0.5-1 mg·kg -1 ·h -1 ) and sufentanil (0.5-1 µg·kg -1 ·h -1 ), inhaled of sevoflurane at a minimum end-tidal concentration of 0.5 to 1 minimal alveolar concentration (MAC), and an intravenous injection of rocuronium according to the procedure of the operation and the intraoperative conditions of the patients. The MAP was maintained at 65 mmHg (± 30% of the baseline value). BIS value was maintained at 40–60. End-tidal carbon dioxide partial pressure was maintained at 35–45 mmHg. The nasopharyngeal temperature was maintained at 36–37°C. The surgical incision performed a median sternotomy for gaining access to the heart. Heparin was given before surgical manipulation of the coronary arteries was started. Based on the condition of coronary artery stenosis, the number and location of the surgery was determined. The hemodynamic was maintained stability during surgery, and gave vasoactive drugs when necessary. After vascular anastomosis, protamine was given to counteract heparin. All patients received intraoperative salvage autologous blood transfusion. Red blood cell transfusions should be considered if the hematocrit level less than 30% during surgery. After surgery, patients were transferred to the intensive care unit (ICU) where they received necessary sedation and analgesia until qualified for tracheal extubation. ciprofol (0.4-1mg·kg -1 ·h -1 ) was used for postoperative sedation. Postoperative pain was managed using patient-controlled intravenous of sufentanil (2µg/kg in 200 ml of 0.9% normal saline) at a background infusion rate of 2 ml/h with 1ml boluses available and a locking time interval of 15 minutes. The goal of analgesia was to maintain the numerical rating scale (NRS) score at 0–3 at rest. Assessment of POD The primary outcome was the occurrence of POD. Assessment of POD was performed twice daily (between 06:00–08:00, and 18:00– 20:00) for postoperative days 1 through 5 by trained POD assessors, using the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) in non-intubated patients [ 25 ] or the CAM for the Intensive Care Unit (CAM-ICU) in intubated patients [ 26 ] . The POD was defined and assessed based on four features: (1) acute onset of mental status changes or a fluctuating course, (2) inattention, (3) disorganized thinking, and (4) altered level of consciousness. POD was diagnosed based on the patient displayed both features 1 and 2, with either 3 or 4 during the assessment period. Applied tests correspond with the DSM-V and has been validated with acceptable sensitivity and specificity [ 25 , 26 ] . Secondary outcome was the severity of POD. The severity of delirium was assessed using the long form of the Chinese version of CAM-Severity (CAM-S) [ 27 ] , with scores ranging from 0 (no delirium features) to 19 (most severe). The researcher responsible for the delirium assessment participates in a training session, at the end of which a quiz is performed and all participants must answer all quiz questions correctly. Delirium assessors were blinded to data collected during surgery and MRI. Imaging All magnetic resonance images were acquired using an MRI scanner (GE SIGNA Architect, 3.0T) at the Second Hospital of Hebei Medical University. MRI was performed 1–5 days before surgery. During the MRI data acquisition, the participants had to remain relaxed and still. Main scanning sequence and parameters: 3D T1-BRAVO and 3D Osag T2-Flair CUBE images displayed anatomic brain structures, diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping, whereas DCE Whole-Brain sequence was used to measure BBB permeability. DCE scanning: axial, TR 4.4ms, TE 1.6ms, slice thickness 2.0 mm with 0.6mm gap, FOV 26.9cm×24cm. The Flip Angle was 12°. The scanning times for T1-BRAVO, T2-FLAIR, and DCE were 224, 367, and 264 seconds, respectively. At the beginning of the third dynamic scan, 0.1mmol/kg gadolinium contrast agent was injected through the elbow vein using a high-pressure syringe 20 ml at an injection rate of 4.5ml/s, and the scan lasted for 40 phases. At the end of scanning, the images obtained by DCE were processed by GE AW4.7 workstation GEN IQ module (GE Medical Systems). The quantitative analysis was based on modified Tofts two-compartment pharmacokinetic model, with an important physiological parameter K trans [ 6 , 16 , 17 , 22 ] . The software has a specialised template for the head, thereby facilitating convenient and accurate operations. Furthermore, it incorporates 3D motion correction technology, which prevents involuntary motion from affecting quantitative accuracy. Before calculating Krans, acquisition of the vascular inflow function was performed in Auto Mode to minimise errors due to flow near vessel boundaries and T1 correction due to the relationship between gadolinium concentration and signal intensity depending on baseline T1. In the process of calculating K trans from the DCE-MRI, the region of interest (ROI) was defined by utilizing the 3D approach and 3D Osag T2-Flair CUBE images. The hippocampus, thalamus, frontal lobe, amygdala, cingulate cortex, precuneus, temporal lobe and parietal lobe were analysed as ROIs. The software was programmed to automatically calculate the K trans value when the ROI was manually labelled. The K trans value was averaged from the each layer. AW Volumeshare 7 software module for GE was used for segmentation and volume estimation, based on 3D Osag T2-Flair CUBE images. There are sagittal, horizontal, coronal, and reconstructed 3D images. Adjust the window width and level based on T1 images to achieve a significant contrast between gray and white matter. The boundaries of the hippocampus, thalamus, and whole brain were delineated according to established criteria from 3D maximum intensity projection. Reconstruct the coronal plane and manually outline the boundary of the ROI. The software will automatically define the edges between adjacent layers and calculate the absolute volume of the ROI. Then hippocampal and thalamus volume were standardized. Standardized volume were calculated as follows: (measured two sides hippocampal volume and thalamus volume /whole brain volume) ×1000 [ 28 ] . MRI was evaluated and processed by two qualified radiologists who were blinded to the clinical and surgical variables. Statistical analysis No formal sample size analysis was performed, because no relevant references exist for the estimation of postoperative outcomes through preoperative BBB status. Accordingly, the sample size was based on two pieces of evidence: first, a study by Chagnot et al. [ 29 ] reporting that the K trans range of low permeability of the BBB is between 10 − 4 and 10 -3 min -1 ; and second, combining Nation et al. [ 6 ] research and preliminary experiments, the BBB in the hippocampus of delirium patients may be within 5×10 -3 min -1 . In our study, the sample size calculation was based on the K trans of patients who have not experienced delirium is approximately half of those who have POD. It is expected expected 40% incidence of POD after cardiac surgery [ 30 , 31 ] . Considering a significance level of 0.05, SD 3, and a power of 0.8 (β = 0.2), a sample size of fifty patients was required. To compensate for missing data and dropouts, the sample size was increased to a total of 60 patients. Statistical calculations were conducted using SPSS 27.0 software program. Patients were categorized into two groups: POD and Non-POD (NPOD), based on the delirium assessment results. Continuous variables were presented as means (standard deviation), if normally distributed, and median (quartile 1– quartile 3) if not. Group comparisons were performed using the independent sample t-test for normally distributed variables and the Mann-Whitney test for non-normally distributed variables. Categorical data were presented as frequencies and percentages, and analyzed using 2-tailed χ2 tests or the Fisher exact test. Univariable logistic regression analysis was used to evaluate the relationship between hippocampus, thalamus, frontal lobe, amygdala, cingulate cortex, precuneus, temporal lobe and parietal lobe K trans values and Brain, Hippocampus, Thalamus volume and POD. Multivariable analysis was conducted by including Hippocampus, Thalamus K trans values and controlling for 2 well-established POD risk factors. Mediation analysis was performed to evaluate whether hippocampus K trans values mediates the association between MoCA-B and POD. We also examined whether the results differed by interleukin-6(IL-6) concentrations, using the P-value of the interaction term in regression models to assess significance. Correlation analyses were conducted using Graphpad Prism 9.5 software program, Bivariate correlations between POD severity and K trans values and volumes of the hippocampus and thalamus using the nonparametric Spearman correlation coefficient. All statistical tests were two-sided, and a P value less than 0.05 was considered statistically significant. Results Characteristics of individuals with and without POD Of the 98 patients assessed for eligibility, 59 patients entered the study of which 9 patients were excluded for reasons described in the diagram (Fig. 1 ) leaving 50 patients for analysis in the formal study. Delirium occurred in 19 (38%) of these 50 patients. The demographic characteristics and perioperative data are presented in Table 1 . The POD group had a median MoCA-B score of 20.8 (95% confidence interval [CI], 19.1–22.5), while the NPOD group scored 23.1 (95% CI, 22.2–24.0) ( P = 0.020). Education level, anxiety, Pittsburgh sleep quality index (PSQI), COVID-19 history, preoperative comorbidity, and severe carotid stenosis, left main coronary artery (LMCA) stenosis were not significantly different. The method of anesthesia, number of coronary artery bypass graft (CABG) vessels, intraoperative drugs, duration of surgery, extubation time and ICU care time were also compared and no statistical differences were observed. IL-6 was tested on the first postoperative day and there was no statistical difference between the two groups. Table 1 Patient Characteristics and Perioperative Data. Demographics POD(n = 19) NPOD(n = 31) P -value Age, y, mean (SD) 68.6 (3.3) 69.2 (3.3) 0.559 Female, n (%) 5 (26.3) 9 (29.0) 0.836 BMI, kg/m 2 , mean (SD) 25.0 (2.9) 24.9 (2.5) 0.911 Smoking, n (%) 6 (31.6) 7 (22.6) 0.481 Drinking, n (%) 6 (31.6) 9 (29.0) 0.894 Education level, n (%) 0.938 Elementary school 2 (10.5) 4 (12.9) Middle school 12 (63.2) 20 (64.5) College and above 5 (26.3) 7 (22.6) COVID-19 history, n (%) 8 (42.1) 12 (38.7) 0.812 MoCA-B, mean (SD) 20.8 (3.6) 23.1 (2.4) 0.020 Anxiety, mean (SD) 5 (26.3) 7 (22.6) 0.764 PSQI, mean (SD) 6.8 (3.8) 4.8 (3.8) 0.079 Preoperative comorbidity, n (%) Hypertension 14 (73.7) 22 (71.0) 0.836 Diabetes 4 (21.1) 14 (45.2) 0.130 Hypercholesterolemia 5 (26.3) 8 (25.8) 0.968 Severe carotid stenosis 1 (5.3) 2 (6.5) 0.864 NYHA (Ⅱ/Ⅲ/Ⅳ) 6/11/2 (31.6,57.9,10.5) 10/17/4 (32.3,54.8,12.9) 0.962 LMCA stenosis, n (%) 5 (26.3) 7 (22.6) 0.764 Types of anesthesia, n (%) 0.332 General 16 (84.2) 22 (71.0) Combined general-regional 3 (15.8) 9 (29.0) Duration of surgery, min, mean (SD) 273 (60) 272 (49) 0.954 Numbers of CABG vessels (2/3/4), n (%) 5/8/6 (26.3,42.1,31.6%) 8/15/8 (25.8,48.4,25.8) 0.886 Intraoperative drugs, mean (SD) Ciprofol, mg 108 (32) 112 (49) 0.743 Sevoflurane, ml 50.5 (11.3) 48.4 (9.6) 0.478 Sufentanil equivalent, µg 330 (65) 325 (88) 0.842 Estimated blood loss, ml, mean (SD) 395 (112) 407 (112) 0.720 Extubation time, h, mean (SD) 18.6 (6.8) 23.1 (10.3) 0.094 ICU care duration, h, mean (SD) 68.0 (20.8) 67.2 (22.8) 0.907 IL-6, pg/ml, median (IQR) 192 (96.8,342) 152 (104,252) 0.490 The data are presented as mean (SD) or median (IQR) for continuous variables and frequency (%) for categorical variables. Abbreviations: POD postoperative delirium; NPOD Non-POD; BMI body mass index; COVID-19 Corona Virus Disease 2019; MoCA-B Montreal Cognitive AssessmentBasic; PSQI Pittsburgh sleep quality index; NYHA New York Heart Association; LMCA Left main coronary artery; CABG coronary artery bypass graft; ICU Intensive Care Unit; IL-6 interleukin 6. MRI results The median hippocampus K trans of the NPOD patients was 3.89(interquartile range [IQR], 3.40,4.68) ×10 -3 min -1 , whereas the median of the POD group was 5.36 (IQR, 3.99,8.39) ×10 -3 min -1 . The median thalamus K trans of the NPOD patients was 3.55 (IQR, 3.05,4.57) ×10 -3 min -1 , the median of the POD group was 4.80 (IQR, 3.60,6.62) ×10 -3 min -1 . Hippocampal and thalamic K trans were statistically higher in the POD group compared to the NPOD group ( P = 0.012 and P = 0.017, respectively) (Fig. 2 ). Frontal lobe, amygdala, cingulate cortex, precuneus, temporal lobe and parietal lobe K trans was not statistically significant. The comprehensive MRI of the distinct characteristics across groups were shown in Table 2 . In the logistic regression model, POD was set as the dependent variable. Univariable logistic regression analysis revealed that higher hippocampus K trans (Odds ratio [OR] per 1×10 -3 min -1 increment, 1.350; 95%CI, 1.048–1.740; P = 0.020) and thalamus K trans (OR, 1.466; 95%CI, 1.017–2.113; P = 0.040) were significantly associated with higher odds of POD, as presented in Table 3 . Multivariable logistic regression analysis, adjustment variables were age, IL-6. The adjusted models revealed that preoperative hippocampus K trans (OR, 1.250; 95% CI, 0.859–1.817; P = 0.244) and thalamus K trans (OR, 1.164; 95% CI, 0.648–2.090; P = 0.611) were not associated with higher odds of POD (See Supplementary Table 1). Table 2 Patient MRI Comparison of Groups (POD Versus NPOD). POD(n = 19) NPOD(n = 31) P -value BBB K trans (×10 − 3 min − 1 ), median (IQR) Hippocampus 5.36 (3.99,8.39) 3.89(3.40,4.68) 0.012 Thalamus 4.80 (3.58,6.62) 3.55 (3.05,4.57) 0.017 Frontal lobe 5.12 (3.77,5.89) 3.80 (3.13,6.29) 0.201 Temporal lobe 8.85(2.82,5.11) 3.29(2.79,3.71) 0.227 Cingulate cortex 2.39(2.14,4.04) 2.65(2.07,3.31) 0.984 Amygdala 5.11(3.80,7.60) 4.14(3.23,5.81) 0.242 Precuneus 3.557(2.54,4.41) 3.029(2.40,4.17) 0.490 Parietal lobe 3.71(2.75,5.79) 3.29(2.64,4.94) 0.204 Volume (cm 3 ), mean (SD) Brain volume 1445 (125) 1450 (139) 0.893 Hippocampus volume 7.05 (0.93) 8.12 (1.01) <0.001 Thalamus volume 6.81 (0.63) 7.53 (0.95) 0.005 Leukoencephalopathy, n (%) 3 (15.8%) 8 (25.8%) 0.498 The data are presented as mean (SD) or median (IQR) for continuous variables and frequency (%) for categorical variables. Abbreviations: POD postoperative delirium; NPOD Non-POD; MRI Magnetic Resonance Imaging, BBB blood-brain barrier Table 3 Univariable Logistic Regression: POD Dependent variable: POD OR 95% CI (OR) Lower Upper P -value Hippocampus K trans 1.350 1.048 1.740 0.020 Thalamus K trans 1.466 1.017 2.113 0.040 Frontal lobe K trans 1.189 0.820 1.724 0.360 Temporal lobe K trans 1.185 0.919 1.526 0.190 Cingulate cortex K trans 1.044 0.777 1.402 0.776 Amygdala K trans 1.150 0.937 1.412 0.181 Precuneus K trans 1.166 0.927 1.468 0.190 Parietal lobe K trans 1.108 0.871 1.409 0.403 Brain volume 1.000 0.995 1.004 0.890 Hippocampus volume 0.297 0.131 0.672 0.004 Thalamus volume 0.304 0.121 0.766 0.012 (n total = 50; POD = 19). Abbreviations: POD postoperative delirium; OR odds ratio; CI confidence interval. The mean hippocampus size of the NPOD patients was 8.12cm 3 (95% CI, 7.75–8.49), whereas the mean of the POD group was 7.05 cm 3 (95%CI, 6.60–7.50). The mean thalamus size of the NPOD patients was 7.53cm 3 (95% CI, 7.18–7.88), whereas the mean of the POD group was 6.81cm 3 (95%CI, 6.51–7.11). Hippocampal and thalamic volume were statistically larger in the NPOD group compared to the POD group( P <0.001 and P = 0.005, respectively). Brain volume was not statistically significant. Leukoencephalopathy was not statistically significantly different between groups. The lower hippocampus volume (OR, 0.297; 95%CI, 0.131–0.672; P = 0.004) and thalamus volume (OR, 0.304; 95%CI, 0.121–0.766; P = 0.012) were significantly associated with higher odds of POD presented in Table 3 . Then we have conducted exploratory analysis. The mediation analysis revealed that the direct effect of Moca-B on POD mediated through hippocampal K trans coefficient (–0.22, 95% CI; − 0.045, 0.015; P = 0.066). We did not find that the association of BBB permeability at baseline with POD was different by pro-inflammatory cytokines IL-6 (all-P-interaction terms > 0.05). In the POD patients, Spearman's rank correlation between POD severity and K trans values in the hippocampus and thalamus were r = 0.442 ( P = 0.058), r = 0.202 ( P = 0.406), respectively. Between POD severity and in the volumes in the hippocampus and thalamus were r = 0.119 ( P = 0.627), r = 0.327 ( P = 0.172), respectively. Discussion In this analysis of a prospective cohort study of older patients undergoing OPCABG, we found that hippocampal and thalamic BBB permeability, but this was not associated with greater odds of POD. The hippocampus plays a crucial role in both learning and memory [ 32 ] . Notably, dysfunction of BBB within the hippocampus is among the first regions impaired by age-related impairment, and the higher in BBB permeability is more pronounced in patients with mild cognitive impairment (MCI) than in age-matched controls without cognitive impairment [ 33 ] . In this study, the median value of K trans in the hippocampus of the POD group was 5.36 ×10 -3 min -1 , which was higher than the median value of the NPOD patients, which was 3.89 ×10 -3 min -1 . Furthermore, the K trans values of the BBB in the hippocampal region showed a slight higher compared to the results of Montagne et al [ 34 ] , which may be attributed to patients with coronary artery disease, who often have more odds factors for cerebral microvascular disease. POD patients have higher hippocampal BBB permeability than NPOD patients. The thalamus also plays an important role in the cognitive areas of declarative memory, executive function, attention, working memory. [ 35 ] The thalamus is a region of interest in POD research. POD patients have higher thalamic BBB permeability than NPOD patients. Our study found that the BBB permeability is higher in the hippocampus than in the thalamus in POD group. In normal aging and MCI, the hippocampus is the first brain region to lose cerebrovascular integrity. [ 6 , 33 ] . Although POD patients have higher BBB permeability, but the hippocampus and thalamus K trans OR values were not significantly associated with higher odds of POD presented. In addition, the impairment of BBB function is associated with various factors, including Apolipoprotein E4 carriers, metabolic syndrome, hypertension, diabetes, chronic inflammation and gut microbiome imbalance [ 34 , 36 , 37 ] . Aside from the role of BBB dysfunction in delirium, our data also show NPOD patients have larger hippocampal volume. This is different from the results of the non-cardiac surgery study [ 38 , 39 ] , which did not find any association between global brain atrophy, hippocampal volume, and the incidence of POD. Previous studies included patients with significantly better cognitive function. However, more patients in our study were MCI. The difference in cognitive level may be the reason of differences in hippocampal volume. The hippocampus has a lower capillary density and narrower diameters than other brain regions. Therefore, it is more likely to cause a lower resting blood flow and oxygenation [ 40 ] , while Aβ deposition, metabolic impairment, functional changes and structural atrophy were featured in succession [ 41 ] . The results of this study also confirm a correlation between the permeability and volume of the hippocampus and thalamic. The role of the neuroinflammation in BBB dysfunction has become an important feature of POD [ 42 ] , particularly Interleukin-6 (IL-6) [ 13 ] . Due to acute BBB dysfunction in cardiac surgery patients often occurs within 24 hours after surgery [ 43 , 44 ] . Therefore, peripheral IL-6 was measured at the first day after surgery and revealed no statistically significant difference. Maybe it has lower sample size, to do with no cardiopulmonary bypass, or high baseline IL-6 levels in the study cohort here. Furthermore, peripheral IL-6 levels may not always correlate with neuroinflammation due to differences in the functional state of the BBB. This study has several limitations. First, our study only conducted preoperative MRI scans. The reason is that the purpose of this study is to evaluate the relationship between preoperative BBB in different brain regions and POD. We did not assess the changes in postoperative BBB, because the BBB dysfunction resulting from the anesthesia and surgery is generally dynamic, transient and reversible [ 9 , 10 ] . Second, the model for measuring the BBB using DCE-MRI technology has not yet been standardized, and this study uses one of the commonly utilized methods. Third, this exploratory study had a moderate sample size. The findings may need to be validated in a larger cohort study. Fourth, stroke is a major determinant of the integrity of the BBB. For this reason, study was carried out prior to the operation in which DWI was performed at the same time and patients with a recent stroke were excluded. Conclusion We showed that POD patients have higher preoperative hippocampal and thalamic BBB permeability, but this was not an independent risk factor for POD. Moreover, our primary analysis should be considered exploratory and a basis for future larger trials. Abbreviations BBB: blood-brain barrier; OPCABG: off-pump coronary artery bypass grafting; POD: postoperative delirium; MRI: magnetic resonance imaging; 3D-CAM: 3-Minute Diagnostic Confusion Assessment Method; CAM-ICU: CAM for the Intensive Care Unit; IL-6: interleukin-6; DCE-MRI: Dynamic contrast-enhanced magnetic resonance imaging; ASA: American Society of Anesthesiologists; MoCA-B: Montreal Cognitive Assessment Scale-Basic; BIS: bispectral index; MAC: minimal alveolar concentration; ICU: intensive care unit; CAM-S: CAM-Severity; DWI: diffusion-weighted imaging; ROI: region of interest; NPOD: Non-POD; PSQI: Pittsburgh sleep quality index; LMCA: left main coronary artery; CABG: coronary artery bypass graft; MCI: mild cognitive impairment Declarations Acknowledgements We are grateful for Dr. Jinghui An, Dr. Chen Yin, Dr. Huajun Wang, Dr. Hongzhan Cui, Dr. Jiqiang Bu and Dr. Zining Liu of the Department of Cardiac Surgery for their contributions to recruiting patients. Additionally, we thank Dr. Yankai Wu of Department of Medical Imaging for providing guidance on MRI scans. Author contributions Lichao Di and Peiying Huang: research design, data analysis, and write and revise the manuscript. Yeju He and Jie Li: blood-brain barrier and brain volume analysis. Yu Liu: patient recruitment and supervision. Liwei Chi and Na Sun: patient recruitment and data acquisition. Rongtian Kang and Lining Huang: research design, supervision and critical revision of the manuscript. All authors reviewed the manuscript. Funding This study was funded by Medical Excellent Talents Project Funded by Hebei Provincial Government in 2022(No.303-2022-27-04), Hebei Provincial Science and Technology Programme Projects for People's Livelihood (No.202030701180328) and Hebei Province Medical Science Research Project Plan in 2024 (20240951). Data Availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study followed the Declaration of Helsinki. It was a prospective study, approved by the ethics committee of The Second Hospital of Hebei Medical University (No.2022-R707), and registered in the Chinese Clinical Trial Registry (No. ChiCTR2200063774). Before surgery, the patients or their family members provided written informed consent. Consent for publication Not applicable. Competing interests The authors declare no conflicts of interest. References Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014. 383: 911-22. Subramaniam B, Shankar P, Shaefi S, et al. Effect of Intravenous Acetaminophen vs Placebo Combined With Propofol or Dexmedetomidine on Postoperative Delirium Among Older Patients Following Cardiac Surgery: The DEXACET Randomized Clinical Trial. JAMA. 2019. 321: 686-696. Kirfel A, Guttenthaler V, Mayr A, Coburn M, Menzenbach J, Wittmann M. Postoperative delirium is an independent factor influencing the length of stay of elderly patients in the intensive care unit and in hospital. J Anesth. 2022. 36: 341-348. Gou RY, Hshieh TT, Marcantonio ER, et al. One-Year Medicare Costs Associated With Delirium in Older Patients Undergoing Major Elective Surgery. JAMA Surg. 2021. 156: 430-442. Fong TG, Inouye SK. The inter-relationship between delirium and dementia: the importance of delirium prevention. Nat Rev Neurol. 2022. 18: 579-596. Nation DA, Sweeney MD, Montagne A, et al. Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med. 2019. 25: 270-276. Cai M, Chen S, Du Y, et al. The Role of Blood-Brain Barrier Dysfunction in Mild Cognitive Impairment: a Scientometric and Visualization Analysis from 2000 to 2021. J Mol Neurosci. 2022. 72: 1977-1989. Sweeney MD, Sagare AP, Zlokovic BV. Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol. 2018. 14: 133-150. Taylor J, Parker M, Casey CP, et al. Postoperative delirium and changes in the blood-brain barrier, neuroinflammation, and cerebrospinal fluid lactate: a prospective cohort study. Br J Anaesth. 2022. 129: 219-230. Devinney MJ, Wong MK, Wright MC, et al. Role of Blood-Brain Barrier Dysfunction in Delirium following Non-cardiac Surgery in Older Adults. Ann Neurol. 2023.94:1024-1035. Li K, Wang J, Chen L, et al. Netrin-1 Ameliorates Postoperative Delirium-Like Behavior in Aged Mice by Suppressing Neuroinflammation and Restoring Impaired Blood-Brain Barrier Permeability. Front Mol Neurosci. 2021. 14: 751570. Yang T, Velagapudi R, Kong C, et al. Protective effects of omega-3 fatty acids in a blood-brain barrier-on-chip model and on postoperative delirium-like behaviour in mice. Br J Anaesth. 2023. 130: e370-e380. Yang S, Gu C, Mandeville ET, et al. Anesthesia and Surgery Impair Blood-Brain Barrier and Cognitive Function in Mice. Front Immunol. 2017. 8: 902. Musaeus CS, Gleerup HS, Høgh P, Waldemar G, Hasselbalch SG, Simonsen AH. Cerebrospinal Fluid/Plasma Albumin Ratio as a Biomarker for Blood-Brain Barrier Impairment Across Neurodegenerative Dementias. J Alzheimers Dis. 2020. 75: 429-436. Jin Z, Hu J, Ma D. Postoperative delirium: perioperative assessment, risk reduction, and management. Br J Anaesth. 2020. 125: 492-504. Heye AK, Culling RD, Valdés Hernández Mdel C, Thrippleton MJ, Wardlaw JM. Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. Neuroimage Clin. 2014. 6: 262-274. Raja R, Rosenberg GA, Caprihan A. MRI measurements of Blood-Brain Barrier function in dementia: A review of recent studies. Neuropharmacology. 2018. 134(Pt B): 259-271. Israeli D, Tanne D, Daniels D, et al. The application of MRI for depiction of subtle blood brain barrier disruption in stroke. Int J Biol Sci. 2010. 7: 1-8. Wu CH, Lirng JF, Wu HM, et al. Blood-Brain Barrier Permeability in Patients With Reversible Cerebral Vasoconstriction Syndrome Assessed With Dynamic Contrast-Enhanced MRI. Neurology. 2021. 97: e1847-e1859. Barnes SR, Ng TS, Montagne A, Law M, Zlokovic BV, Jacobs RE. Optimal acquisition and modeling parameters for accurate assessment of low Ktrans blood-brain barrier permeability using dynamic contrast-enhanced MRI. Magn Reson Med. 2016. 75: 1967-1977. Moretti R, Janjusevic M, Fluca AL, et al. Common Shared Pathogenic Aspects of Small Vessels in Heart and Brain Disease. Biomedicines. 2022. 10: 1009. Chen W, Jin F, Cao G, et al. ApoE4 May be a Promising Target for Treatment of Coronary Heart Disease and Alzheimer's Disease. Curr Drug Targets. 2018. 19: 1038-1044. Sun L, Zhou M, Ji Y, Wang X, Wang X. Off-pump versus on-pump coronary artery bypass grafting for octogenarians: A meta-analysis involving 146 372 patients. Clin Cardiol. 2022. 45: 331-341. Szwed K, Pawliszak W, Szwed M, Tomaszewska M, Anisimowicz L, Borkowska A. Reducing delirium and cognitive dysfunction after off-pump coronary bypass: A randomized trial. J Thorac Cardiovasc Surg. 2021. 161: 1275-1282.e4. Oberhaus J, Wang W, Mickle AM, et al. Evaluation of the 3-Minute Diagnostic Confusion Assessment Method for Identification of Postoperative Delirium in Older Patients. JAMA Netw Open. 2021. 4: e2137267. Ely EW, Inouye SK, Bernard GR, et al. Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. 2001. 286: 2703-2710. Mei X, Chen Y, Zheng H, et al. The Reliability and Validity of the Chinese Version of Confusion Assessment Method Based Scoring System for Delirium Severity (CAM-S). J Alzheimers Dis. 2019. 69: 709-716. Chen MH, Liao Y, Rong PF, Hu R, Lin GX, Ouyang W. Hippocampal volume reduction in elderly patients at risk for postoperative cognitive dysfunction. J Anesth. 2013. 27: 487-492. Chagnot A, Barnes SR, Montagne A. Magnetic Resonance Imaging of Blood-Brain Barrier permeability in Dementia. Neuroscience. 2021. 474: 14-29. Brown CH, Kim AS, Yanek L, et al. Association of perioperative plasma concentration of neurofilament light with delirium after cardiac surgery: a nested observational study. Br J Anaesth. 2024. 132: 312-319. Xiong X, Shao Y, Chen D, Chen B, Lan X, Shi J. Effect of Esketamine on Postoperative Delirium in Patients Undergoing Cardiac Valve Replacement with Cardiopulmonary Bypass: A Randomized Controlled Trial. Anesth Analg. 2024. Milner B, Klein D. Loss of recent memory after bilateral hippocampal lesions: memory and memories-looking back and looking forward. J Neurol Neurosurg Psychiatry. 2016. 87: 230. Montagne A, Barnes SR, Sweeney MD, et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron. 2015. 85: 296-302. Montagne A, Nation DA, Sagare AP, et al. APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline. Nature. 2020. 581: 71-76. Van der Werf YD, Scheltens P, Lindeboom J, Witter MP, Uylings HB, Jolles J. Deficits of memory, executive functioning and attention following infarction in the thalamus; a study of 22 cases with localised lesions. Neuropsychologia. 2003. 41: 1330-1344. Parker A, Fonseca S, Carding SR. Gut microbes and metabolites as modulators of blood-brain barrier integrity and brain health. Gut Microbes. 2020. 11: 135-157. Van Dyken P, Lacoste B. Impact of Metabolic Syndrome on Neuroinflammation and the Blood-Brain Barrier. Front Neurosci. 2018. 12: 930. Cavallari M, Hshieh TT, Guttmann CR, et al. Brain atrophy and white-matter hyperintensities are not significantly associated with incidence and severity of postoperative delirium in older persons without dementia. Neurobiol Aging. 2015. 36: 2122-2129. Huang C, Mårtensson J, Gögenur I, Asghar MS. Exploring Postoperative Cognitive Dysfunction and Delirium in Noncardiac Surgery Using MRI: A Systematic Review. Neural Plast. 2018. 2018: 1281657. Shaw K, Bell L, Boyd K, et al. Neurovascular coupling and oxygenation are decreased in hippocampus compared to neocortex because of microvascular differences. Nat Commun. 2021. 12: 3190. Iturria-Medina Y, Sotero RC, Toussaint PJ, Mateos-Pérez JM, Evans AC. Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis. Nat Commun. 2016. 7: 11934. Subramaniyan S, Terrando N. Neuroinflammation and Perioperative Neurocognitive Disorders. Anesth Analg. 2019. 128: 781-788. Merino JG, Latour LL, Tso A, et al. Blood-brain barrier disruption after cardiac surgery. AJNR Am J Neuroradiol. 2013. 34(3): 518-523. Abrahamov D, Levran O, Naparstek S, et al. Blood-Brain Barrier Disruption After Cardiopulmonary Bypass: Diagnosis and Correlation to Cognition. Ann Thorac Surg. 2017. 104(1): 161-169. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4986382","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348172053,"identity":"301c1532-e295-4670-b71b-35ad0f8b5f0b","order_by":0,"name":"Lichao Di","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lichao","middleName":"","lastName":"Di","suffix":""},{"id":348172056,"identity":"abc216b3-231d-40d9-9f1c-e498562cd46c","order_by":1,"name":"Peiying Huang","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peiying","middleName":"","lastName":"Huang","suffix":""},{"id":348172057,"identity":"4b3c19a4-66bf-4034-a16b-7bcc8f612160","order_by":2,"name":"Yeju He","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yeju","middleName":"","lastName":"He","suffix":""},{"id":348172059,"identity":"906fd245-effe-489d-9fc6-f95741cc2ce2","order_by":3,"name":"Jie Li","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""},{"id":348172060,"identity":"23ccc75c-39d0-4182-b632-2b7b12f2da9f","order_by":4,"name":"Yu Liu","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Liu","suffix":""},{"id":348172061,"identity":"12311d99-3a79-47a4-b282-ceaca4103a5e","order_by":5,"name":"Liwei Chi","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liwei","middleName":"","lastName":"Chi","suffix":""},{"id":348172062,"identity":"9bdfe786-bf74-4043-a18e-32220be97ade","order_by":6,"name":"Na Sun","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Sun","suffix":""},{"id":348172063,"identity":"63915f96-9731-41ba-8c41-b93be7a4d16b","order_by":7,"name":"Rongtian Kang","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rongtian","middleName":"","lastName":"Kang","suffix":""},{"id":348172064,"identity":"4da7ea9f-e715-4d0a-9b4b-d9a26adb9d8a","order_by":8,"name":"Lining Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACfvn3Hw4kVNjI8UswsEGEDhDQItmQYPjgwZk0Y8kZxGoxOJBgbPiw7XCiwQ1itTAcOJAmkcDGnGB8u/nYo5ttDHJ8NxIYPxfg0cHY2HBMIoGHLc/szrF049w2BmPJGwnM0jPwaGFmZmyTSJDgKTa7kWMmDdSSuOEG0FIePFrY2JjZJBIMJBI3z8j/BtJST1ALDw8bs0FCgkHiBokcNpCWBANCWiQkeBgfJACDTeJGmrlxzjkJw5lnHjZL49Nif4OH4eDPf//l+GckP3ucU2Yjz3c8+eBnfFowbAVixgYSNIyCUTAKRsEowAYAbEFM0dDXVgQAAAAASUVORK5CYII=","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lining","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-08-27 18:28:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4986382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4986382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66939451,"identity":"d0810a5c-d80d-439e-801c-4e9bbd2eaca2","added_by":"auto","created_at":"2024-10-18 08:40:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62591,"visible":true,"origin":"","legend":"\u003cp\u003eSTROBE diagram of the study. Abbreviations: \u003cem\u003eSTROBE\u003c/em\u003eStrengthening the Reporting of Observational Studies in Epidemiology; \u003cem\u003ePOD\u003c/em\u003epostoperative delirium; \u003cem\u003eNPOD\u003c/em\u003e Non-POD; \u003cem\u003eMoCA-B\u003c/em\u003e Montreal Cognitive Assessment‑Basic; \u003cem\u003eMRI\u003c/em\u003e Magnetic Resonance Imaging;\u003cem\u003e DWI\u003c/em\u003e diffusion-weighted imaging\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4986382/v1/a8bd5bf4456780c7472c22de.png"},{"id":66939452,"identity":"5d8dc6e3-7c39-4147-8ccc-3cb6f68b5f0b","added_by":"auto","created_at":"2024-10-18 08:40:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":370282,"visible":true,"origin":"","legend":"\u003cp\u003eA, B and C show preoperative DCE-MRI images of the hippocampus of patients who underwent OPCABG and developed POD. D, E and F show preoperative DCE-MRI images of the hippocampus of patients who underwent OPCABG without developed POD. Abbreviations: \u003cem\u003eDCE-MRI \u003c/em\u003eDynamic contrast-enhanced magnetic resonance imaging; \u003cem\u003ePOD\u003c/em\u003e postoperative delirium; \u003cem\u003eOPCABG\u003c/em\u003e off-pump coronary artery bypass grafting\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4986382/v1/320fb006442d332f7148ac30.png"},{"id":70756858,"identity":"dc24cfb9-ed95-4233-81a4-f12096075db1","added_by":"auto","created_at":"2024-12-06 10:32:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1169007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4986382/v1/86ff1f0c-fb9f-4198-a51f-c13858ceffc0.pdf"},{"id":66940295,"identity":"f1dce938-e0a6-44a3-91ca-ae18df3f5210","added_by":"auto","created_at":"2024-10-18 08:48:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16933,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4986382/v1/3cf10866b2de305ffa0559a6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation between preoperative blood–brain barrier dysfunction and postoperative delirium in older patients undergoing cardiac surgery: a prospective cohort study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePostoperative delirium (POD) is an acute, transient, fluctuating neuropsychiatric syndrome in attention, consciousness, and cognition\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. POD is common in older patients following cardiac surgery\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e and is associated with longer hospital stay\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, increased healthcare costs\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, significant mortality and long-term dementia risk\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.To date, there are no highly efficacious medicines for the prevention and treatment of POD, mainly due to the limited understanding of its underlying pathophysiology.\u003c/p\u003e \u003cp\u003eThe blood-brain barrier (BBB) is a physiological structure that separates the central nervous system from the periphery. It plays a critical role in protecting the brain from potentially harmful substances of peripheral origin, preventing the entry of toxins, pathogens and inflammatory agents, thereby maintaining brain homeostasis. It has been reported that the BBB breakdown is an early biomarker of human cognitive dysfunction\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, including Alzheimer\u0026rsquo;s disease and mild cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The pathogenesis of POD is considered to be related to BBB dysfunction\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and BBB plays a key protective role. Animal models of postoperative delirium-like behavior exhibit significant BBB\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The BBB dysfunction resulting from the inflammatory response to anesthesia and surgery is generally dynamic, transient and reversible\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. It is unclear whether POD patients have preoperative BBB dysfunction. The common method for evaluating the BBB permeability is cerebrospinal fluid/plasma albumin ratio\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, which is invasive and difficult to perform routinely during the perioperative period or episodes of POD. However, It is important to assess the patient's preoperative BBB permeability to identify potential etiology of POD\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is currently the most widely used non-invasive imaging technique to assess BBB breakdown\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. It offers new possibilities to understand how the brain functions in health and disease and allows the detection of subtle regional changes in the BBB integrity\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The physiological parameter volume transfer constant (K\u003csub\u003etrans\u003c/sub\u003e) is the rate at which contrast agent is delivered to the extravascular extracellular space per volume of tissue and contrast agent concentration in the arterial blood plasma. The K\u003csub\u003etrans\u003c/sub\u003e is the most common and classical method for assessing BBB in the neuroimaging\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Patients with coronary artery disease may have concomitant impairment of the BBB because they often share common causative factors, such as ApoE4\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The off-pump coronary artery bypass grafting (OPCABG) may be an effective surgical strategy for myocardial revascularization\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Compared to non-cardiac surgery, OPCABG usually leads to a higher incidence of POD, which can seriously affect clinical recovery\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is important to explore whether preoperative BBB dysfunction associated with POD in older patients with coronary heart disease undergoing OPCABG, especially in different brain regions. Accordingly, this study aimed to elucidate the association of the BBB dysfunction with the odds of developing POD. We hypothesized that patients with preoperative higher BBB would have an increased odds of developing POD.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis prospective observational study was approved by the Research Ethics Committee of The Second Hospital of Hebei Medical University (No.2022-R707), registered in the Chinese Clinical Trial Registry (No. ChiCTR2200063774), and conducted at The Second Hospital of Hebei Medical University from September 2022 to June 2023. Written informed consent for study participation was obtained from all patients or their legal representatives.\u003c/p\u003e \u003cp\u003eAfter obtaining written informed consent, patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, with an American Society of Anesthesiologists (ASA) physical status of Ⅲ\u0026ndash;Ⅳ, and scheduled for OPCABG were recruited from October 2022 to June 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for STROBE diagram). The exclusion criteria were any of the following: (1) education\u0026thinsp;\u0026lt;\u0026thinsp;6 years; (2) Montreal Cognitive Assessment Scale-Basic (MoCA-B)\u0026thinsp;\u0026lt;\u0026thinsp;18; (3) pre-existing psychological and/or mental illness; (4) Parkinson's disease or Epileptic disease; (5) history of previous brain surgery; (6) taking sedatives or antidepressants in the last year; (7) severe hepatic insufficiency, acute kidney injury or renal insufficiency (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e), aortic arch atherosclerosis thicker than 4 mm; (8) alcoholism or drug abuse; (9) audition, vision or language troubles impeding communication; (10) situations unsuitable for an MRI scan (claustrophobia); (11) contraindications to gadolinium contrast agents. The eliminate criteria were: (1) emergency intraoperative extracorporeal circulation; (2) secondary postoperative thoracotomy; or (3) postoperative stroke.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnesthesia and perioperative management\u003c/h2\u003e \u003cp\u003eAll patients underwent standardized anesthetic management. All preoperative cardiac medications were continued until the morning of surgery. In the operating room, all patients were monitored using electrocardiography, pulse oximetry, end-tidal carbon dioxide, bispectral index (BIS), body temperature, invasive blood pressure, and central venous pressure and received general anesthesia, with or without a parasternal nerve block at the discretion of the attending anesthesiologist. Anesthesia was induced with midazolam (0.03\u0026ndash;0.05 mg/kg), etomidate (0.2\u0026ndash;0.3 mg/kg), sufentanil (0.4\u0026ndash;0.6 \u0026micro;g/kg) and rocuronium (0.9 mg/kg). Anesthesia was maintained with continuous intravenous infusion of ciprofol (0.5-1 mg\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e\u0026middot;h\u003csup\u003e-1\u003c/sup\u003e) and sufentanil (0.5-1 \u0026micro;g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e\u0026middot;h\u003csup\u003e-1\u003c/sup\u003e), inhaled of sevoflurane at a minimum end-tidal concentration of 0.5 to 1 minimal alveolar concentration (MAC), and an intravenous injection of rocuronium according to the procedure of the operation and the intraoperative conditions of the patients. The MAP was maintained at 65 mmHg (\u0026plusmn;\u0026thinsp;30% of the baseline value). BIS value was maintained at 40\u0026ndash;60. End-tidal carbon dioxide partial pressure was maintained at 35\u0026ndash;45 mmHg. The nasopharyngeal temperature was maintained at 36\u0026ndash;37\u0026deg;C. The surgical incision performed a median sternotomy for gaining access to the heart. Heparin was given before surgical manipulation of the coronary arteries was started. Based on the condition of coronary artery stenosis, the number and location of the surgery was determined. The hemodynamic was maintained stability during surgery, and gave vasoactive drugs when necessary. After vascular anastomosis, protamine was given to counteract heparin. All patients received intraoperative salvage autologous blood transfusion. Red blood cell transfusions should be considered if the hematocrit level less than 30% during surgery.\u003c/p\u003e \u003cp\u003eAfter surgery, patients were transferred to the intensive care unit (ICU) where they received necessary sedation and analgesia until qualified for tracheal extubation. ciprofol (0.4-1mg\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e\u0026middot;h\u003csup\u003e-1\u003c/sup\u003e) was used for postoperative sedation. Postoperative pain was managed using patient-controlled intravenous of sufentanil (2\u0026micro;g/kg in 200 ml of 0.9% normal saline) at a background infusion rate of 2 ml/h with 1ml boluses available and a locking time interval of 15 minutes. The goal of analgesia was to maintain the numerical rating scale (NRS) score at 0\u0026ndash;3 at rest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of POD\u003c/h2\u003e \u003cp\u003eThe primary outcome was the occurrence of POD. Assessment of POD was performed twice daily (between 06:00\u0026ndash;08:00, and 18:00\u0026ndash; 20:00) for postoperative days 1 through 5 by trained POD assessors, using the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) in non-intubated patients\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e or the CAM for the Intensive Care Unit (CAM-ICU) in intubated patients\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The POD was defined and assessed based on four features: (1) acute onset of mental status changes or a fluctuating course, (2) inattention, (3) disorganized thinking, and (4) altered level of consciousness. POD was diagnosed based on the patient displayed both features 1 and 2, with either 3 or 4 during the assessment period. Applied tests correspond with the DSM-V and has been validated with acceptable sensitivity and specificity\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Secondary outcome was the severity of POD. The severity of delirium was assessed using the long form of the Chinese version of CAM-Severity (CAM-S)\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, with scores ranging from 0 (no delirium features) to 19 (most severe). The researcher responsible for the delirium assessment participates in a training session, at the end of which a quiz is performed and all participants must answer all quiz questions correctly. Delirium assessors were blinded to data collected during surgery and MRI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImaging\u003c/h2\u003e \u003cp\u003eAll magnetic resonance images were acquired using an MRI scanner (GE SIGNA Architect, 3.0T) at the Second Hospital of Hebei Medical University. MRI was performed 1\u0026ndash;5 days before surgery. During the MRI data acquisition, the participants had to remain relaxed and still. Main scanning sequence and parameters: 3D T1-BRAVO and 3D Osag T2-Flair CUBE images displayed anatomic brain structures, diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping, whereas DCE Whole-Brain sequence was used to measure BBB permeability. DCE scanning: axial, TR 4.4ms, TE 1.6ms, slice thickness 2.0 mm with 0.6mm gap, FOV 26.9cm\u0026times;24cm. The Flip Angle was 12\u0026deg;. The scanning times for T1-BRAVO, T2-FLAIR, and DCE were 224, 367, and 264 seconds, respectively. At the beginning of the third dynamic scan, 0.1mmol/kg gadolinium contrast agent was injected through the elbow vein using a high-pressure syringe 20 ml at an injection rate of 4.5ml/s, and the scan lasted for 40 phases.\u003c/p\u003e \u003cp\u003eAt the end of scanning, the images obtained by DCE were processed by GE AW4.7 workstation GEN IQ module (GE Medical Systems). The quantitative analysis was based on modified Tofts two-compartment pharmacokinetic model, with an important physiological parameter K\u003csub\u003etrans\u003c/sub\u003e\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The software has a specialised template for the head, thereby facilitating convenient and accurate operations. Furthermore, it incorporates 3D motion correction technology, which prevents involuntary motion from affecting quantitative accuracy. Before calculating Krans, acquisition of the vascular inflow function was performed in Auto Mode to minimise errors due to flow near vessel boundaries and T1 correction due to the relationship between gadolinium concentration and signal intensity depending on baseline T1. In the process of calculating K\u003csub\u003etrans\u003c/sub\u003e from the DCE-MRI, the region of interest (ROI) was defined by utilizing the 3D approach and 3D Osag T2-Flair CUBE images. The hippocampus, thalamus, frontal lobe, amygdala, cingulate cortex, precuneus, temporal lobe and parietal lobe were analysed as ROIs. The software was programmed to automatically calculate the K\u003csub\u003etrans\u003c/sub\u003e value when the ROI was manually labelled. The K\u003csub\u003etrans\u003c/sub\u003e value was averaged from the each layer.\u003c/p\u003e \u003cp\u003eAW Volumeshare 7 software module for GE was used for segmentation and volume estimation, based on 3D Osag T2-Flair CUBE images. There are sagittal, horizontal, coronal, and reconstructed 3D images. Adjust the window width and level based on T1 images to achieve a significant contrast between gray and white matter. The boundaries of the hippocampus, thalamus, and whole brain were delineated according to established criteria from 3D maximum intensity projection. Reconstruct the coronal plane and manually outline the boundary of the ROI. The software will automatically define the edges between adjacent layers and calculate the absolute volume of the ROI. Then hippocampal and thalamus volume were standardized. Standardized volume were calculated as follows: (measured two sides hippocampal volume and thalamus volume /whole brain volume) \u0026times;1000\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. MRI was evaluated and processed by two qualified radiologists who were blinded to the clinical and surgical variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNo formal sample size analysis was performed, because no relevant references exist for the estimation of postoperative outcomes through preoperative BBB status. Accordingly, the sample size was based on two pieces of evidence: first, a study by Chagnot et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e reporting that the K\u003csub\u003etrans\u003c/sub\u003e range of low permeability of the BBB is between 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and 10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e; and second, combining Nation et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e research and preliminary experiments, the BBB in the hippocampus of delirium patients may be within 5\u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e. In our study, the sample size calculation was based on the K\u003csub\u003etrans\u003c/sub\u003e of patients who have not experienced delirium is approximately half of those who have POD. It is expected expected 40% incidence of POD after cardiac surgery\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Considering a significance level of 0.05, SD 3, and a power of 0.8 (β\u0026thinsp;=\u0026thinsp;0.2), a sample size of fifty patients was required. To compensate for missing data and dropouts, the sample size was increased to a total of 60 patients.\u003c/p\u003e \u003cp\u003eStatistical calculations were conducted using SPSS 27.0 software program. Patients were categorized into two groups: POD and Non-POD (NPOD), based on the delirium assessment results. Continuous variables were presented as means (standard deviation), if normally distributed, and median (quartile 1\u0026ndash; quartile 3) if not. Group comparisons were performed using the independent sample t-test for normally distributed variables and the Mann-Whitney test for non-normally distributed variables. Categorical data were presented as frequencies and percentages, and analyzed using 2-tailed χ2 tests or the Fisher exact test. Univariable logistic regression analysis was used to evaluate the relationship between hippocampus, thalamus, frontal lobe, amygdala, cingulate cortex, precuneus, temporal lobe and parietal lobe K\u003csub\u003etrans\u003c/sub\u003e values and Brain, Hippocampus, Thalamus volume and POD. Multivariable analysis was conducted by including Hippocampus, Thalamus K\u003csub\u003etrans\u003c/sub\u003e values and controlling for 2 well-established POD risk factors. Mediation analysis was performed to evaluate whether hippocampus K\u003csub\u003etrans\u003c/sub\u003e values mediates the association between MoCA-B and POD. We also examined whether the results differed by interleukin-6(IL-6) concentrations, using the P-value of the interaction term in regression models to assess significance. Correlation analyses were conducted using Graphpad Prism 9.5 software program, Bivariate correlations between POD severity and K\u003csub\u003etrans\u003c/sub\u003e values and volumes of the hippocampus and thalamus using the nonparametric Spearman correlation coefficient. All statistical tests were two-sided, and a \u003cem\u003eP\u003c/em\u003e value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of individuals with and without POD\u003c/h2\u003e \u003cp\u003eOf the 98 patients assessed for eligibility, 59 patients entered the study of which 9 patients were excluded for reasons described in the diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) leaving 50 patients for analysis in the formal study. Delirium occurred in 19 (38%) of these 50 patients. The demographic characteristics and perioperative data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The POD group had a median MoCA-B score of 20.8 (95% confidence interval [CI], 19.1\u0026ndash;22.5), while the NPOD group scored 23.1 (95% CI, 22.2\u0026ndash;24.0) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). Education level, anxiety, Pittsburgh sleep quality index (PSQI), COVID-19 history, preoperative comorbidity, and severe carotid stenosis, left main coronary artery (LMCA) stenosis were not significantly different. The method of anesthesia, number of coronary artery bypass graft (CABG) vessels, intraoperative drugs, duration of surgery, extubation time and ICU care time were also compared and no statistical differences were observed. IL-6 was tested on the first postoperative day and there was no statistical difference between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Characteristics and Perioperative Data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOD(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNPOD(n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.6 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA-B, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.8 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.1 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative comorbidity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere carotid stenosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNYHA (Ⅱ/Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/11/2\u003c/p\u003e \u003cp\u003e(31.6,57.9,10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/17/4\u003c/p\u003e \u003cp\u003e(32.3,54.8,12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMCA stenosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of anesthesia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined general-regional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of surgery, min, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e273 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumbers of CABG vessels\u003c/p\u003e \u003cp\u003e(2/3/4), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/8/6\u003c/p\u003e \u003cp\u003e(26.3,42.1,31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/15/8\u003c/p\u003e \u003cp\u003e(25.8,48.4,25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative drugs, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCiprofol, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevoflurane, ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.5 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.4 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSufentanil equivalent, \u0026micro;g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated blood loss, ml, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395 (112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407 (112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtubation time, h, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.1 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU care duration, h, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.0 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.2 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6, pg/ml, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (96.8,342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (104,252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe data are presented as mean (SD) or median (IQR) for continuous variables and frequency (%) for categorical variables. Abbreviations: \u003cem\u003ePOD\u003c/em\u003e postoperative delirium; \u003cem\u003eNPOD\u003c/em\u003e Non-POD; \u003cem\u003eBMI\u003c/em\u003e body mass index; \u003cem\u003eCOVID-19\u003c/em\u003e Corona Virus Disease 2019; \u003cem\u003eMoCA-B\u003c/em\u003e Montreal Cognitive AssessmentBasic; \u003cem\u003ePSQI\u003c/em\u003e Pittsburgh sleep quality index; \u003cem\u003eNYHA\u003c/em\u003e New York Heart Association; \u003cem\u003eLMCA\u003c/em\u003e Left main coronary artery; \u003cem\u003eCABG\u003c/em\u003e coronary artery bypass graft; \u003cem\u003eICU\u003c/em\u003e Intensive Care Unit; \u003cem\u003eIL-6\u003c/em\u003e interleukin 6.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMRI results\u003c/h2\u003e \u003cp\u003eThe median hippocampus K\u003csub\u003etrans\u003c/sub\u003e of the NPOD patients was 3.89(interquartile range [IQR], 3.40,4.68) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e, whereas the median of the POD group was 5.36 (IQR, 3.99,8.39) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e. The median thalamus K\u003csub\u003etrans\u003c/sub\u003e of the NPOD patients was 3.55 (IQR, 3.05,4.57) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e, the median of the POD group was 4.80 (IQR, 3.60,6.62) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e. Hippocampal and thalamic K\u003csub\u003etrans\u003c/sub\u003e were statistically higher in the POD group compared to the NPOD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Frontal lobe, amygdala, cingulate cortex, precuneus, temporal lobe and parietal lobe K\u003csub\u003etrans\u003c/sub\u003e was not statistically significant. The comprehensive MRI of the distinct characteristics across groups were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the logistic regression model, POD was set as the dependent variable. Univariable logistic regression analysis revealed that higher hippocampus K\u003csub\u003etrans\u003c/sub\u003e (Odds ratio [OR] per 1\u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e increment, 1.350; 95%CI, 1.048\u0026ndash;1.740; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) and thalamus K\u003csub\u003etrans\u003c/sub\u003e (OR, 1.466; 95%CI, 1.017\u0026ndash;2.113; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) were significantly associated with higher odds of POD, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Multivariable logistic regression analysis, adjustment variables were age, IL-6. The adjusted models revealed that preoperative hippocampus K\u003csub\u003etrans\u003c/sub\u003e (OR, 1.250; 95% CI, 0.859\u0026ndash;1.817; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.244) and thalamus K\u003csub\u003etrans\u003c/sub\u003e (OR, 1.164; 95% CI, 0.648\u0026ndash;2.090; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.611) were not associated with higher odds of POD (See Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient MRI Comparison of Groups (POD Versus NPOD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOD(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNPOD(n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBB K\u003csub\u003etrans\u003c/sub\u003e (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003emin\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.36 (3.99,8.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.89(3.40,4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.80 (3.58,6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55 (3.05,4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.12 (3.77,5.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.80 (3.13,6.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.85(2.82,5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.29(2.79,3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCingulate cortex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.39(2.14,4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.65(2.07,3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmygdala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.11(3.80,7.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.14(3.23,5.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.557(2.54,4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.029(2.40,4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.71(2.75,5.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.29(2.64,4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume (cm\u003csup\u003e3\u003c/sup\u003e), mean (SD)\u003c/p\u003e \u003cp\u003eBrain volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1445 (125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1450 (139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.05 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.12 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThalamus volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.81 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.53 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukoencephalopathy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe data are presented as mean (SD) or median (IQR) for continuous variables and frequency (%) for categorical variables. Abbreviations: \u003cem\u003ePOD\u003c/em\u003e postoperative delirium; \u003cem\u003eNPOD\u003c/em\u003e Non-POD; \u003cem\u003eMRI\u003c/em\u003e Magnetic Resonance Imaging, \u003cem\u003eBBB\u003c/em\u003e blood-brain barrier\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable Logistic Regression: POD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable: POD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e95% CI (OR)\u003c/span\u003e\u003c/p\u003e \u003cp\u003eLower Upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThalamus K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal lobe K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal lobe K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCingulate cortex K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmygdala K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecuneus K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal lobe K\u003csub\u003etrans\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThalamus volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e(n total\u0026thinsp;=\u0026thinsp;50; POD\u0026thinsp;=\u0026thinsp;19). Abbreviations: \u003cem\u003ePOD\u003c/em\u003e postoperative delirium; \u003cem\u003eOR\u003c/em\u003e odds ratio; \u003cem\u003eCI\u003c/em\u003e confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean hippocampus size of the NPOD patients was 8.12cm\u003csup\u003e3\u003c/sup\u003e (95% CI, 7.75\u0026ndash;8.49), whereas the mean of the POD group was 7.05 cm\u003csup\u003e3\u003c/sup\u003e (95%CI, 6.60\u0026ndash;7.50). The mean thalamus size of the NPOD patients was 7.53cm\u003csup\u003e3\u003c/sup\u003e (95% CI, 7.18\u0026ndash;7.88), whereas the mean of the POD group was 6.81cm\u003csup\u003e3\u003c/sup\u003e (95%CI, 6.51\u0026ndash;7.11). Hippocampal and thalamic volume were statistically larger in the NPOD group compared to the POD group(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, respectively). Brain volume was not statistically significant. Leukoencephalopathy was not statistically significantly different between groups. The lower hippocampus volume (OR, 0.297; 95%CI, 0.131\u0026ndash;0.672; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and thalamus volume (OR, 0.304; 95%CI, 0.121\u0026ndash;0.766; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) were significantly associated with higher odds of POD presented in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThen we have conducted exploratory analysis. The mediation analysis revealed that the direct effect of Moca-B on POD mediated through hippocampal K\u003csub\u003etrans\u003c/sub\u003e coefficient (\u0026ndash;0.22, 95% CI; \u0026minus;\u0026thinsp;0.045, 0.015; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.066). We did not find that the association of BBB permeability at baseline with POD was different by pro-inflammatory cytokines IL-6 (all-P-interaction terms\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the POD patients, Spearman's rank correlation between POD severity and K\u003csub\u003etrans\u003c/sub\u003e values in the hippocampus and thalamus were \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.442 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058), \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.202 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.406), respectively. Between POD severity and in the volumes in the hippocampus and thalamus were \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.119 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.627), \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.327 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.172), respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this analysis of a prospective cohort study of older patients undergoing OPCABG, we found that hippocampal and thalamic BBB permeability, but this was not associated with greater odds of POD.\u003c/p\u003e \u003cp\u003eThe hippocampus plays a crucial role in both learning and memory\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Notably, dysfunction of BBB within the hippocampus is among the first regions impaired by age-related impairment, and the higher in BBB permeability is more pronounced in patients with mild cognitive impairment (MCI) than in age-matched controls without cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In this study, the median value of K\u003csub\u003etrans\u003c/sub\u003e in the hippocampus of the POD group was 5.36 \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e, which was higher than the median value of the NPOD patients, which was 3.89 \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e. Furthermore, the K\u003csub\u003etrans\u003c/sub\u003e values of the BBB in the hippocampal region showed a slight higher compared to the results of Montagne \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, which may be attributed to patients with coronary artery disease, who often have more odds factors for cerebral microvascular disease. POD patients have higher hippocampal BBB permeability than NPOD patients. The thalamus also plays an important role in the cognitive areas of declarative memory, executive function, attention, working memory.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e The thalamus is a region of interest in POD research. POD patients have higher thalamic BBB permeability than NPOD patients. Our study found that the BBB permeability is higher in the hippocampus than in the thalamus in POD group. In normal aging and MCI, the hippocampus is the first brain region to lose cerebrovascular integrity.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Although POD patients have higher BBB permeability, but the hippocampus and thalamus K\u003csub\u003etrans\u003c/sub\u003e OR values were not significantly associated with higher odds of POD presented. In addition, the impairment of BBB function is associated with various factors, including Apolipoprotein E4 carriers, metabolic syndrome, hypertension, diabetes, chronic inflammation and gut microbiome imbalance\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAside from the role of BBB dysfunction in delirium, our data also show NPOD patients have larger hippocampal volume. This is different from the results of the non-cardiac surgery study\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, which did not find any association between global brain atrophy, hippocampal volume, and the incidence of POD. Previous studies included patients with significantly better cognitive function. However, more patients in our study were MCI. The difference in cognitive level may be the reason of differences in hippocampal volume. The hippocampus has a lower capillary density and narrower diameters than other brain regions. Therefore, it is more likely to cause a lower resting blood flow and oxygenation\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, while Aβ deposition, metabolic impairment, functional changes and structural atrophy were featured in succession\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The results of this study also confirm a correlation between the permeability and volume of the hippocampus and thalamic. The role of the neuroinflammation in BBB dysfunction has become an important feature of POD\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, particularly Interleukin-6 (IL-6) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Due to acute BBB dysfunction in cardiac surgery patients often occurs within 24 hours after surgery\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Therefore, peripheral IL-6 was measured at the first day after surgery and revealed no statistically significant difference. Maybe it has lower sample size, to do with no cardiopulmonary bypass, or high baseline IL-6 levels in the study cohort here. Furthermore, peripheral IL-6 levels may not always correlate with neuroinflammation due to differences in the functional state of the BBB.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, our study only conducted preoperative MRI scans. The reason is that the purpose of this study is to evaluate the relationship between preoperative BBB in different brain regions and POD. We did not assess the changes in postoperative BBB, because the BBB dysfunction resulting from the anesthesia and surgery is generally dynamic, transient and reversible\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Second, the model for measuring the BBB using DCE-MRI technology has not yet been standardized, and this study uses one of the commonly utilized methods. Third, this exploratory study had a moderate sample size. The findings may need to be validated in a larger cohort study. Fourth, stroke is a major determinant of the integrity of the BBB. For this reason, study was carried out prior to the operation in which DWI was performed at the same time and patients with a recent stroke were excluded.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe showed that POD patients have higher preoperative hippocampal and thalamic BBB permeability, but this was not an independent risk factor for POD. Moreover, our primary analysis should be considered exploratory and a basis for future larger trials.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBBB: blood-brain barrier; OPCABG: off-pump coronary artery bypass grafting; POD: postoperative delirium; MRI: magnetic resonance imaging; 3D-CAM: 3-Minute Diagnostic Confusion Assessment Method; CAM-ICU: CAM for the Intensive Care Unit; IL-6: interleukin-6; DCE-MRI: Dynamic contrast-enhanced magnetic resonance imaging; ASA: American Society of Anesthesiologists; MoCA-B: Montreal Cognitive Assessment Scale-Basic; BIS: bispectral index; MAC: minimal alveolar concentration; ICU: intensive care unit; CAM-S: CAM-Severity; DWI: diffusion-weighted imaging; ROI: region of interest; NPOD: Non-POD; PSQI: Pittsburgh sleep quality index; LMCA: left main coronary artery; CABG: coronary artery bypass graft; MCI: mild cognitive impairment\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for Dr. Jinghui An, Dr. Chen Yin, Dr. Huajun Wang, Dr. Hongzhan Cui, Dr. Jiqiang Bu and Dr. Zining Liu of the Department of Cardiac Surgery for their contributions to recruiting patients. Additionally, we thank Dr. Yankai Wu of Department of Medical Imaging for providing guidance on MRI scans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLichao Di and Peiying Huang:\u0026nbsp;research design, data analysis, and write and revise the manuscript.\u0026nbsp;Yeju He and Jie Li: blood-brain barrier and brain volume analysis.\u0026nbsp;Yu Liu: patient recruitment and supervision.\u0026nbsp;Liwei Chi and Na Sun: patient recruitment and data acquisition.\u0026nbsp;Rongtian Kang and Lining Huang:\u0026nbsp;research design, supervision and critical revision of the manuscript.\u0026nbsp;All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Medical Excellent Talents Project Funded by Hebei Provincial Government in 2022(No.303-2022-27-04), Hebei Provincial Science and Technology Programme Projects for People\u0026apos;s Livelihood (No.202030701180328) and Hebei Province Medical Science Research Project Plan in 2024 (20240951).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed the Declaration of Helsinki. It was a prospective study, approved by the ethics committee of The Second Hospital of Hebei Medical University (No.2022-R707), and registered in the Chinese Clinical Trial Registry (No. ChiCTR2200063774). Before surgery, the patients or their family members provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eInouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014. 383: 911-22.\u003c/li\u003e\n \u003cli\u003eSubramaniam B, Shankar P, Shaefi S, et al. 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Effect of Esketamine on Postoperative Delirium in Patients Undergoing Cardiac Valve Replacement with Cardiopulmonary Bypass: A Randomized Controlled Trial. Anesth Analg. 2024.\u003c/li\u003e\n \u003cli\u003eMilner B, Klein D. Loss of recent memory after bilateral hippocampal lesions: memory and memories-looking back and looking forward. J Neurol Neurosurg Psychiatry. 2016. 87: 230.\u003c/li\u003e\n \u003cli\u003eMontagne A, Barnes SR, Sweeney MD, et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron. 2015. 85: 296-302.\u003c/li\u003e\n \u003cli\u003eMontagne A, Nation DA, Sagare AP, et al. APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline. Nature. 2020. 581: 71-76.\u003c/li\u003e\n \u003cli\u003eVan der Werf YD, Scheltens P, Lindeboom J, Witter MP, Uylings HB, Jolles J. Deficits of memory, executive functioning and attention following infarction in the thalamus; a study of 22 cases with localised lesions. 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Neurovascular coupling and oxygenation are decreased in hippocampus compared to neocortex because of microvascular differences. Nat Commun. 2021. 12: 3190.\u003c/li\u003e\n \u003cli\u003eIturria-Medina Y, Sotero RC, Toussaint PJ, Mateos-P\u0026eacute;rez JM, Evans AC. Early role of vascular dysregulation on late-onset Alzheimer\u0026apos;s disease based on multifactorial data-driven analysis. Nat Commun. 2016. 7: 11934.\u003c/li\u003e\n \u003cli\u003eSubramaniyan S, Terrando N. Neuroinflammation and Perioperative Neurocognitive Disorders. Anesth Analg. 2019. 128: 781-788.\u003c/li\u003e\n \u003cli\u003eMerino JG, Latour LL, Tso A, et al. Blood-brain barrier disruption after cardiac surgery. AJNR Am J Neuroradiol. 2013. 34(3): 518-523.\u003c/li\u003e\n \u003cli\u003eAbrahamov D, Levran O, Naparstek S, et al. Blood-Brain Barrier Disruption After Cardiopulmonary Bypass: Diagnosis and Correlation to Cognition. Ann Thorac Surg. 2017. 104(1): 161-169.\u003c/li\u003e\n\u003c/ol\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":"Postoperative delirium, Blood-brain barrier, Hippocampus, Thalamus, Off-pump coronary artery bypass grafting, Magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-4986382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4986382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious research indicates that the breakdown of the blood-brain barrier (BBB) is an early biomarker of cognitive dysfunction in humans, and it deteriorates with age. Patients with coronary heart disease may have concomitant impairment of the BBB. The off-pump coronary artery bypass grafting (OPCABG) is an effective surgical strategy for myocardial revascularization. However, cardiac surgery leads to a high incidence of postoperative delirium (POD), which can seriously affect clinical recovery. Therefore, it is important to explore whether preoperative BBB dysfunction is associated with POD in older patients undergoing OPCABG.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective observational study was performed on OPCABG patients. Fifty older patients with coronary heart disease were recruited. Before surgery, patients underwent Gadolinium-enhanced magnetic resonance imaging. BBB was assessed using GE AW4.7 workstation GEN IQ module. The physiological parameter volume transfer constant (K\u003csub\u003etrans\u003c/sub\u003e) is the most common and classical method for assessing BBB in the neuroimaging. All patients underwent standardized anesthetic management. Participants were assessed for POD twice daily for 5 days using the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) in non-intubated patients or the CAM for the Intensive Care Unit in intubated patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e19 patients (38%) were diagnosed with POD. The preoperative median hippocampus K\u003csub\u003etrans\u003c/sub\u003e of the POD and NPOD patients were 5.36 (IQR, 3.99,8.39) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e, and 3.89 (IQR, 3.40,4.68) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e. The preoperative median thalamus K\u003csub\u003etrans\u003c/sub\u003e of the POD and NPOD patients were 4.80 (IQR, 3.60,6.62) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e, and 3.55 (IQR, 3.05,4.57) \u0026times;10\u003csup\u003e-3\u003c/sup\u003emin\u003csup\u003e-1\u003c/sup\u003e. Hippocampal and thalamic K\u003csub\u003etrans\u003c/sub\u003e were statistically higher in the POD group compared to the NPOD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). Univariable logistic regression analysis revealed that higher hippocampus K\u003csub\u003etrans\u003c/sub\u003e (OR, 1.350; 95%CI, 1.048\u0026ndash;1.740; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) and thalamus K\u003csub\u003etrans\u003c/sub\u003e (OR, 1.466; 95%CI, 1.017\u0026ndash;2.113; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) were significantly associated with higher odds of POD. Multivariable logistic regression analysis, adjustment variables were age, interleukin-6. The adjusted models revealed that preoperative hippocampus K\u003csub\u003etrans\u003c/sub\u003e (OR, 1.250; 95%CI, 0.859\u0026ndash;1.817; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.244) and thalamus K\u003csub\u003etrans\u003c/sub\u003e (OR, 1.164; 95% CI, 0.648\u0026ndash;2.090; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.611) were not associated with higher odds of POD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePOD patients have higher preoperative hippocampal and thalamic BBB permeability, but this was not an independent risk factor for POD.\u003c/p\u003e","manuscriptTitle":"Association between preoperative blood–brain barrier dysfunction and postoperative delirium in older patients undergoing cardiac surgery: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 08:40:22","doi":"10.21203/rs.3.rs-4986382/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":"d467fd0a-a31a-4ea8-a9f6-d1d3b0648f21","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-06T10:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 08:40:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4986382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4986382","identity":"rs-4986382","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}