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.
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.
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.
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.
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.
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.
Acknowledgements
Not applicable.
Footnotes
Funding information: Not applicable.
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.
Conflict of interest: All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare.
Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- [1].Bramley P, McArthur K, Blayney A, McCullagh I. Risk factors for postoperative delirium: an umbrella review of systematic reviews. Int J Surg. 2021;93:106063. 10.1016/j.ijsu.2021.106063. [DOI] [PubMed]
- [2].Zhao Q, Wan H, Pan H, Xu Y. Postoperative cognitive dysfunction-current research progress. Front Behav Neurosci. 2024;18:1328790. 10.3389/fnbeh.2024.1328790. [DOI] [PMC free article] [PubMed]
- [3].Feinkohl I, Janke J, Slooter AJC, Winterer G, Spies C, Pischon T. Metabolic syndrome and the risk of postoperative delirium and postoperative cognitive dysfunction: a multi-centre cohort study. Br J Anaesth. 2023;131(2):338–47. 10.1016/j.bja.2023.04.031. [DOI] [PubMed]
- [4].Zeng K, Long J, Li Y, Hu J. Preventing postoperative cognitive dysfunction using anesthetic drugs in elderly patients undergoing noncardiac surgery: a systematic review and meta-analysis. Int J Surg. 2023;109(1):21–31. 10.1097/js9.0000000000000001. [DOI] [PMC free article] [PubMed]
- [5].He L, Duan X, Li S, Zhang R, Dai X, Lu M. Unveiling the role of astrocytes in postoperative cognitive dysfunction. Ageing Res Rev. 2024;95:102223. 10.1016/j.arr.2024.102223. [DOI] [PubMed]
- [6].Zhang S, Liu C, Sun J, Li Y, Lu J, Xiong X, et al. Bridging the gap: investigating the link between inflammasomes and postoperative cognitive dysfunction. Aging Dis. 2023;14(6):1981–2002. 10.14336/ad.2023.0501. [DOI] [PMC free article] [PubMed]
- [7].Wu WF, Lin JT, Qiu YK, Dong W, Wan J, Li S, et al. The role of epigenetic modification in postoperative cognitive dysfunction. Ageing Res Rev. 2023;89:101983. 10.1016/j.arr.2023.101983. [DOI] [PubMed]
- [8].Yang X, Huang X, Li M, Jiang Y, Zhang H. Identification of individuals at risk for postoperative cognitive dysfunction (POCD). Ther Adv Neurol Disord. 2022;15:17562864221114356. 10.1177/17562864221114356. [DOI] [PMC free article] [PubMed]
- [9].Jin Z, Hu J, Ma D. Postoperative delirium: perioperative assessment, risk reduction, and management. Br J Anaesth. 2020;125(4):492–504. 10.1016/j.bja.2020.06.063. [DOI] [PubMed]
- [10].Travica N, Lotfaliany M, Marriott A, Safavynia SA, Berk M, Skvarc D, et al. Peri-operative risk factors associated with post-operative cognitive dysfunction (POCD): an umbrella review of meta-analyses of observational studies. J Clin Med. 2023;12(4):1610. 10.3390/jcm12041610. [DOI] [PMC free article] [PubMed]
- [11].Dilmen OK, Meco BC, Evered LA, Radtke FM. Postoperative neurocognitive disorders: a clinical guide. J Clin Anesth. 2024;92:111320. 10.1016/j.jclinane.2023.111320. [DOI] [PubMed]
- [12].Marcucci M, Chan MTV, Smith EE, Absalom AR, Devereaux PJ. Prevention of perioperative stroke in patients undergoing non-cardiac surgery. Lancet Neurol. 2023;22(10):946–58. 10.1016/s1474-4422(23)00209-0. [DOI] [PubMed]
- [13].Mendelson SJ, Prabhakaran S. Diagnosis and management of transient ischemic attack and acute ischemic stroke: a review. JAMA. 2021;325(11):1088–98. 10.1001/jama.2020.26867. [DOI] [PubMed]
- [14].Businger J, Fort AC, Vlisides PE, Cobas M, Akca O. Management of acute ischemic stroke-specific focus on anesthetic management for mechanical thrombectomy. Anesth Analg. 2020;131(4):1124–34. 10.1213/ane.0000000000004959. [DOI] [PubMed]
- [15].Hindman BJ, Dexter F. Anesthetic management of emergency endovascular thrombectomy for acute ischemic stroke, part 2: integrating and applying observational reports and randomized clinical trials. Anesth Analg. 2019;128(4):706–17. 10.1213/ane.0000000000004045. [DOI] [PubMed]
- [16].Ilyas A, Chen CJ, Ding D, Foreman PM, Buell TJ, Ironside N, et al. Endovascular mechanical thrombectomy for acute ischemic stroke under general anesthesia versus conscious sedation: a systematic review and meta-analysis. World Neurosurg. 2018;112:e355–67. 10.1016/j.wneu.2018.01.049. [DOI] [PubMed]
- [17].Geraldini F, De Cassai A, Napoli M, Marini S, De Bon F, Sergi M, et al. Risk factors for general anesthesia conversion in anterior circulation stroke patients undergoing endovascular treatment. Cerebrovasc Dis. 2022;51(4):481–7. 10.1159/000520929. [DOI] [PubMed]
- [18].Orlický M, Hrbáč T, Sameš M, Vachata P, Hejčl A, Otáhal D, et al. Anesthesia type determines risk of cerebral infarction after carotid endarterectomy. J Vasc Surg. 2019;70(1):138–47. 10.1016/j.jvs.2018.10.066. [DOI] [PubMed]
- [19].Bhushan S, Huang X, Duan Y, Xiao Z. The impact of regional versus general anesthesia on postoperative neurocognitive outcomes in elderly patients undergoing hip fracture surgery: a systematic review and meta-analysis. Int J Surg. 2022;105:106854. 10.1016/j.ijsu.2022.106854. [DOI] [PubMed]
- [20].Li T, Li J, Yuan L, Wu J, Jiang C, Daniels J, et al. Effect of regional vs general anesthesia on incidence of postoperative delirium in older patients undergoing hip fracture surgery: the RAGA randomized trial. JAMA. 2022;327(1):50–8. 10.1001/jama.2021.22647. [DOI] [PMC free article] [PubMed]
- [21].Tzimas P, Samara E, Petrou A, Korompilias A, Chalkias A, Papadopoulos G. The influence of anesthetic techniques on postoperative cognitive function in elderly patients undergoing hip fracture surgery: general vs spinal anesthesia. Injury. 2018;49(12):2221–6. 10.1016/j.injury.2018.09.023. [DOI] [PubMed]
- [22].Liang F, Wu Y, Wang X, Yan L, Zhang S, Jian M, et al. General anesthesia vs conscious sedation for endovascular treatment in patients with posterior circulation acute ischemic stroke: an exploratory randomized clinical trial. JAMA Neurol. 2023;80(1):64–72. 10.1001/jamaneurol.2022.3018. [DOI] [PMC free article] [PubMed]
- [23].Chabanne R, Geeraerts T, Begard M, Balança B, Rapido F, Degos V, et al. Outcomes after endovascular therapy with procedural sedation vs general anesthesia in patients with acute ischemic stroke: the AMETIS randomized clinical trial. JAMA Neurol. 2023;80(5):474–83. 10.1001/jamaneurol.2023.0413. [DOI] [PMC free article] [PubMed]
- [24].Campbell D, Butler E, Campbell RB, Ho J, Barber PA. General anesthesia compared with non-GA in endovascular thrombectomy for ischemic stroke: a systematic review and meta-analysis of randomized controlled trials. Neurology. 2023;100(16):e1655–63. 10.1212/wnl.0000000000207066. [DOI] [PMC free article] [PubMed]
- [25].Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. 2018 guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2018;49(3):e46–110. 10.1161/str.0000000000000158. [DOI] [PubMed]
- [26].Liu B, Huang D, Guo Y, Sun X, Chen C, Zhai X, et al. Recent advances and perspectives of postoperative neurological disorders in the elderly surgical patients. CNS Neurosci Ther. 2022;28(4):470–83. 10.1111/cns.13763. [DOI] [PMC free article] [PubMed]
- [27].Granger KT, Barnett JH. Postoperative cognitive dysfunction: an acute approach for the development of novel treatments for neuroinflammation. Drug Discov Today. 2021;26(5):1111–4. 10.1016/j.drudis.2021.01.019. [DOI] [PubMed]
- [28].Müller J, Nowak S, Weidemeier M, Vogelgesang A, Ruhnau J, von Sarnowski B, et al. Duration of surgery and intraoperative blood pressure management are modifiable risk factors for postoperative neurocognitive disorders after spine surgery: results of the prospective CONFESS study. Spine (Phila Pa 1976). 2023;48(16):1127–37. 10.1097/brs.0000000000004722. [DOI] [PMC free article] [PubMed]
- [29].Esmael A, Elsherief M, Eltoukhy K. Prevalence of cognitive impairment in acute ischaemic stroke and use of Alberta Stroke Programme Early CT Score (ASPECTS) for early prediction of post-stroke cognitive impairment. Neurol Neurochir Pol. 2021;55(2):179–85. 10.5603/PJNNS.a2021.0006. [DOI] [PubMed]
- [30].Lee M, Lim JS, Kim CH, Lee SH, Kim Y, Hun Lee J, et al. High neutrophil-lymphocyte ratio predicts post-stroke cognitive impairment in acute ischemic stroke patients. Front Neurol. 2021;12:693318. 10.3389/fneur.2021.693318. [DOI] [PMC free article] [PubMed]
- [31].Li Y, Geng W, Zhang X, Mi B. Risk factors and characteristics analysis of cognitive impairment in patients with cerebral infarction during recovery period. Int J Neurosci. 2024;1–6. 10.1080/00207454.2024.2336189. [DOI] [PubMed]
- [32].Tang A, Liu S, Wang Z, Shao H, Cai X, Li T. A new nomogram model for individualized prediction of cognitive impairment in patients with acute ischemic stroke. J Stroke Cerebrovasc Dis. 2022;31(7):106515. 10.1016/j.jstrokecerebrovasdis.2022.106515. [DOI] [PubMed]
- [33].Gulen MF, Samson N, Keller A, Schwabenland M, Ablasser A, Favre L, et al. cGAS-STING drives ageing-related inflammation and neurodegeneration. Nature. 2023;620(7973):374–80. 10.1038/s41586-023-06373-1. [DOI] [PMC free article] [PubMed]
- [34].Hosoki S, Hansra GK, Ihara M, Jayasena T, Poljak A, Kovacic JC, et al. Molecular biomarkers for vascular cognitive impairment and dementia. Nat Rev Neurol. 2023;19(12):737–53. 10.1038/s41582-023-00884-1. [DOI] [PubMed]
- [35].Ashraf-Ganjouei A, Moradi K, Bagheri S, Aarabi MH. The association between systemic inflammation and cognitive performance in healthy adults. J Neuroimmunol. 2020;345:577272. 10.1016/j.jneuroim.2020.577272. [DOI] [PubMed]
- [36].Tan S, Chen W, Kong G, Wei L, Xie Y. Peripheral inflammation and neurocognitive impairment: correlations, underlying mechanisms, and therapeutic implications. Front Aging Neurosci. 2023;15:1305790. 10.3389/fnagi.2023.1305790. [DOI] [PMC free article] [PubMed]
- [37].Greene C, Connolly R, Brennan D, Laffan A, O’Keeffe E, Zaporojan L, et al. Blood-brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment. Nat Neurosci. 2024;27(3):421–32. 10.1038/s41593-024-01576-9. [DOI] [PMC free article] [PubMed]
- [38].Guo Z, Zheng Y, Geng J, Wu Z, Wei T, Shan G, et al. Unveiling the link between systemic inflammation markers and cognitive performance among older adults in the US: a population-based study using NHANES 2011–2014 data. J Clin Neurosci. 2024;119:45–51. 10.1016/j.jocn.2023.11.004. [DOI] [PubMed]
- [39].Cheng Y, Zhu H, Liu C, Li L, Lin F, Guo Y, et al. Systemic immune-inflammation index upon admission correlates to post-stroke cognitive impairment in patients with acute ischemic stroke. Aging (Albany NY). 2024;16(10):8810–21. 10.18632/aging.205839. [DOI] [PMC free article] [PubMed]
- [40].Cao MM, Zhang YW, Sheng RW, Gao W, Kang QR, Gao YC, et al. General anesthesia versus regional anesthesia in the elderly patients undergoing hip fracture surgeries: a systematic review and meta-analysis of randomized clinical trials. World J Surg. 2023;47(6):1444–56. 10.1007/s00268-023-06949-y. [DOI] [PubMed]