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Journal of Clinical Nursing, 2024; 0:1–15 https://doi.org/10.1111/jocn.17596

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Journal of Clinical Nursing

EMPIRICAL RESEARCH QUANTITATIVE

Incidence and Associated Factors of Postoperative Delirium in Adults Undergoing Cardiac Surgery With Cardiopulmonary Bypass: A Prospective Cohort Study Yating Guo1,2 | Chengyang Li3 | Yan Mu4 | Tingting Wu5 | Xiuxia Lin4

1Department of Nursing, Zhangzhou Affiliated Hospital of Fujian Medical University and Zhangzhou Municipal Hospital of Fujian Province, Zhangzhou, Fujian, China | 2College of Nursing, Fujian University of Traditional Chinese Medicine, Fujian, China | 3School of Nursing, Fujian Medical University, Fuzhou, Fujian, China | 4Department of Nursing, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China | 5Department of Nursing, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China

Correspondence: Yan Mu ([email protected])

Received: 9 October 2024 | Revised: 6 November 2024 | Accepted: 26 November 2024

Funding: The authors received no specific funding for this work.

Keywords: associated factors | cardiac surgery | cardiopulmonary bypass | incidence | postoperative delirium

ABSTRACT Background: Delirium is one of the most common and serious complications after cardiac surgery with cardiopulmonary by- pass (CPB). A comprehensive assessment of independent risk factors for postoperative delirium (POD) is essential for early de- tection and prevention. Aims and Objectives: To investigate the incidence and independent associated factors of POD in adults undergoing cardiac surgery with CPB. Design: Prospective cohort design. Methods: A total of 203 patients were enrolled in this study from October 2022 to December 2023 in China. Richmond agitation and sedation scale (RASS) and confusion assessment method- intensive care unit (CAM- ICU) were used for assessing delirium symptom. This study analysed various factors for POD, including demographic, physical, psychological, social, spiritual and environmental aspects. Using logistic regression analysis to identify the independent associated factors. Results: A totla of 60.1% (n = 122) of patients had POD. Of these cases, 86 (70.5%) were hypoactive delirium, 4 (3.3%) were hy- peractive delirium and 32 (26.2%) were mixed delirium. Advanced age (OR = 1.069, 95% confidence interval [CI]: 1.031–1.107; p < 0.001), preoperative depression (OR = 1.847, 95% CI: 1.246–2.736; p = 0.002), postoperative albumin level (OR = 0.921, 95% CI: 0.851–0.997; p = 0.042) and duration of mechanical ventilation (OR > 1.000, 95% CI: 1.000–1.001; p < 0.001) were independent predictors of POD. Conclusions: The incidence of POD in patients undergoing cardiac surgery with CPB was high. This study identified advanced age, preoperative depression, postoperative albumin level and duration of mechanical ventilation as significant and independent predictors of POD. Relevance to Clinical Practice: The study's findings highlight the urgent necessity for improved clinical vigilance and proac- tive management strategies. Patient or Public Contribution: No patient or public contribution.

© 2024 John Wiley & Sons Ltd.

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1 | Introduction and Background

As the incidence of cardiovascular disease increases annually (Roth et al.  2020; Aldwikat, Manias, and Nicholson 2020), an increasing number of patients in China are undergoing cardiac surgery, with 63.5% of these procedures being performed using cardiopulmonary bypass (CPB) (Circulatio  2022; Aldwikat, Manias, and Nicholson 2020). Postoperative delirium (POD) is a common neurological complication of cardiac surgery with CPB and is one of the main factors leading to poor prognosis in patients undergoing cardiac surgery (Andrási et  al.  2022; Aldwikat, Manias, and Nicholson  2020). POD is defined as delirium occurring 1–7 days after surgery (Hughes et al. 2020; Aldwikat, Manias, and Nicholson  2020). It is an acute, fluc- tuating and degenerative syndrome of the central nervous system characterised by significant consciousness disorders, cognitive changes, lack of concentration and disrupted sleep cycles (European Delirium Association; American Delirium Society 2014; Aldwikat, Manias, and Nicholson 2020). POD is not only a psychological transformation but also a clinical ill- ness characterised by pathological and physiological alterations, with a complicated occurrence process. It is thought to be caused by a variety of pathophysiological factors, including neu- roinflammation theory, neurotransmitter theory, neuronal met- abolic disorder theory, etc. (Shioiri et al. 2016; Brown et al. 2015; Taylor et al. 2022). However, it is difficult to explain the onset and progression of POD using a single pathophysiological cause, and the particular pathophysiological mechanisms underlying delirium are unknown. In the past decade, numerous studies have described the incidence, diagnosis, evaluation criteria and methods, clinical symptoms, pathophysiological research and treatment of POD after cardiac surgery. The incidence and missed diagnosis rates of POD in cardiac surgery patients are still high, which is still an unavoidable problem for cardiac sur- gery physicians and nurses.

Due to differences in assessment methods, study popula- tions and types of surgery, the reported incidence of delir- ium after CPB in adults ranges from 11.2% to 45.5% (Ibala et  al.  2021; Theologou, Giakoumidakis, and Charitos  2018a; Shi et  al.  2019; Aldwikat, Manias, and Nicholson  2020). POD significantly prolongs patient hospitalisation and

rehabilitation time (Cheng et al. 2021; Aldwikat, Manias, and Nicholson 2020), increases healthcare costs (Potter et al. 2019; Aldwikat, Manias, and Nicholson  2020), reduces postopera- tive quality of life and functional status (Labaste et al. 2020; Aldwikat, Manias, and Nicholson 2020) and is positively cor- related with postoperative mortality and cognitive impairment (Brown et al. 2018; Aldwikat, Manias, and Nicholson 2020), placing an enormous burden on both patients and society. Approximately 50% of hospitalised patients experience de- lirium, which is preventable (Hshieh et al.  2018). Therefore, identifying high- risk patients for delirium is critical for de- signing clinical nursing strategies and allocating resources efficiently.

Although all effective delirium prevention measures may po- tentially be routinely delivered to hospitalised patients, their implementation is constrained by the resource conditions of most medical institutions. On the other hand, medical staff have few options for efficiently preventing and treating delirium in practical practice. A meta- analysis of 15 trials (2812 partici- pants) found that the use of nonpharmacological interventions to reduce the incidence and duration of delirium in critically ill patients is not supported, and even the multicomponent non- pharmacological intervention methods recommended in the guidelines did not yield compelling results in the meta- analysis (Bannon et al. 2019). A multicentre, wedge- shaped, cluster ran- domised controlled trial discovered that multicomponent non- pharmacological interventions (including preoperative delirium education for patients, delirium education for nurses and ward environment intervention) did not reduce the incidence of de- lirium in high- risk populations (Rood et al. 2021). This shows that present delirium prevention strategies remain ineffective and that advancements in delirium research require a fresh approach.

Gómez Tovar and Henao Castaño  (2020) proposed a new per- spective of understanding delirium as a symptom to promote its prevention. Brant, Beck, and Miaskowski  (2010) created a dynamic symptoms model (DSM) by comparing and evaluat- ing the symptom management theory, discomfort symptom theory, symptom experience model and symptom time expe- rience model. This theory emerged during the comparison of theories and models for addressing symptom phenomena. In Gómez Tovar and Henao Castaño  (2020) provided a thorough analytical technique for delirium symptoms based on the DSM model. This approach offers a new research perspective for ap- plying the DSM model to delirium. Gómez Tovar and Henao Castaño  (2020) conducted a literature analysis based on the DSM and determined that the four primary elements impacting delirium are demographic; physiological; psychological, social spiritual; and environmental.

1.1 | Demographic

Age was associated with POD incidence in patients undergo- ing CPB cardiac surgery. Several studies have reported that the incidence of delirium after heart surgery increases with age (Kotfis et  al.  2018; Ordóñez- Velasco and Hernández- Leiva 2021; Chen et al. 2021; Kapoor 2020). Two retrospective investigations indicated no significant difference between the

Summary

• What Does This Paper Contribute to the Wider Global Clinical Community? ○ The incidence of delirium after cardiac surgery with

cardiopulmonary bypass (CPB) was 60.1% and 70.5% of patients had hypoactive delirium.

○ Early identification of high- risk reversible risk fac- tors for postoperative delirium (POD), including ad- vanced age, preoperative depression and duration of mechanical ventilation, may benefit from targeted prevention strategies.

○ Postoperative albumin levels independently corre- lated with the incidence of POD. We need to pay at- tention to and increase the albumin levels of patients undergoing cardiac surgery with CPB and enhanced nutrition to prevent delirium.

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probability of POD occurrence and age in patients after car- diac surgery with CPB (Salem et al. 2021, 2020). In addition, Mufti and Hirsch (2017) discovered that male sex was an in- dependent risk factor for POD in patients undergoing cardiac surgery. Another large retrospective study indicated that fe- male patients were more likely to experience POD (Aldwikat, Manias, and Nicholson 2020). Furthermore, the patients' de- gree of education influenced their comprehension and compli- ance with therapy and nursing. Consequently, the association between demographic characteristics such as age, sex and POD remains controversial.

1.2 | Physiological

The duration of mechanical ventilation (Shirvani, Sedighi, and Shahzamani  2022), type of cardiac surgery (Mailhot et  al.  2019), duration of surgery, duration of CPB (Ordóñez- Velasco and Hernández- Leiva  2021), disease severity, previ- ous history (diabetes, atrial fibrillation, etc.) (Ordóñez- Velasco and Hernández- Leiva 2021; Chen et al. 2021), inflammatory markers (Ordóñez- Velasco and Hernández- Leiva  2021), mal- nutrition (Velayati et al. 2019) and impairment of daily func- tion (Ordóñez- Velasco and Hernández- Leiva  2021) were all associated with the occurrence of POD in CPB cardiac sur- gery. Biomarkers included PO2 (Spiropoulou et  al.  2022), albumin (Shin, Choi, and Na  2021a, 2021b), creatinine (Theologou, Giakoumidakis, and Charitos  2018b), lactate (Wang et al. 2023) and haemoglobin (Bajracharya et al. 2023) levels.

1.3 | Psychosocial, Social and Spiritual

Several studies have reported a link between anxiety, de- pression and POD in patients undergoing cardiac surgery (Eshmawey et  al.  2019; Falk, Eriksson, and Stenman  2020). However, two investigations found that preoperative anxiety was not associated with the development of POD in patients undergoing cardiovascular surgery (Fukunaga et  al.  2022; Milisen et  al.  2020). In addition, a study indicated that per- sonality factors, such as neuroticism and conscientiousness, might predispose patients to POD (Shin et al. 2016), whereas Fukunaga et al. (2022) discovered that agreeableness is an in- dependent predictor of POD. Furthermore, few studies have examined the relationship between individual personality qualities and POD in the Chinese population. In addition, few studies have examined the effects of preoperative social sup- port on POD.

1.4 | Environmental

The study findings on the effects of environmental pressure variables on POD are not yet evident. Surgery, anaesthesia and the intensive care unit (ICU) environment may reduce messenger RNA levels of synaptic nuclear protein alpha, neurotrophic receptor tyrosine kinase 1 and synaptic protein 1a in the hippocampus, resulting in attention, memory and thinking disorders (Illendula et  al.  2020). Zaal et  al.  (2013) discovered that the number of delirium days in a single ICU

fell by 0.4 days when compared to a typical ICU, suggesting that the ICU environment may influence the course of delir- ium in patients. Arenson et al.  (2013) sought to reduce POD by modifying the CSICU atmosphere but observed no signif- icant decrease in the overall incidence of POD in the CSICU. Therefore, the link between environmental stressors and POD in patients undergoing heart surgery with CPB requires addi- tional investigation.

To address this issue, this study used the delirium DSM as a theoretical guide to comprehensively analyse the independent determinants of POD from multiple factors, assisting medical staff in identifying delirium determinants, intervening early in modifiable risk factors and providing a theoretical basis for the precise management of delirium symptoms.

2 | Methods

This prospective cohort study investigated the prevalence of POD and its associated factors in patients undergoing cardiac surgery with CPB. Figure 1 illustrated the research framework. This study was approved by the Ethics Committee of Fujian Provincial Hospital (approval date: August 26, 2022, approval number: K2022- 08- 032). All the patients provided written in- formed consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist (Supporting Information S1).

2.1 | Study Design and Participants

The research subjects were patients who underwent cardiac sur- gery with CPB at a tertiary hospital in Fujian Province between August 2022 and October 2023.

• Inclusion criteria include (1) age ≥ 18 years; (2) heart dis- ease (coronary atherosclerotic heart disease, valvular heart disease, congenital heart disease, etc.) or major vascular disease (aortic dissection, Marfan syndrome, aortic aneurysm, etc.) who chose to undergo cardiac sur- gery with CPB; (3) patients who needed to stay in the ICU for ≥ 12 h after surgery; and (4) informed consent and vol- untary participation.

• Exclusion criteria include (1) inability to undergo delir- ium assessment due to severe neurological or psychiatric abnormalities or a history of treatment for severe mental disorders; (2) patients who already have severe cognitive impairment before surgery (score < 9 on the Mini- Mental State Examination); (3) patients who already have de- lirium before surgery; (4) patients who already have se- vere hearing impairment, visual abnormalities, slurred speech, etc., and cannot communicate normally before surgery; (5) patients who were pregnant or breastfeeding before surgery; and (6) patients who died within 24 h after surgery.

The patient's preoperative, intraoperative and postoperative nursing and pain treatment plans were carried out by the hospi- tal's procedure, with no modifications made to the participants.

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The usual nursing plan calls for ICU nurses to do 20- to 30- min preoperative visits to all patients the day before surgery. ICU nurses advise patients and their families about ICU hospitalisa- tion precautions and preparations, document patients' particular needs and patiently answer their inquiries. Patients undergoing heart surgery should follow the hospital's regular postoperative care plan, which includes delirium assessment, pain assessment and management, round- the- clock assistance, early activity and exercise, psychosocial care and so on. Remifentanil, sufentanil and dexmedetomidine are frequently used to provide pain relief during and after surgery. Piperidine and morphine are used to treat severe postoperative pain that cannot be alleviated by the analgesics listed above.

2.2 | Delirium Assessment

Clinical nurses and researchers who have received standardised training conducted face- to- face screening for delirium using the Chinese version of the Richmond Agitation and Sedation Scale (RASS) and the Chinese version of the Confusion Assessment Method (CAM)- ICU scale 1 day before surgery. From postoper- ative Days 1–7, clinical nurses and researchers used the RASS and CAM- ICU scales to assess delirium face- to- face for all study

subjects, regardless of whether they were in the ICU or ward. To capture the phenomenon of delirium ‘sunset’ (usually occur- ring at sunset or dusk), avoid the assessment process affecting patients' rest and nighttime sleep, as well as facilitating nurses' workflow, this study chose to conduct delirium assessment on patients during two time periods: 08:00–10:00 and 18:00–20:00 every day.

This study used the CAM- ICU and RASS measures to assess de- lirium subtypes. First, CAM- ICU was utilised to make a qualita- tive diagnosis of delirium, followed by RASS for categorisation and judgement. When the RASS score was −3 to 0, it indicated hypoactive delirium. When the RASS score was 1–4, it was con- sidered hyperactive delirium. Mixed delirium was defined as a RASS score that varied between positive and negative values.

2.3 | Selections of Variables

The content of the collected data was established in accordance with the research objectives and the comprehensiveness of clinical data collection. General information and clinical data for all patients were extracted from the hospital information system. All data were collected prospectively. The assessment

FIGURE 1 | The framework for the study of POD in patients after cardiac surgery with CPB based on the delirium dynamic symptom model (DSM).

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scales were administered in person by researchers 1 day prior to surgery, allowing patients to complete questionnaires or respond to inquiries individually. Based on the patients' re- sponses, researchers assisted them in completing each item on the questionnaires.

2.3.1 | Demographic- Related Factors

The variables collected were as follows: age, gender (male/fe- male), body mass index (BMI, kg/m2), education level (illiter- acy/primary school/Junior middle school/high or polytechnic school/university and above), marital status (married/single/di- vorced/widowed), medical insurance (new rural medical insur- ance/basic medical insurance for urban residents/basic medical insurance for urban employees/commercial insurance/none), smoking and drinking history (yes/no).

2.3.2 | Physiological- Related Factors

The variables collected were as follows: (1) general data: medical history, including a history of hypertension, diabetes and cere- brovascular disease. (2) Clinical data: preoperative data included liver and kidney function tests and electrocardiogram rhythm. Intraoperative data encompassed the type of surgery, duration of surgery, duration of CPB and whether deep hypothermic cir- culatory arrest was performed. Postoperative data included the severity of the disease, duration of mechanical ventilation and the first postoperative venous blood transfusion, which was as- sessed through albumin level, glutamic- pyruvic transaminase, glutamic oxaloacetic transaminase, creatinine, serum lactate dehydrogenase (LDH) and haemoglobin level. Additionally, the first arterial blood transfusion after surgery was evaluated based on the lactate level and oxygenation index, along with the Acute Physiology and Chronic Health Evaluation (APACHE) II score. Furthermore, this study assessed patients' preoperative sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and evaluated preoperative physical functioning status through the Activities of Daily Living (ADL) scale and the Medical Outcomes Study Short Form- 12 (SF- 12) assessment tools.

The PSQI scale has a total score range of 0–21, with higher values indicating poorer sleep quality. A score greater than 5 implies clinically substantial discomfort or inadequate sleep. The ADL scale has a total score range of 14–56 points, with higher scores indicating worse daily living abilities. A total score of 14 shows that ADL is normal; 15–21 indicates mild impairment of ADL; and > 22 indicates significant impairment of ADL. The SF- 12 scale has a total score range of 0–100 points, with higher scores indicating greater health- related quality of life for patients.

2.3.3 | Psychological- , Social- and Spiritual- Related Factors

The variables collected in this study included the patient's preop- erative cognitive function, assessed using the Mini- Mental State Examination (MMSE); preoperative anxiety and depression lev- els, measured with the Hospital Anxiety and Depression Scale (HADS); preoperative social support levels, evaluated through

the Social Support Rating Scale (SSRS); and preoperative per- sonality types, determined using the Ten- Item Personality Inventory in China (TIPI- C).

The overall score on the MMSE scale is 30 points. A score of 27–30 suggests normal cognition, whereas a score below 27 shows cognitive impairment (> 21 indicates mild cognitive impairment, 10–20 indicates moderate cognitive impairment and < 9 indicates severe cognitive impairment). The HADS scale consists of two subscales: anxiety and depression, each having a total score range of 0–21 points. A score of 0–7 indicates as- ymptomatic, 8–10 indicates probable anxiety or depression and 11–21 indicates confirmed anxiety or depression. The entire score range on the SSRS scale is 12–66 points: 22 is designated as low support, 23–44 as broad support and 45–66 as high sup- port. The TIPI- C scale has five dimensions: agreeableness (A), conscientiousness (C), emotional stability (ES), extraversion (E) and openness (O). This measure has a Likert 7- point scale rang- ing from 1 (strongly disagree) to 7 (strongly agree).

2.3.4 | Environmental- Related Factors

If the patient were moved from the ICU to the general ward within 7 days following surgery, the researcher would conduct an ICU Environmental Stressor Scale (ICUES) scale evaluation with the study subjects on the day of transfer. If the patient did not remove the endotracheal tube within 7 days following sur- gery, the researchers would evaluate the study participants in person after the tube had been removed. This scale's total score runs from 42 to 168, with higher scores indicating more strain on patients in the ICU setting.

2.4 | Statistical Analysis

The Kruskal–Wallis H test and the Mann–Whitney U test were employed for the analysis of quantitative data. Qualitative data were assessed using either the Chi- square test or Fisher's exact probability method. Statistical analyses were performed utilis- ing SPSS version 23.0 software. Descriptive statistics, including frequency, rate and composition ratio, were applied to charac- terise the qualitative data. Quantitative data are presented as mean ± standard deviation (x ± s) or median (quartiles) (P50 (P25, P75)), contingent upon the adherence to a normal distri- bution. Initially, a univariate analysis was conducted with de- lirium and its various subtypes serving as dependent variables, while each risk factor was treated as an independent variable. The values of the independent variables are defined as follows: continuous variables are input according to their original scale and categorical variables are coded as no = 0 and yes = 1. In the one- way analysis, if the quantitative variables satisfy the crite- ria for normal distribution and homogeneity of variance, one- way analysis of variance (ANOVA) or t- tests are employed. In instances where the data do not conform to the assumptions of normal distribution and homogeneity of variance, it is advisable to conduct nonparametric diagnostic analyses on the statistically significant risk factors individually. Through multicollinearity analysis, a total of 20 variables may be incorporated into a bi- nary logistic regression analysis, allowing for the calculation of their odds ratios (ORs) and 95% con

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