Home Medicine The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia
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The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia

  • Yangning Zhou , Yan Wang EMAIL logo and Limin Xu
Published/Copyright: December 17, 2024
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Open Medicine
From the journal Open Medicine Volume 19 Issue 1

Abstract

Background

Postoperative cognitive dysfunction (POCD) frequently occurs following endovascular therapy for acute ischemic stroke (AIS). Given the complexity of predicting AIS clinically, there is a pressing need to develop a preemptive prediction model and investigate the impact of anesthesia depth on AIS.

Methods

A total of 333 patients diagnosed with AIS were included in the study, comprising individuals with non-POCD (n = 232) or POCD (n = 101). Univariate and multivariate logistic regression analyses were utilized to examine the independent risk factors associated with POCD. A calibration, decision curve analysis, and precision–recall curves were employed to assess the model’s goodness of fit.

Results

Multivariate regression analysis identified two inflammatory indicators, high-sensitivity C reactive protein (hs-CRP) and systemic immune inflammatory index (SII), and three brain injury indicators, National Institute of Health Stroke Scale (NIHSS) score, N-terminal pro-brain natriuretic peptide (NT-proBNP), and soluble protein-100 β (S100-β), which were used to construct a nomogram model.

Conclusion

The composite predictive model incorporating NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β demonstrated efficacy in predicting POCD following AIS. Additionally, our results suggest a potential association between depth of anesthesia, cognitive impairment, and inflammatory response in AIS patients.

Keywords: cognitive impairment; acute ischemic stroke; nomogram; prediction; anesthesia depth

1 Introduction

Postoperative cognitive dysfunction (POCD) is characterized by impairments in memory, abstract thinking, and orientation following anesthesia surgery, often accompanied by a decrease in social activity. This includes changes in personality, social ability, cognitive function, and skills [1,2]. More than 20% of surgical patients may experience postoperative delirium on the first day following surgery, and between 10 and 25% may develop POCD within 3–6 months postoperatively [3]. The prevalence of POCD is particularly high among elderly patients post-surgery, and a study reported that the incidence of POCD in elderly patients following major noncardiac surgery was 25.8% [4]. The pathogenesis of POCD may involve neuroinflammation, mitochondrial dysfunction, oxidative stress, blood–brain barrier disruption, and damage to the brain–gut axis [2,5–7]; however, the etiology of POCD remains incompletely elucidated. Empirical evidence from clinical investigations suggests that variables including anesthesia modality, surgical duration, patient age, educational attainment, American Society of Anesthesiologists classification, surgical type, length of hospitalization, incidence of delirium, and opioid utilization may serve as predictive indicators of cognitive decline following surgery [1,8–11]. Consequently, a thorough examination of the risk factors associated with POCD can offer valuable insights into its underlying pathophysiological mechanisms.

Acute ischemic stroke (AIS) is the most prevalent form of stroke, representing approximately 70% of all cases [12,13]. The timely restoration of blocked vascular perfusion, preservation of ischemic penumbra, reduction of core infarction volume, and prompt vascular recanalization are crucial for the effective treatment of AIS patients and can significantly enhance patient prognosis [12,13]. Furthermore, there is a growing interest in the impact of anesthesia techniques and management on neurological outcomes following endovascular therapy (EVT) in individuals with AIS [14,15]. The anesthesia methods most frequently utilized for EVT in AIS patients are local anesthesia/conscious sedation and general anesthesia, each presenting distinct advantages and disadvantages and varying impacts on neurological outcomes. Local/conscious sedation offers the benefit of allowing the patient to remain conscious throughout the procedure, facilitating the assessment of neurological function [14,15]. The initiation of surgical treatment demonstrates a brief start-up time, with perioperative hemodynamics remaining stable. However, notable drawbacks include patient agitation and uncooperativeness, intraoperative body movements leading to treatment time prolongation, potential risks of respiratory depression, carbon dioxide retention, and reflux aspiration in the absence of airway protection [14,15]. General anesthesia offers the advantage of airway control and protection against reflux aspiration, facilitating ease of patient management and procedural execution. While artificial airway placement aids in ventilation control, it also presents significant disadvantage, such as pneumonia [14–16]. Studies have shown conflicting results regarding the impact of anesthesia type on the neurological prognosis of patients with surgical operation [17,18]. No statistically significant variance in the prevalence of POCD was observed between general anesthesia and regional anesthesia in elderly patients undergoing hip fracture surgery. Comparable outcomes were noted for both methods at 24 h, 3 days, and 7 days postoperatively [10,19–21]. Conscious sedation does not demonstrate superior efficacy compared to general anesthesia in EVT for patients with post-circulatory AIS, particularly with respect to the primary outcome of functional recovery, and may even be inferior in terms of the secondary outcome of successful reperfusion [22,23]. Furthermore, research has demonstrated that among ischemic stroke patients undergoing EVT, general anesthesia is associated with a higher rate of recurrence and enhanced functional recovery at the 3-month mark when compared to non-general anesthesia techniques [24]. General anesthesia is advised to be prioritized as the preferred option in the majority of EVT interventions for AIS [24].

These studies indicate that general anesthesia may offer advantages in the prognosis of EVT for AIS. Nevertheless, there remains uncertainty surrounding the selection of risk factors linked to perioperative cognitive impairment and prognostic markers associated with general anesthesia. In this study, we collected general clinical data, markers of brain injury, and inflammatory markers from patients undergoing EVT under general anesthesia for AIS patients in order to identify independent prognostic risk factors associated with POCD and establish the relevant prediction model.

2 Methods

2.1 Study participants and design

A retrospective study was conducted to collect clinical data from 521 patients with AIS between January 2019 and January 2024. Inclusion criteria for the study required a diagnosis of AIS based on the guideline of the American Heart Association/American Stroke Association [25]. The study excluded participants based on the following criteria: (1) Alzheimer’s disease, Parkinson’s disease, epilepsy, brain tumors, and other medical conditions related to cognitive dysfunction; (2) depression, anxiety, mania, and other mental disorders; (3) biomarker data incomplete; (4) malignant tumors; and (5) hepatic renal insufficiency. About 188 AIS patients were excluded. Ultimately, a total of 333 patients diagnosed with AIS were included in the study, comprising individuals with non-POCD (n = 232) or POCD (n = 101) as shown in Figure 1. The authors bear responsibility for all facets of the research, including addressing any concerns regarding the accuracy or integrity of the work.

Figure 1 A flowchart about the criteria for exclusion: 521 patients with AIS were recruited and 188 patients were excluded. Ultimately, a total of 333 patients were included in the study.
Figure 1

A flowchart about the criteria for exclusion: 521 patients with AIS were recruited and 188 patients were excluded. Ultimately, a total of 333 patients were included in the study.

2.2 Data collection

General information, including age, gender, body mass index (BMI), time of operation, Trial of ORG 10172 in acute stroke treatment (TOAST) classification, level of education, smoking history, diseases history (hypertension history and diabetes history), length of stay (LOS), systolic blood pressure (SBP), diastolic blood pressure (DBP), National Institute of Health Stroke Scale (NIHSS), bispectral index (BIS), biochemical markers, such as creatinine (Cre), homocysteine (Hcy), uric acid (UA), d-dimer (d-D), high-sensitivity C reactive protein (hs-CRP), platelet lymphocyte ratio (PLR), neutrophil lymphocyte ratio (NLR), systemic inflammatory response index (SIRI), systemic immune inflammatory index (SII), triglyceride (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), N-terminal pro-brain natriuretic peptide (NT-proBNP), and soluble protein-100 β (S100-β) were collected to filtrate POCD related risk factors. POCD is defined as a Mini-Mental State Examination (MMSE) score of 26 points or lower, while non-POCD is operationally defined as an MMSE score ranging from 27 to 30. In addition, a total of 227 patients diagnosed with AIS were included, comprising individuals with high BIS (n = 163) or low BIS (n = 64) to analyze the correlation of BIS with clinical parameters in AIS patients. The low BIS group is defined as individuals scoring between 30 and 49, while the high BIS group is characterized by scores falling between 50 and 60.

2.3 Statistical methods

Statistical analyses were performed using IBM SPSS Statistics (version 25.0, IBM, Armonk, NY, USA), GraphPad Prism 9.0 (GraphPad Software, Inc., La Jolla, CA, USA), R software (version 4.2.1) with the rms package (version 6.3-0), pROC package (version 1.18.0), and ggplot2 package (version 3.3.6). Descriptive statistics, such as the median and interquartile range for non-normally distributed data or the mean and standard deviation for normally distributed data, were calculated. The study utilized Fisher’s exact test or chi-square test for categorical variables, Wilcoxon signed-rank test for continuous variables, t-tests, and Mann–Whitney test for group differences assessment. The independent risk factors associated with POCD were examined through binary logistic regression analysis. Receiver operating characteristic (ROC) curves with the area under the curve (AUC) was used to evaluate the diagnostic performance, in addition to indicators including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and cut-off value, which were computed using optimal thresholds derived from ROC curve analysis. The glm function was utilized to develop a binary logistic model, while the rms (version 6.4.0) and ResourceSelection (version 0.3-5) packages were employed to construct a nomogram model and facilitate its visualization. The goodness-of-fit of the nomogram model was assessed using statistical measures such as the likelihood-ratio test, C index, and Hosmer-Lemeshow goodness fit. The stdca.R file was employed for conducting decision curve analysis (DCA). The rms package (version 6.3-0) was utilized for calibration analysis and data visualization. The pROC package (version 1.18.0) was utilized to conduct precision–recall (PR) analysis on the dataset, and the outcomes were graphically represented using ggplot2 (version 3.3.6). A two-tailed approach was employed for all statistical analyses, with statistical significance defined as P-values less than 0.05.

  1. Ethical approval: The study adhered to the Declaration of Helsinki (2013 revision) and received approval from the Ethics Committees of the Shanghai United Family Hospital and Shanghai SinoUnited Hospital (Approval number: 20240089).

3 Results

3.1 Baseline subject’s characteristics and risk factors of POCD

A total of 333 patients diagnosed with AIS were included in the study, comprising individuals with non-POCD (n = 232) or POCD (n = 101). Baseline data analysis revealed that several key indicators, including DBP, atrial fibrillation incidents, NIHSS score, d-D, hs-CRP, NLR, SIRI, SII, NT-proBNP, as well as the S100-β, were significantly elevated in the POCD group compared to the non-POCD group (Table 1). In order to investigate independent risk factors linked to POCD, we employed univariate and multivariate logistic regression analyses on multiple clinical parameters. Our findings revealed that NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β were identified as independent risk factors for POCD (Table 2).

Table 1

Baseline characteristics of the study population

Characteristics Non-POCD (N = 232) POCD (N = 101) P value Statistical method
Age (years), median (IQR) 64 (58, 71) 63 (55, 72) 0.491 Wilcoxon
Gender, n (%) 0.847 Chisq test
Male 156 (46.8%) 69 (20.7%)
Female 76 (22.8%) 32 (9.6%)
Time of operation (min), median (IQR) 84 (72, 103) 90 (75, 102) 0.280 Wilcoxon
TOAST classification, n (%) 0.293 Chisq test
Small vessel occlusion 89 (26.7%) 28 (8.4%)
Cardioembolism 47 (14.1%) 24 (7.2%)
Large-artery atherosclerosis 72 (21.6%) 35 (10.5%)
Others 24 (7.2%) 14 (4.2%)
TICI score, n (%) 0.687 Chisq test
2b-3 208 (62.5%) 92 (27.6%)
0-2a 24 (7.2%) 9 (2.7%)
Pneumonia, n (%) 0.226 Chisq test
Yes 92 (27.6%) 33 (9.9%)
No 140 (42.0%) 68 (20.4%)
Level of education, n (%) 0.470 Chisq test
High school or higher 80 (24.0%) 39 (11.7%)
Junior middle school or lower 152 (45.6%) 62 (18.6%)
LOS (days), median (IQR) 10 (8, 12) 9 (7, 11) 0.957 Wilcoxon
BMI (kg/m2), median (IQR) 25.4 (22.3, 26.6) 25.8 (23.7, 26.6) 0.180 Wilcoxon
Hypertension history, n (%) 0.705 Chisq test
No 152 (45.6%) 64 (19.2%)
Yes 80 (24.0%) 37 (11.1%)
SBP (mmHg), median (IQR) 150 (137, 161) 151 (134, 157) 0.078 Wilcoxon
DBP (mmHg), median (IQR) 87 (80, 96.25) 93 (83, 97) 0.024 Wilcoxon
Atrial fibrillation, n (%) 0.027 Chisq test
No 158 (47.4%) 56 (16.8%)
Yes 74 (22.2%) 45 (13.5%)
Diabetes history, n (%) 0.940 Chisq test
Yes 86 (25.8%) 37 (11.1%)
No 146 (43.8%) 64 (19.2%)
Stroke history, n (%) 0.555 Chisq test
No 157 (47.1%) 65 (19.5%)
Yes 75 (22.5%) 36 (10.8%)
NIHSS score, median (IQR) 4 (3, 5) 8 (7, 9) 0.000 Wilcoxon
Hemoglobin (g/dL), median (IQR) 11.2 (10.9, 11.7) 11.0 (10.8, 11.3) 0.227 Wilcoxon
Cre (mg/dL), median (IQR) 1.1 (0.9, 1.5) 1.2 (1.0, 1.5) 0.162 Wilcoxon
Hcy (μmol/L), median (IQR) 16.95 (14.6, 21.1) 18 (16.0, 21.1) 0.397 Wilcoxon
UA (μmol/L), median (IQR) 310.9 (179.7, 414.7) 277.1 (175.9, 399.2) 0.632 Wilcoxon
d-D (mg/L), median (IQR) 1.0 (0.8, 1.7) 1.6 (1.4, 1.7) 0.000 Wilcoxon
hs-CRP (mg/L), median (IQR) 4.6 (3.8, 5.4) 8.7 (7.7, 8.9) 0.000 Wilcoxon
PLR, median (IQR) 125.6 (114.6, 140.4) 133.7 (118.9, 142.2) 0.083 Wilcoxon
NLR, median (IQR) 2.7 (2.4, 3.1) 4.1 (3.8, 4.3) 0.000 Wilcoxon
SIRI, median (IQR) 1.6 (1.3, 2.3) 3.4 (3.1, 4.1) 0.000 Wilcoxon
SII, median (IQR) 460.9 (411.2, 562.1) 701.3 (583.4, 804.6) 0.000 Wilcoxon
TG (mmol/L), median (IQR) 2.0 (1.8, 2.1) 2.0 (1.8, 2.1) 0.248 Wilcoxon
TC (mmol/L), median (IQR) 4.3 (3.9, 5.1) 4.2 (3.9, 4.7) 0.046 Wilcoxon
LDL-C (mmol/L), median (IQR) 3.1 (2.7, 3.4) 3 (2.8, 3.3) 0.472 Wilcoxon
HDL-C (mmol/L), median (IQR) 1.2 (1.0, 1.4) 1.2 (1.0, 1.4) 0.594 Wilcoxon
NT-proBNP (ng/L), median (IQR) 406.6 (364.4, 477.6) 563.2 (425.5, 654.4) 0.000 Wilcoxon
S100-β (ng/L), median (IQR) 139.9 (126.4, 159.9) 232.2 (212.5, 262.2) 0.000 Wilcoxon

POCD, postoperative cognitive dysfunction; IQR, interquartile range; TOAST, Trial of ORG 10172 in acute stroke treatment; LOS, length of stay; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institute of Health Stroke Scale; TICI, thrombolysis in cerebral infarction; Cre, creatinine; Hcy, homocysteine; UA, uric acid; d-D, d-dimer; hs-CRP, high-sensitivity C reactive protein; PLR, platelet lymphocyte ratio; NLR, neutrophil lymphocyte ratio; SIRI, systemic inflammatory response index; SII, systemic immune inflammatory index; TG, triglyceride; TC, total cholesterol; LDL-C, low density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; NT-proBNP, N-terminal pro-brain natriuretic peptide; S100-β, soluble protein-100 β.

Table 2

Univariate and multivariate analysis of POCD related risk factors in AIS patients

Characteristics Total (N) OR (95% CI) Univariate analysis P value OR (95% CI) Multivariate analysis P value
Age (years) 333 0.99 (0.96–1.02) 0.484
Gender 333
Male 225 Reference
Female 108 0.95 (0.58–1.57) 0.847
Time of operation (min) 333 1.01 (0.99–1.02) 0.330
Pneumonia 333
Yes 125 Reference
No 208 1.35 (0.83–2.22) 0.227
Level of education 333
High school or higher 119 Reference
Junior middle school or lower 214 0.84 (0.52–1.36) 0.470
LOS (days) 333 1.01 (0.91–1.11) 0.924
BMI (kg/m2) 333 1.08 (0.98–1.19) 0.131
Hypertension history 333
No 216 Reference
Yes 117 1.09 (0.68–1.79) 0.706
SBP (mmHg) 333 0.98 (0.97–1.00) 0.044 1.01 (0.89–1.14) 0.817
DBP (mmHg) 333 1.03 (1.01–1.06) 0.025 0.96 (0.79–1.17) 0.704
Atrial fibrillation 333
No 214 Reference Reference
Yes 119 1.72 (1.06–2.77) 0.027 1.49 (0.07–32.20) 0.798
Stroke history 333
No 222 Reference
Yes 111 1.16 (0.71–1.89) 0.555
NIHSS score 333 4.18 (3.01–5.79) <0.001 15.59 (2.14–113.49) 0.007
Hemoglobin (g/dL) 333 1.16 (0.94–1.42) 0.164
Cre (mg/dL) 333 1.56 (0.82–2.94) 0.168
Hcy (μmol/L) 333 1.04 (0.97–1.11) 0.295
UA (μmol/L) 333 1.00 (0.99–1.00) 0.620
d-D (mg/L) 333 14.28 (7.03–28.97) <0.001 0.38 (0.01–21.18) 0.637
hs-CRP (mg/L) 333 2.73 (2.24–3.34) <0.001 3.42 (1.34–8.72) 0.010
PLR 333 1.02 (0.99–1.03) 0.071 1.09 (0.98–1.23) 0.113
SII 333 1.01 (1.01–1.02) <0.001 1.04 (1.00–1.07) 0.034
TG (mmol/L) 333 1.60 (0.62–4.15) 0.331
TC (mmol/L) 333 0.67 (0.47–0.96) 0.029 2.92 (0.25–34.49) 0.395
LDL-C (mmol/L) 333 0.87 (0.52–1.46) 0.599
HDL-C (mmol/L) 333 1.34 (0.45–3.93) 0.598
NT-proBNP (ng/L) 333 1.01 (1.01–1.01) <0.001 1.01 (1.00–1.03) 0.039
S100-β (ng/L) 333 1.03 (1.03–1.04) <0.001 1.07 (1.02–1.13) 0.007

POCD, postoperative cognitive dysfunction; AIS, acute ischemic stroke; OR, odds ratio; LOS, length of stay; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institute of Health Stroke Scale; Cre, creatinine; Hcy, homocysteine; UA, uric acid; d-D, d-dimer; hs-CRP, high-sensitivity C reactive protein; PLR, platelet lymphocyte ratio; SII, systemic immune inflammatory index; TG, triglyceride; TC, serum total cholesterol; LDL-C, low density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; NT-proBNP, N-terminal pro-brain natriuretic peptide; S100-β, soluble protein-100 β.

3.2 Diagnostic performance of risk factors for POCD in AIS patients

In order to investigate the diagnostic performance of NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β in distinguishing between patients with POCD or non-POCD, a total of 333 AIS patients were recruited. The corresponding AUC values for NIHSS score (Figure 2a), hs-CRP (Figure 2b), SII (Figure 2c), NT-proBNP (Figure 2d), and S100-β (Figure 2e) were determined to be 0.950 (95% CI: 0.929–0.972), 0.911 (95% CI: 0.878–0.945), 0.880 (95% CI: 0.842–0.918), 0.764 (0.700–0.827), and 0.853 (0.806–0.901), respectively. The combination of five independent risk factors has excellent diagnostic efficacy for POCD (AUC = 0.999; 95% CI: 0.998–1.000) (Figure 2f). The cut-off value, sensitivity, specificity, accuracy, PPV, and NPV of NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β for the diagnosis of POCD are shown in Table 3.

Figure 2 Diagnostic performance of risk factors for POCD in AIS patients. In order to investigate the diagnostic performance of NIHSS score (a), hs-CRP (b), SII (c), NT-proBNP (d), S100-β (e), and combination diagnosis model (f) in distinguishing between patients with POCD or non-POCD. ROC with AUC was used to evaluate the diagnostic performance in 333 AIS patients.
Figure 2

Diagnostic performance of risk factors for POCD in AIS patients. In order to investigate the diagnostic performance of NIHSS score (a), hs-CRP (b), SII (c), NT-proBNP (d), S100-β (e), and combination diagnosis model (f) in distinguishing between patients with POCD or non-POCD. ROC with AUC was used to evaluate the diagnostic performance in 333 AIS patients.

Table 3

Diagnostic performance of variables for POCD in AIS patients

Variables Cut-off Sensitivity Specificity Accuracy PPV NPV
NIHSS score 6.50 0.782 0.978 0.918 0.940 0.912
hs-CRP (mg/L) 7.40 0.772 0.953 0.898 0.876 0.9066
SII 521.800 0.931 0.698 0.769 0.573 0.959
NT-proBNP (ng/L) 520.200 0.594 0.922 0.822 0.769 0.839
S100-β (ng/L) 186.600 0.792 0.866 0.844 0.721 0.905

POCD, postoperative cognitive dysfunction; AIS, acute ischemic stroke; NIHSS, National Institute of Health Stroke Scale; hs-CRP, high-sensitivity C reactive protein; SII, systemic immune inflammatory index; NT-proBNP, N-terminal pro-brain natriuretic peptide; S100-β, soluble protein-100 β; PPV, positive predictive value; NPV, negative predictive value.

3.3 Develop a nomogram model for the evaluation of POCD risk

The independent risk factors for predicting POCD patients from AIS patients were determined using multiple logistic regression analysis. The nomogram effectively integrates these five predictors, NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β, and illustrates their interconnectedness within the predictive framework by means of calibrated line segments on a shared plane. The cumulative scores for the five variables were calculated, and subsequently used to determine the probability of POCD risk (Figure 3a). Calibration curves (Figure 3b), DCA (Figure 3c), and PR curves (Figure 3d) were used to evaluate the stability of nomogram models. The statistical analysis, including the likelihood-ratio test (χ 2 = 381.02; P < 0.001), C index (0.999; 95% CI: 0.998–1.000; P < 0.001), and Hosmer-Lemeshow goodness fit (χ 2 = 0.311; P = 1.000), indicates that the model exhibits a favorable level of fit (Figure 3b). DCA curve analysis is employed to depict the alteration in the net return value, considering the intervention condition and the predicted value of the model, as the risk probability threshold undergoes modification. The five variables surpass the reference line within a substantial interval of probability threshold, thereby signifying the efficacy of the variable model (Figure 3c). The PR graph is a curve that reflects the relationship between precision and recall. The AUC value of NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β was 0.950 (95% CI: 0.929–0.972), 0.911 (95% CI: 0.878–0.945), 0.880 (95% CI: 0.842–0.918), 0.764 (95% CI: 0.700–0.827), and 0.853 (95% CI: 0.806–0.901), respectively, suggesting that five variables have good diagnostic effect in predicting POCD (Figure 3d).

Figure 3 Develop a nomogram model for the evaluation of POCD risk. The independent risk factors for predicting POCD patients from AIS patients were determined using multiple logistic regression analysis. The nomogram effectively integrates these five predictors, NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β (a). A calibration curve (b), DCA (c), and PR (d) curves were employed to assess the model’s goodness of fit.
Figure 3

Develop a nomogram model for the evaluation of POCD risk. The independent risk factors for predicting POCD patients from AIS patients were determined using multiple logistic regression analysis. The nomogram effectively integrates these five predictors, NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β (a). A calibration curve (b), DCA (c), and PR (d) curves were employed to assess the model’s goodness of fit.

3.4 Correlation of BIS with clinical parameters in AIS patients

A total of 227 patients diagnosed with AIS were included, comprising individuals with high BIS (n = 163) or low BIS (n = 64) to analyze the correlation of BIS with clinical parameters in AIS patients. Baseline data analysis revealed that several key indicators, including NIHSS score, d-D, hs-CRP, NLR, SIRI, SII, NT-proBNP, and S100-β, were significantly elevated in the low BIS group compared to the high BIS group (Table 4). However, MMSE score was significantly lower in the low BIS group compared to the high BIS group (Table 4). Then we performed spearman correlation analysis and found that BIS score was positively correlated with MMSE score (r = 0.300; P < 0.001). It was negatively correlated with NIHSS score (r = −0.364; P < 0.001), d-D (r = −0.331; P < 0.001), hs-CRP (r = −0.322; P < 0.001), NLR (r = −0.381; P < 0.001), SIRI (r = −0.359; P < 0.001), SII (r = −0.261; P < 0.001), NT-proBNP (r = −0.251; P < 0.001), and S100-β (r = −0.342; P < 0.001) (Figure 4).

Table 4

Baseline characteristics of AIS patients with high BIS (50–60) or low BIS (30–49)

Characteristics High (n = 163) Low (n = 64) P value Statistical method
MMSE score, median (IQR) 27.5 (27, 28.6) 21.4 (18.6, 24.9) 0.000 Wilcoxon
Age (years), median (IQR) 64 (58, 70) 63 (56, 72) 0.708 Wilcoxon
Gender, n (%) 0.541 Chisq test
Male 113 (49.8%) 47 (20.7%)
Female 50 (22.0%) 17 (7.5%)
Time of operation (min), median (IQR) 85 (72.0, 102.0) 90 (74.5, 105.0) 0.294 Wilcoxon
TOAST classification, n (%) 0.059 Chisq test
Cardioembolism 38 (16.7%) 14 (6.2%)
Large-artery atherosclerosis 45 (19.8%) 24 (10.6%)
Small vessel occlusion 67 (29.5%) 16 (7.0%)
Others 13 (5.7%) 10 (4.4%)
TICI, n (%) 0.621 Chisq test
2b-3 144 (63.4%) 58 (25.6%)
0-2a 19 (8.4%) 6 (2.6%)
Pneumonia, n (%) 0.072 Chisq test
No 99 (43.6%) 47 (20.7%)
Yes 64 (28.2%) 17 (7.5%)
Level of education, n (%) 0.366 Chisq test
Junior middle school or lower 99 (43.6%) 43 (18.9%)
High school or higher 64 (28.2%) 21 (9.3%)
LOS (days), median (IQR) 10 (8, 12) 10 (8, 11) 0.846 Wilcoxon
BMI (kg/m2), median (IQR) 24.2 (22.1, 26.6) 25.3 (23.5, 26.6) 0.419 Wilcoxon
Hypertension history, n (%) 0.693 Chisq test
No 105 (46.3%) 43 (18.9%)
Yes 58 (25.6%) 21 (9.3%)
SBP (mmHg), median (IQR) 150.0 (136.5, 161.0) 143.5 (132.0, 155.0) 0.129 Wilcoxon
DBP (mmHg), median (IQR) 87 (79, 95) 93 (83, 99) 0.000 Wilcoxon
Atrial fibrillation, n (%) 0.971 Chisq test
Yes 59 (26.0%) 23 (10.1%)
No 104 (45.8%) 41 (18.1%)
Diabetes history, n (%) 0.687 Chisq test
Yes 59 (26%) 25 (11%)
No 104 (45.8%) 39 (17.2%)
Stroke history, n (%) 0.754 Chisq test
No 108 (47.6%) 41 (18.1%)
Yes 55 (24.2%) 23 (10.1%)
NIHSS score, median (IQR) 4 (3, 5) 7 (5, 8) 0.000 Wilcoxon
Hemoglobin (g/dL), median (IQR) 11.1 (10.8, 11.8) 11 (10.8, 11.3) 0.408 Wilcoxon
Cre (mg/dL), median (IQR) 1.2 (0.9, 1.5) 1.2 (1, 1.5) 0.115 Wilcoxon
Hcy (μmol/L), median (IQR) 16.9 (14.6, 21.1) 16.8 (14.5, 20.9) 0.727 Wilcoxon
UA (μmol/L), median (IQR) 278.1 (176.4, 399.2) 241.7 (174.3, 410.2) 0.798 Wilcoxon
d-D (mg/L), median (IQR) 1.1 (0.8, 1.6) 1.6 (1.4, 1.8) 0.000 Wilcoxon
hs-CRP (mg/L), median (IQR) 4.9 (3.9, 6.9) 8.6 (5.3, 8.9) 0.000 Wilcoxon
PLR, median (IQR) 131.4 (114.7, 140.9) 133.7 (119.3, 143.5) 0.164 Wilcoxon
NLR, median (IQR) 2.8 (2.4, 3.3) 4.1 (3.45, 4.2) 0.000 Wilcoxon
SIRI, median (IQR) 1.6 (1.3, 2.7) 3.3 (3, 3.8) 0.000 Wilcoxon
SII, median (IQR) 486.3 (414.3, 603.2) 604.8 (535.7, 765.5) 0.000 Wilcoxon
TG (mmol/L), median (IQR) 1.9 (1.8, 2.1) 2 (1.9, 2.1) 0.056 Wilcoxon
TC (mmol/L), median (IQR) 4.5 (4, 5.15) 4 (3.8, 4.4) 0.000 Wilcoxon
LDL-C (mmol/L), median (IQR) 3.1 (2.7, 3.4) 3.05 (2.9, 3.3) 0.924 Wilcoxon
HDL-C (mmol/L), median (IQR) 1.2 (1.0, 1.4) 1.2 (1, 1.5) 0.235 Wilcoxon
NT-proBNP (ng/L), median (IQR) 421.3 (368.2, 490.4) 562.2 (445.5, 631.9) 0.000 Wilcoxon
S100-β (ng/L), median (IQR) 144.4 (126.5, 173.7) 244.95 (214.1, 272.8) 0.000 Wilcoxon

BIS, bispectral index; MMSE, mini mental status examination; IQR, interquartile range; TOAST, Trial of ORG 10172 in acute stroke treatment; LOS, length of stay; BMI, body mass index; TICI, thrombolysis in cerebral infarction; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institute of Health Stroke Scale; Cre, creatinine; Hcy, homocysteine; UA, uric acid; d-D, d-dimer; hs-CRP, high-sensitivity C reactive protein; PLR, platelet lymphocyte ratio; NLR, neutrophil lymphocyte ratio; SIRI, systemic inflammatory response index; SII, systemic immune inflammatory index; TG, triglyceride; TC, total cholesterol; LDL-C, low density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; NT-proBNP, N-terminal pro-brain natriuretic peptide; S100-β, soluble protein-100 β.

Figure 4 Correlation of BIS with clinical parameters in AIS patients. A total of 227 patients diagnosed with AIS were included, comprising individuals with high BIS (n = 163) or low BIS (n = 64) to analyze the correlation of BIS with clinical parameters, including NIHSS score, d-D, hs-CRP, NLR, SIRI, SII, NT-proBNP, and S100-β, in AIS patients with Spearman correlation.
Figure 4

Correlation of BIS with clinical parameters in AIS patients. A total of 227 patients diagnosed with AIS were included, comprising individuals with high BIS (n = 163) or low BIS (n = 64) to analyze the correlation of BIS with clinical parameters, including NIHSS score, d-D, hs-CRP, NLR, SIRI, SII, NT-proBNP, and S100-β, in AIS patients with Spearman correlation.

4 Discussion

POCD is a prevalent complication among patients with AIS, with its incidence being associated with cerebrovascular lesions and strongly correlated with advancing age. Elderly individuals who have suffered a stroke are particularly vulnerable to developing POCD [26]. Following AIS in the elderly population, localized tissue ischemia may directly precipitate oxidative stress and inflammation, ultimately leading to brain injury [27]. Brain tissue is highly susceptible to hypoxia, and delayed irreversible nerve damage can occur even with timely thrombolysis and thrombectomy [27]. Previous studies have indicated that CRP, NLR, reduced HDL-C, BMI, NIHSS score, and SBP are risk factors for perioperative POCD [3,28–30]. Li et al. identified elderly age, the presence of carotid artery plaques, a high NIHSS score, multiple infarct lesions, and specific infarct types as significant risk factors for cognitive dysfunction in patients recovering from cerebral infarction [31]. Tang and colleagues [32] employed gender, age, baseline NIHSS score, hyperhomocysteinemia, and multiple lesions in the development of a nomogram model for the prediction of acute cognitive impairment following stroke, achieving an AUC value of 0.859. In light of the significance of the inflammatory response and brain injury in contributing to cognitive dysfunction, we utilized multivariate regression analysis to identify two inflammatory indicators (hs-CRP and SII) and three brain injury indicators (NIHSS score, NT-proBNP, and S100-β), which were used to construct a nomogram model. Evaluation through calibration curves, DCA, and PR curves demonstrated the satisfactory predictive performance of the nomogram model. Our results reaffirm the role of elevated inflammation and increased brain damage in exacerbating POCD in patients with AIS.

Numerous studies have established a correlation between inflammation and cognitive impairment, with inflammation identified as a potential mechanism contributing to POCD [33,34]. Impairment of the blood–brain barrier further intensifies central inflammation, facilitating the infiltration of peripheral immune cells into the brain and fostering inflammation [33–35]. Peripheral inflammation is recognized as a primary factor in inducing neuroinflammation within the brain, as pro-inflammatory cytokines have been observed to breach the blood–brain barrier and incite central inflammatory responses [35–37]. Guo et al. [38] indicated a significant negative relationship between systemic inflammation markers, including SIRI, SII, pan-immune-inflammation value, and cognitive functioning, implying that targeting inflammation may be a viable strategy for improving cognitive well-being and reducing cognitive decline associated with aging. In comparison to patients without POCD, those with POCD exhibited significantly higher SII and SIRI values. Both SII and SIRI were found to have negative correlations with Montreal cognitive assessment scores. Furthermore, multivariable logistic regression analysis revealed that SII was independently associated with POCD, while SIRI was not significantly associated [39]. Consistent of these findings [38,39], this study revealed a significant upregulation of SII and hs-CRP in patients with POCD compared to those without POCD in AIS patients. Furthermore, both SII and hs-CRP were identified as independent risk factors for the development of POCD in AIS patients.

The present findings suggest that there is no statistically significant disparity in the prevalence of cognitive impairment among elderly individuals following general anesthesia compared to regional anesthesia [19]. The outcomes of both anesthesia modalities remained comparable for a duration of up to 7 days post-surgery, implying that the choice of anesthesia may be contingent upon the unique characteristics of each patient rather than anticipated variations in clinical consequences [19,20,40]. While previous research has indicated that the depth of anesthesia may not have a direct impact on cognitive function, this study categorizes AIS patients undergoing general anesthesia based on BIS score with high score (50–60) or low score (30–49). Our findings demonstrate a significant difference in MMSE scores between the low BIS and high BIS groups, implying that depth of anesthesia could potentially contribute to cognitive decline in patients receiving general anesthesia. Our findings indicate a significant negative correlation between BIS score and various clinical measures, such as NIHSS score, d-D, hs-CRP, PLR, NLR, SIRI, SII, NT-proBNP, and S100-β. Increased depth of anesthesia may be linked to brain injury and inflammatory response, with five of these parameters identified as independent risk factors for the development of POCD. Overall, the depth of anesthesia may be a factor in the occurrence of POCD in AIS patients undergoing general anesthesia. However, Spearman’s correlation coefficient indicates that the correlation between depth of anesthesia and cognitive impairment is not a strong correlation. Consequently, further investigation in a larger sample is required to validate this relationship.

Our research successfully developed a novel nomogram model for predicting POCD in patients with AIS, demonstrating a strong level of accuracy. Additionally, our findings suggest a potential association between anesthesia depth, cognitive impairment, and inflammatory response. However, it is important to acknowledge the limitations of this study. Initially, the study did not include an analysis of the comparative impacts of varying anesthesia types on cognitive function. Additionally, the examination did not address the potential effects of narcotic drug utilization on cognitive function. Furthermore, while a correlation was identified between the anesthesia depth and cognitive impairment and inflammatory response, the strength of this association was deemed insufficient and necessitated validation through a more extensive sample size. Finally, this study did not include parameters related to perioperative complications. Future research will aim to investigate the impact of perioperative complications on POCD.

5 Conclusion

In summary, our findings indicate that the composite predictive model incorporating NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β demonstrates efficacy in predicting POCD following AIS. Additionally, our results suggest a potential association between depth of anesthesia, cognitive impairment, and inflammatory response in AIS patients.

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Acknowledgements

Not applicable.

  1. Funding information: Not applicable.

  2. Author contributions: Study design was performed by Y.Z. and Y.W.; literature research was performed by Y.Z., Y.W., and L.X.; experimental studies, data acquisition, statistical analysis were executed by Y.Z., Y.W., and L.X.; manuscript writing and editing were performed by Y.Z., Y.W., and L.X.; manuscript review was performed by Y.Z., Y.W., and L.X.; manuscript was approved by all authors.

  3. Conflict of interest: All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare.

  4. Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Received: 2024-06-24
Revised: 2024-09-20
Accepted: 2024-10-14
Published Online: 2024-12-17

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  209. Retraction
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  211. Corrigendum
  212. Corrigendum to “Investigating hormesis, aging, and neurodegeneration: From bench to clinics”
  213. Corrigendum to “Frankincense: A neuronutrient to approach Parkinson’s disease treatment”
  214. Special Issue The evolving saga of RNAs from bench to bedside - Part II
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https://doi.org/10.1515/med-2024-1085
Keywords for this article
cognitive impairment; acute ischemic stroke; nomogram; prediction; anesthesia depth
Creative Commons
BY 4.0

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  118. Paraquat disrupts the blood–brain barrier by increasing IL-6 expression and oxidative stress through the activation of PI3K/AKT signaling pathway
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  123. Laboratory variables‐based artificial neural network models for predicting fatty liver disease: A retrospective study
  124. Decreased BIRC5-206 promotes epithelial–mesenchymal transition in nasopharyngeal carcinoma through sponging miR-145-5p
  125. Sepsis induces the cardiomyocyte apoptosis and cardiac dysfunction through activation of YAP1/Serpine1/caspase-3 pathway
  126. Assessment of iron metabolism and iron deficiency in incident patients on incident continuous ambulatory peritoneal dialysis
  127. Tibial periosteum flap combined with autologous bone grafting in the treatment of Gustilo-IIIB/IIIC open tibial fractures
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  130. Role of FOXM1 and AURKB in regulating keratinocyte function in psoriasis
  131. Parental control attitudes over their pre-school children’s diet
  132. The role of auto-HSCT in extranodal natural killer/T cell lymphoma
  133. Significance of negative cervical cytology and positive HPV in the diagnosis of cervical lesions by colposcopy
  134. Echinacoside inhibits PASMCs calcium overload to prevent hypoxic pulmonary artery remodeling by regulating TRPC1/4/6 and calmodulin
  135. ADAR1 plays a protective role in proximal tubular cells under high glucose conditions by attenuating the PI3K/AKT/mTOR signaling pathway
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  143. Temporal muscle thickness is not a prognostic predictor in patients with high-grade glioma, an experience at two centers in China
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  145. Therapeutic role of respiratory exercise in patients with tuberculous pleurisy
  146. Effects of CFTR-ENaC on spinal cord edema after spinal cord injury
  147. Irisin-regulated lncRNAs and their potential regulatory functions in chondrogenic differentiation of human mesenchymal stem cells
  148. DMD mutations in pediatric patients with phenotypes of Duchenne/Becker muscular dystrophy
  149. Combination of C-reactive protein and fibrinogen-to-albumin ratio as a novel predictor of all-cause mortality in heart failure patients
  150. Significant role and the underly mechanism of cullin-1 in chronic obstructive pulmonary disease
  151. Ferroptosis-related prognostic model of mantle cell lymphoma
  152. Observation of choking reaction and other related indexes in elderly painless fiberoptic bronchoscopy with transnasal high-flow humidification oxygen therapy
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  156. Role of COX6C and NDUFB3 in septic shock and stroke
  157. Trends in disease burden of type 2 diabetes, stroke, and hypertensive heart disease attributable to high BMI in China: 1990–2019
  158. Purinergic P2X7 receptor mediates hyperoxia-induced injury in pulmonary microvascular endothelial cells via NLRP3-mediated pyroptotic pathway
  159. Investigating the role of oviductal mucosa–endometrial co-culture in modulating factors relevant to embryo implantation
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  161. Elevated serum miR-142-5p correlates with ischemic lesions and both NSE and S100β in ischemic stroke patients
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  164. Predictive role of neuron-specific enolase and S100-β in early neurological deterioration and unfavorable prognosis in patients with ischemic stroke
  165. The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia
  166. Fluoxetine inhibited RANKL-induced osteoclastic differentiation in vitro
  167. Detection of serum FOXM1 and IGF2 in patients with ARDS and their correlation with disease and prognosis
  168. Rhein promotes skin wound healing by activating the PI3K/AKT signaling pathway
  169. Differences in mortality risk by levels of physical activity among persons with disabilities in South Korea
  170. Review Articles
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  172. XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis
  173. A narrative review on adverse drug reactions of COVID-19 treatments on the kidney
  174. Emerging role and function of SPDL1 in human health and diseases
  175. Adverse reactions of piperacillin: A literature review of case reports
  176. Molecular mechanism and intervention measures of microvascular complications in diabetes
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  178. Molecular landscape of borderline ovarian tumours: A systematic review
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  180. Investigating hormesis, aging, and neurodegeneration: From bench to clinics
  181. Frankincense: A neuronutrient to approach Parkinson’s disease treatment
  182. Sox9: A potential regulator of cancer stem cells in osteosarcoma
  183. Early detection of cardiovascular risk markers through non-invasive ultrasound methodologies in periodontitis patients
  184. Advanced neuroimaging and criminal interrogation in lie detection
  185. Maternal factors for neural tube defects in offspring: An umbrella review
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  187. CBD’s potential impact on Parkinson’s disease: An updated overview
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  191. Lidocaine infusion for the treatment of complex regional pain syndrome: Case series and literature review
  192. Trends and future directions of autophagy in osteosarcoma: A bibliometric analysis
  193. Iron in ventricular remodeling and aneurysms post-myocardial infarction
  194. Case Reports
  195. Sirolimus potentiated angioedema: A case report and review of the literature
  196. Identification of mixed anaerobic infections after inguinal hernia repair based on metagenomic next-generation sequencing: A case report
  197. Successful treatment with bortezomib in combination with dexamethasone in a middle-aged male with idiopathic multicentric Castleman’s disease: A case report
  198. Complete heart block associated with hepatitis A infection in a female child with fatal outcome
  199. Elevation of D-dimer in eosinophilic gastrointestinal diseases in the absence of venous thrombosis: A case series and literature review
  200. Four years of natural progressive course: A rare case report of juvenile Xp11.2 translocations renal cell carcinoma with TFE3 gene fusion
  201. Advancing prenatal diagnosis: Echocardiographic detection of Scimitar syndrome in China – A case series
  202. Outcomes and complications of hemodialysis in patients with renal cancer following bilateral nephrectomy
  203. Anti-HMGCR myopathy mimicking facioscapulohumeral muscular dystrophy
  204. Recurrent opportunistic infections in a HIV-negative patient with combined C6 and NFKB1 mutations: A case report, pedigree analysis, and literature review
  205. Letter to the Editor
  206. Letter to the Editor: Total parenteral nutrition-induced Wernicke’s encephalopathy after oncologic gastrointestinal surgery
  207. Erratum
  208. Erratum to “Bladder-embedded ectopic intrauterine device with calculus”
  209. Retraction
  210. Retraction of “XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis”
  211. Corrigendum
  212. Corrigendum to “Investigating hormesis, aging, and neurodegeneration: From bench to clinics”
  213. Corrigendum to “Frankincense: A neuronutrient to approach Parkinson’s disease treatment”
  214. Special Issue The evolving saga of RNAs from bench to bedside - Part II
  215. Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
  216. Unlocking the future of hepatocellular carcinoma treatment: A comprehensive analysis of disulfidptosis-related lncRNAs for prognosis and drug screening
  217. Elevated mRNA level indicates FSIP1 promotes EMT and gastric cancer progression by regulating fibroblasts in tumor microenvironment
  218. Special Issue Advancements in oncology: bridging clinical and experimental research - Part I
  219. Ultrasound-guided transperineal vs transrectal prostate biopsy: A meta-analysis of diagnostic accuracy and complication rates
  220. Assessment of diagnostic value of unilateral systematic biopsy combined with targeted biopsy in detecting clinically significant prostate cancer
  221. SENP7 inhibits glioblastoma metastasis and invasion by dissociating SUMO2/3 binding to specific target proteins
  222. MARK1 suppress malignant progression of hepatocellular carcinoma and improves sorafenib resistance through negatively regulating POTEE
  223. Analysis of postoperative complications in bladder cancer patients
  224. Carboplatin combined with arsenic trioxide versus carboplatin combined with docetaxel treatment for LACC: A randomized, open-label, phase II clinical study
  225. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part I
  226. Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma
  227. Identification of signatures associated with microsatellite instability and immune characteristics to predict the prognostic risk of colon cancer
  228. Single-cell analysis identified key macrophage subpopulations associated with atherosclerosis
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