Abstract
Background
Postoperative cognitive dysfunction (POCD) significantly affects recovery, hospitalization duration, and quality of life following cardiac surgery. Identifying reliable biomarkers for predicting POCD could improve patient outcomes and perioperative care. Among these, S100 calcium-binding protein beta (S100β) and neuron-specific enolase (NSE) have emerged as promising indicators of cerebral injury and neurocognitive dysfunction.
Objectives
This systematic review and meta-analysis aimed to assess within-subject perioperative changes in S100β and NSE levels among patients who developed POCD after cardiac surgery, to evaluate whether these biomarkers consistently rise in association with POCD.
Methods
Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science up to October 2024. Studies included peer-reviewed articles evaluating S100β and NSE levels in relation to POCD in cardiac surgery patients. Two reviewers independently extracted data and assessed the quality using the ROBINS-I tool. Meta-analyses were conducted using a random-effects model.
Results
Thirty studies were included. Among patients who developed POCD, both S100β and NSE levels were significantly elevated postoperatively compared to preoperative baselines. The pooled standardized mean difference (SMD) was 1.52 (95% CI 0.57–2.48; I2 = 93.1%) for S100β and 1.19 (95% CI 0.42–1.96; I2 = 88.7%) for NSE, indicating large effect sizes. Sensitivity analyses confirmed the robustness of these findings despite substantial heterogeneity.
Conclusions
Among patients who developed POCD, S100β and NSE levels significantly increased from preoperative to postoperative measurements, indicating a potential association with cerebral injury. However, as non-POCD patients were not analyzed for the same biomarker changes, causality or specificity to POCD cannot be confirmed and future research should be directed toward between group changes.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40001-025-03081-6.
Keywords: Postoperative cognitive dysfunction (POCD), S100β protein, Neuron-specific enolase (NSE), Cardiac surgery, Biomarkers, Neurocognitive disorders, Perioperative risk stratification
Introduction
Postoperative cognitive dysfunction (POCD) represents a significant clinical complication that adversely impacts patient recovery, prolongs hospitalization, and diminishes the overall quality of life in patients undergoing cardiac surgery [1]. Despite advancements in surgical techniques, anesthetic protocols, and comprehensive perioperative management strategies, the prevalence of POCD remains notably high, particularly in patients undergoing complex cardiac procedures, such as valve replacement or cardiopulmonary bypass. This neurocognitive impairment can range from subtle cognitive deficits to more profound disturbances, adversely affecting patients' ability to resume normal daily activities, leading to prolonged rehabilitation and increased healthcare utilization [1].
Reported prevalence rates of POCD vary depending on the timing of assessment and diagnostic criteria used, but studies suggest that up to 30–50% of patients may experience cognitive decline in the early postoperative period, with persistent deficits reported in approximately 10–20% of cases at 3–12 month post-surgery [2].
POCD's pathophysiology's complexity underscores the critical need for reliable and predictive biomarkers to identify individuals at increased risk, guide early intervention, and optimize perioperative clinical management [3]. In recent decades, significant attention has been devoted to identifying and validating biochemical biomarkers indicative of neuronal injury or cerebral distress, among which S100 calcium-binding protein beta (S100β) has emerged as an up-and-coming candidate. This protein, predominantly found in glial cells, regulates neuronal survival, growth, and apoptosis. Clinically, elevated serum or cerebrospinal fluid levels of S100β have consistently been correlated with various forms of cerebral injury, including stroke, traumatic brain injuries, and neurodegenerative diseases [4].
Recent research has increasingly suggested a correlation between perioperative elevations of S100β and the development of POCD in cardiac surgery patients. Nevertheless, variability in study designs, patient populations, biomarker measurement protocols, and definitions of cognitive dysfunction has complicated the interpretation of results and hindered definitive conclusions. Thus, a systematic synthesis and rigorous meta-analytic evaluation of existing studies are necessary to elucidate the true significance of S100β as a biomarker for POCD [5].
In addition to S100β, neuron-specific enolase (NSE) has also been investigated as a biomarker of neurocognitive injury in the perioperative setting. NSE is a glycolytic enzyme found predominantly in neurons and neuroendocrine cells, and elevated serum levels may indicate neuronal cytoplasmic damage. Like S100β, NSE has been associated with cerebral ischemia, traumatic brain injury, and postoperative cognitive decline [6]. However, its role in the context of cardiac surgery remains less consistently characterized. Including both biomarkers in the present review allows for a more comprehensive evaluation of their respective associations with POCD and their potential as complementary indicators of perioperative cerebral insult [7].
This systematic review and meta-analysis aim to assess the extent to which S100β and NSE levels change from preoperative to postoperative states within patients who develop POCD following cardiac surgery. Rather than comparing POCD vs. non-POCD groups, this study synthesizes within-patient biomarker changes to explore their potential association with POCD.
Methods
Study design and protocol
This systematic review and meta-analysis adhered to the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [8]. The study protocol was registered in the Open Science Framework (OSF) under registration number 10.17605/OSF.IO/QH9WT.
Search strategy
A comprehensive literature search was performed using PubMed, Scopus, and Web of Science databases up to October 2, 2024. The search strategy was developed using a combination of keywords related to biomarkers indicative of cerebral injury, specifically S100β protein and neuron-specific enolase (NSE), and various cardiac surgical procedures. Keywords were combined using Boolean operators to maximize sensitivity. In addition, manual searches of reference lists from retrieved articles and relevant systematic reviews were conducted to identify any studies missed by electronic searches.
Eligibility criteria
Inclusion criteria were original, peer-reviewed human studies evaluating the relationship between S100β and NSE biomarkers and postoperative cognitive dysfunction (POCD) in patients undergoing any type of cardiac surgery, including valve replacement, coronary artery bypass grafting (CABG), congenital heart surgeries, and procedures with or without cardiopulmonary bypass (CPB), across both adult and pediatric populations. Articles were excluded if they were non-human, in-vitro studies, non-English publications, conference abstracts, case series, case reports, or review articles. Duplicate studies were also removed. Our synthesis and meta-analysis focuses on within-patient comparisons of biomarker levels before and after surgery in patients who developed POCD.
Study selection and data extraction
Two reviewers independently screened articles using the Rayyan Intelligent, systematic review tool. Disagreements were resolved through consensus and the involvement of a third reviewer. Data extracted included authors, publication year, country, number of POCD and non-POCD patients, anesthesia method, type of cardiac surgery performed, biomarker levels (pre- and postoperative values of S100β and NSE), and cognitive assessment methods. Data were summarized and presented in structured tables.
Quality assessment
The ROBINS-I tool was applied to evaluate the potential risk of bias across seven domains [9]: (1) bias due to confounding, (2) bias in participant selection, (3) bias in classification of interventions, (4) deviations from intended interventions, (5) missing data, (6) outcome measurement, and (7) selection of reported results. Findings from the quality assessments were visualized using a traffic-light system to illustrate the risk level clearly.
Statistical analysis
Meta-analysis used a random-effects model to account for anticipated heterogeneity across studies. Effect sizes were computed as standardized mean differences (SMD) with corresponding 95% confidence intervals (CI). For each included study, the preoperative biomarker level was treated as the baseline (control), and the postoperative level as the post-exposure condition. The standardized mean difference thus reflects within-subject changes in biomarker levels among patients with POCD.
Heterogeneity was evaluated using the I2 statistic, and funnel plots were used to assess potential publication bias visually. Sensitivity analyses were conducted by sequentially removing individual studies to test the robustness of the results.
Results
Study selection
The PubMed, Scopus, and Web of Science databases identified 708 records. After excluding 234 duplicates, 474 unique titles and abstracts were screened. Of these, 132 were removed for not meeting the predefined inclusion criteria (e.g., non-human studies, irrelevant biomarkers, or non-English articles), leaving 342 articles for full-text review. Subsequently, 320 articles were excluded due to insufficient or irrelevant data. Ultimately, 30 studies fulfilled all eligibility requirements and were included in this systematic review and meta-analysis (see PRISMA flow diagram in Fig. 1) [10–36].
Fig. 1.
Prisma flow diagram of the study selection procedure
Data extraction
Key information, such as authors, publication year, country, number of POCD vs. non-POCD patients, anesthesia type, type of cardiac surgery, S100β and NSE levels (pre- and post-surgery), and cognitive assessment methods, was extracted from each of the 30 included studies (Supplementary Table 1). Most studies focused on adult cardiac surgical procedures involving cardiopulmonary bypass (CPB), although a few addressed pediatric cases or off-pump surgeries. Reported POCD assessments ranged from standardized neuropsychological batteries (e.g., Mini-Mental State Examination, MoCA) to clinical diagnoses based on DSM-III-R or DSM-IV criteria.
Quality assessment
Figure 2 presents the traffic-light summary of the risk of bias (ROB) judgments for each included study across seven domains, as assessed by the ROBINS-I tool.
Fig. 2.
Risk-of-bias assessment of included studies using ROBINS-I tool. This figure summarizes the risk of bias evaluation across seven predefined domains for the included studies, as assessed using the ROBINS-I (risk of bias in non-randomized studies of interventions) tool. Domains include: D1—bias due to confounding, D2—bias due to selection of participants, D3—bias in classification of interventions, D4—bias due to deviations from intended interventions, D5—bias due to missing data, D6—bias in measurement of outcomes, D7—bias in selection of the reported result. Judgments are color-coded: green circles (+) indicate low risk, yellow circles (−) indicate moderate risk, red circles (×) indicate serious risk, and blue circles (?) represent no information. Overall bias risk for each study is indicated in the final column. This assessment highlights variability in methodological quality, with most studies rated as low-to-moderate risk of bias, although a few studies exhibit serious concerns in specific domains
Bias due to confounding (D1): Most studies had a low or moderate risk in this domain, reflecting efforts to control key confounders (e.g., age and comorbidities). However, a few studies lacked detailed reporting of patient characteristics, leading to higher concern in D1.
Bias in selection of participants (D2): A subset of studies showed serious risk of bias here, primarily due to unclear or non-consecutive recruitment methods and incomplete inclusion/exclusion criteria reporting.
Bias in classification of interventions (D3): All studies were rated low or moderate in this domain, as surgical procedures and biomarkers were clearly defined.
Bias due to deviations from intended interventions (D4) and bias due to missing data (D5): These domains were generally rated low-to-moderate risk. Some studies reported losses to follow-up or missing biomarker measurements without a clear explanation.
Bias in measurement of outcomes (D6) and bias in selection of the reported result (D7): Most articles used validated cognitive assessment tools. However, the variation in timing and outcome measurements of some studies led to moderate ratings in D6. Reporting bias (D7) was typically low or mild, with only a few studies omitting relevant outcome details.
Despite occasional serious risk in specific domains, often due to incomplete methodological descriptions, included studies were deemed suitable for meta-analysis.
Meta-analysis
While several included studies reported biomarker levels in both POCD and non-POCD patients, our pooled analyses exclusively focused on within-subject comparisons, evaluating the change from preoperative to postoperative biomarker levels among POCD patients only. Studies that did not report stratified data for POCD patients were excluded from the pooled analysis.
Meta-analysis of NSE levels
Figure 3 (forest plot) shows the pooled effect of neuron-specific enolase (NSE) in patients who developed postoperative cognitive dysfunction (POCD), comparing postoperative values to preoperative baselines. NSE levels were significantly elevated after surgery, with a pooled standardized mean difference (SMD) of 1.19 (95% CI 0.42–1.96, p < 0.001). Heterogeneity was substantial (I2 = 88.7%), prompting additional exploration through Galbraith plots (Fig. 4). Sensitivity analyses, removing one study at a time, did not substantially alter the overall effect size, supporting the robustness of this observed postoperative increase in NSE among POCD cases.
Fig. 3.
Forest plot of neuron-specific enolase (NSE) levels in patients with postoperative cognitive dysfunction (POCD). Forest plot displaying the comparison of postoperative NSE comparing postoperative vs. preoperative biomarker levels in POCD patients. The analysis reveals a significant increase in NSE levels among patients with POCD, suggesting its potential as a biomarker for cognitive impairment following cardiac surgery
Fig. 4.
Forest plot of S100β levels in patients with postoperative cognitive dysfunction (POCD). This forest plot depicts the standardized mean differences (SMD) and 95% confidence intervals (CI) for postoperative S100β levels comparing postoperative vs. preoperative biomarker levels in POCD patient across the included studies. The pooled analysis indicates significantly elevated S100β levels in patients who developed POCD
Meta-analysis of S100β levels
Figure 5 illustrates the pooled within-subject comparison of postoperative vs. preoperative S100β levels in patients who developed POCD. Postoperative S100β levels were significantly higher, with a pooled SMD of 1.52 (95% CI 0.57–2.48, p < 0.001). Heterogeneity was high (I2 = 93.1%), but subsequent sensitivity analyses (Fig. 6) confirmed the consistency of the effect estimate, indicating a reliable elevation of S100β following surgery in patients exhibiting POCD.
Fig. 5.
Galbraith plot for heterogeneity analysis of S100β levels. Galbraith plot illustrating the heterogeneity among studies included in the meta-analysis of S100β levels. This plot helps identify potential sources of heterogeneity by plotting the standardized effect sizes against their precision
Fig. 6.
Funnel plot for publication bias assessment of S100β and NSE analyses. Funnel plot evaluating potential publication bias in studies reporting S100β and NSE levels in relation to POCD. The symmetry of the funnel plot suggests minimal evidence of publication bias across the included studies
Publication bias and sensitivity analysis
Funnel plots (Fig. 7) showed minimal evidence of publication bias for both NSE and S100β analyses. In addition, sequentially excluding individual studies from each meta-analysis did not materially change the summary effect estimates or confidence intervals, indicating that the results are robust and not driven by any single outlier study.
Fig. 7.
Sensitivity analysis plot (leave-one-out analysis) for the meta-analysis of S100β levels in relation to postoperative cognitive dysfunction (POCD). This plot demonstrates the influence of each individual study on the overall pooled estimate by performing a leave-one-out sensitivity analysis. The graph shows the meta-analysis effect estimates (yellow circles) and the corresponding lower and upper confidence intervals (yellow lines) when each study is omitted sequentially. The consistency of the estimates across the omissions indicates the robustness and stability of the meta-analysis results
The PRISMA-guided search and selection process identified 30 studies meeting the inclusion criteria. Despite some variability in study quality, most studies were rated as having a low-to-moderate risk of bias, supporting their suitability for pooled analysis. The meta-analyses demonstrated that both NSE and S100β levels rise significantly in patients who develop POCD, indicating that these biomarkers may be valuable for detecting and monitoring postoperative cognitive changes in the cardiac surgery population.
Discussion
This systematic review and meta-analysis provide robust evidence that elevated postoperative levels of S100β and neuron-specific enolase (NSE) are significantly associated with postoperative cognitive dysfunction (POCD) in patients undergoing cardiac surgery. The pooled standardized mean differences (SMDs) were 1.52 for S100β and 1.19 for NSE, indicating moderate to large effect sizes. These findings highlight a clinically meaningful association, suggesting that these biomarkers may reflect ongoing cerebral injury in the perioperative context and hold potential for enhancing neurocognitive monitoring strategies.
These effect sizes are considered large by conventional standards, suggesting that the differences in biomarker levels are not only statistically significant but also potentially clinically meaningful. The magnitude of biomarker elevation from preoperative to postoperative timepoints supports the utility of S100β and NSE as robust indicators of postoperative neurocognitive risk.
Clinical implications
The consistent increase in S100β and NSE among POCD cases supports their use as early indicators of cerebral stress or damage following cardiac surgery. Their integration into routine perioperative care could improve risk stratification, allowing for more proactive interventions to protect cognitive function [37]. However, most of the included studies measured these biomarkers postoperatively, indicating that they may primarily serve as early signatures of injury rather than predictive markers. Clarifying this distinction is crucial, as biomarkers measured preoperatively may inform preventive strategies, while postoperative elevations are more likely to signal the onset of neurocognitive decline [38, 39].
The application of these biomarkers could be particularly useful in elderly patients and in those undergoing high-risk procedures, such as prolonged cardiopulmonary bypass or complex valve replacements. Real-time monitoring of these markers may aid in tailoring perfusion strategies, guiding anesthetic choices, and informing postoperative care plans [40, 41].As our analysis reflects within-patient comparisons, these markers may serve as early signals of developing POCD, rather than as preoperative predictors.
Methodological considerations and limitations
Interpretation of these results must take into account several methodological challenges. One major source of heterogeneity was the timing of biomarker sampling. While some studies measured S100β and NSE immediately after bypass, others used longer windows, such as 24–72 h postoperatively. The absence of consistent preoperative measurements limits our ability to evaluate the predictive value of these markers [42, 43].
In addition, definitions of POCD varied widely across studies. Different cognitive assessment tools, including the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and DSM-based clinical criteria, were employed with varying thresholds for diagnosis. These inconsistencies reduce the precision of cross-study comparisons and limit the interpretability of pooled outcomes [44].
Confounding remains another concern. Only a minority of studies adjusted for essential variables, such as age, educational level, comorbidities, pre-existing cognitive function, or intraoperative factors, such as cerebral embolism or hypotension. As a result, some of the observed associations may reflect underlying clinical or procedural risk rather than biomarker-specific effects. Furthermore, most studies focused on short-term cognitive outcomes, often within the first few days or weeks post-surgery, with limited data on longer term cognitive trajectories [45].
A major limitation across included studies was the lack of consistent reporting on the timing of biomarker sampling. In most cases, only postoperative measurements were provided, and preoperative levels were either not measured or not reported in sufficient detail. This restricts the interpretation of whether S100β and NSE function primarily as predictive markers or as early indicators of cerebral injury. Future studies should standardize the timing of biomarker collection to distinguish baseline vulnerability from postoperative pathophysiological changes.
Despite these challenges, most studies were judged to have low-to-moderate risk of bias according to the ROBINS-I criteria, supporting the reliability of our meta-analytic findings.
Specificity of biomarker changes to POCD
A critical limitation of this study is the lack of comparison with patients who did not develop POCD. While we observed significant within-subject increases in S100β and NSE levels among POCD patients, we did not evaluate whether similar biomarker changes occur in patients without cognitive decline. Therefore, the findings cannot confirm whether the observed biomarker elevations are specific to POCD or simply reflect general postoperative cerebral changes. Future studies should include comparative analyses with non-POCD groups to establish biomarker specificity.
Addressing heterogeneity
A notable limitation of this meta-analysis is the substantial heterogeneity observed across studies (I2 = 93.1% for S100β and 88.7% for NSE). This likely reflects variability in multiple domains, including surgical techniques (e.g., CPB vs. off-pump), timing of biomarker sampling, types of cognitive assessments used, and differing definitions of POCD. While a random-effects model was applied to account for between-study variability, the magnitude of heterogeneity limits the precision of pooled estimates. Unfortunately, subgroup analyses were not feasible due to the inconsistent reporting of key modifiers, such as sampling timepoints and surgical subtypes. Future research should aim to harmonize methodologies and report stratified biomarker data to enable more nuanced meta-analytic comparisons and reduce residual heterogeneity.
Addressing confounding factors
Another critical limitation involves the potential impact of unmeasured or inadequately controlled confounding factors. Variables such as age, baseline cognitive status, comorbidities (e.g., diabetes and hypertension), type and duration of anesthesia, and intraoperative events (e.g., hypotension, microembolism, and inflammatory responses) may independently influence both biomarker expression and the risk of developing POCD. Most of the included studies did not report multivariable adjustments or stratified analyses to account for these factors. As a result, the observed associations between S100β, NSE, and POCD may, in part, reflect residual confounding. Future studies should aim to incorporate these covariates into multivariate models or propensity-matched designs to better isolate the independent effect of these biomarkers on neurocognitive outcomes.
Comparison with prior literature
Our results are consistent with existing literature showing that S100β and NSE levels rise in response to cerebral injury during and after cardiac surgery. S100β, primarily expressed in astrocytes, is known to indicate blood–brain barrier disruption and glial stress. NSE, a glycolytic enzyme found in neurons, reflects neuronal cytoplasmic damage. Together, these biomarkers offer complementary insights into the different cellular pathways implicated in POCD pathogenesis [46, 47].
While previous reviews have examined these markers individually, few have undertaken a combined meta-analytic approach across multiple cardiac surgery populations. This study strengthens the evidence base by synthesizing data from 30 studies and directly comparing both markers in relation to cognitive outcomes [10–36, 48–50].
Future research and clinical translation
Standardizing biomarker measurement
One major barrier to clinical translation is the lack of consistency in biomarker sampling protocols across studies. Timing of measurement varied widely, with most studies assessing postoperative levels at different intervals, while preoperative data were rarely available. This variability limits comparability and hinders meta-analytic precision. Future studies should adopt standardized perioperative timepoints, ideally including preoperative baselines, immediate post-bypass sampling, and longer term postoperative follow-up, to map dynamic changes in S100β and NSE levels and determine their true prognostic value.
Embedding biomarkers into interventional studies
To move beyond associative findings, future trials should incorporate S100β and NSE as surrogate endpoints in interventions aimed at reducing POCD risk. For instance, studies evaluating neuroprotective strategies, perfusion optimization, or anesthetic regimens could assess whether modulating perioperative care alters biomarker trajectories and cognitive outcomes. Such integration would not only validate the biological relevance of these markers but also establish their potential role in guiding clinical decisions.
Toward multimodal and predictive modeling
Combining S100β and NSE with additional neuroinflammatory, glial, and imaging-based biomarkers may yield more specific and sensitive risk stratification tools. Advances in machine learning and systems biology could facilitate the development of multimodal predictive models tailored to individual risk profiles. These models would be especially powerful if trained on harmonized data sets that account for surgical type, patient comorbidities, and neuropsychological baselines. The next generation of research must shift from descriptive biomarker studies toward predictive, interventional, and mechanistically informed investigations that support clinical translation.
Conclusion
This meta-analysis provides compelling evidence that Postoperative elevations of S100β and NSE relative to preoperative levels are significantly associated with POCD in cardiac surgery patients. These biomarkers may serve as early indicators of neurocognitive changes in patients with POCD. However, because we did not analyze biomarker trajectories in patients who did not develop POCD, it remains unclear whether these perioperative changes are specific to POCD or reflect general cerebral stress during surgery. As such, implications for risk stratification remain speculative.
Supplementary Information
Supplementary Material 1: Table 1. Summary characteristics of included studies.
Supplementary Material 2: Table 2. curated search strategies across searched databases.
Acknowledgements
None.
Abbreviations
- POCD
Postoperative cognitive dysfunction
- S100β
S100 calcium-binding protein beta
- NSE
Neuron-specific enolase
- CPB
Cardiopulmonary bypass
- CABG
Coronary artery bypass grafting
- PRISMA
Preferred reporting items for systematic reviews and meta-analyses
- ROBINS-I
Risk of bias in non-randomized studies of interventions
- CI
Confidence interval
- SMD
Standardized mean difference
- DSM
Diagnostic and statistical manual of mental disorders
- MoCA
Montreal cognitive assessment
- ELISA
Enzyme-linked immunosorbent assay
Author contributions
Study concept and design: MAA Acquisition of the data: MAA Analysis and interpretation of the data:MAA Drafting of the manuscript: OR.; critical revision of the manuscript for important intellectual content: SKSR,AR,SMM,SS,IA,AT,AM,KAS,AK,PE,FK,MS,MHA,KAH administrative, technical, and material support: SKSR,AR,SMM,SS,IA,AT,AM,KAS,AF,AR,MR study supervision: MAA.
Funding
None.
Data availability
Data is provided in the manuscript.
Declarations
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Mehdi Hassani Ahangar, Komeil Aghazadeh-Habashi and Ali Rahi have contributed equally to this work.
References
- 1.Vacas S, Cole DJ, Cannesson M. Cognitive decline associated with anesthesia and surgery in older patients. JAMA. 2021;326(9):863–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Newman MF, Kirchner JL, Phillips-Bute B, Gaver V, Grocott H, Jones RH, et al. Longitudinal assessment of neurocognitive function after coronary-artery bypass surgery. N Engl J Med. 2001;344(6):395–402. [DOI] [PubMed] [Google Scholar]
- 3.Zhao Q, Wan H, Pan H, Xu Y. Postoperative cognitive dysfunction—current research progress. Front Behav Neurosci. 2024. 10.3389/fnbeh.2024.1328790. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Gao L, Taha R, Gauvin D, Othmen LB, Wang Y, Blaise G. Postoperative cognitive dysfunction after cardiac surgery. Chest. 2005;128(5):3664–70. [DOI] [PubMed] [Google Scholar]
- 5.Zhang L, Qiu Y, Zhang ZF, Zhao YF, Ding YM. Current perspectives on postoperative cognitive dysfunction in geriatric patients: insights from clinical practice. Front Med. 2024;11: 1466681. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Isgrò MA, Bottoni P, Scatena R. Neuron-specific enolase as a biomarker: biochemical and clinical aspects. Adv Exp Med Biol. 2015;867:125–43. [DOI] [PubMed] [Google Scholar]
- 7.Haque A, Polcyn R, Matzelle D, Banik NL. New insights into the role of neuron-specific enolase in neuro-inflammation, neurodegeneration, and neuroprotection. Brain Sci. 2018. 10.3390/brainsci8020033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372: n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Igelström E, Campbell M, Craig P, Katikireddi SV. Cochrane’s risk of bias tool for non-randomized studies (ROBINS-I) is frequently misapplied: a methodological systematic review. J Clin Epidemiol. 2021;140:22–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Johnsson P, Lundqvist C, Lindgren A, Ferencz I, Alling C, Ståhl E. Cerebral complications after cardiac surgery assessed by S-100 and NSE levels in blood. J Cardiothorac Vasc Anesth. 1995;9(6):694–9. [DOI] [PubMed] [Google Scholar]
- 11.Isgro F, Schmidt C, Pohl P, Saggau W. A predictive parameter in patients with brain related complications after cardiac surgery? Eur J Cardiothorac Surg. 1997;11(4):640–4. [DOI] [PubMed] [Google Scholar]
- 12.Taggart DP, Mazel JW, Bhattacharya K, Meston N, Standing SJ, Kay JDS, et al. Comparison of serum S-100β levels during CABG and intracardiac operations. Ann Thorac Surg. 1997;63(2):492–6. [DOI] [PubMed] [Google Scholar]
- 13.Schmitt B, Bauersfeld U, Schmid ER, Tuchschmid P, Molinari L, Fanconi S, et al. Serum and CSF levels of neuron-specific enolase (NSE) in cardiac surgery with cardiopulmonary bypass: a marker of brain injury? Brain Dev. 1998;20(7):536–9. [DOI] [PubMed] [Google Scholar]
- 14.Herrmann M, Ebert AD, Tober D, Hann J, Huth C. A contrastive analysis of release patterns of biochemical markers of brain damage after coronary artery bypass grafting and valve replacement and their association with the neurobehavioral outcome after cardiac surgery. Eur J Cardiothorac Surg. 1999;16(5):513–8. [DOI] [PubMed] [Google Scholar]
- 15.Herrmann M, Ebert AD, Galazky I, Wunderlich MT, Kunz WS, Huth C. Neurobehavioral outcome prediction after cardiac surgery: role of neurobiochemical markers of damage to neuronal and glial brain tissue. Stroke. 2000;31(3):645–50. [DOI] [PubMed] [Google Scholar]
- 16.Westaby S, Saatvedt K, White S, Katsumata T, Van Oeveren W, Bhatnagar NK, et al. Is there a relationship between serum S-100β protein and neuropsychologic dysfunction after cardiopulmonary bypass? J Thorac Cardiovasc Surg. 2000;119(1):132–7. [DOI] [PubMed] [Google Scholar]
- 17.Oki A, Ohtake H, Okada Y, Kawada T, Takaba T. Simultaneous monitoring of somatosensory evoked potentials and regional cerebral oxygen saturation combined with serial measurement of plasma levels of cerebral specific proteins for the early diagnosis of postoperative brain damage in cardiovascular surgery. J Artif Organs. 2004;7(1):13–8. [DOI] [PubMed] [Google Scholar]
- 18.Snyder-Ramos SA, Gruhlke T, Bauer H, Bauer M, Luntz AP, Motsch J, et al. Cerebral and extracerebral release of protein S100B in cardiac surgical patients. Anaesthesia. 2004;59(4):344–9. [DOI] [PubMed] [Google Scholar]
- 19.Stolz E, Gerriets T, Kluge A, Klövekorn WP, Kaps M, Bachmann G. Diffusion-weighted magnetic resonance imaging and neurobiochemical markers after aortic valve replacement: implications for future neuroprotective trials? Stroke. 2004;35(4):888–92. [DOI] [PubMed] [Google Scholar]
- 20.Wang KJ, Wu HH, Fang SY, Yang YR, Tseng ACC. Serum S-100 β protein during coronary artery bypass graft surgery with or without cardiopulmonary bypass. Ann Thorac Surg. 2005;80(4):1371–4. [DOI] [PubMed] [Google Scholar]
- 21.Sulemanji DS, Dönmez A, Aldemir D, Sezgin A, Türkoglu S. Dexmedetomidine during coronary artery bypass grafting surgery: is it neuroprotective? - a preliminary study. Acta Anaesthesiol Scand. 2007;51(8):1093–8. [DOI] [PubMed] [Google Scholar]
- 22.Vidlund M, Holm J, Håkanson E, Friberg T, Sunnermalm L, Vanky F, et al. The S-100B substudy of the GLUTAMICS trial: glutamate infusion not associated with sustained elevation of plasma S-100B after coronary surgery. Clin Nutr. 2010;29(3):358–64. [DOI] [PubMed] [Google Scholar]
- 23.Tamura A, Imamaki M, Shimura H, Niitsuma Y, Miyazaki M. Release of serum s-100β protein and neuron-specific enolase after off-pump coronary artery bypass grafting with and without intracranial and cervical artery stenosis. Ann Thorac Cardiovasc Surg. 2011;17(1):33–8. [DOI] [PubMed] [Google Scholar]
- 24.Reinsfelt B, Ricksten SE, Zetterberg H, Blennow K, Fredén-Lindqvist J, Westerlind A. Cerebrospinal fluid markers of brain injury, inflammation, and blood-brain barrier dysfunction in cardiac surgery. Ann Thorac Surg. 2012;94(2):549–55. [DOI] [PubMed] [Google Scholar]
- 25.Ugur EH, Yapici N, Kudsioglu T, Ugur SY, Yapici MF, Saracoglu A, et al. N-acetylcysteine is ineffective on short-term neuron-specific enolase levels following coronary artery bypass graft surgery. J Anesth. 2012;26(3):477–8. [DOI] [PubMed] [Google Scholar]
- 26.Tsaousi GG, Pitsis AA, Deliaslani DV, Amaniti EN, Karakoulas KA, Vasilakos DG. Cerebral oxygenation impairment and S-100β protein release during off-pump coronary artery revascularization. J Cardiothorac Vasc Anesth. 2013;27(2):245–52. [DOI] [PubMed] [Google Scholar]
- 27.Yuan SM. Cerebral damage in cardiac surgery assessed by serum S100 proteins. Int J Cardiol. 2013;168(3):3075–6. [DOI] [PubMed] [Google Scholar]
- 28.Abu-Sultaneh S, Hehir DA, Murkowski K, Ghanayem NS, Liedel J, Hoffmann RG, et al. Changes in cerebral oxygen saturation correlate with S100B in infants undergoing cardiac surgery with cardiopulmonary bypass. Pediatr Crit Care Med. 2014;15(3):219–28. [DOI] [PubMed] [Google Scholar]
- 29.Sanchez-De-Toledo J, Chrysostomou C, Munoz R, Lichtenstein S, Sao-Avilés CA, Wearden PD, et al. Cerebral regional oxygen saturation and serum neuromarkers for the prediction of adverse neurologic outcome in pediatric cardiac surgery. Neurocrit Care. 2014;21(1):133–9. [DOI] [PubMed] [Google Scholar]
- 30.Gailiušas M, Andrejaitienė J, Širvinskas E, Krasauskas D, Švagždienė M, Kumpaitienė B. Association between serum biomarkers and postoperative delirium after cardiac surgery. Acta Med Litu. 2019;26(1):8–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Trubnikova OA, Tarasova IV, Moskin EG, Kupriyanova DS, Argunova YA, Pomeshkina SA, et al. Beneficial effects of a short course of physical prehabilitation on neurophysiological functioning and neurovascular biomarkers in patients undergoing coronary artery bypass grafting. Front Aging Neurosci. 2021. 10.3389/fnagi.2021.699259. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Wan Z, Li Y, Ye H, Zi Y, Zhang G, Wang X. Plasma S100b and neuron-specific enolase, but not neuroglobin, are associated with early cognitive dysfunction after total arch replacement surgery a pilot study. Medicine (Baltimore). 2021. 10.1097/MD.0000000000025446. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Matsuishi Y, Hoshino H, Enomoto Y, Shimojo N, Matsubara M, Kato H, et al. Pediatric delirium is associated with increased brain injury marker levels in cardiac surgery patients. Sci Rep. 2022;12(1):18681. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Wiberg S, Holmgaard F, Zetterberg H, Nilsson JC, Kjaergaard J, Wanscher M, et al. Biomarkers of cerebral injury for prediction of postoperative cognitive dysfunction in patients undergoing cardiac surgery. J Cardiothorac Vasc Anesth. 2022;36(1):125–32. [DOI] [PubMed] [Google Scholar]
- 35.Nübel J, Buhre C, Hoffmeister M, Oess S, Labrenz O, Jost K, et al. Association between neuron-specific enolase, memory function, and postoperative delirium after transfemoral aortic valve replacement. J Cardiovasc Dev Dis. 2023. 10.3390/jcdd10110441. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Tarasova I, Kukhareva I, Kupriyanova D, Temnikova T, Gorbatovskaya E, Trubnikova O. Electrical activity changes and neurovascular unit markers in the brains of patients after cardiac surgery: effects of multi-task cognitive training. Biomedicines. 2024. 10.3390/biomedicines12040756. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Yuan SM. S100 and S100β: biomarkers of cerebral damage in cardiac surgery with or without the use of cardiopulmonary bypass. Rev Bras Cir Cardiovasc. 2014;29(4):630–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Stef A, Bodolea C, Bocsan IC, Cainap SS, Achim A, Serban A, et al. The value of biomarkers in major cardiovascular surgery necessitating cardiopulmonary bypass. Rev Cardiovasc Med. 2024;25(10):355. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Yan F, Chen B, Ma Z, Chen Q, Jin Z, Wang Y, et al. Exploring molecular mechanisms of postoperative delirium through multi-omics strategies in plasma exosomes. Sci Rep. 2024;14(1):29466. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Tang Z, Lin A, Liu H, Zhao M. What is new in cardiac anesthesia in 2024? J Anesth Transl Med. 2025;4(2):33–41. [Google Scholar]
- 41.Majewski P, Zegan-Barańska M, Karolak I, Kaim K, Żukowski M, Kotfis K. Current evidence regarding biomarkers used to aid postoperative delirium diagnosis in the field of cardiac surgery-review. Medicina (Kaunas). 2020. 10.3390/medicina56100493. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Oris C, Kahouadji S, Durif J, Bouvier D, Sapin V. S100B, actor and biomarker of mild traumatic brain injury. Int J Mol Sci. 2023. 10.3390/ijms24076602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Liu X, Yu Y, Zhu S. Inflammatory markers in postoperative delirium (POD) and cognitive dysfunction (POCD): a meta-analysis of observational studies. PLoS ONE. 2018;13(4): e0195659. 10.1371/journal.pone.0195659. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Wang G, Estrella A, Hakim O, Milazzo P, Patel S, Pintagro C, et al. Mini-mental state examination and Montreal cognitive assessment as tools for following cognitive changes in Alzheimer’s disease neuroimaging initiative participants. J Alzheimers Dis. 2022;90(1):263–70. [DOI] [PubMed] [Google Scholar]
- 45.Maas AIR, Menon DK, Manley GT, Abrams M, Åkerlund C, Andelic N, et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol. 2022;21(11):1004–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Gayger-Dias V, Vizuete AF, Rodrigues L, Wartchow KM, Bobermin L, Leite MC, et al. How S100B crosses brain barriers and why it is considered a peripheral marker of brain injury. Exp Biol Med (Maywood). 2023;248(22):2109–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Kawata K, Liu CY, Merkel SF, Ramirez SH, Tierney RT, Langford D. Blood biomarkers for brain injury: what are we measuring? Neurosci Biobehav Rev. 2016;68:460–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Ramlawi B, Rudolph JL, Mieno S, Khabbaz K, Sodha NR, Boodhwani M, et al. Serologic markers of brain injury and cognitive function after cardiopulmonary bypass. Ann Surg. 2006;244(4):593–600. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.He X, Wen LJ, Cui C, Li DR, Teng JF. The significance of S100β protein on postoperative cognitive dysfunction in patients who underwent single valve replacement surgery under general anesthesia. Eur Rev Med Pharmacol Sci. 2017;21(9):2192–8. [PubMed] [Google Scholar]
- 50.Georgiadis D, Berger A, Kowatschev E, Lautenschläger C, Börner A, Lindner A, et al. Predictive value of S-100beta and neuron-specific enolase serum levels for adverse neurologic outcome after cardiac surgery. J Thorac Cardiovasc Surg. 2000;119(1):138–47. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1: Table 1. Summary characteristics of included studies.
Supplementary Material 2: Table 2. curated search strategies across searched databases.
Data Availability Statement
Data is provided in the manuscript.