The multidimensional physiological, psychological, and social influencing factors of perioperative neurocognitive disorders in elderly Chinese patients undergoing major abdominal surgery: study protocol of a prospective cohort study

Abstract

Background

Perioperative neurocognitive disorders (PND) in elderly patients undergoing abdominal surgery seriously affect their prognosis and long-term quality of life, yet the aetiology and pathogenesis of PND remain poorly understood. This study aims to identify the multidimensional physiological, psychological and social factors influencing PND in elderly patients undergoing abdominal surgery, to develop objective biomarkers for diagnosis, and to provide guidelines for treatment response, prognosis, early detection and intervention.

Methods

This is a multidimensional cohort study of the physiological, psychological, and social influencing factors of PND in elderly patients undergoing abdominal surgery. We plan to recruit more than 500 elderly patients aged 65–90 years who require elective abdominal surgery within 2 years. The study will include one baseline and four follow-up assessments: multidimensional influence factors and outcome indicators will be collected at baseline (T1), at surgery (T2), at discharge (postoperative days 1–7; T3), 1 month (T4), and 3 months (T5). The entire study will run from 11 December 2023 to 31 December 2025.

Discussion

In this study, a multimodal observation was implemented, combining physiological status and psychosocial factors. To our knowledge, this is the first longitudinal cohort study to examine multidimensional physiological (including biomarkers) and psychosocial factors. This study aims to provide early personalized intervention for elderly surgical patients, reducing the mortality rate and disease burden of elderly patients undergoing major abdominal surgery.

Trial registration

The protocol is registered at the ClinicalTrials.gov on December 21st, 2023. The trial registration number: NCT06182215.

Background

In recent years, both the world and China have noticed population aging. At the end of 2023, the population aged 65 and above in China was 216.76 million, accounting for 15.4% of the total population. The proportion of population aged 65 and above is expected to reach 37.4% by 2060. With the deepening of population aging, the demand for abdominal surgery among the elderly is gradually increasing. However, the physiology and reserves of the elderly are impaired, and perioperative neurocognitive disorders (PND) have become a common complication in such patients during the perioperative period [1], with an incidence rate of 41% to 75% and increasing with age [2]. PND includes preoperative cognitive impairment (preCI), postoperative delirium (POD), delayed neurocognitive recovery (dNCR), and postoperative cognitive dysfunction (POCD) and meets the diagnostic criteria for neurocognitive impairment in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). The time frame for PND includes preoperatively and within 12 months postoperatively [3]. Research has shown that up to 11–45% of elderly patients may experience postoperative delirium (short-term brain dysfunction caused by acute attacks) [4, 5], and up to 10% of elderly patients may suffer from long-term neurocognitive dysfunction [6, 7].

PND not only prolongs the recovery period from abdominal surgery, but also affects their future quality of life, placing a heavy burden on families and society. Early detection and intervention are therefore essential.

Currently, the biggest obstacle to early identification and intervention is the lack of predictive indicators of disease risk, progression and intervention effectiveness. Although previous studies have found that various factors, including aging, physiological and psychological vulnerability, anesthetic drugs, decreased cerebral oxygen saturation, and severe pain, are associated with PND [8], there is still a lack of multidimensional physiological, psychological, and social intervention strategies to effectively address these issues.

Due to the complexity of disease and surgical types, as well as the heterogeneity of preoperative physiological and psychological health status, family and social status, and age dependence, the intervention and treatment of perioperative neurocognitive disorders in elderly patients undergoing abdominal surgery are more complex. Based on the developmental characteristics of symptoms, physiological and psychological factors, social risk factors, and functional outcomes, dividing the clinical manifestations of research subjects into different dimensions can provide important information about the ways in which perioperative neurocognitive disorders occur throughout the entire development process. Therefore, long-term longitudinal observation is necessary to capture biological, psychological, and social characteristics from multiple dimensions and identify accurate biomarkers.

The physiological and psychological vulnerability during the perioperative period is considered an important factor in the occurrence of PND. The incidence of complications in elderly patients undergoing abdominal surgery is high, such as heart disease, diabetes or previous stroke history. These diseases may increase the risk of PND [9, 10]. Perioperative mental health conditions, such as perioperative anxiety and sleep disorders, are also considered to be important factors in the occurrence of PND [11–13]. Social support and high quality of life may help patients adjust their mental state and play a buffering role in the occurrence of PND [14–17].

At present, there is a lack of cohort studies on perioperative neurocognitive disorders in elderly patients undergoing major abdominal surgery both domestically and internationally. Especially for elderly patients in non Western countries such as China, there is limited prospective data on the comprehensive assessment of preoperative physiological and psychological symptoms, as well as the capture of social protection factors and biomarkers. To bridge this gap, our team designed a comprehensive framework of multi domain and multi-level units (Fig. 1), all of which aim to prevent and alleviate the disease burden of PND in elderly patients undergoing abdominal surgery.

Fig. 1.

Design of the Cohort Study; inclusion of patients receiving elective major abdominal surgery, 65–90 years

In this study, the investigation of PND focuses mainly on POD, dNCR, and POCD. The specific aims are (1) to reveal the development track of perioperative neurocognitive level of elderly patients undergoing abdominal surgery; (2) to evaluate the relationship between perioperative physiological and psychosocial factors and POD, dNCR and POCD; (3) to analyse the correlation between biomarkers and the onset and progression of POD, dNCR and POCD.

Methods and design

Study design

This multidimensional cohort study on perioperative neurocognitive disorders in elderly Chinese patients undergoing major abdominal surgery began in December 2023 and will end in December 2025. The study is currently being conducted at the Sixth Affiliated Hospital of Sun Yat-sen University in Guangdong Province, China. Considering that the occurrence of perioperative neurocognitive disorders in elderly abdominal surgery patients is mainly concentrated within 3 months before and after surgery [3], we plan to conduct a 3-month follow-up period. The first face-to-face clinical interview will be conducted at T1 (the baseline time point) for each participant. Afterwards, assessments will be conducted at T2 (during surgery), T3 ( postoperative days 1–7), T4 (1-month follow-up), and T5 (3-month follow-up). We plan to recruit 500 elderly patients aged 65–90 undergoing major abdominal surgery between December 2023 and December 2025.

The baseline information of participants, including demographic information, Preoperative disease data, preoperative neurocognitive level, physiological and psychological status, and social support, was collected through face-to-face interviews by trained professionals using self-report tools or scales. The blood samples of the participants were collected 15 min after anesthesia induction during the surgery. The collection of postoperative delirium, neurocognitive level, physiological and psychological status will be arranged for in hospital face-to-face follow-up from the first to the seventh day after surgery, and blood samples will be collected again on the third day after surgery. The collection of relevant data at week 4, week 12 after surgery will be conducted through face-to-face, telephone, or video interviews based on discharge status. Fig. 1 shows the design of the study.

Participants

All patients were included with informed consent prior to the baseline assessment. During the research process, no randomized or protocol driven treatments will be administered or provided to the subjects. If applicable in clinical practice, the treating physician shall make treatment decisions and choose treatment plans at their discretion.

The inclusion criteria at baseline are:

1. Voluntary signing of informed consent form;

2. 65–90 years old undergoing elective major abdominal surgery (expected surgery time greater than 2 h);

3. American society of Anesthesiologists (ASA) physical status I-III.

The exclusion criteria are:

• 4.Preoperative Mini-Mental State Examination (MMSE) score < 15;
• 5.Renal failure (requiring dialysis) or liver failure (Child-Pugh score > 5);
• 6.Being treated with anti-neurological drugs;
• 7.Severe impairment due to structural or hypoxic brain injury, more than 2 days in ICU one month before surgery;
• 8.Have undergone major heart, lung or abdominal surgery within the past 1 year;
• 9.The cognitive assessment cannot be completed because the subject is blind, deaf, or unable to communicate in Mandarin;
• 10.Long-term follow-up unavailable (homelessness, active psychosis or substance abuse, etc.).

The exit criteria are:

• 11.Planning to undergo a second surgical procedure during hospitalization;
• 12.Postoperative intubation for more than 12 h;
• 13.Actual operative duration less than 2 h.

Sample size

According to the pre-experimental results, the incidence of delirium in the group with clinical preoperative anxiety was 15.6%, and the incidence of delirium in the group without clinical preoperative anxiety was 7%, which was calculated using the tests for a two aspects design model in PASS, with type 1 error and power kept at 5% and 80%, two-sided, 1:1 group allocation. The result was that each group needed 209 participants, and the estimated data loss rate was 15%. The final sample size was 240 participants for each group or 480 participants for both groups. If the actual shedding rate is greater than 20%, in order to ensure sufficient sample size, our total sample size is adjusted to over 500 cases.

Data collection

The constructs of these dimensions are shown in Table 1.

Table 1.

Detailed evaluation tools and measurement standards

Dimension	Subdimension	Assessments
Basic Information	Demographic information	Gender, age, education level, marital status, number of children, residence status, medical payment method, and primary caregiver during hospitalization
Preoperative disease data	Clinical profile	Preoperative diagnosis, name of the operation to be performed, preoperative chemotherapy or not, ASA classification (Grade I-III)
	Disease comorbidity risk	Age-adjusted Charlson Comorbidity Index (A-CCI)
Cognitive function	Preoperative cognitive status	Mini-Mental State Examination (MMSE), Abbreviated Mental Test Score (AMTS)
	Postoperative delirium	3-minute Diagnostic Interview for Confusion Assessment Method (3D-CAM)
		Confusion Assessment Method for the Intensive Care Unit (CAM-ICU)
	Postoperative cognitive status	Abbreviated Mental Test Score (AMTS)
Physical condition	Biochemistry	Serum biomarkers, p-Tau 181, NfL, determination of IL-8 concentration, intestinal flora, anthropometry (e.g., height and weight)
	Quality of life	Short Form 36 Questionnaire (SF-36)
	Sleep quality	Athens Insomnia Scale (AIS)
	Pain	Numerical Rating Scale (NRS)
Mental health	Severity of anxiety symptoms	The Hamilton Anxiety Scale (HAMA)
	Social support	Social Support Rating Scale (SSRS)

Demographic information

Demographic information is collected by trained professionals using self-report tools. Demographic information includes gender, age, education level, marital status, number of children, living conditions, medical payment methods, and primary caregivers during hospitalization.

Preoperative disease data

Collecting electronic medical records includes preoperative diagnosis, proposed surgical name, presence or absence of chemotherapy, and ASA physical status. The American Society of Anesthesiologists (ASA) classifies patients into six levels based on their physical condition and surgical risk before anesthesia, as patients in levels 4–6 have extremely high anesthesia risk. This study selected patients with ASA grades I-III.

Assess the preoperative comorbidity risk of patients using the age-adjusted Charlson Comorbidity Index (A-CCI). A-CCI is a method used to evaluate the impact of multiple diseases coexisting on the health status of patients, and is considered the gold standard for assessing comorbidity risk in clinical studies [18]. By evaluating the type and quantity of diseases that patients suffer from, and calculating a comprehensive score after assigning weights to different diseases, the overall health status of patients is measured. This includes cardiovascular diseases, respiratory diseases, tumors, diabetes, etc. The higher the score, the more types of diseases the patient suffers at the same time, and his health may be more complex and fragile [19].

Neurocognitive function assessment

This study will continue to collect and evaluate the perioperative neurocognitive level of older patients undergoing abdominal surgery, starting from a preoperative baseline and continuing for three months after surgery.

The Mini-Mental State Examination (MMSE) is used to assess a patient’s preoperative cognitive level in order to screen eligible study subjects. It covers a wide range of cognitive items, is highly sensitive, easy to administer, and is widely used around the world. The MMSE measures orientation, memory, calculation, language, visuospatial, utilization, and attention, with a total of 10 subscales and a total score of 30 [20]. It covers a wide range of cognitive items, is highly sensitive, easy to manage, and widely used worldwide.

Postoperative delirium examination uses the 3-minute Diagnostic Interview for Confusion Assessment Method (3D-CAM), a short and practical clinical tool. It is an improved version of CAM, mainly providing evaluation opinions by asking patients a series of questions and observing their status. This scale contains 20 question items (10 cognitive test items, 10 interviewer observations). 3D-CAM can effectively screen and diagnose delirium [21]. According to relevant studies, 3D-CAM shows good consistency in diagnostic results and has high sensitivity and specificity for detecting postoperative delirium [22].

For patients admitted to the ICU unit after surgery, we will use the Confusion Assessment Method (CAM-ICU) for the diagnosis of postoperative delirium. Since the CAM-ICU is designed to assess delirium in intubated ICU patients, all questions can be answered using nonverbal responses. It consists of four diagnostic features: acute onset or recurrent fluctuation of symptoms, inattention, disorganized thinking, and disorganized speech, which can be assessed by patient observation and questioning [23].

The cognitive function assessment of patients at 1 day before surgery, 4 weeks, 12 weeks after surgery was conducted using the Abbreviated Mental Test Score (AMTS), a rapid screening cognitive impairment scale developed in 1974. It consists of 10 questions that assess basic cognitive functions such as orientation, memory, attention, and calculation. This test takes approximately 3 min to complete and is suitable for people from different cultural backgrounds due to its simplicity and independence from reading or writing skills. The scoring system rewards one point for each correct answer, with a total score ranging from 0 to 10 points. A score below 7–8 indicates cognitive impairment, while a higher score indicates normal cognitive function [24, 25].

At baseline, both the Mini-Mental State Examination (MMSE) and the Abbreviated Mental Test Score (AMTS) will be administered. MMSE will be used to screen eligibility and describe preoperative cognitive status, whereas AMTS will serve as the primary cognitive measure for longitudinal comparisons across all time points.

Definitions of PND, POD, dNCR and POCD

In accordance with the 2018 consensus recommendations on the nomenclature of cognitive change associated with anaesthesia and surgery, perioperative neurocognitive disorders (PND) are used in this study as an umbrella term encompassing preoperative cognitive impairment (preCI), postoperative delirium (POD), delayed neurocognitive recovery (dNCR) and postoperative cognitive dysfunction (POCD) [26].

POD will be defined as delirium occurring from the end of surgery until postoperative day 7 or hospital discharge, whichever comes first. POD will be diagnosed using the 3-minute Diagnostic Interview for Confusion Assessment Method (3D-CAM) on the ward and the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) for intubated ICU patients. Patients who fulfil the diagnostic criteria of 3D-CAM or CAM-ICU on any assessment during this period will be classified as having POD.

Delayed neurocognitive recovery (dNCR) will be defined at the early postoperative follow-up visit as a decline of ≥ 2 points in AMTS score compared with the preoperative baseline, together with an AMTS score ≤ 7, in the absence of ongoing delirium. This definition reflects both a clinically meaningful decline from the patient’s own baseline and a level of performance consistent with cognitive impairment.

Postoperative cognitive dysfunction (POCD) will be defined at the late postoperative follow-up visit as a decline of ≥ 2 points in AMTS score compared with the preoperative baseline, together with an AMTS score ≤ 7, accompanied by a clinically meaningful deterioration in health-related quality of life on the SF-36.

Assessment of physiological, psychological, and social state

Monitoring and evaluating the changes in physiological status, mental health, and social support of patients before and after surgery can help us identify the risk and protective factors that affect perioperative neurocognitive disorders in elderly abdominal surgery patients.

Blood sample explanation

1. Sampling

This study will extract blood samples from patients who meet the inclusion criteria and have signed informed consent. Blood samples will be collected at the following time points: before anesthesia induction in the operating room, 15 min after induction, 30 min after induction, and on the 3rd day post-operation. Preoperative blood draws will be performed after the patient enters the operating room and the nurse opens venous access to the patient using a 22G or 20G model infusion needle and draws venous blood, marking the hospitalization number, subject number, and time of blood draw. Blood draws after induction of anesthesia will be taken intraoperatively by the anesthesiologist via an invasive arterial access, and before the blood is drawn discard the heparinized water at the distal end of the arterial line to prevent saline dilution of the blood sample. Postoperative blood draws will be taken by a nurse via peripheral venous access on the patient’s third postoperative morning at 7:00–8:00 a.m. Venous blood will be stored in an EDTA anticoagulation tube.

• (2)Storage and determination of p-Tau 181, NfL, IL-8 concentrations

Each blood sample will be collected into 5-mL serum separator tubes without anticoagulant. All blood samples will be allowed to stand at room temperature for 5 min after sampling and then temporarily stored in a refrigerator at 4 °C. After 12 h, the samples will be centrifuged uniformly and the upper layer of serum will be removed by professionally trained laboratory personnel. The centrifugation is performed by the same investigator, and the parameters and procedures are strictly adhered to the study protocol. The serum obtained by centrifugation will be stored in 1 ml EP tubes, labeled, and stored in a − 80℃ freezer until analysis.

Concentrations of serum phosphorylated Tau (p-Tau181), neurofilament light chain (NfL), and interleukin-8 (IL-8) will be measured using validated immunoassay methods. To maintain analytical consistency and minimize inter-assay variability—particularly given the anticipated sample size of over 500 specimens—all samples will be analyzed centrally in a single batch after study collection is complete, using the same lot of reagents for each biomarker. (1) p-Tau181 will be quantified using the Quanterix Simoa p-Tau181 Advantage Kit (catalog #103714) based on Simoa technology on the HD-X Analyzer. (2) NfL will be measured using the Macklin ELISA Kit (catalog #H710131-48T/EA). (3) IL-8 will be assayed using the Abbkine EliKine™ Human IL-8 ELISA Kit (catalog KTE6018-48T). All laboratory personnel involved in biomarker testing will be blinded to patients’ clinical outcomes and group assignments to prevent potential measurement bias.

By analyzing the concentration changes of serum biomarkers and intergroup differences, the study aims to assess whether the concentration changes of serum target biomarkers postoperatively are potential mechanisms for the occurrence and development of postoperative delirium.

Physiological state assessment

Physiological state assessment includes the patient’s quality of life, sleep quality, circadian rhythm, and pain condition before and after surgery.

The 36 Item Short Form Health Survey (SF-36) was used to evaluate the quality of life of patients before and after surgery. SF-36 is one of the global indicators developed by the American Medical Association to measure health-related quality of life. It consists of 8 dimensions and 36 questions, covering physical function, vitality, physical pain, general health status, social function, emotional roles, psychological health, and health changes, to assess patients’ health status and quality of life. It does not have a total score, but calculates the total score for each subscale, ranging from 0 (poor health) to 100 (good health). The higher the score, the better the quality of life in the measured field [27].

The Athens Insomnia Scale (AIS) is used to measure the sleep quality and circadian rhythm of patients before and after surgery. The scale consists of 8 items, with the first 5 items assessing participants’ sleep quality and the last 3 items assessing participants’ daytime state. All projects assess the severity of insomnia on a scale of 0–3. The total score ranges from 0 to 24, from none to severe. A total score less than 4 indicates no sleep disorders, 4 to 6 indicates suspected insomnia, and 6 or more indicates insomnia [28].

The Numerical Rating Scale (NRS) is used to measure the pain status of patients before and after surgery. NRS is a self-assessment tool used to evaluate pain levels ranging from 1 to 10. Among them, 0 represents painless, 1 to 3 represents mild pain, 4 to 6 represents moderate pain, and 7 to 10 represents severe pain. The NRS score is accurate and concise, and is considered the gold standard for pain assessment by the American Pain Society [29, 30]. In this study, elderly patients undergoing major abdominal surgery were asked to rate the degree of pain in the abdomen and around the anus on a scale of 1 to 10 before surgery. Joint pain, headaches, and other pain caused by old age or other reasons cannot be considered as pain data collected in this study.

Assessment of mental health and social support

The Hamilton Anxiety Scale (HAMA) was used to assess the severity of anxiety symptoms in patients before and after surgery. This scale has two subscales: mental anxiety and physical anxiety, consisting of 14 items, all of which are scored on a 5-point scale from 0 to 4. Each level has the following criteria: none, mild, moderate, severe, and very severe. The higher the score, the more severe the anxiety symptoms [31].

The Social Support Rating Scale (SSRS) is used to measure an individual’s perception of social resources such as friends, family, and community, in order to assess their level of social support. It can also be used to predict the risk of postoperative delirium and evaluate the recovery outcomes of postoperative delirium patients. It includes three dimensions: subjective support, objective support, and helpful support, each with specific scoring items. The total score of this scale is 38 points, and the higher the total score, the higher the level of social support that an individual feels [32].

Follow-up

In order to ensure that as many patients as possible complete all follow-up assessments, we use a number of methods to improve our follow-up rates. First, our team had adequate human resources covering a wide range of clinical, psychiatric and psychological disciplines. Second, we fully inform participants and their families about the study to ensure that they understand what the study is about before they participate. Thirdly, we provide practical assistance to patients according to the actual situation, such as informing patients of the assessment results and assisting clinicians to provide assistance to patients when necessary. Finally, during the actual study, we will continue to improve the follow-up protocol and make adjustments based on participants’ feedback in order to improve the follow-up rate and study quality. The specific follow-up time and multidimensional framework assessment content are shown in Table 2.

Table 2.

Complete overview of the study period

Time point	Study period
	Baseline	Surgery	Discharge (1-7days)	1-month follow-up	3-months follow-up
	T1	T2	T3	T4	T5
Assessments
Laboratory testing		√	√
Investigator
Baseline patient characteristics	√
MMSE	√
AMTS	√		√	√	√
A-CCI and ASA	√
NRS	√		√	√	√
AIS	√		√	√	√
3D-CAM(CAM-ICU)			√
HAMA	√		√	√	√
SF-36	√		√	√	√
SSRS	√		√	√	√

MMSE Mini-Mental State Examination, AMTS Abbreviated Mental Test Score, A-CCI Age-adjusted Charlson Comorbidity Index, ASA American Society of Anesthesiologists, NRS Numerical Rating Scale, AIS Athens Insomnia Scale, 3D-CAM 3-minute Diagnostic Interview for Confusion Assessment Method, CAM-ICU Confusion Assessment Method for the Intensive Care Unit, HAMA Hamilton Anxiety Scale, SF-36 Short Form 36 Questionnaire, SSRS Social Support Rating Scale

To ensure that cognitive assessments are standardized, prior to the assessment, all assessors are required to complete uniform training and pass a consistency test using standardized instructions and scoring rubrics. The AMTS is an assessment scale based on objective evaluation. It has clear scoring criteria for all items, which minimises the subjective bias of assessors and ensures the reliability of the data. A pre-assessment equipment checklist, a checklist of operating practices during the assessment, and immediate post-assessment data entry requirements are also developed. During the assessment, the telephone follow-up will be conducted in a quiet, private, and well-equipped follow-up office and will be scheduled, as far as possible, from 9 to 11 a.m., when the cognitive status of the elderly subjects is relatively stable. After the assessment, outliers such as internal contradictions within the scale and other conditions are reviewed.

Statistical analysis

Data analysis will be performed using R version 3.5.1. Descriptive analysis will be used to characterise the baseline characteristics of the study population and levels of perioperative neurocognitive function at different time points. Repeated measures analysis of variance will be used to determine whether there are significant differences in neurocognitive levels at different time points between patients with different characteristics. Categorical variables will be expressed as numbers and percentages (N, %), and group comparisons were conducted using the Chi-square test or Fisher’s exact test. Continuous variables conforming to a normal distribution were presented as Mean ± Standard Deviation (SD) and compared using independent samples t-tests; non-normally distributed continuous variables were reported as Median (Interquartile Range, IQR) and compared using the Mann-Whitney U test. The number of POD incidents was first calculated according to anxiety grouping. Univariable logistic regression was performed to identify potential risk factors associated with postoperative delirium. Least Absolute Shrinkage and Selection Operator (LASSO) regression was then applied to further filter the most valuable variables. The relationship between preoperative risk factors and POD was evaluated using multivariable logistic regression analysis. The variance inflation factor (VIF) was calculated to assess multicollinearity in regression analysis. Multiple imputation was used to handle missing long-term cognitive follow-up data, and a generalized linear regression model was applied to assess the impact of preoperative significant risk factors on long-term cognitive function.

Discussion

PND is a common perioperative complication in elderly patients, severely affecting their recovery and quality of life. Using a combination of longitudinal and cross-sectional research methods, this study aims to explore the relationship between perioperative physiological factors (including biomarkers), socio-psychological factors and PND in older patients undergoing abdominal surgery, and to provide multidimensional data to support early prevention and management of PND.

Compared with previous studies, this study addressed the lack of a large-sample longitudinal cohort study of PND in elderly patients undergoing major abdominal surgery in China. It will provide the research community with information on the trajectory of change in the perioperative neurocognitive level of elderly patients undergoing abdominal surgery in China. It will also track multidimensional outcomes.

This study will analyze whether there are differences in PND among elderly patients undergoing abdominal surgery in China due to differences in the number of children, living conditions, and medical payment methods. Determining whether physiological conditions such as quality of life, sleep disorders, pain, as well as psychological and social factors such as preoperative anxiety and social support, are protective or risk factors for PND can provide surgeons with rich early intervention information. Most importantly, we initiated a long-term longitudinal study that collected multiple objective marker units at different time points to help identify neuroepigenetic biomarkers that guide postoperative delirium diagnosis and severity. Lastly, the results of this study are expected to describe the characteristics of high-risk patients with PND, thereby identifying individuals who would benefit most from commonly used prevention measures, providing valuable guidance for clinical practice. The results of the study are expected to be directly translated into clinical action. In the perioperative period, psychosocial counseling resources can be targeted and allocated based on patients’ psychosocial risk scores (e.g., anxiety, low social support), and preoperative cognitive-behavioral therapy and postoperative psychosocial support can be implemented for patients at high psychological risk. Perioperative nutritional interventions are available for malnourished patients. Postoperatively, patients are effectively monitored according to their delirium risk level, and high-risk patients may be prioritized for inclusion in a multidisciplinary follow-up program. This “assessment-prediction-intervention” closed-loop model facilitates direct information for clinical action. Ultimately, this study’s empirical data can provide evidence-based support for formulating or updating clinical prevention and treatment guidelines for PND.

We will correlate changes in NfL concentration to delirium occurrence. We would like to see if intrathecal morphine, an inexpensive addition to bupivacaine, could reduce the incidence of delirium with limited side-effects.

The disadvantage of this comprehensive study is that it takes a long time to obtain clinical information, which may lead to an increase in the dropout rate of subjects. Furthermore, despite our efforts to minimise respondent burden, older adults with more severe physical or cognitive impairment may be less likely to complete the full assessment battery or long-term follow-up. This may result in a positive selection of cognitively fitter participants. We will take measures such as gradual evaluation and strengthening health education to encourage patient follow-up. In addition, this study only analyzes data from a single medical center, which may limit the external validity of the study results, affecting the generalizability of the results to other medical centers or different environments. Furthermore, the identification of dNCR and POCD in this protocol is based on pragmatic operational criteria using the AMTS and SF-36 rather than a comprehensive neuropsychological test battery and formal DSM-5 diagnostic procedures. Therefore, our findings should be interpreted as reflecting probable perioperative neurocognitive disorders, and some degree of misclassification of long-term cognitive outcomes cannot be entirely excluded.

Based on the statistical analysis of the existing results, preoperative anxiety has a statistically significant predictive effect on the occurrence of POD and provides the optimal critical point of anxiety index for screening positive for POD, improving the predictability of POD and providing clinical guidance for improving prognosis, thus achieving research translation. In the future, combining the performance and characteristics of POD under different surgical backgrounds, it is hoped to gradually establish treatment plans and nursing measures for patients under different surgical backgrounds, improving patients’ recovery and quality of life.

Abbreviations

A-CCI

Age-adjusted Charlson Comorbidity Index

AIS

Athens Insomnia Scale

AMTS

Abbreviated Mental Test Score

ASA

American Society of Anesthesiologists

CAM-ICU

Confusion Assessment Method for the Intensive Care Unit

dNCR

Delayed Neurocognitive Recovery

DSM-5

Diagnostic and Statistical Manual of Mental Disorders-fifth edition

HAMA

Hamilton Anxiety Scale

MMSE

Mini-Mental State Examination

NRS

Numerical Rating Scale

PND

Perioperative Neurocognitive Disorders

POCD

Postoperative Cognitive Dysfunction

POD

Postoperative Delirium

preCI

Preoperative Cognitive Impairment

SF-36

36-Item Short Form Health Survey

SSRS

Social Support Rating Scale

3D-CAM

3-minute Diagnostic Interview for Confusion Assessment Method

Authors’ contributions

XZ conceives the research, leads the development of the proposal and protocols and contributes to the research design and selection of research tools. XR conceives the research, leads the development of the proposal and protocols, and provides guiding solutions and suggestions for modifications to issues encountered during the research process. HC and MD contribute to the development of the research proposal, draft this protocol, and participate in data collection. XM contributes to the research design and selection of research tools, and participates in data collection and statistical analysis. WD and BL provide professional guidance on experimental methods. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (81271196) and the Guangzhou Science, Technology and Innovation Commission (202206060004).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The research program was reviewed and approved by the Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (Ethics Number: E2023212). No randomized or any protocol-driven treatment will be administered or provided to the subjects during the study. Written informed consent was provided by all participants or their legal guardians. All the selected groups do not involve special groups, and patient privacy information is strictly protected.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.