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

doi:10.21203/rs.3.rs-4986382/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-4986382/v1

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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^-3min^-1, and 3.89 (IQR, 3.40,4.68) ×10^-3min^-1. The preoperative median thalamus K_trans of the POD and NPOD patients were 4.80 (IQR, 3.60,6.62) ×10^-3min^-1, and 3.55 (IQR, 3.05,4.57) ×10^-3min^-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

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.

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/1.73m²), 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^-3min^-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^-3min^-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.

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/m², 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^-3min^-1, whereas the median of the POD group was 5.36 (IQR, 3.99,8.39) ×10^-3min^-1. The median thalamus K_trans of the NPOD patients was 3.55 (IQR, 3.05,4.57) ×10^-3min^-1, the median of the POD group was 4.80 (IQR, 3.60,6.62) ×10^-3min^-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^-3min^-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^− 3min^− 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 (cm³), 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 8.12cm³ (95% CI, 7.75–8.49), whereas the mean of the POD group was 7.05 cm³ (95%CI, 6.60–7.50). The mean thalamus size of the NPOD patients was 7.53cm³ (95% CI, 7.18–7.88), whereas the mean of the POD group was 6.81cm³ (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.

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^-3min^-1, which was higher than the median value of the NPOD patients, which was 3.89 ×10^-3min^-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.

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.

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

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.

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No competing interests reported.

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

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Abstract

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Introduction

Materials and methods

Study design and participants

Anesthesia and perioperative management

Assessment of POD

Imaging

Statistical analysis

Results

Characteristics of individuals with and without POD

MRI results

Discussion

Conclusion

Abbreviations

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