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% confidence intervals (95% CIs).

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This approach facilitates a more nuanced examination of the adjusted effects of various risk factors on the incidence of delir- ium following CPB during cardiac surgery. All statistical tests are considered significant at a threshold of p < 0.05 (two- tailed).

3 | Results

3.1 | The Incidence of POD

This study involved the screening of 273 patients scheduled for cardiac surgery with CPB. Of these, 70 patients were excluded for failing to meet the established inclusion and exclusion cri- teria. Consequently, a total of 203 patients were included in the analysis, and the process of inclusion and exclusion of study sub- jects is illustrated in Figure 2. All 203 patients who participated in this study were monitored for 1 week postsurgery. Within this follow- up period, 122 patients exhibited signs of delirium, resulting in an incidence rate of 60.1%. Among those affected, 86 patients (70.5%) experienced hypoactive delirium, 4 patients (3.3%) exhibited hyperactive delirium and 32 patients (26.2%) presented with mixed delirium.

3.2 | Differences Between Patients With and Without POD

3.2.1 | Demographic- Related Factors

The average age of 203 patients receiving CPB cardiac surgery was 59.0 (52.0, 66.0), with a range of 20–80 years. 87 (42.9%) of them were older than 60. The influence of age, gender and education on the development of POD in adults following car- diac surgery with CPB was confirmed through our univariate

analysis (p < 0.05). Conversely, marital status, medical insur- ance, BMI and history of smoking and/or alcohol consumption were not identified as significant risk factors for delirium in our study (p > 0.05). For further details, please refer to Table 2.

3.2.2 | Physiological- Related Factors

Univariate analysis indicated that 14 physiological factors were significantly associated with POD (p < 0.05). These factors in- cluded a history of diabetes, preoperative electrocardiogram (ECG) rhythm, preoperative ADL, preoperative quality of life, type of surgery, duration of surgery, duration of CPB, postop- erative albumin levels, postoperative serum glutamic oxaloace- tic transaminase, postoperative blood lactate dehydrogenase, postoperative haemoglobin levels, postoperative lactate lev- els, APACHE score and duration of mechanical ventilation. Conversely, no statistically significant differences (p > 0.05) were observed in preoperative liver and kidney function, history of hypertension or history of cerebrovascular events between the two patient groups. See Table 2 for details.

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

The findings from the univariate analysis indicated that there were statistically significant differences in preoperative cogni- tive function, preoperative anxiety and preoperative depression levels between the POD group and the non- POD, as illustrated in Table 2. Conversely, preoperative personality traits and pre- operative social support did not demonstrate a statistically sig- nificant association with the incidence or prevalence of POD in the univariate analysis (p > 0.05).

FIGURE 2 | The flowchart of the study.

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3.2.4 | Environmental- Related Factors

The scores on the ICUES scale for the POD and the non- POD were 93 and 91, respectively. However, univariate analysis re- vealed no statistically significant difference in ICU environmen- tal stress levels between the two groups (p > 0.05).

3.3 | Independent Factors Influencing POD in Cardiac Surgery Patients With CPB

We selected 20 variables with statistically significant find- ings from the univariate analysis (as shown in Table  1) and conducted a collinearity diagnostic analysis as a preliminary step. The F- value of the model was 4.205, with a significance level of p < 0.001 for the independent variable. The variance inflation factor (VIF) for these 20 variables was less than 3, and the tolerance (Tol) was greater than 0.1, indicating the absence of significant multicollinearity among the indepen- dent variables (refer to Table A1). Additionally, delirium was designated as the dependent variable in a binary logistic re- gression analysis involving the 20 statistically relevant factors identified in the univariate study. A comprehensive list of variable assignments is provided in Table A2. The results in- dicated that the postoperative albumin level (OR = 0.921, 95% CI: 0.851–0.997; p = 0.042) served as an independent protec- tive factor for POD in adult patients undergoing cardiac sur- gery with CPB. Conversely, advanced age (OR = 1.069, 95% CI: 1.031–1.107; p < 0.001), preoperative depression (OR = 1.847, 95% CI: 1.246–2.736; p = 0.002) and the duration of mechanical ventilation (OR > 1.000, 95% CI: 1.000–1.001; p < 0.001) were identified as independent risk factors for POD in this patient population (as shown in Table 2). The Cox and Snell R2 and Nagelkerke's R2 for this regression equation were 0.327 and 0.442, respectively. The Hosmer–Lemeshow test yielded a sig- nificant p value > 0.05, suggesting that the model's predicted probability of POD in cardiac surgery patients closely approxi- mated the actual values observed in this study. Consequently, the model was deemed to fit the data well, demonstrating an excellent fitting effect.

4 | Discussion

POD represents a significant and prevalent complication among adult patients undergoing cardiac surgery with CPB, primarily manifesting as neurological dysfunction. The find- ings of this study indicated that 122 patients who underwent CPB cardiac surgery experienced delirium within 7 days postoperatively, resulting in an incidence rate of 60.1%. This rate was comparable to, yet notably higher than, the reported incidence rates of 11.2%–45.5% in the recent literature (Shi et  al.  2019; Theologou, Giakoumidakis, and Charitos  2018a; Ibala et  al.  2021). The observed discrepancies may be at- tributed to variations in inclusion and exclusion criteria, baseline characteristics of the patients, the type of surgery, cardioplegia type, CPB time, assessment tools for delirium, timing and frequency of assessments, as well as geographical differences in the populations studied. This investigation spe- cifically included patients who were admitted to the ICU for a minimum of 12 h following surgery. The delirium observation

period for all participants extended to 7 days postsurgery, with evaluations conducted bi- daily. Prior to the implementation of the study, raters underwent systematic training focused on delirium- related knowledge to ensure the reliability of the assessment outcomes. Notably, among the patients diagnosed with POD in this study, those exhibiting hypoactive delir- ium constituted the largest subgroup, accounting for 70.5%. Hypoactive delirium was characterised by reduced alertness and a lack of verbal communication, and was frequently observed in elderly populations, which aligned with the av- erage age of delirious participants in this study, recorded at 63.0 years (range: 55.3–68.8 years). The duration of delirium among patients within the 7- day postoperative period varied from 0 to 168 h, with a median duration of 12 h. The prolonged duration of delirium may be influenced by factors such as the type of surgical procedure, length of surgery, surgical tech- niques employed, duration of CPB as well as the specific types of delirium and their triggering factors. The elevated inci- dence of delirium and its potentially severe ramifications war- rant significant attention from clinical medical personnel. It is imperative that healthcare providers enhance their vigilance in monitoring and managing delirium symptoms, employ standardised assessment tools for delirium and improve their competencies in recognising and addressing these symptoms effectively.

The findings of this study indicated that age served as the most significant and immutable predictor of POD in patients under- going cardiac surgery with CPB. In contrast, factors such as gender, educational background and histories of smoking and alcohol consumption did not demonstrate a statistically sig- nificant effect on the incidence and progression of delirium following cardiac surgery. Specifically, the risk of developing POD increased by a factor of 1.069 for each additional year of patient age, a finding that was consistent with previous research (Kupiec et  al.  2020; Andrási et  al.  2022; Lu et  al.  2024). Prior studies have consistently identified age as an independent risk factor for predicting POD in this patient population (Kupiec et  al.  2020, Andrási et  al.  2022, Lu et  al.  2024). This correla- tion may be attributed to the fact that, as individuals age, the presence of chronic conditions such as atherosclerosis can lead to diminished cardiac reserve and reduced cerebral blood flow perfusion. Consequently, this decline in physiological capac- ity might result in decreased tolerance to surgical trauma and anaesthesia, thereby precipitating the onset and progression of POD (Chan and Aneman 2019).

This study found that patients undergoing cardiac surgery with prolonged mechanical ventilation had a higher risk of devel- oping POD, which is similar to the results of a previous study (Shirvani, Sedighi, and Shahzamani  2022). Mechanical ven- tilation support is a routine supportive treatment for cardiac surgery patients admitted to the ICU after surgery. However, due to its invasive nature and significant limitations on pa- tient activity, it can easily cause ventilator- associated lung in- jury, leading to pulmonary and even systemic inflammatory reactions. Inflammatory mediators can penetrate the blood– brain barrier and cause inflammatory damage and neuronal apoptosis (Breitbart et al. 1997), leading to nerve damage and increasing the likelihood of POD occurrence. Therefore, to minimise the risk of postoperative neurological complications

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TABLE 1 | Univariate analysis of factors for POD (n = 203) (

N(%)∕P50 (

P25, P75 )

∕x ± s )

.

Variable

POD

Statistic pYes (n = 122) No (n = 81)

Age 63.0 (55.3, 68.8) 55 (49, 59.8) −5.157a < 0.001

Gender

Male 54 (44.3) 48 (67.6) 4.380b 0.036

Female 68 (55.7) 33 (32.4)

Marital status

Married 119 (97.5) 77 (96.6) 1.317b 0.579

Single 2 (1.6) 3 (2.5)

Divorced 1 (0.8) 1 (1.0)

Widowed 0 (0.0) 0 (0.0)

Education

Illiteracy 35 (28.7) 16 (19.8) −2.533a 0.011

Primary school 38 (31.1) 16 (19.8)

Junior middle school 32 (26.2) 32 (39.5)

High or polytechnic school 10 (8.2) 13 (16.0)

University and above 6 (4.9) 4 (4.9)

Medical insurance

New rural medical insurance 84 (68.9) 50 (61.7) 2.124b 0.730

Basic medical insurance for urban residents 12 (9.8) 10 (12.3)

Basic medical insurance for urban employees 23 (18.9) 17 (21.0)

Commercial insurance 1 (0.8) 1 (1.2)

None 2 (1.6) 3 (3.7)

BMI (kg/m2) 23.0 (20.8, 25.7) 23.5 ± 4.1 −1.148a 0.251

Smoking

Yes 37 (30.3) 33 (40.7) 2.336b 0.126

No 85 (69.7) 48 (59.3)

Alcohol

Yes 40 (32.8) 29 (35.8) 0.197b 0.657

No 82 (67.2) 52 (64.2)

History of diabetes

Yes 20 (16.4) 5 (6.2) 4.709b 0.030

No 102 (83.6) 76 (93.8)

History of hypertension

Yes 40 (32.8) 21 (25.9) 1.090b 0.296

No 82 (67.2) 60 (74.1)

History of cerebrovascular diseases

Yes 10 (8.2) 7 (8.6) 0.013b 0.911

No 112 (91.8) 74 (91.4)

(Continues)

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Variable

POD

Statistic pYes (n = 122) No (n = 81)

Preoperative electrocardiogram rhythm

SR 75 (61.5) 60 (74.1) 18.438b < 0.001

Af 46 (37.7) 14 (17.3)

AF 0 (0.0) 4 (4.9)

Af + AF 0 (0.0) 1 (1.2)

Accelerated atrial autonomous rhythm 0 (0.0) 2 (2.5)

Pacing rhythm 1 (0.8) 0 (0.0)

First preoperative venous blood transfusion

ALT (U/L) 20.0 (13.3, 31.0) 20.0 (12.0, 29.3) −0.630a 0.529

AST(U/L) 22.0 (17.0, 27.0) 20.0 (15.0, 26.0) −1.859a 0.063

Cr (umol/L) 71.5 (58.3, 85.0) 71.5 (58.3 86.3) −0.122a 0.903

Preoperative sleep quality

≤ 5: sleep well 13 (10.7) 11 (13.6) −0.630a 0.528

> 5: clinically significant distress or poor sleep 109 (89.3) 70 (86.4)

Preoperative activities of daily living

14: normal 74 (60.7) 64 (79.0) −2.754a 0.006

15–21: mild damage 20 (16.4) 8 (9.9)

> 21: severe damage 28 (23.0) 9 (11.1)

Preoperative cognitive function

27–30: normal 44 (36.1) 44 (54.3) −2.992a 0.003

21–26: mild cognitive impairment 44 (36.1) 27 (33.3)

10–20: moderate cognitive impairment 34 (27.9) 10 (12.3)

< 9: severe cognitive impairment 0 (0.0) 0 (0.0)

Preoperative anxiety

0~7: asymptomatic 60 (49.2) 55 (67.9) −2.767a 0.006

8~10: suspected anxiety 29 (23.8) 15 (18.5)

11~21: existence of anxiety 33 (27.0) 11 (13.6)

Preoperative depression

0~7: asymptomatic 39 (32.0) 51 (63.0) −4.350a < 0.001

8~10: suspected depression 23 (18.9) 11 (13.6)

11~21: existence of depression 60 (49.2) 19 (23.5)

Preoperative personality traits

Extraversion score 8 (8, 8) 8 (7, 9) −0.108a 0.914

Agreeableness score 8 (8, 9) 8 (8, 9) −0.239a 0.811

Responsibility score 8 (7, 8) 8 (7, 8) −1.034a 0.301

Emotional stability score 8 (8, 9) 8 (8, 9) −0.677a 0.498

Openness score 8 (7, 8) 8 (7, 9) −0.657a 0.511

(Continues)

TABLE 1 | (Continued)

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in patients, it is recommended that medical staff improve the efficiency of team communication, strengthen teamwork, ac- curately evaluate the timing of endotracheal intubation and

extubation and shorten postoperative mechanical ventilation time as much as possible, thereby reducing the incidence of POD.

Variable

POD

Statistic pYes (n = 122) No (n = 81)

Preoperative social support

23~44: general support 115 (94.3) 74 (91.4) −0.798a 0.425

45~66: high support 7 (5.7) 7 (8.6)

Preoperative quality of life 80.9 ± 15.9 85.9 ± 14.1 2.305c 0.022

Surgical type

Valve replacement/shaping surgery 95 (77.9) 66 (81.5) 22.132b < 0.001

CABG 5 (4.1) 1 (1.2)

Valve replacement/shaping surgery + CABG 9 (7.4) 1 (1.2)

Large vessel surgery 2 (1.6) 2 (2.5)

Cardiac tumour resection surgery 0 (0.0) 9 (11.1)

Large vessel surgery + Valve replacement/shaping surgery

9 (7.4) 2 (2.5)

Large vessel surgery + CABG 2 (1.6) 0 (0.0)

Surgical duration (min) 310 (266.3, 407.5) 295 (242.5, 350) −2.040a 0.041

Duration of CPB (min) 179.5 (137.3, 225.8) 156.5 (124.0, 203.0) −2.387a 0.017

Deep hypothermic circulatory arrest

Yes 3 (2.5) 2 (2.5) 0.000b 1.000

No 119 (97.5) 79 (97.5)

The first postoperative venous blood transfusion

ALB (g/L) 32.0 (28.0, 35.0) 33.5 (31.0, 36.0) −3.231a 0.001

ALT (U/L) 23.5 (18.0, 34.0) 24.5 (17, 30) −0.692a 0.489

AST(U/L) 89.5 (56.3, 133.3) 72.0 (51, 109) −2.241a 0.025

Cr (μmol/L) 84.0 (69.3, 105.8) 75.0 (68.0, 100.5) −1.641a 0.101

LDH (U/L) 532.5 (404.0, 636.8) 463.0 (344.5, 582.5) −2.555a 0.011

Hb (g/L) 105.04 ± 18.13 111.43 ± 16.14 −2.936c 0.004

The first arterial blood transfusion after surgery

Oxygenation index (mmHg) 298.8 (239.5, 423.8) 335.0 (252.5, 432.5) −0.717a 0.473

Lac (mmHg) 6.7 ± 3.5 5.7 ± 2.9 −2.178c 0.031

APACHE 28.5 (25.0, 32.0) 25.0 (22.0, 28.8) −3.674a < 0.001

During of mechanical ventilation (min) 2652.5 (1246.3, 6717.5)

1235.0 (1128.8, 2526.3)

−5.106a < 0.001

ICUESS score 93.0 (81.0, 107.0) 91.0 (79.5, 109.0) −0.388a 0.698

Abbreviations: Af, atrial fibrillation; AF, atrial flutter; ALB, Albumin; ALT, alanine aminotransferase; APACHE II, Association of Peoria Area Christian Home Educators II; AST, aspartate transaminase; BMI, body mass index; CABG, coronary artery bypass surgery; CPB, cardiopulmonary bypass; Cr, creatinine; Hb, haemoglobin; ICUESS, Intensive Care Unite Environmental Stressor Scale; Lac, lactic acid; LDH, lactic dehydrogenase; POD, postoperative delirium; SR, sinus rhythm. aThe result calculated using Mann–Whitney U test is the Z- value. bThe results calculated using Chi square test show a statistical value of χ2. cThe result calculated using a two sample t- test is the t- value.

TABLE 1 | (Continued)

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In addition, the results of this study showed that albumin is a protective factor against POD in patients undergoing CPB cardiac surgery, which is similar to previous studies (Shin, Choi, and Na 2021a, 2021b; Cereghetti et al. 2017). Thus, al- bumin may be a helpful and straightforward biomarker for predicting POD in patients following cardiac surgery with CPB. However, previous studies (Shin, Choi, and Na  2021a, 2021b; Cereghetti et al. 2017) only clarified when the preop- erative albumin level can predict delirium after cardiac sur- gery. In this study, it was found that the patient's first venous albumin level after surgery is a protective factor for delirium in patients undergoing CPB cardiac surgery. The higher the patient's first venous albumin level after surgery, the lower the risk of POD occurrence. This may provide some basis for precise intervention timing of delirium. However, the study's findings did not establish that early postoperative nutritional assistance or other nutritional support strategies might en- hance blood albumin levels in heart surgery patients, lower- ing the risk of delirium. Low serum albumin levels were often related to malnutrition. However, according to one research [48], calorie and protein nutritional supplementation did not affect blood albumin levels. This was because nonnutritional variables controlled hepatic protein production, and in acute illnesses, the drop in serum albumin levels might be due to inflammatory responses rather than nutritional conditions. There was no convincing indication that increasing food in- take after managing inflammation would increase blood al- bumin levels. As a result, the pathophysiological mechanism linking albumin and delirium occurrence requires further in- vestigation, as well as additional studies to better understand whether nutritional support and when to use it, can increase albumin levels and reduce delirium incidence in patients who underwent cardiac surgery.

In our study, we used the HADS- D to screen for preoperative depression in patients undergoing cardiac surgery with CPB. Multivariate regression analysis revealed that patients with pre- existing depression had a 1.847 times higher risk of developing POD compared to those no or suspected depressive symptoms. This is consistent with the findings of Segernas et al. (2022) and Falk et al.  (2022). This indicates the need for preoperative de- pression screening for patients undergoing cardiac surgery with CPB. It suggests that clinical medical staff can use standardised depression assessment tools to identify high- risk populations for POD at an early stage and provide patients with comprehen- sive and regular preoperative education and postrehabilitation- related information as early as possible. This can help them

reduce negative emotions and enhance their confidence in disease recovery in unfamiliar environments and/or delirium states. Specific interventions include guiding patients to watch delirium education videos before surgery (Wheeler et al. 2023). However, a systematic review (Nan, Yanqiu, and Lan 2022) have found no statistically significant association between preopera- tive depression and POD in adult cardiac surgery. This may be because different studies used different research tools, and the definition and classification of depression varies among different research tools. At present, the pathophysiological mechanism between depression and POD is not clear. Possible mecha- nisms include decreased serotonin activity, increased cortisol concentration and disturbed glucocorticoid levels in the brain, but the causal relationship is still unclear (Fatehi Hassanabad et  al.  2021). Therefore, further basic and multicentre clinical studies are recommended to confirm the association between preoperative depression and POD in patients undergoing car- diac surgery with CPB.

5 | Limitations

The limitations of this study include constraints related to time, manpower, material resources and finances. The survey was conducted at only one hospital, which limited the repre- sentativeness of the sample size. The research team plans to conduct multicentre studies in the future. Additionally, our study followed up on patients' delirium symptoms for only 7 days postsurgery, despite the volatile nature of these symp- toms. Consequently, the incidence rate of POD measured in this study may not accurately reflect the true incidence. However, each patient was evaluated twice daily to minimise the rates of missed diagnoses of POD. Furthermore, this study solely employed the PSQI scale to evaluate patients' sleep be- fore surgery. This study did not assess patients' sleep quality following surgery since tracheal intubation, confusion and other factors may make it difficult for them to communicate their genuine sleep quality verbally. Future studies can use more advanced tools, like as polysomnography, to investi- gate the association between postoperative sleep quality and POD in patients. Furthermore, while the occurrence and pro- gression of delirium are influenced by multiple factors, this study identified only one biomarker associated with delirium symptoms. Future research could employ omics methods to explore multiple biomarkers of delirium, aiming to identify additional potential targets for the precise treatment of delir- ium symptoms.

TABLE 2 | Binary logistic regression analysis of factors for POD (N = 203).

Variables β BE Wald χ2 p OR

95%CI

Lower Upper

Age 0.066 0.018 13.503 < 0.001 1.069 1.031 1.107

Preoperative existence of depression 0.613 0.201 9.349 0.002 1.847 1.246 2.736

Postoperative albumin level (g/L) −0.082 0.04 4.136 0.042 0.921 0.851 0.997

During of mechanical ventilation (min)

0.000 0.000 16.707 < 0.001 > 1.000 1.000 1.001

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6 | Conclusions

The incidence of delirium following cardiac surgery with CPB was notably high, necessitating increased attention from clin- ical medical staff. Factors such as advanced age, preoperative depression, postoperative albumin levels and duration of me- chanical ventilation could serve as predictors for the occurrence of delirium in these patients. We recommend implementing pre- ventive and personalised interventions that focus on assessing delirium and identifying risk factors, facilitating early extuba- tion, providing psychological assistance and preventing delir- ium. Future study is needed to investigate the mechanism of postoperative albumin levels and delirium in patients following cardiac surgery with CPB, to develop more precise intervention options.

7 | Relevance to Clinical Practice

This study has significant implications for understanding the specific manifestation of delirium symptoms in patients after cardiac surgery involving CPB. For the first time, our research demonstrates a notable incidence of POD in Chinese patients following cardiac surgery with CPB, along with a correlation to postoperative albumin levels. Our findings emphasise the ne- cessity for medical staff to actively assess and identify delirium symptoms after cardiac surgery with CPB, as well as to recog- nise high- risk patients, to improve clinical outcomes.

Author Contributions

Yating Guo was involved in conceptualisation, data curation, for- mal analysis, methodology, investigation, project administra- tion, resources, software, visualisation and writing- original draft. Chengyang Li was involved in writing- review and editing. Yan Mu was involved in resources and supervision. Tingting Wu was involved in writing- review and editing. Xiuxia Lin was involved in resources. The authors have disclosed that they do not have any potential con- flicts of interest.

Acknowledgements

The authors thank the study participants as well as Fujian Provincial Hospital for providing the source of the study participants.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Supporting Information

Additional supporting information can be found online in the Supporting Information section.

Appendix A

TABLE A1 | Collinearity diagnosis.

Variable Tolerance (Tol) Variance inflation factor (VIF)

Age 0.672 1.487

Gender 0.763 1.311

Education 0.579 1.726

Previous diabetes 0.875 1.142

Preoperative electrocardiogram rhythm 0.827 1.209

Preoperative activities of daily living 0.710 1.409

Preoperative cognitive function 0.458 2.182

Preoperative anxiety 0.539 1.857

Preoperative depression 0.409 2.443

Preoperative quality of life 0.503 1.986

Surgical type 0.879 1.138

Surgical duration (min) 0.386 2.589

Duration of CPB (min) 0.565 1.771

The first postoperative venous blood transfusion

ALB (g/L) 0.647 1.545

AST (U/L) 0.635 1.575

LDH (U/L) 0.529 1.891

Hb (g/L) 0.636 1.572

The first arterial blood transfusion after surgery

Lac (mmHg) 0.729 1.371

APACHE 0.728 1.374

During of mechanical ventilation (min) 0.565 1.771

Abbreviations: ALB, albumin; AST, aspartate transaminase; CPB, cardiopulmonary bypass; Hb, hemoglobin; Lac, lactic acid; LDH, lactic dehydrogenase.

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TABLE A2 | Variable assignment and meaning.

Variable meaning Assignment

Age Continuous variable

Gender 0 = Female; 1 = Male

Education 0 = Illiteracy; 1 = Primary school; 2 = Middle school; 3 = High school/ Vocational school; 4 = University or above

Previous diabetes 0 = No; 1 = Yes

Preoperative AF 0 = No; 1 = Yes

Preoperative activities of daily living 0 = Normal; 1 = Mild damage; 2 = Severe damage

Preoperative cognitive function 0 = Normal; 1 = mild cognitive impairment; 2 = Moderate cognitive impairment; 3 = Severe cognitive impairment

Preoperative anxiety 0 = Asymptomatic; 1 = Suspected anxiety; 2 = Existence of anxiety

Preoperative depression 0 = Asymptomatic; 1 = Suspected depression; 2 = Existence of depression

Preoperative quality of life Continuous variable

Surgical type 0 = Simple surgery; 1 = Mixed surgery or aortic surgery

Surgical duration (min) Continuous variable

Duration of CPB (min) Continuous variable

The first postoperative venous blood transfusion

ALB (g/L) Continuous variable

AST (U/L) Continuous variable

LDH (U/L) Continuous variable

Hb (g/L) Continuous variable

The first arterial blood transfusion after surgery

Lac (mmHg) Continuous variable

APACHE Continuous variable

During of mechanical ventilation (min) Continuous variable

Abbreviations: AF, atrial fibrillation; ALB, albumin; AST, aspartate transaminase; CPB, cardiopulmonary bypass; Hb, hemoglobin; Lac, lactic acid; LDH, lactic dehydrogenase.

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Concepts and Applications of Information Technology Class Project

Purpose of this Assignment The Class Project is the most significant assignment in this course, Concepts and Applications of Information Technology. As such, it accounts for 58% of the course points. This assignment is comprised of two deliverables; a SWOT Analysis and a Presentation. The SWOT Analysis is due in Week 4 (worth 33% of course grade), and the Presentation is due in Week 8 (worth 25% of course grade).

This assignment gives you the opportunity to demonstrate your ability to research, evaluate, and describe business strategy focused on information technology tools and services. This assignment specifically addresses the following course outcomes:

• Identify the basic components of the information system: hardware, software, data, processes, and people, and how these components are used to support strategic decision making.

• Apply information technology tools for research, data gathering and information analysis, problem-solving, decision-making, and communicating information that aligns with business needs and objectives.

Start Here The Class Project for this course focuses on the evaluation of a business or organization and how they might strengthen their operations through technology, including their information systems. There are two parts to the Class Project; the SWOT Analysis and the Presentation. You will use the same business/organization for both parts; however, the grade received on the SWOT Analysis will not affect the grade for the Presentation (they are separate assignments).

Step 1: Choose a Business or Organization To begin, choose a business or organization that you would like to evaluate – it can be where you work, a school, a place of worship, government entity (e.g., DMV, Secretary of State, courthouse, etc.), or any other type of organization. Since the focus of the analysis will involve information technology, the best type of organization to choose is one where you can envision technology playing a key role in improving products, processes, or services.

*From this point on, the instructions focus on the first deliverable, the SWOT Analysis*

Step 2: The SWOT Analysis (due week 4) Now that you have chosen a business or organization for your Class Project, it is time to complete the first deliverable – the SWOT analysis. A SWOT analysis is a framework for identifying and analyzing an organization's strengths, weaknesses, opportunities and threats -- SWOT stands for: Strength, Weakness, Opportunity, Threat. Commonly used by businesses, this tool focuses on factors that are important to strategic decision making. These factors include both internal and external influences on the viability of the organization.

For more explanations on a SWOT Analysis, go to: • YouTube “How to Perform a SWOT Analysis”

• Forbes: What Is A SWOT Analysis?

• SWOT Analysis: Strengths, Weaknesses, Opportunities, and Threats

• Refer to the SWOT examples on Heineken and Walt Disney World (below)

You will use a SWOT analysis to help analyze the current health of your organization, and (in week 8), you will identify possible ways the information technology could be used to make it stronger.

The SWOT analysis offers a visual way of identifying both the positive attributes of an organization, and areas that need to be recognized and addressed. Thinking of the organization you chose, start by filling out a simple table listing its internal strengths and weaknesses, and external opportunities and threats.

SWOT Table:

Task: List 2-3 Strengths and 2-3 Weaknesses Strengths and weaknesses are associated with internal resources and experiences and include:

• Characteristics of the business that give it an advantage over others in the industry.

• Positive tangible and intangible attributes, internal to an organization. ▪ Human resources - staff, volunteers, board members, target population

• Physical resources - location, building, equipment

• Financial – products/services, other sources of income

• Activities and processes – programs and processes, online presence

• Past experiences - building blocks for learning and success, your reputation in the community

Task: List 2-3 Opportunities and 2-3 Threats Opportunities and threats are factors outside of business operations that can contribute to either the make the organization stronger or be troublesome. The ability of a business to identify, control, and adapt to these external factors can make it more profitable:

• Market expansion

• Complacent/aggressive competition

• Changing customer needs and tastes

• Economic swings

• Changing government deregulations

Step 3: Write the SWOT Analysis Now that you have identified the organization you will analyze and completed a SWOT table listing the strengths, weaknesses, opportunities, and threats for the organization, it is time to write the analysis paper that will be submitted to your instructor/classroom.

The paper should include all of the following: 1. Title page - the title of paper, company/organization name, your name, course, and date of submission.

2. Purpose – briefly describe what the SWOT methodology is and the business/organization you are focusing on.

3. SWOT Analysis – include a SWOT Analysis table and describe each quadrant (strengths, weaknesses, opportunities, threats) for your organization. Each quadrant should be clearly identified in the analysis and the description should include the importance of the attributes to the organization.

4. Conclusion – synthesize the findings from the SWOT analysis.

5. References - cite at least two resources with APA formatted citation and reference.

Format • Double spaced. Any 11- or 12-point font.

• Paper should be approximately 3-4 pages in length, excluding title page and references.

• Cite at least two resources with APA formatted citation and reference. Incorporate at least two resources correctly; one reference should be from the course materials and one reference should be external. An external resource is a resource other than those provided in the class or textbook. Incorporate properly formatted APA citations in the

text of your document for each reference used. Then, place an APA style reference page at the end of your document.

• Consider your audience – you are writing in the role of a business analyst and your audience is upper management of the organization.

• Compare your work to the Grading Rubric below to be sure you have met content and quality criteria.

• Submit your paper as a Word or PDF document to the appropriate folder under Activities and Assessments > Assignments in the classroom.

SWOT Analysis – Examples Read the following: Heineken and Walt Disney World SWOT Analysis Now, let’s take the information from the article and create SWOT tables for Heineken and Walt Disney World. SWOT Table – Heineken:

SWOT Table – Disney World:

GRADING RUBRIC:

SWOT Analysis

Template