Preoperative Risk Factors for Postoperative Delirium in Older Patients with Cancer Undergoing Surgery: A Case–control Study

Preoperative Risk Factors for Postoperative Delirium in Older Patients with Cancer Undergoing Surgery: A Case–control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preoperative Risk Factors for Postoperative Delirium in Older Patients with Cancer Undergoing Surgery: A Case–control Study Yusuke Kumura, Michio Maruta, Yoshie Yoshida, Hiroki Yokota, Shun Sugioka, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6537403/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Postoperative delirium (POD) is a common complication in older patients with cancer undergoing surgery, leading to prolonged hospitalization and cognitive decline. This study examined the relationship between preoperative psychological and cognitive assessments and POD incidence in these patients. Methods This case–control study included 195 patients (≥ 65 years) undergoing elective surgery. Patients were classified into delirium (n = 53) and non-delirium (n = 142) groups based on the delirium screening tool and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Preoperative assessments included physical function (performance status, bedside mobility scale, functional independence measure, grip strength), fatigue (cancer fatigue scale), psychological state (hospital anxiety and depression scale [HADS-A and HADS-D], vitality index, numerical rating scale), and cognitive function (mini-mental state examination [MMSE] and frontal assessment battery). The demographic, clinical, and preoperative characteristics of the two groups were compared. Multivariate logistic regression analysis was performed to identify POD-associated factors. Results Patients with POD had lower MMSE (p < .001) and higher HADS-A (p < .001) and HADS-D (p < .001) scores. Logistic regression identified HADS-A (odd ratio [OR] 1.505; 95% confidence intervals (CI), 1.225–1.849, p < .001) and HADS-D (OR, 1.280; 95% CI, 1.085–1.510, p = .003) as independent risk factors, whereas higher MMSE scores (OR, 0.811; 95% CI, 0.719 to 0.916, p < .001) were associated with a reduced risk. Conclusions Preoperative MMSE and HADS predict POD in older patients with cancer, emphasizing the importance of cognitive and psychological assessments and the need for effective preoperative interventions. Delirium Older adults Cancer Rehabilitation Figures Figure 1 Introduction Postoperative delirium in older patients POD is a significant complication that occurs in approximately 15–53% of older patients undergoing surgery, with even higher rates reported in those treated for malignancies [ 1 , 2 ]. POD presents as a sudden alteration in consciousness and cognitive function, often fluctuating over time. Its clinical manifestations, including disorientation, inattention, and memory disturbances, lead to diagnostic and management difficulties [ 3 , 4 ]. Importantly, POD has been identified as an independent predictor of prolonged hospitalization, increased mortality, and long-term functional decline [ 5 , 6 ]. The prevalence of POD is high among older patients with cancer due to a confluence of factors, which include advanced age, physiological stress induced by cancer and surgery, and the cumulative effect of comorbidities [ 7 , 8 ]. Thus, addressing these challenges requires an integrated approach to perioperative care, focusing on the early identification and management of risk factors. Risk factors for POD The development of POD involves a complex interplay between intrinsic vulnerabilities and extrinsic triggers. Among extrinsic factors, prolonged surgical duration, anesthesia type, and postoperative complications increase the POD risk [ 9 ]. In oncologic surgery, additional risks stem from the physiological burden of cancer and multimorbidity [ 10 ]. Intrinsic factors include cognitive impairment, anxiety, depression, malnutrition, and frailty, which have been identified as major risk factors for POD in older adults [ 11 , 12 ]. In particular, patients with cancer along with chemotherapy-related cognitive impairment and systemic inflammation are at increased risk [ 13 , 14 ]. In addition, chronic physical stress and the use of medications, particularly opioids and anticholinergic drugs, have been linked to POD [ 15 , 16 ]. Given the cumulative influence of these intrinsic and extrinsic factors, older patients with cancer are at a particularly high risk of POD. In recent years, research on preoperative risk factors for POD in older patients with cancer has increased [ 17 – 30 ], identifying factors such as low physical activity [ 23 ], frailty [ 20 – 22 ], sarcopenia [ 18 ], and cognitive impairment [ 17 , 19 , 24 ]. However, existing studies have limitations, including (1) inadequate POD assessment (e.g., reliance on medical records or single evaluators), (2) insufficient consideration of psychological factors (e.g., limited assessment of anxiety and depression), and (3) lack of analysis of intraoperative factors (e.g., effects of anesthesia and surgical stress). In patients with cancer, not only physical frailty but also cancer-related fatigue, psychological stress, anxiety, and depression should be considered key contributors to POD. These patients frequently experience chronic fatigue and psychological distress owing to their disease and treatment; however, the effect of these factors on POD remains largely unknown. Some studies have used the hospital anxiety and depression scale (HADS) to assess POD risk [ 25 , 31 , 32 ]. Historically, POD risk assessment has focused primarily on cognitive impairment using tools such as the mini-mental state examination (MMSE) [ 17 , 19 , 22 , 24 , 25 ], whereas comprehensive evaluations incorporating psychological factors remain scarce. Furthermore, factors such as prolonged surgical duration, anesthesia type, and postoperative complications increase the POD risk in older patients [ 28 , 29 ]. Preoperative cognitive impairment is one of the strongest risk factors, and evidence shows that cognitive decline significantly increases the risk in those with both POD and long-term cognitive impairment [ 32 , 33 ]. However, no studies have sufficiently analyzed the combined effects of cancer-related fatigue, psychological stress, anxiety, depression, and physical function decline on POD risk. A comprehensive approach that integrates physical, cognitive, and psychological assessments is essential for accurately evaluating the POD risk of older patients with cancer [ 17 – 33 ]. Although previous studies have demonstrated the association between physical function and POD risk [ 26 , 27 ], the influence of cancer-related fatigue and psychological distress remains insufficiently explored. Thus, future research should incorporate both preoperative cognitive assessments (e.g., MMSE) and psychological stress evaluations for a more comprehensive risk assessment. Aim of Study This study aimed to examine the relationship between preoperative cognitive and psychological function and POD incidence in older patients with cancer. By gaining a comprehensive understanding of how preoperative mental health status influences POD risk, this study may contribute to the development of more effective perioperative management strategies, including rehabilitation, for this vulnerable population. Methods Patients This retrospective case–control study was conducted at St. Mary’s Hospital from April 2021 to March 2024 and was approved by the Institutional Review Board of St. Mary’s Hospital (Approval No. 23–0605). The study included patients admitted to the hospital’s acute care ward. A total of 320 older patients with cancer (aged ≥ 65 years) who met the following inclusion criteria were initially screened: (1) presence of a malignant tumor, (2) undergoing elective surgery, and (3) age 65 years. Among these patients, 125 were excluded based on the following criteria: (1) preoperative assessment was not performed (n = 72), (2) the patient had preexisting dementia or psychiatric disorders (n = 20), (3) severe adverse events occurred (n = 20), and (4) the scheduled surgery was not performed (n = 13). After applying these exclusion criteria, 195 patients were included in the final analysis, which was further categorized into the delirium group (n = 53) and non-delirium group (n = 142) based on POD diagnosis (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5] criteria). The patient selection and exclusion process is summarized in Fig. 1 . Diagnosis of POD POD was diagnosed using both the DSM-5 and the delirium screening tool (DST). Patients who met the diagnostic criteria for delirium in both assessments were classified into the delirium group, whereas those who did not meet the criteria in either evaluation were included in the non-delirium group. The DSM-5 defines POD as an acute disturbance in attention and cognition, which fluctuates over time and is often accompanied by impairments in memory, orientation, language, visuospatial ability, or perception. Underlying medical conditions, medications, or other physiological factors contribute to its onset. The DST consists of 11 items divided into three domains: (A) level of consciousness and awareness of surroundings (7 items), (B) cognitive changes (2 items), and (C) symptom fluctuations (2 items). The DST can be completed in approximately 5 min and is a practical tool for early detection. In this study, DSM-5 evaluations were conducted by physicians over a 1-week postoperative period, whereas occupational therapists performed daily DST screenings. The final diagnosis was determined through interdisciplinary team discussions. Preoperative factors Preoperative evaluations were conducted by occupational therapists and included assessments of physical function, fatigue, psychological state, and cognitive function. Physical function Physical function was assessed using the performance status (PS) scale, bedside mobility scale (BMS), functional independence measure (FIM), and grip strength. PS was scored from 0 to 4, with higher scores indicating worse overall physical condition. The BMS (0–40 points) evaluated patients’ ability to perform basic movements such as sitting up, standing, and transferring between positions, which are essential for mobility. The FIM (18–126 points) assessed the level of independence in performing activities of daily living, including self-care, sphincter control, mobility, locomotion, communication, and social cognition. Grip strength was measured using a hand dynamometer and indicated upper limb function, overall muscle strength, and a potential risk factor for POD, as lower grip strength has been associated with increased vulnerability to postoperative complications. Fatigue Fatigue was assessed using the cancer fatigue scale (CFS). The CFS evaluated fatigue severity, including physical (0–28), mental (0–16), and cognitive fatigue (0–16), with a total score range of 0–60 points, where higher scores indicate greater fatigue. Psychological state : Psychological assessments included the HADS, vitality index (VI), and numerical rating scale (NRS) for stress. HADS consists of two subscales: HADS-A for anxiety and HADS-D for depression, with scores categorized as normal (≤ 7), borderline (8–11), or abnormal (12–21)[ 34 ]. VI is a 10-point scale measuring motivation, where higher scores indicate greater motivation. The NRS assessed stress severity on a scale from 0 (no stress) to 10 (worst stress). Cognitive function Cognitive function was assessed using the MMSE and the frontal assessment battery (FAB). MMSE (0–30 points) was used for general cognitive screening [ 5 ], whereas FAB (0–18 points) evaluated frontal lobe function [ 35 ]. Demographic and clinical variables Demographic and clinical information was extracted from electronic medical records, including age, sex, surgery type (endoscopic or open), preoperative and postoperative hospital stay duration, surgery duration, intraoperative blood loss, and serum albumin levels as an indicator of nutritional status. Statistical analysis To examine differences between the delirium and non-delirium groups and identify independent risk factors for POD, statistical analyses were conducted as follows: Continuous variables were tested for normality using the Shapiro–Wilk test and presented as means ± standard deviations or medians with interquartile ranges (IQR), depending on their distribution. Independent t-tests or Mann–Whitney U tests were used to compare continuous variables between groups, whereas chi-square tests were applied to categorical variables. The effect sizes were calculated using Cohen’s d (for continuous variables) and Cramer’s V (for categorical variables) to quantify the magnitude of the differences. To identify the independent predictors of POD while adjusting for confounders, a multivariate logistic regression analysis was performed. A forward stepwise selection method was applied to determine the best-fit model. Variables with p < 0.10 in the univariate analyses, along with clinically relevant covariates, were initially included, and nonsignificant variables were sequentially removed. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were reported for each predictor. In the logistic regression model, POD status was treated as a binary outcome variable (0 = non-delirium, 1 = delirium), with the non-delirium group as the reference category. All statistical analyses were performed using R version 4.3.2, with a two-tailed p-value < 0.05 considered significant. Results Demographic and clinical characteristics Table 1 summarizes the baseline characteristics of the patients. Among the 195 older patients with cancer who underwent elective surgery, 142 (73%) did not experience POD, whereas 53 (27%) did. The study cohort included 103 male (53%) and 92 female (47%) patients, with an age range of 65–97 years. Regarding the surgical approaches, 145 (74%) patients underwent endoscopic surgery, whereas 50 (26%) underwent open surgery. The distribution of cancer types was diverse, with the most common being rectal (n = 42), ascending colon (n = 29), and liver (n = 22) cancer. Other malignancies included stomach (n = 15), pancreatic (n = 4), ovarian (n = 4), and breast (n = 5) cancer. These baseline characteristics provide an overview of the study population, reflecting the diversity in cancer types and surgical approaches. Table 1 Baseline Characteristics of Patients (n = 195) Attribute Description Total number of patients n = 195 Sex (male/female) 103 (53%) / 92 (47%) Age range (years) 65–97 Delirium occurrence (yes/no) 53 (27%) / 142 (73%) Surgical approach (endoscopic/open) 145 (74%) / 50 (26%) Cancer type Number of patients Ascending colon 29 Transverse colon 18 Descending colon 14 Sigmoid colon 19 Rectum 42 Stomach 15 Duodenum 8 Small intestine 2 Lung 9 Ovary 4 Breast 5 Bladder 2 Ureter 1 Liver 22 Pancreas 4 Parotid gland 1 Table 2: Demographic and clinical characteristics of the delirium and non-delirium groups Variable Non-delirium group (n = 142) Delirium group (n = 53) Cohen’s d p-value Age (mean ± SD, years) 77.8 ± 6.0 79.8 ± 6.3 0.184 0.052 Sex (female, n(%)) 73(51.4) 19(35.9) 0.232 0.053 Surgical procedure (endoscopic, n(%)) 104(73.2) 40(75.5) 0.323 0.420 Preoperative hospital stay (mean ± SD, days) 2.8 ± 2.5 3.5 ± 2.7 0.265 0.003 Postoperative hospital stay (mean ± SD, days) 13.0 ± 5.2 21.9 ± 10.1 0.598 <.001 Surgery duration (mean ± SD, min) 269.7 ± 104.4 299.6 ± 146.9 0.079 0.400 Blood loss (mean ± SD, mL) 91.1 ± 189.3 159.3 ± 311.3 0.140 0.120 Serum albumin levels (mean ± SD, dL) 3.8 ± 0.5 3.5 ± 0.5 −0.190 0.040 Sex (%) and surgical procedure (%) were analyzed using the chi-square test, whereas all other variables were analyzed using an independent t -test. Data are presented as mean ± SD or number (%), and p -values <0.05 were considered significant. Table 3: Preoperative comparison of functional and cognitive measures between the delirium and non-delirium groups Variable Max Non-delirium group (n = 142) Delirium group (n = 53) Cohen’s d p-value PS (mean ± SD, points) 4 0.7 ± 0.6 1.1 ± 0.4 0.364 <.001 BMS (mean ± SD, points) 40 38.9 ± 3.7 37.1 ± 5.7 −0.320 <.001 FIM (mean ± SD, points) 126 118.9 ± 13.8 111.6 ± 16.8 −0.468 <.001 Grip strength (mean ± SD, kg) - 21.1 ± 7.4 17.8 ± 7.8 −0.260 0.005 CFS (mean ± SD, points) 60 15.2 ± 7.5 24.7 ± 9.6 0.560 <.001 NRS (mean ± SD, points) 10 4.6 ± 4.1 6.4 ± 2.9 0.383 <.001 HADS-A (mean ± SD, points) 21 3.9 ± 2.6 9.7 ± 3.2 0.819 <.001 HADS-D (mean ± SD, points) 21 4.8 ± 3.7 10.1 ± 3.2 0.806 <.001 Vitality index (mean ± SD, points) 10 8.8 ± 1.6 6.7 ± 2.0 −0.549 <.001 MMSE (mean ± SD, points) 30 27.2 ± 3.7 23.7 ± 5.5 −0.585 <.001 FAB (mean ± SD, points) 18 15.6 ± 2.6 13.4 ± 3.2 −0.513 <.001 PS, performance status, BMS, bedside mobility scale, FIM, functional independence measure, CFS, cancer fatigue scale, NRS, numerical rating scale, HADS, Hospital anxiety and depression scale (HADS-A for anxiety and HADS-D for depression); MMSE, Mini-Mental State Examination. FAB, frontal assessment battery, Sex (%) and surgical procedure (%) were analyzed using the chi-square test, whereas all other variables were analyzed using an independent t-test. Data are presented as mean ± SD or number (%), and p-values <0.05 were considered significant. Table 4: Logistic regression analysis of factors associated with delirium Variable B p-value OR 95% CI lower 95% CI upper HADS-A ＋0.409 <0.001 1.505 1.225 1.849 HADS-D ＋0.247 0.003 1.280 1.085 1.510 MMSE −0.209 <0.001 0.811 0.719 0.916 HADS, Hospital anxiety and depression scale (HADS-A for anxiety and HADS-D for depression); MMSE, Mini-Mental State Examination. Logistic regression analysis was conducted to examine the factors associated with delirium. Data are presented as regression coefficients (B), odds ratios (OR), and 95% confidence intervals (CI). A p-value <0.05 was considered significant. The dependent variable was coded as 0 = non-delirium, 1 = delirium. A forward stepwise selection method was used to build the regression model. Variables with p < 0.10 in the univariate analysis were initially included. Variables not contributing significantly were removed step by step. Among the generated models (Models 0–3), Model 3 demonstrated the best fit. Model 3 was selected based on the lowest Akaike Information Criterion (AIC = 112.2). Demographic and clinical characteristics of the delirium and non-delirium groups A comparison of the demographic and clinical characteristics between the delirium and non-delirium groups is presented in Table 2. The mean age was slightly higher in the delirium group (79.8 ± 6.3 years) than in the non-delirium group (77.8 ± 6.0 years); however, this difference did not reach significance (p = 0.052). The proportion of female patients was lower in the delirium group (35.9%) than in the non-delirium group (51.4%); however, the difference was not significant (p = 0.053). No significant difference in the proportion of surgical procedures performed laparoscopically was noted between the two groups (p = 0.42). However, the delirium group had a significantly longer postoperative hospital stay (21.9 ± 10.1 vs. 13.0 ± 5.2 days, p < 0.001) and longer preoperative hospital stay (3.5 ± 2.7 vs. 2.8 ± 2.5 days, p = 0.003). No significant differences were found in surgical duration or intraoperative blood loss. Comparison of the preoperative functional and cognitive measures Significant differences were observed in the preoperative functional and cognitive assessments between the delirium and non-delirium groups (Table 3). The delirium group had significantly higher PS scores (p < 0.001, d = 0.364) and significantly lower BMS (p < 0.001, d = − 0.320) and FIM (p < 0.001, d = − 0.468) scores. Moreover, grip strength was significantly lower in the delirium group (p < 0.005, d = − 0.260), suggesting a potential association between reduced physical function and POD. As regards fatigue and psychological assessments, the delirium group had significantly higher CFS scores (p < 0.001, d = 0.560) and NRS scores for pain and stress (p < 0.001, d = 0.383). Furthermore, the HADS-A and HADS-D scores were significantly higher in the delirium group (p < 0.001, d = 0.819 and 0.806, respectively), whereas the VI was significantly lower (p < 0.001, d = − 0.549). The cognitive function assessments also showed significant differences between the two groups. The delirium group had significantly lower MMSE (p < 0.001, d = − 0.585) and FAB (p < 0.001, d = − 0.513) scores than the non-delirium group. These findings suggest that preoperative decline in physical function, psychological distress, and cognitive function may be associated with POD. Logistic regression analysis of factors associated with delirium To identify the independent predictors of POD, a multivariate logistic regression analysis was conducted (Table 4). Variables that were significantly different between the delirium and non-delirium groups in the univariate analysis, including preoperative hospital stay, serum albumin levels, PS, BMS score, FIM score, grip strength, CFS score, NRS score, HADS-A score, HADS-D score, VI, MMSE score, and FAB score, were entered into the model. A forward stepwise selection method was applied to determine the most relevant POD predictors. Variables with p < 0.10 in the univariate analysis were initially included, and those that did not contribute significantly were sequentially removed until the final model was obtained. Of the generated models 0–3, model 3 with the best model fit (Akaike Information Criterion [AIC], 112.2) was adopted. After eliminating nonsignificant variables, HADS-A, HADS-D, and MMSE scores remained significant independent predictors. Higher HADS-A and HADS-D scores were significantly associated with an increased POD risk (OR, 1.505; 95% CI, 1.225–1.849, p < 0.001 for HADS-A; OR, 1.280; 95% CI, 1.085–1.510, p = 0.003 for HADS-D), indicating that preoperative psychological distress, including anxiety and depression, may contribute to POD. Conversely, higher MMSE scores were associated with a decreased POD risk (OR, 0.811; 95% CI, 0.719 to 0.916, p < 0.001), suggesting that better cognitive function serves as a protective factor against POD. Discussion This study highlights that preoperative cognitive impairment (MMSE) and psychological distress (HADS-D and HADS-A) are significant predictors of POD in patients with cancer undergoing surgery. In addition, our results demonstrated that patients who had POD had significantly longer postoperative hospital stays, underscoring the clinical effect of POD on recovery and healthcare utilization. Although this study did not directly measure the physical activity levels. Given these findings, the early identification of high-risk groups and the implementation of prehabilitation strategies focusing on cognitive, psychological, and functional aspects may be key to reducing the incidence of POD. In this study, despite excluding patients with cognitive and psychiatric comorbidities as identified in previous research, the incidence of POD was relatively high at 27%. Hypoactive delirium tends to be more challenging to differentiate from conditions such as depression, apathy, and dementia compared with hyperactive delirium, making it more likely to be overlooked. DST includes an assessment of hypoactivity, which may help prevent POD underdiagnosis. Furthermore, by accurately evaluating hypoactive patients and sharing their delirium status with attending physicians through DSM-5-based diagnoses, a more appropriate approach to management can be achieved. Furthermore, by accurately evaluating hypoactive patients and sharing their delirium status with attending physicians through DSM-5-based diagnoses, a more appropriate approach to management can be achieved. Our main findings reinforce prior evidence that preoperative cognitive impairment is a strong predictor of POD [ 36 ]. MMSE, commonly used to assess cognitive status, was significantly lower in the delirium group than in the non-delirium group, indicating that patients with preexisting cognitive deficits may be more vulnerable to perioperative neurocognitive dysfunction. Similarly, higher HADS-A and HADS-D scores were associated with increased POD risk, consistent with studies revealing that preoperative anxiety and depression may contribute to an exaggerated neuroinflammatory response and increased vulnerability to postoperative cognitive disturbances [ 17 , 25 , 28 , 30 , 31 ]. These results emphasize the need for systematic psychological screening as part of preoperative assessment protocols. In other medical conditions, numerous studies have reported the association of HADS and MMSE with POD. For example, in patients undergoing cardiovascular or orthopedic surgery, preoperative cognitive function and psychological factors were identified as significant contributors to POD risk [ 1 ]. In the present study, despite excluding patients with cognitive and psychiatric comorbidities as identified in previous research, the incidence of POD was relatively high at 27%. Hypoactive delirium tends to be more challenging to differentiate from conditions such as depression, apathy, and dementia compared with hyperactive delirium, making it more likely to be overlooked. Our findings indicate that preoperative psychological and cognitive assessments are crucial for identifying POD risk in patients with cancer, reinforcing the applicability of previous research findings from other disease domains to the field of oncology. To further elucidate the mechanisms underlying POD in patients with cancer, future studies should investigate the effect of perioperative inflammatory responses and hormonal changes as potential physiological contributors. This study has several limitations. First, as a retrospective case–control study, causal relationships between preoperative cognitive and psychological factors and POD cannot be established. Prospective cohort studies are needed to confirm these associations. Second, this study was conducted at a single institution, which may limit the generalizability of our findings. Multicenter studies with larger sample sizes are necessary to validate these results. Third, although we controlled for multiple confounders, unmeasured variables such as intraoperative factors or perioperative medication use might have influenced the POD risk. Future research should incorporate these factors into the analysis. In addition, this study provides valuable insights into the preoperative risk factors for POD in older patients with cancer; however, further research is needed to explore potential interventions. Future studies should evaluate the effectiveness of multidisciplinary prehabilitation programs in reducing the incidence of POD through prospective interventional studies. Moreover, investigating the effect of early cognitive screening on long-term surgical outcomes remains an area of interest. Longitudinal studies are also warranted to determine whether interventions targeting cognitive and psychological factors help sustained improvements in postoperative functional independence. A multidisciplinary approach, integrating cognitive and psychological assessments into perioperative care, is essential for effective POD prevention. Given the strong association between preoperative psychological distress and POD, early screening and appropriate interventions may help mitigate the risk of POD in high-risk patients. Conclusion This study demonstrates that preoperative cognitive impairment (MMSE) and psychological anxiety and depression (HADS-D and HADS-A) are significant predictors of POD in older patients with cancer undergoing surgery. In addition, POD was associated with prolonged postoperative hospitalization, highlighting its effect on recovery and healthcare utilization. Given these findings, more studies are expected to explore targeted interventions aimed at improving cognitive and psychological resilience in high-risk patients. Declarations Acknowledgments We would like to express our gratitude to St. Mary’s Hospital for their support and contributions to this study. Author contributions Y.K. and T. Tabira conceived and designed the study. Data were collected conducted by Y.K., Y.Y., H.Y., S.S., and T. Tanaka. Data were analyzed by Y.K. and M.M. Data were interpreted by T. Tabira, M.M., H.Y., K.I., and T. Tanaka. The manuscript was drafted by Y.K., T. Tabira., M.M., Y.A., and T. Tanaka. All authors have read and approved the final manuscript. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethics approval and consent to participate: This study was approved by the Institutional Review Board of St. Mary’s Hospital (Approval No. 23-0605) and was conducted in accordance with the Declaration of Helsinki. Consent to Participate declaration: Not applicable. 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Ann Surg Oncol 31(13):9039-9047. https://doi.org/10.1245/s10434-024-16034-w Dong B, Yu D, Zhang H, Li P, Li Y, Li C, Li J (2024) Association between preoperative sarcopenia and postoperative delirium in older patients undergoing gastrointestinal cancer surgery. Front Aging Neurosci 16:1416569. https://doi.org/10.3389/fnagi.2024.1416569 Sugi T, Enomoto T, Ohara Y, Furuya K, Kitaguchi D, Moue S, Akashi Y, Ogawa K, Owada Y, Oda T (2023) Risk factors for postoperative delirium in elderly patients undergoing gastroenterological surgery: A single-center retrospective study. Ann Gastroenterol Surg 7(6):918-928. https://doi.org/10.1002/ags3.12676 Tian JY, Hao XY, Cao FY, Liu JJ, Li YX, Guo YX, Mi WD, Tong L, Fu Q (2023) Preoperative Frailty Assessment Predicts Postoperative Mortality, Delirium and Pneumonia in Elderly Lung Cancer Patients: A Retrospective Cohort Study. Ann Surg Oncol 30(12):7442-7451. https://doi.org/10.1245/s10434-023-13696-w Hoffmann AJ, Tin AL, Vickers AJ, Shahrokni A (2023) Preoperative frailty vs. cognitive impairment: Which one matters most for postoperative delirium among older adults with cancer? J Geriatr Oncol 14(4):101479. https://doi.org/10.1016/j.jgo.2023.101479 Tsai CY, Liu KH, Lai CC, Hsu JT, Hsueh SW, Hung CY, Yeh KY, Hung YS, Lin YC, Chou WC (2023) Association of preoperative frailty and postoperative delirium in older cancer patients undergoing elective abdominal surgery: A prospective observational study in Taiwan. Biomed J 46(4):100557. https://doi.org/10.1016/j.bj.2022.08.003 Yanagisawa T, Tatematsu N, Horiuchi M, Migitaka S, Yasuda S, Itatsu K, Kubota T, Sugiura H (2022) Preoperative Low Physical Activity is a Predictor of Postoperative Delirium in Patients with Gastrointestinal Cancer: A Retrospective Study. Asian Pac J Cancer Prev 23(5):1753-1759. https://doi.org/10.31557/APJCP.2022.23.5.1753 Ristescu AI, Pintilie G, Moscalu M, Rusu D, Grigoras I (2021) Preoperative Cognitive Impairment and the Prevalence of Postoperative Delirium in Elderly Cancer Patients A Prospective Observational Study. Diagnostics (Basel) 11(2):275. https://doi.org/10.3390/diagnostics11020275 Liu Q, Li L, Wei J, Xie Y (2023) Correlation and influencing factors of preoperative anxiety, postoperative pain, and delirium in elderly patients undergoing gastrointestinal cancer surgery. BMC Anesthesiol 23(1):78. https://doi.org/10.1186/s12871-023-02036-w Arita A, Takahashi H, Ogino T, Miyoshi N, Uemura M, Akasaka H, Sugimoto K, Rakugi H, Doki Y, Eguchi H (2022) Grip strength as a predictor of postoperative delirium in patients with colorectal cancers. Ann Gastroenterol Surg６(2):265-272. https://doi.org/10.1002/ags3.12519 Sugi T, Enomoto T, Ohara Y, Furuya K, Kitaguchi D, Moue S, Akashi Y, Ogawa K, Owada Y, Oda T (2023) Risk factors for postoperative delirium in elderly patients undergoing gastroenterological surgery: A single-center retrospective study. Asian J Surg 46(12):4416-4423. https://doi.org/10.1016/j.asjsur.2023.08.088 Wilson JE, Mart MF, Cunningham C, Shehabi Y, Girard TD, MacLullich AMJ, Slooter AJC, Ely EW (2020) Delirium. Nat Rev Dis Primers 6(1):90. https://doi.org/10.1038/s41572-020-00223-4 Brown CH 4th, Laflam A, Max L, Lymar D, Neufeld KJ, Tian J, Shah AS, Whitman GJ, Hogue CW (2016) The Impact of Delirium After Cardiac Surgical Procedures on Postoperative Resource Use. Ann Thorac Surg 101(5):1663-1669. https://doi.org/10.1016/j.athoracsur.2015.12.074 Hshieh TT, Inouye SK, Oh ES (2020) Delirium in the Elderly. Clin Geriatr Med 36(2):183-199. https://doi.org/10.1016/j.cger.2020.01.005 Vasunilashorn SM, Ngo L, Inouye SK, Libermann TA, Jones RN, Alsop DC, Guess J, Jastrzebski S, McElhaney JE, Kuchel GA, Marcantonio ER (2015) Cytokines and postoperative delirium in older patients undergoing major elective surgery. J Gerontol A Biol Sci Med Sci 70(10):1289-1295. https://doi.org/10.1093/gerona/glv083 Pandharipande PP, Girard TD, Jackson JC, Morandi A, Thompson JL, Pun BT, Brummel NE, Hughes CG, Vasilevskis EE, Shintani AK, Moons KG, Geevarghese SK, Canonico A, Hopkins RO, Bernard GR, Dittus RS, Ely EW (2013) Long-term cognitive impairment after critical illness. N Engl J Med 369(14):1306-1316. https://doi.org/10.1056/NEJMoa1301372 Lundström M, Edlund A, Karlsson S, Brännström B, Bucht G, Gustafson Y (2005) A multifactorial intervention program reduces the duration of delirium, length of hospitalization, and mortality in delirious patients. J Am Geriatr Soc 53(4):622-628. https://doi.org/10.1111/j.1532-5415.2005.53210.x Luckett T, Butow PN, King MT, Oguchi M, Heading G, Hackl NA, Rankin N, Price MA (2010) A review and recommendations for optimal outcome measures of anxiety, depression and general distress in studies evaluating psychosocial interventions for English-speaking adults with heterogeneous cancer diagnoses. Support Care Cancer 18(10):1241-1262. https://doi.org/10.1007/s00520-010-0932-8 Dubois B, Slachevsky A, Litvan I, Pillon B (2000) The FAB: a Frontal Assessment Battery at bedside. Neurology 55(11):1621-1626. https://doi.org/10.1212/wnl.55.11.1621 McCusker J, Cole MG, Dendukuri N, Belzile E (2003) Does delirium increase hospital stay? J Am Geriatr Soc 51(11):1539-1546. https://doi.org/10.1046/j.1532-5415.2003.51509.x Additional Declarations No competing interests reported. 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Kumura","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACA/kDDIwNIBYzmMsgB6IOPMCnRYIBqoW9AazFGKwlgSgtPAfAAolgDj4t5tLNDxhnVBxmMJdIfsDMU3AnfX7Y4YdAW+zkdBuwa7Gcc8yAccOZwwyWM9IMmHkMnuVuvJ1mANSSbGx2AIfDDiQYMD5sO8xgcCPBgDnH4HDuxtkJIC0HErfh1JL+gfHhP5CW9A8gLemGs9M/4NdyI8eAcWMDUMuZM2BbEuSlcwjYcuZMwcEZx9J5DI73FBz+Y3DYcIN0TgHQtXj8crx948OeGms5g8PsGx/O+HNYXn52+uYPHyrs5HBpAQGgVDMPlAHyHZjErRwK6hBM+QaCqkfBKBgFo2CEAQCPaGltWQNydAAAAABJRU5ErkJggg==","orcid":"","institution":"Doctoral Program of Clinical Neuropsychiatry, Graduate School of Health Science, Kagoshima University","correspondingAuthor":true,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Kumura","suffix":""},{"id":467208316,"identity":"8a6fe8af-de8c-41fa-86f2-292aea95fda6","order_by":1,"name":"Michio Maruta","email":"","orcid":"","institution":"Nagasaki University","correspondingAuthor":false,"prefix":"","firstName":"Michio","middleName":"","lastName":"Maruta","suffix":""},{"id":467208317,"identity":"5acbca68-81d9-40e8-bd51-c697d575c767","order_by":2,"name":"Yoshie Yoshida","email":"","orcid":"","institution":"Rehabilitation Unit, St. Mary’s Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Yoshie","middleName":"","lastName":"Yoshida","suffix":""},{"id":467208318,"identity":"bf56615f-f770-4e69-bc41-a17bc580631d","order_by":3,"name":"Hiroki Yokota","email":"","orcid":"","institution":"Rehabilitation Unit, St. Mary’s Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Yokota","suffix":""},{"id":467208319,"identity":"f1ba76d2-7929-491c-b9c8-7b1203db209e","order_by":4,"name":"Shun Sugioka","email":"","orcid":"","institution":"Rehabilitation Unit, St. Mary’s Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Sugioka","suffix":""},{"id":467208320,"identity":"27665ae9-a69c-4114-b3be-c6c6a861a5c4","order_by":5,"name":"Kiyonori Izumi","email":"","orcid":"","institution":"Rehabilitation Unit, St. Mary’s Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Kiyonori","middleName":"","lastName":"Izumi","suffix":""},{"id":467208321,"identity":"239d4b28-dc1b-4b53-9b83-d2b2dfba6f76","order_by":6,"name":"Takako Tanaka","email":"","orcid":"","institution":"Rehabilitation Unit, St. Mary’s Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Takako","middleName":"","lastName":"Tanaka","suffix":""},{"id":467208322,"identity":"7ad90cee-7c1b-4b16-baaa-edbc97b2a1c6","order_by":7,"name":"Yasuaki Akasaki","email":"","orcid":"","institution":"Department of Occupational Therapy, School of Health Sciences, Faculty of Medicine, Kagoshima University","correspondingAuthor":false,"prefix":"","firstName":"Yasuaki","middleName":"","lastName":"Akasaki","suffix":""},{"id":467208323,"identity":"763a7e3b-2b77-44ad-a1a8-90897662eb22","order_by":8,"name":"Takayuki Tabira","email":"","orcid":"","institution":"Department of Occupational Therapy, School of Health Sciences, Faculty of Medicine, Kagoshima University","correspondingAuthor":false,"prefix":"","firstName":"Takayuki","middleName":"","lastName":"Tabira","suffix":""}],"badges":[],"createdAt":"2025-04-27 02:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6537403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6537403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84305861,"identity":"db174240-adef-4cb6-a88b-e29faf838fdf","added_by":"auto","created_at":"2025-06-10 11:24:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242995,"visible":true,"origin":"","legend":"\u003cp\u003ePatient selection and exclusion\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6537403/v1/61736881ce552e807af4127c.png"},{"id":93544894,"identity":"5aaf8a13-d6a9-4cd1-b1fb-6f29d04fcdd7","added_by":"auto","created_at":"2025-10-15 03:01:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1068979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6537403/v1/15fc9b5c-3daf-41cb-aa94-3ba36e2c2848.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative Risk Factors for Postoperative Delirium in Older Patients with Cancer Undergoing Surgery: A Case–control Study","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003ePostoperative delirium in older patients\u003c/h2\u003e \u003cp\u003ePOD is a significant complication that occurs in approximately 15\u0026ndash;53% of older patients undergoing surgery, with even higher rates reported in those treated for malignancies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. POD presents as a sudden alteration in consciousness and cognitive function, often fluctuating over time. Its clinical manifestations, including disorientation, inattention, and memory disturbances, lead to diagnostic and management difficulties [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Importantly, POD has been identified as an independent predictor of prolonged hospitalization, increased mortality, and long-term functional decline [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The prevalence of POD is high among older patients with cancer due to a confluence of factors, which include advanced age, physiological stress induced by cancer and surgery, and the cumulative effect of comorbidities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, addressing these challenges requires an integrated approach to perioperative care, focusing on the early identification and management of risk factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for POD\u003c/h2\u003e \u003cp\u003eThe development of POD involves a complex interplay between intrinsic vulnerabilities and extrinsic triggers. Among extrinsic factors, prolonged surgical duration, anesthesia type, and postoperative complications increase the POD risk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In oncologic surgery, additional risks stem from the physiological burden of cancer and multimorbidity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Intrinsic factors include cognitive impairment, anxiety, depression, malnutrition, and frailty, which have been identified as major risk factors for POD in older adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In particular, patients with cancer along with chemotherapy-related cognitive impairment and systemic inflammation are at increased risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, chronic physical stress and the use of medications, particularly opioids and anticholinergic drugs, have been linked to POD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Given the cumulative influence of these intrinsic and extrinsic factors, older patients with cancer are at a particularly high risk of POD.\u003c/p\u003e \u003cp\u003eIn recent years, research on preoperative risk factors for POD in older patients with cancer has increased [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], identifying factors such as low physical activity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], frailty [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], sarcopenia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and cognitive impairment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, existing studies have limitations, including (1) inadequate POD assessment (e.g., reliance on medical records or single evaluators), (2) insufficient consideration of psychological factors (e.g., limited assessment of anxiety and depression), and (3) lack of analysis of intraoperative factors (e.g., effects of anesthesia and surgical stress).\u003c/p\u003e \u003cp\u003eIn patients with cancer, not only physical frailty but also cancer-related fatigue, psychological stress, anxiety, and depression should be considered key contributors to POD. These patients frequently experience chronic fatigue and psychological distress owing to their disease and treatment; however, the effect of these factors on POD remains largely unknown. Some studies have used the hospital anxiety and depression scale (HADS) to assess POD risk [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Historically, POD risk assessment has focused primarily on cognitive impairment using tools such as the mini-mental state examination (MMSE) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], whereas comprehensive evaluations incorporating psychological factors remain scarce.\u003c/p\u003e \u003cp\u003eFurthermore, factors such as prolonged surgical duration, anesthesia type, and postoperative complications increase the POD risk in older patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Preoperative cognitive impairment is one of the strongest risk factors, and evidence shows that cognitive decline significantly increases the risk in those with both POD and long-term cognitive impairment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, no studies have sufficiently analyzed the combined effects of cancer-related fatigue, psychological stress, anxiety, depression, and physical function decline on POD risk.\u003c/p\u003e \u003cp\u003eA comprehensive approach that integrates physical, cognitive, and psychological assessments is essential for accurately evaluating the POD risk of older patients with cancer [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although previous studies have demonstrated the association between physical function and POD risk [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the influence of cancer-related fatigue and psychological distress remains insufficiently explored. Thus, future research should incorporate both preoperative cognitive assessments (e.g., MMSE) and psychological stress evaluations for a more comprehensive risk assessment.\u003c/p\u003e \u003cp\u003eAim of Study\u003c/p\u003e \u003cp\u003eThis study aimed to examine the relationship between preoperative cognitive and psychological function and POD incidence in older patients with cancer. By gaining a comprehensive understanding of how preoperative mental health status influences POD risk, this study may contribute to the development of more effective perioperative management strategies, including rehabilitation, for this vulnerable population.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This retrospective case\u0026ndash;control study was conducted at St. Mary\u0026rsquo;s Hospital from April 2021 to March 2024 and was approved by the Institutional Review Board of St. Mary\u0026rsquo;s Hospital (Approval No. 23\u0026ndash;0605). The study included patients admitted to the hospital\u0026rsquo;s acute care ward. A total of 320 older patients with cancer (aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years) who met the following inclusion criteria were initially screened: (1) presence of a malignant tumor, (2) undergoing elective surgery, and (3) age 65 years. Among these patients, 125 were excluded based on the following criteria: (1) preoperative assessment was not performed (n\u0026thinsp;=\u0026thinsp;72), (2) the patient had preexisting dementia or psychiatric disorders (n\u0026thinsp;=\u0026thinsp;20), (3) severe adverse events occurred (n\u0026thinsp;=\u0026thinsp;20), and (4) the scheduled surgery was not performed (n\u0026thinsp;=\u0026thinsp;13). After applying these exclusion criteria, 195 patients were included in the final analysis, which was further categorized into the delirium group (n\u0026thinsp;=\u0026thinsp;53) and non-delirium group (n\u0026thinsp;=\u0026thinsp;142) based on POD diagnosis (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5] criteria). The patient selection and exclusion process is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiagnosis of POD\u003c/h3\u003e\n\u003cp\u003ePOD was diagnosed using both the DSM-5 and the delirium screening tool (DST). Patients who met the diagnostic criteria for delirium in both assessments were classified into the delirium group, whereas those who did not meet the criteria in either evaluation were included in the non-delirium group. The DSM-5 defines POD as an acute disturbance in attention and cognition, which fluctuates over time and is often accompanied by impairments in memory, orientation, language, visuospatial ability, or perception. Underlying medical conditions, medications, or other physiological factors contribute to its onset. The DST consists of 11 items divided into three domains: (A) level of consciousness and awareness of surroundings (7 items), (B) cognitive changes (2 items), and (C) symptom fluctuations (2 items). The DST can be completed in approximately 5 min and is a practical tool for early detection. In this study, DSM-5 evaluations were conducted by physicians over a 1-week postoperative period, whereas occupational therapists performed daily DST screenings. The final diagnosis was determined through interdisciplinary team discussions.\u003c/p\u003e\n\u003ch3\u003ePreoperative factors\u003c/h3\u003e\n\u003cp\u003ePreoperative evaluations were conducted by occupational therapists and included assessments of physical function, fatigue, psychological state, and cognitive function.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePhysical function\u003c/strong\u003e \u003cp\u003ePhysical function was assessed using the performance status (PS) scale, bedside mobility scale (BMS), functional independence measure (FIM), and grip strength. PS was scored from 0 to 4, with higher scores indicating worse overall physical condition. The BMS (0\u0026ndash;40 points) evaluated patients\u0026rsquo; ability to perform basic movements such as sitting up, standing, and transferring between positions, which are essential for mobility. The FIM (18\u0026ndash;126 points) assessed the level of independence in performing activities of daily living, including self-care, sphincter control, mobility, locomotion, communication, and social cognition. Grip strength was measured using a hand dynamometer and indicated upper limb function, overall muscle strength, and a potential risk factor for POD, as lower grip strength has been associated with increased vulnerability to postoperative complications.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFatigue\u003c/strong\u003e \u003cp\u003eFatigue was assessed using the cancer fatigue scale (CFS). The CFS evaluated fatigue severity, including physical (0\u0026ndash;28), mental (0\u0026ndash;16), and cognitive fatigue (0\u0026ndash;16), with a total score range of 0\u0026ndash;60 points, where higher scores indicate greater fatigue.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePsychological state\u003c/b\u003e: Psychological assessments included the HADS, vitality index (VI), and numerical rating scale (NRS) for stress. HADS consists of two subscales: HADS-A for anxiety and HADS-D for depression, with scores categorized as normal (\u0026le;\u0026thinsp;7), borderline (8\u0026ndash;11), or abnormal (12\u0026ndash;21)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. VI is a 10-point scale measuring motivation, where higher scores indicate greater motivation. The NRS assessed stress severity on a scale from 0 (no stress) to 10 (worst stress).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCognitive function\u003c/strong\u003e \u003cp\u003eCognitive function was assessed using the MMSE and the frontal assessment battery (FAB). MMSE (0\u0026ndash;30 points) was used for general cognitive screening [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], whereas FAB (0\u0026ndash;18 points) evaluated frontal lobe function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical variables\u003c/h2\u003e \u003cp\u003eDemographic and clinical information was extracted from electronic medical records, including age, sex, surgery type (endoscopic or open), preoperative and postoperative hospital stay duration, surgery duration, intraoperative blood loss, and serum albumin levels as an indicator of nutritional status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo examine differences between the delirium and non-delirium groups and identify independent risk factors for POD, statistical analyses were conducted as follows: Continuous variables were tested for normality using the Shapiro\u0026ndash;Wilk test and presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or medians with interquartile ranges (IQR), depending on their distribution. Independent t-tests or Mann\u0026ndash;Whitney U tests were used to compare continuous variables between groups, whereas chi-square tests were applied to categorical variables. The effect sizes were calculated using Cohen\u0026rsquo;s d (for continuous variables) and Cramer\u0026rsquo;s V (for categorical variables) to quantify the magnitude of the differences.\u003c/p\u003e \u003cp\u003eTo identify the independent predictors of POD while adjusting for confounders, a multivariate logistic regression analysis was performed. A forward stepwise selection method was applied to determine the best-fit model. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in the univariate analyses, along with clinically relevant covariates, were initially included, and nonsignificant variables were sequentially removed. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were reported for each predictor. In the logistic regression model, POD status was treated as a binary outcome variable (0\u0026thinsp;=\u0026thinsp;non-delirium, 1\u0026thinsp;=\u0026thinsp;delirium), with the non-delirium group as the reference category. All statistical analyses were performed using R version 4.3.2, with a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic and clinical characteristics\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003esummarizes the baseline characteristics of the patients. Among the 195 older patients with cancer who underwent elective surgery, 142 (73%) did not experience POD, whereas 53 (27%) did. The study cohort included 103 male (53%) and 92 female (47%) patients, with an age range of 65\u0026ndash;97 years. Regarding the surgical approaches, 145 (74%) patients underwent endoscopic surgery, whereas 50 (26%) underwent open surgery. The distribution of cancer types was diverse, with the most common being rectal (n\u0026thinsp;=\u0026thinsp;42), ascending colon (n\u0026thinsp;=\u0026thinsp;29), and liver (n\u0026thinsp;=\u0026thinsp;22) cancer. Other malignancies included stomach (n\u0026thinsp;=\u0026thinsp;15), pancreatic (n\u0026thinsp;=\u0026thinsp;4), ovarian (n\u0026thinsp;=\u0026thinsp;4), and breast (n\u0026thinsp;=\u0026thinsp;5) cancer. These baseline characteristics provide an overview of the study population, reflecting the diversity in cancer types and surgical approaches.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of Patients (n\u0026thinsp;=\u0026thinsp;195)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttribute\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal number of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (53%) / 92 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge range (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelirium occurrence (yes/no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (27%) / 142 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgical approach (endoscopic/open)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 (74%) / 50 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAscending colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransverse colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescending colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSigmoid colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuodenum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall intestine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUreter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePancreas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParotid gland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable 2: Demographic and clinical characteristics of the delirium and non-delirium groups\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"840\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNon-delirium group\u003c/p\u003e\n \u003cp\u003e(n = 142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDelirium group\u003c/p\u003e\n \u003cp\u003e(n = 53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eAge (mean \u0026plusmn; SD, years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e77.8 \u0026plusmn; 6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e79.8 \u0026plusmn; 6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSex (female, n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e73(51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e19(35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSurgical procedure (endoscopic, n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e104(73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e40(75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePreoperative hospital stay (mean \u0026plusmn; SD, days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.8 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePostoperative hospital stay (mean \u0026plusmn; SD, days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e13.0 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e21.9 \u0026plusmn; 10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSurgery duration (mean \u0026plusmn; SD, min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e269.7 \u0026plusmn; 104.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e299.6 \u0026plusmn; 146.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eBlood loss (mean \u0026plusmn; SD, mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e91.1 \u0026plusmn; 189.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e159.3 \u0026plusmn; 311.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSerum albumin levels (mean \u0026plusmn; SD, dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026minus;0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u003cbr\u003eSex (%) and surgical procedure (%) were analyzed using the chi-square test, whereas all other variables were analyzed using an independent \u003cem\u003et\u003c/em\u003e-test.\n \u003c/div\u003e\n \u003cp\u003eData are presented as mean \u0026plusmn; SD or number (%), and \u003cem\u003ep\u003c/em\u003e-values \u0026lt;0.05 were considered significant.\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTable 3: Preoperative comparison of functional and cognitive measures between the delirium and non-delirium groups\u003c/div\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"793\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNon-delirium group\u003c/p\u003e\n \u003cp\u003e(n = 142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eDelirium group\u003c/p\u003e\n \u003cp\u003e(n = 53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003ePS (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.7 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eBMS (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e38.9 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e37.1 \u0026plusmn; 5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026minus;0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eFIM (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e118.9 \u0026plusmn; 13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e111.6 \u0026plusmn; 16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026minus;0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eGrip strength (mean \u0026plusmn; SD, kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e21.1 \u0026plusmn; 7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e17.8 \u0026plusmn; 7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026minus;0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCFS (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e15.2 \u0026plusmn; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e24.7 \u0026plusmn; 9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eNRS (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.6 \u0026plusmn; 4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e6.4 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eHADS-A (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3.9 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e9.7 \u0026plusmn; 3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eHADS-D (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e10.1 \u0026plusmn; 3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eVitality index (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e8.8 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e6.7 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026minus;0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eMMSE (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e27.2 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e23.7 \u0026plusmn; 5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026minus;0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eFAB (mean \u0026plusmn; SD, points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e15.6 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e13.4 \u0026plusmn; 3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026minus;0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003ePS, performance status, BMS, bedside mobility scale, FIM, functional independence measure, CFS, cancer fatigue scale, NRS, numerical rating scale, HADS, Hospital anxiety and depression scale (HADS-A for anxiety and HADS-D for depression); MMSE, Mini-Mental State Examination. FAB, frontal assessment battery, Sex (%) and surgical procedure (%) were analyzed using the chi-square test, whereas all other variables were analyzed using an independent t-test. Data are presented as mean \u0026plusmn; SD or number (%), and p-values \u0026lt;0.05 were considered significant.\u003c/p\u003e\n \u003cp\u003eTable 4: Logistic regression analysis of factors associated with delirium\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"670\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e95% CI lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e95% CI upper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eHADS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e＋0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eHADS-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e＋0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026minus;0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHADS, Hospital anxiety and depression scale (HADS-A for anxiety and HADS-D for depression); MMSE, Mini-Mental State Examination. Logistic regression analysis was conducted to examine the factors associated with delirium. Data are presented as regression coefficients (B), odds ratios (OR), and 95% confidence intervals (CI). A p-value \u0026lt;0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003eThe dependent variable was coded as 0 = non-delirium, 1 = delirium. A forward stepwise selection method was used to build the regression model. Variables with p \u0026lt; 0.10 in the univariate analysis were initially included. Variables not contributing significantly were removed step by step. Among the generated models (Models 0\u0026ndash;3), Model 3 demonstrated the best fit. Model 3 was selected based on the lowest Akaike Information Criterion (AIC = 112.2).\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic and clinical characteristics of the delirium and non-delirium groups\u003c/h2\u003e\n \u003cp\u003eA comparison of the demographic and clinical characteristics between the delirium and non-delirium groups is presented in Table\u0026nbsp;2. The mean age was slightly higher in the delirium group (79.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 years) than in the non-delirium group (77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 years); however, this difference did not reach significance (p\u0026thinsp;=\u0026thinsp;0.052). The proportion of female patients was lower in the delirium group (35.9%) than in the non-delirium group (51.4%); however, the difference was not significant (p\u0026thinsp;=\u0026thinsp;0.053). No significant difference in the proportion of surgical procedures performed laparoscopically was noted between the two groups (p\u0026thinsp;=\u0026thinsp;0.42). However, the delirium group had a significantly longer postoperative hospital stay (21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1 vs. 13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and longer preoperative hospital stay (3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 vs. 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 days, p\u0026thinsp;=\u0026thinsp;0.003). No significant differences were found in surgical duration or intraoperative blood loss.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eComparison of the preoperative functional and cognitive measures\u003c/h2\u003e\n \u003cp\u003eSignificant differences were observed in the preoperative functional and cognitive assessments between the delirium and non-delirium groups (Table\u0026nbsp;3). The delirium group had significantly higher PS scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.364) and significantly lower BMS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.320) and FIM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.468) scores. Moreover, grip strength was significantly lower in the delirium group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.260), suggesting a potential association between reduced physical function and POD. As regards fatigue and psychological assessments, the delirium group had significantly higher CFS scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.560) and NRS scores for pain and stress (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.383). Furthermore, the HADS-A and HADS-D scores were significantly higher in the delirium group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.819 and 0.806, respectively), whereas the VI was significantly lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.549). The cognitive function assessments also showed significant differences between the two groups. The delirium group had significantly lower MMSE (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.585) and FAB (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.513) scores than the non-delirium group. These findings suggest that preoperative decline in physical function, psychological distress, and cognitive function may be associated with POD.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eLogistic regression analysis of factors associated with delirium\u003c/h2\u003e\n \u003cp\u003eTo identify the independent predictors of POD, a multivariate logistic regression analysis was conducted (Table\u0026nbsp;4). Variables that were significantly different between the delirium and non-delirium groups in the univariate analysis, including preoperative hospital stay, serum albumin levels, PS, BMS score, FIM score, grip strength, CFS score, NRS score, HADS-A score, HADS-D score, VI, MMSE score, and FAB score, were entered into the model. A forward stepwise selection method was applied to determine the most relevant POD predictors. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in the univariate analysis were initially included, and those that did not contribute significantly were sequentially removed until the final model was obtained. Of the generated models 0\u0026ndash;3, model 3 with the best model fit (Akaike Information Criterion [AIC], 112.2) was adopted. After eliminating nonsignificant variables, HADS-A, HADS-D, and MMSE scores remained significant independent predictors. Higher HADS-A and HADS-D scores were significantly associated with an increased POD risk (OR, 1.505; 95% CI, 1.225\u0026ndash;1.849, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for HADS-A; OR, 1.280; 95% CI, 1.085\u0026ndash;1.510, p\u0026thinsp;=\u0026thinsp;0.003 for HADS-D), indicating that preoperative psychological distress, including anxiety and depression, may contribute to POD.\u003c/p\u003e\n \u003cp\u003eConversely, higher MMSE scores were associated with a decreased POD risk (OR, 0.811; 95% CI, 0.719 to 0.916, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that better cognitive function serves as a protective factor against POD.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights that preoperative cognitive impairment (MMSE) and psychological distress (HADS-D and HADS-A) are significant predictors of POD in patients with cancer undergoing surgery. In addition, our results demonstrated that patients who had POD had significantly longer postoperative hospital stays, underscoring the clinical effect of POD on recovery and healthcare utilization. Although this study did not directly measure the physical activity levels. Given these findings, the early identification of high-risk groups and the implementation of prehabilitation strategies focusing on cognitive, psychological, and functional aspects may be key to reducing the incidence of POD.\u003c/p\u003e \u003cp\u003eIn this study, despite excluding patients with cognitive and psychiatric comorbidities as identified in previous research, the incidence of POD was relatively high at 27%. Hypoactive delirium tends to be more challenging to differentiate from conditions such as depression, apathy, and dementia compared with hyperactive delirium, making it more likely to be overlooked. DST includes an assessment of hypoactivity, which may help prevent POD underdiagnosis. Furthermore, by accurately evaluating hypoactive patients and sharing their delirium status with attending physicians through DSM-5-based diagnoses, a more appropriate approach to management can be achieved. Furthermore, by accurately evaluating hypoactive patients and sharing their delirium status with attending physicians through DSM-5-based diagnoses, a more appropriate approach to management can be achieved.\u003c/p\u003e \u003cp\u003eOur main findings reinforce prior evidence that preoperative cognitive impairment is a strong predictor of POD [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. MMSE, commonly used to assess cognitive status, was significantly lower in the delirium group than in the non-delirium group, indicating that patients with preexisting cognitive deficits may be more vulnerable to perioperative neurocognitive dysfunction. Similarly, higher HADS-A and HADS-D scores were associated with increased POD risk, consistent with studies revealing that preoperative anxiety and depression may contribute to an exaggerated neuroinflammatory response and increased vulnerability to postoperative cognitive disturbances [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These results emphasize the need for systematic psychological screening as part of preoperative assessment protocols. In other medical conditions, numerous studies have reported the association of HADS and MMSE with POD. For example, in patients undergoing cardiovascular or orthopedic surgery, preoperative cognitive function and psychological factors were identified as significant contributors to POD risk [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the present study, despite excluding patients with cognitive and psychiatric comorbidities as identified in previous research, the incidence of POD was relatively high at 27%. Hypoactive delirium tends to be more challenging to differentiate from conditions such as depression, apathy, and dementia compared with hyperactive delirium, making it more likely to be overlooked. Our findings indicate that preoperative psychological and cognitive assessments are crucial for identifying POD risk in patients with cancer, reinforcing the applicability of previous research findings from other disease domains to the field of oncology. To further elucidate the mechanisms underlying POD in patients with cancer, future studies should investigate the effect of perioperative inflammatory responses and hormonal changes as potential physiological contributors.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as a retrospective case\u0026ndash;control study, causal relationships between preoperative cognitive and psychological factors and POD cannot be established. Prospective cohort studies are needed to confirm these associations. Second, this study was conducted at a single institution, which may limit the generalizability of our findings. Multicenter studies with larger sample sizes are necessary to validate these results. Third, although we controlled for multiple confounders, unmeasured variables such as intraoperative factors or perioperative medication use might have influenced the POD risk. Future research should incorporate these factors into the analysis. In addition, this study provides valuable insights into the preoperative risk factors for POD in older patients with cancer; however, further research is needed to explore potential interventions. Future studies should evaluate the effectiveness of multidisciplinary prehabilitation programs in reducing the incidence of POD through prospective interventional studies. Moreover, investigating the effect of early cognitive screening on long-term surgical outcomes remains an area of interest. Longitudinal studies are also warranted to determine whether interventions targeting cognitive and psychological factors help sustained improvements in postoperative functional independence. A multidisciplinary approach, integrating cognitive and psychological assessments into perioperative care, is essential for effective POD prevention. Given the strong association between preoperative psychological distress and POD, early screening and appropriate interventions may help mitigate the risk of POD in high-risk patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that preoperative cognitive impairment (MMSE) and psychological anxiety and depression (HADS-D and HADS-A) are significant predictors of POD in older patients with cancer undergoing surgery. In addition, POD was associated with prolonged postoperative hospitalization, highlighting its effect on recovery and healthcare utilization. Given these findings, more studies are expected to explore targeted interventions aimed at improving cognitive and psychological resilience in high-risk patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to St. Mary\u0026rsquo;s Hospital for their support and contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.K. and T. Tabira conceived and designed the study. Data were collected conducted by Y.K., Y.Y., H.Y., S.S., and T. Tanaka. Data were analyzed by Y.K. and M.M. Data were interpreted by T. Tabira, M.M., H.Y., K.I., and T. Tanaka. The manuscript was drafted by Y.K., T. Tabira., M.M., Y.A., and T. Tanaka. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Institutional Review Board of St. Mary\u0026rsquo;s Hospital (Approval No. 23-0605) and was conducted in accordance with the Declaration of Helsinki. Consent to Participate declaration: Not applicable.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eDisclosure of potential conflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eResearch involving human participants and/or animals\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Not applicable.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnsaloni L, Catena F, Chattat R, Fortuna D, Franceschi C, Mascitti P, Melotti RM (2010) Risk factors and incidence of postoperative delirium in elderly patients after elective and emergency surgery. 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Neurology 55(11):1621-1626. https://doi.org/10.1212/wnl.55.11.1621 \u003c/li\u003e\n\u003cli\u003eMcCusker J, Cole MG, Dendukuri N, Belzile E (2003) Does delirium increase hospital stay? J Am Geriatr Soc 51(11):1539-1546. https://doi.org/10.1046/j.1532-5415.2003.51509.x \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Delirium, Older adults, Cancer, Rehabilitation","lastPublishedDoi":"10.21203/rs.3.rs-6537403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6537403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003ePostoperative delirium (POD) is a common complication in older patients with cancer undergoing surgery, leading to prolonged hospitalization and cognitive decline. This study examined the relationship between preoperative psychological and cognitive assessments and POD incidence in these patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis case\u0026ndash;control study included 195 patients (\u0026ge;\u0026thinsp;65 years) undergoing elective surgery. Patients were classified into delirium (n\u0026thinsp;=\u0026thinsp;53) and non-delirium (n\u0026thinsp;=\u0026thinsp;142) groups based on the delirium screening tool and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Preoperative assessments included physical function (performance status, bedside mobility scale, functional independence measure, grip strength), fatigue (cancer fatigue scale), psychological state (hospital anxiety and depression scale [HADS-A and HADS-D], vitality index, numerical rating scale), and cognitive function (mini-mental state examination [MMSE] and frontal assessment battery). The demographic, clinical, and preoperative characteristics of the two groups were compared. Multivariate logistic regression analysis was performed to identify POD-associated factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients with POD had lower MMSE (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and higher HADS-A (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and HADS-D (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) scores. Logistic regression identified HADS-A (odd ratio [OR] 1.505; 95% confidence intervals (CI), 1.225\u0026ndash;1.849, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and HADS-D (OR, 1.280; 95% CI, 1.085\u0026ndash;1.510, p\u0026thinsp;=\u0026thinsp;.003) as independent risk factors, whereas higher MMSE scores (OR, 0.811; 95% CI, 0.719 to 0.916, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were associated with a reduced risk.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative MMSE and HADS predict POD in older patients with cancer, emphasizing the importance of cognitive and psychological assessments and the need for effective preoperative interventions.\u003c/p\u003e","manuscriptTitle":"Preoperative Risk Factors for Postoperative Delirium in Older Patients with Cancer Undergoing Surgery: A Case–control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 11:23:55","doi":"10.21203/rs.3.rs-6537403/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4bccffcf-498e-43fe-a6df-00e28fc98f39","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T02:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 11:23:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6537403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6537403","identity":"rs-6537403","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}