Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective study

Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective 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 Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective study Hao Wang, Jie Chen, Jing Chen, Yanhua Chen, Yinying Qin, Tianxiao Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4065304/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Supportive Care in Cancer → Version 1 posted 9 You are reading this latest preprint version Abstract Background Analyze the risk factors for postoperative delirium (POD) in elderly patients undergoing radical prostatectomy, built a predictive nomogram model for early identification of high-risk individuals and develop strategies for preventive interventions. Methods A total of 156 patients was recruited and categorized according to the development of POD within 7 days. After identifying independent risk factors through univariate and multivariate logistic regression analyses, predictive models were established. The discrimination and calibration were determined by C-index and calibration curve, with five-fold cross-validation executed. A nomogram model representing the optimal model was constructed based on the results. Results POD occurred in 24 (15.38%) patients. Significant differences were observed in age, anxiety, physical status, sleep disorders, blood glucose, age-adjusted Charlson comorbidity index (ACCI), anticholinergic, blood loss, postoperative infection, and numerical rating scale (NRS). Logistic regression analyses showed that sleep disorders (OR:12.931, 95% CI:1.191-140.351, P = 0.035), ACCI (OR:2.608, 95% CI:1.143–5.950, P = 0.023), postoperative infection (OR:19.298, 95% CI:2.53-147.202, P = 0.04), and NRS (OR:4.033, 95% CI:1.062–15.324, P = 0.041) were independent risk factors for POD. Model 1 (postoperative infection, ACCI, preoperative sleep disorder, NRS showed better diagnostic performance than the others, of which the area under the curve (AUC) was 0.973. The best diagnostic performance was found in model 1 through five-fold cross-validation, with a C-index of 0.963. Conclusions This prospective cohort study highlighted that ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection were identified as independent risk factors for POD. Furthermore, the nomogram derived from model 1 proved to be effective in predicting POD in elderly patients undergoing radical prostatectomy. Prostate cancer Elderly patients Postoperative delirium Risk factors Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Prostate cancer ranks as the second most prevalent malignant neoplasm in males and stands as the fifth primary contributor to male mortality on a global scale [ 1 ]. Prostate cancer primarily affects older individuals [ 2 ]. As China's population continues to age, the incidence and mortality rates of prostate cancer are on the rise. Radical prostatectomy stands as an effective treatment for local prostate cancer. Most postoperative delirium (POD) occurs within 3 days after operation, with a higher prevalence among elderly patients. The main characteristics of POD include acute attention disorder and cognitive dysfunction [ 3 ]. A previous study has shown that the overall incidence of POD was 19% after elective surgery and 32% after emergency surgery [ 4 ]. Researchers found that the incidence of POD in otorhinolaryngology was 12%, 13% in general surgery, 29% in aortic surgery, 50% in major abdominal surgery and 51% in cardiac surgery [ 5 ]. Delirium not only prolongs the length of hospital stay, but impacts the postoperative quality of life for patients. There is currently no perfect clinical treatment plan for POD, and the underlying mechanism remains unclear. The factors contributing to POD are multifaceted, involving preoperative, intraoperative, and postoperative elements. It is clinically significant to timely identify and manage the risk factors associated with POD in order to decrease its occurrence in elderly patients. Predictive models have the potential to support physicians in implementing precision medicine, thus enabling the development of individualized and effective treatment strategies [ 6 – 9 ]. The objective of this investigation was to examine the present status and potential risk factors linked to POD in elderly individuals undergoing radical prostatectomy, as there has been little focus on them. Therefore, we aimed to gather and analyze clinical data in this prospective cohort analysis to establish a predictive model that can serve as a reference for identifying high-risk patients with POD and formulating preventive measures in future clinical practice. Material and methods Patients This was a single-center, observational, prospective cohort study with the approval of the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (Ethics No.2024-E093-01) in accordance with the Declaration of Helsinki. Between November 2020 and January 2023, a total of 183 elderly patients who received radical prostatectomy were collected from the First Affiliated Hospital of Guangxi Medical University. The patient data was retrieved from the hospital's electronic health record system, and postoperative follow-up was conducted for 7 days. The inclusion criteria included: (1) radical prostatectomy under general anesthesia, (2) postoperative hospitalization for at least 3 days, (3) American Association of Anesthesiologists (ASA) grade I to III, (4) no language communication barriers, with certain language comprehension and reading abilities, (5) patient and family members provided consent to be involved in the research, (6) patient age ≥ 65 years old. The exclusion criteria were delineated as follows: (1) patient or their families refused to be involved in the research, (2) had a confirmed history of delirium or dementia, (3) emergency surgery, (4) entered ICU after surgery, (5) with severe visual or auditory impairments, (6) preoperative cognitive impairment is defined as having a MMSE score of 17 or lower for individuals who are illiterate, a score of 20 or lower for those with a primary school education, or a score of 24 or lower for individuals with a middle school education or higher. All patients provided signed informed consent forms. Data collection General clinical data of patients such as age, height, weight, education, past medical history, smoking history, drinking history, medication history, ASA grade, preoperative urinary tract infection, hospitalization days, etc. were collected. The following laboratory findings were collected: hemoglobin, serum albumin, serum potassium, serum sodium and blood glucose. Intraoperative information was gathered by searching through the Anesthesia Clinical Information System: anesthetic drugs and dosage, type of surgical procedure, duration of surgery, length of anesthesia, duration of resuscitation, blood gas analysis, and blood loss. Assessment scales Prior to the surgery, the patients' baseline cognitive function was assessed using the Mini-Mental State Examination (MMSE) [ 10 , 11 ]. The levels of anxiety were measured using the Generalized Anxiety Disorder-7 (GAD-7) [ 12 ], while the levels of depression were evaluated using the Patient Health Questionnaire (PHQ-9) [ 13 ]. The frailty of the patients was assessed using the FRAIL Scale [ 14 , 15 ], and their sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) [ 16 ]. Preoperative comorbidities were determined by the age-adjusted Charlson comorbidity index (ACCI) [ 17 ], and physical status was assessed using the metablic equivalents (METs) [ 18 ]. Following the surgery, pain levels were quantified using the numerical rating scale (NRS) [ 19 ], and delirium was assessed using the 3-minute Diagnostic Interview for the Confusion Assessment Method (3D-CAM) [ 20 , 21 ]. Predictive Model Univariate and multivariate logistic regression analyses were utilized to assess potential risk factors linked to POD. Once independent risk factors had been identified, random combinations were employed to establish models. The stepwise regression approach was utilized to evaluate the models with strong differentiation. R software (version 4.3.0) was then used to evaluate and plot the ROC curve, C-index, calibration curve, and DCA curve for each model, with 5-fold cross-validation executed. The resulting data was used to generate a nomogram representing the optimal model. Statistical analysis The study employed the Kolmogorov-Smirnov test to assess the normality of the data. Continuous variables exhibiting normal distributions were represented as means ± standard deviations (SDs) and compared using the independent samples t-test. Skewed continuous variables were presented as medians (interquartile ranges) and analyzed using the Mann-Whitney U test. Counting data was expressed as percentages [n (%)], and group comparisons were conducted using the Chi-square test. Statistical analysis was conducted using SPSS software (version 27.0), with a significance level set at p < 0.05. Results Demographic and Clinical Characteristics of Patients Our study involved 183 patients in total, with 156 patients (85%) successfully completing the follow-up process. Among them, 24 patients (15.38%) developed POD. The demographic and clinical details of the patients can be found in Table 1 . There were notable variations between the POD group and the non-POD group in age (P < 0.001), anxiety levels (P = 0.013), frailty (P < 0.001), sleep disorder (P < 0.001), blood glucose levels (P = 0.02), ACCI scores (P < 0.001) and MET scores (P < 0.001). Regarding the intraoperative risk factors outlined in Table 2 , there were no statistically significant differences identified in surgical method, benzodiazepine use, electrolyte disturbances, surgical time, anesthesia time, resuscitation time, and hypotension time between the two groups. Nonetheless, there was a significantly lower proportion of anticholinergic use (P = 0.019) observed in the POD group compared to the non-POD group. Additionally, the intraoperative blood loss (P = 0.015) was significantly higher in the POD group. Table 3 , which provides information on postoperative indicators, revealed significant differences in postoperative infections (P < 0.001) and postoperative pain (P < 0.001) between the two cohorts. There were no statistically significant variations observed in the levels of serum sodium, serum potassium, and serum albumin between the two groups. Table 1 General characteristics and preoperative factors of patients Total POD p (n = 156) Yes (n = 24) No (n = 132) Age [median (IQR)] 156 74 (7) 69 (6) Z = -4.102 < 0.001* BMI (kg/m 2 ) 156 23.45 23.83 t = -0.625 0.533 ASA, n (%) χ² = 2.797 0.247 I 3 1 (33.33%) 2 (66.67%) II 100 12 (12%) 88 (88%) III 53 11 (20.8%) 42 (79.2%) Anxiety, n (%) χ² = 6.205 0.013* Yes 56 14 (25%) 42 (75%) No 100 10 (10%) 90 (90%) Depression, n (%) χ² = 0.837 0.36 Yes 23 5 (21.7%) 18 (78.3%) No 133 19 (14.3%) 114 (85.7%) Frailty, n (%) χ² = 13.6 < 0.001* Yes 76 20 (26.3%) 56 (73.7%) No 80 4 (5%) 76 (95%) Sleep disorder, n (%) χ² = 40.789 < 0.001* Yes 54 22 (40.7%) 32 (59.3%) No 102 2 (1.96%) 100 (98.04%) Smoking, n (%) χ² = 0.222 0.638 Yes 52 7 (13.5%) 45 (86.5%) No 104 17 (16.3% ) 87 (83.7% ) Drinking, n (%) χ² = 0.541 0.462 Yes 49 6 (12.2%) 43 (87.8%) No 107 18 (16.8%) 89 (83.2%) Urinary tract infection χ² = 2.781 0.095 Yes 22 6 ( 27.3%) 16 ( 72.7%) No 134 18 (13.4%) 116 (86.6%) Serum sodium χ² = 0.175 0.676 Normal 146 22 (15.1%) 124 (84.9%) Abnormal 10 2 (20%) 8 (80%) Serum potassium χ² = 0.487 0.485 Normal 131 19 (14.5%) 112 (85.5%) Abnormal 25 5 (20%) 20 (80%) Hemoglobin [g/L, median (IQR)] 156 129 (20.5) 133 (20.5) Z = -1.673 0.094 Blood glucose [mmol/L, median (IQR)] 156 6.47 (1.64) 5.41 (1.61) Z = -3.125 0.02* ACCI scores 156 7 (0) 5 (1) Z = -6.66 < 0.001* MET values 156 3 (2) 4 (1) Z = -3.422 < 0.001* POD, postoperative delirium; BMI, body mass index; ASA, American Society of Anesthesiologists; ACCI, age-adjusted Charlson comorbidity index; MET, metabolic equivalent. * p < 0.05 Table 2 Intraoperative factors of patients Total POD p (n = 156) Yes (n = 24) No (n = 132) Types of surgery, n (%) χ² = 1.266 0.542 Robot-assisted laparoscopic radical prostatectomy 83 14 (16.9%) 69 (83.1%) Laparoscopic radical prostatectomy 70 9 (12.9%) 61 (87.1%) Open radical prostatectomy 3 1 (33.33%) 2 (66.67%) Anticholinergics χ² = 5.522 0.019* Yes 36 10 (27.8%) 26 (72.2%) No 120 14 (11.7%) 106 (88.3%) Benzodiazepines χ² = 0.355 0.552 Yes 26 3 (11.5%) 23 (88.5%) No 130 21 (16.2%) 109 (83.8%) Electrolyte disturbance χ² = 0.16 0.898 Yes 31 5 (16.1%) 26 (83.9%) No 125 19 (15.4%) 106 (84.6%) Duration of surgery (min) 156 284.42 248.98 t = 1.517 0.141 Duration of anesthesia (min) 156 336.67 301.21 t = 1.546 0.133 Duration of recovery [min, median (IQR)] 156 92 (35) 88 (33) Z = -0.349 0.727 Blood loss [ml, median (IQR)] 156 200 (200) 100 (128) Z = -2.444 0.015* Duration of hypotension [min, median (IQR)] 156 10 (5) 10 (9) Z = -0.432 0.666 POD, postoperative delirium. * p < 0.05 Table 3 Postoperative factors of patients Total POD p (n = 156) Yes (n = 24) No (n = 132) Serum sodium χ²=0.292 0.589 Normal 152 23 (15.1%) 129 (84.9%) Abnormal 4 1 (25%) 3 (75%) Serum potassium χ²=0.093 0.76 Normal 113 18 (15.9%) 95 (84.1%) Abnormal 43 6 (14%) 37 (86%) Postoperative infection χ²=77.46 < 0.001* Yes 15 14 (93.3%) 1 (6.7%) No 141 10 (7.1%) 131(92.9%) Serum albumin [mg/L, median (IQR)] 156 33.83 35.02 t=-1.317 0.19 NRS scores [median (IQR)] 156 4.14 (1) 4 (1.26) Z=-4.155 < 0.001* POD, postoperative delirium; NRS, numerical rating scale. * p < 0.05 Feature Selection and Prediction Model Construction Subsequently, eleven possible risk factors were included in logistic regression analysis, as demonstrated in Table 4 . The results revealed that ACCI (OR: 2.608, 95%CI: 1.143–5.950, P = 0.023), preoperative sleep disorder (OR: 12.931, 95%CI: 1.191-140.351, P = 0.035), postoperative pain (OR:4.033, 95%CI: 1.062–15.324, P = 0.041), and postoperative infection (OR:19.298, 95%CI: 2.53-147.202, P = 0.04) were found to be independent risk factors for POD. These risk factors were randomly combined to establish models, and five models with good differentiation were selected for further analysis. These models include: Model 1 (PI + ACCI + PSD + NRS), Model 2 (PI + ACCI + PSD), Model 3 (PI + PSD + NRS), Model 4 (PI + PSD), Model 5 (ACCI + NRS). Table 4 Multivariate logistic regression analyses of POD Variables OR (95%CI) P Age 0.938 (0.626–1.406) 0.757 Anxiety 0.442 (0.061–3.191) 0.418 Frailty 0.632 (0.054–7.376) 0.714 Sleep disorder 12.931 (1.191-140.351) 0.035* Blood glucose 1.423 (0.704–2.879) 0.326 ACCI 2.608 (1.143–5.950) 0.023* MET 0.776 (0.162–3.724) 0.751 Anticholinergics 3.103 (0.597–16.143) 0.178 Blood loss 1.001 (0.999–1.002) 0.213 Postoperative pain (NRS) 4.033 (1.062–15.324) 0.041* Postoperative infection 19.298 (2.53-147.202) 0.04* POD, postoperative delirium; ACCI, age-adjusted Charlson comorbidity index; MET, metabolic equivalent; NRS, numerical rating scale. * p < 0.05 Construction of POD Nomogram The results of ROC curve for each model are presented in Fig. 1 . Model 1 in Table 5 showed the highest AUC of 0.973 (95%CI: 0.949–0.996) in predicting the occurrence of POD. Furthermore, all combined models exhibited better diagnostic efficacy compared to individual indicators. The C-index and the calibration curve for each model are shown in Table 6 and Fig. 2 , highlighting their robust consistency both in theory and application. Following internal verification via five-fold cross-validation, a new C-index was obtained as presented in Table 7 . Model 1 continued to exhibit the utmost diagnostic efficiency with a new C-index of 0.963 (95%CI: 0.944–0.983), substantiating its exceptional innate repeatability. Furthermore, the decision curve analysis (DCA) indicated that the net benefit of using the Model 1 to predict POD was superior to other models, as shown in Fig. 3 . Based on Model 1, a nomogram was constructed in Fig. 4 to provide a more precise prediction of individualized risk of POD. Table 5 AUC of fusion model and single variable AUC 95%CI ACCI 0.916 0.870–0.962 Preoperative sleep disorder (PSD) 0.837 0.758–0.917 Postoperative infection (PI) 0.765 0.640–0.890 NRS 0.783 0.698–0.867 Model 1 (PI + ACCI + PSD + NRS) 0.973 0.949–0.996 Model 2 (PI + ACCI + PSD) 0.968 0.924–0.994 Model 3 (PI + PSD + NRS) 0.956 0.926–0.986 Model 4 (PI + PSD) 0.918 0.876–0.959 Model 5 (ACCI + NRS) 0.936 0.898–0.973 AUC, area under the curve; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale Table 6 C-index of fusion model and single variable C-index 95%CI ACCI 0.916 0.870–0.962 Preoperative sleep disorder (PSD) 0.837 0.758–0.917 Postoperative infection (PI) 0.765 0.640–0.890 NRS 0.783 0.698–0.867 Model 1 (PI + ACCI + PSD + NRS) 0.973 0.949–0.996 Model 2 (PI + ACCI + PSD) 0.968 0.924–0.994 Model 3 (PI + PSD + NRS) 0.956 0.926–0.986 Model 4 (PI + PSD) 0.918 0.876–0.959 Model 5 (ACCI + NRS) 0.936 0.898–0.973 C-index, concordance index; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale Table 7 C-index of models after five-fold cross-validation C-index 95%CI Model 1 (PI + ACCI + PSD + NRS) 0.963 0.944–0.983 Model 2 (PI + ACCI + PSD) 0.957 0.934–0.981 Model 3 (PI + PSD + NRS) 0.941 0.920–0.962 Model 4 (PI + PSD) 0.915 0.890–0.940 Model 5 (ACCI + NRS) 0.938 0.915–0.961 C-index, concordance index; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale Discussion In this prospective cohort study, the incidence of POD was 15.38%, a figure that similar to the previously reported 19% incidence in a retrospective study of elderly cancer patients [ 22 ]. Our study identified ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection as factors that can predict the occurrence of POD. We constructed and validated a predictive model for POD and depicted the impact of different factors on POD through a nomogram. At present, most research on POD focuses on patients undergoing cardiac and orthopedic surgeries [ 23 ]. The prevalence of POD in cardiac surgery patients varies from 25–50%[ 24 ], while orthopedic surgery patients have a POD incidence of 17.3% [ 25 ]. There is a noticeable disparity in the occurrence rates of POD among different surgical procedures, indicating that the nature of the surgery itself may pose a considerable risk factor for POD. Therefore, developing a predictive model for POD in elderly patients after a specific type of surgery may be more feasible and valuable in clinical practice. A recent study extracted 576 variables and concluded that older age was the most influential factor in the onset of POD [ 26 ]. Similarly, studies found that older age and the presence of comorbidities were associated with an increased risk of POD [ 27 , 28 ]. Elderly patients typically have multiple comorbidities and tend to become more frail as they age [ 29 ]. ACCI is a reliable predictor of overall survival and mortality across various surgeries, and it has the potential to accurately predict the onset of POD [ 30 ]. Researchers observed that 25% of spinal surgery patients developed POD, and 24% were frail, with frail patients having a 6.6 times higher probability of developing POD compared to robust patients [ 31 ]. In the older population undergoing major planned noncardiac surgical procedures, the probability of developing POD was 2.7 times higher in frail patients compared to robust patients. However, frailty was not found to be related to postoperative cognitive decline [ 32 ]. According to previous studies, preoperative anxiety has been recognized as a contributing factor for POD [ 33 , 34 ]. There is an evident association between preoperative anxiety and sleep disorder [ 35 ]. Postoperative sleep disorder was among one of the risk factors for POD in elderly patients who underwent elective spinal surgery [ 36 ]. Another study also determined that sleep disorder increased the probability of developing POD [ 37 ]. Perioperative individuals with sleep disorders demonstrated heightened neuroinflammation, increased oxidative stress, compromised blood-brain barrier integrity, and impaired glymphatic pathway function, and accumulation of amyloid-beta proteins, all of which were linked to postoperative neurocognitive dysfunction [ 38 ]. Moreover, patients who experienced sleep disturbances prior to surgery exhibited notably elevated occurrences of delayed neurocognitive improvement and postoperative infection compared to those without sleep disorders [ 39 ]. Melatonin, a hormone produced by the pineal gland, has been shown to regulate sleep disturbances. Studies demonstrated that melatonin can notably reduce the occurrence of delirium [ 40 ], suggesting that improving perioperative sleep may decrease the likelihood of POD and postoperative cognitive dysfunction (POCD). Our study solely concentrated on the influence of preoperative sleep disorder on POD and did not assess postoperative sleep quality. Subsequent research should further investigate the connection between postoperative sleep disorder and POD. Researchers discovered that after surgery, the pain experienced by the patient may lead to an increased likelihood of POD. However, the effect of pain on other postoperative neurocognitive disorders remained uncertain [ 41 ]. Advanced age, smoking, cognitive impairment, and postoperative pain were identified as risk factors for subsyndromal delirium in a descriptive correlational study [ 42 ]. Postoperative acute pain could worsen neuroinflammation and associated cognitive functional impairments in an animal study [ 43 ]. However, studies found that preoperative chronic pain was not an independent predictor of POD [ 44 ]. Our research found that elevated postoperative pain levels in elderly individuals were linked to a higher likelihood of developing POD. The association between resting pain and POD was even more pronounced, with higher scores and longer duration of resting pain potentially elevating the risk of POD. In our study, we found that postoperative infection was the most prominent independent indicator for POD. Factors such as trauma, surgical manipulation, intraoperative bleeding, hypoxia, electrolyte disturbances, acid-base imbalance, and the use of specific anesthetic drugs can stimulate the body's immune system and peripheral inflammatory response, resulting in the liberation of a substantial quantity of inflammatory mediators. These factors can disrupt the blood-brain barrier, allowing the inflammatory factors to reach the brain parenchymal cells through various pathways and ultimately contributing to the occurrence of POD [ 45 ]. Previous research has shown an association between POD and POCD with the levels of inflammatory markers in the peripheral tissues. Certain markers, including IL-6 and CRP, have been associated with both POD and POCD [ 46 ]. Additionally, studies have linked the elevation of inflammatory markers CPAR and S100B to the occurrence of POD [ 47 ]. Furthermore, it has been discovered that the axon guidance molecule Netrin-1 possesses properties that are anti-inflammatory and neuroprotective. Netrin-1 was found to decrease the levels of IL-6 and HMGB-1 in peripheral blood, prefrontal cortex, and hippocampus, while concurrently increasing the expression of IL-10. Additionally, Netrin-1 was observed to diminish the activation of microglial cells in the prefrontal cortex and hippocampus, and ameliorate behavior characteristic of POD in mice [ 48 ]. Another study observed a significant association between the increase in systemic immune-inflammation index and the presence of POD [ 49 ], although this finding was not validated in our study. The study has a limited sample size and did not encompass perioperative drug use that lacked statistical significance. Subsequent research should enlarge the sample size and include it. Moreover, the onset of POD varies, and may occur beyond the duration of our follow-up. Many assessment scales rely on subjective accounts from patients, potentially leading to biased outcomes. It is expected that more comprehensive research will be conducted to provide early intervention for high-risk patients with POD in the future. Conclusions In this research, we have emphasized that factors such as ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection independently contribute to the risk of POD in elderly patients with prostate cancer. Furthermore, we have developed a nomogram model based on these factors, which demonstrates an effective ability to predict the occurrence of POD in this patient population. Declarations Acknowledgements None. Funding This work was supported by the Special Fund of Neurotoxicity of General Anesthetics and Its Prevention and Treatment Innovation Team of the First Affiliated Hospital of Guangxi Medical University (No. YYZS2022001), the Guangxi Clinical Research Center for Anesthesiology (No. GK AD22035214) and the Key Project of Natural Science Foundation of Guangxi (No. 2020GXNSFDA238025). Author Contribution Hao Wang, Yubo Xie, Jie Chen and Sining Pan designed the experiments; Hao Wang, Jie Chen and Sining Pan analyzed the data and drafted the work; Hao Wang, Jie Chen, Sining Pan, Yanhua Chen, Yinying Qin, Jing Chen and Tianxiao Liu performed the experiments and prepared the figures and tables. References Sung H, Ferlay J, Siegel RL, et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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J Am Geriatr Soc 62:2383–2390. https://doi.org/10.1111/jgs.13138 Sanjanwala RM, Hiebert B, Kent D, et al (2021) A Quality Improvement Initiative to Reduce Postoperative Delirium among Cardiac Surgery Patients. Geriatrics (Basel) 6:111. https://doi.org/10.3390/geriatrics6040111 Scott JE, Mathias JL, Kneebone AC (2015) Incidence of delirium following total joint replacement in older adults: a meta-analysis. Gen Hosp Psychiatry 37:223–229. https://doi.org/10.1016/j.genhosppsych.2015.02.004 Mevorach L, Forookhi A, Farcomeni A, et al (2023) Perioperative risk factors associated with increased incidence of postoperative delirium: systematic review, meta-analysis, and Grading of Recommendations Assessment, Development, and Evaluation system report of clinical literature. Br J Anaesth 130:e254–e262. https://doi.org/10.1016/j.bja.2022.05.032 Yang Y, Zhao X, Dong T, et al (2017) Risk factors for postoperative delirium following hip fracture repair in elderly patients: a systematic review and meta-analysis. Aging Clin Exp Res 29:115–126. https://doi.org/10.1007/s40520-016-0541-6 Papaconstantinou D, Frountzas M, Ruurda JP, et al (2023) Risk factors and consequences of post-esophagectomy delirium: a systematic review and meta-analysis. Dis Esophagus 36:doac103. https://doi.org/10.1093/dote/doac103 Esmaeeli S, Franco-Garcia E, Akeju O, et al (2022) Association of preoperative frailty with postoperative delirium in elderly orthopedic trauma patients. Aging Clin Exp Res 34:625–631. https://doi.org/10.1007/s40520-021-01961-5 Liu J, Li J, He J, et al (2022) The Age-adjusted Charlson Comorbidity Index predicts post-operative delirium in the elderly following thoracic and abdominal surgery: A prospective observational cohort study. Front Aging Neurosci 14:979119. https://doi.org/10.3389/fnagi.2022.979119 Susano MJ, Grasfield RH, Friese M, et al (2020) Brief Preoperative Screening for Frailty and Cognitive Impairment Predicts Delirium after Spine Surgery. Anesthesiology 133:1184–1191. https://doi.org/10.1097/ALN.0000000000003523 Mahanna-Gabrielli E, Zhang K, Sieber FE, et al (2020) Frailty Is Associated With Postoperative Delirium But Not With Postoperative Cognitive Decline in Older Noncardiac Surgery Patients. Anesth Analg 130:1516–1523. https://doi.org/10.1213/ANE.0000000000004773 Fukunaga H, Sugawara H, Koyama A, et al (2022) Relationship between preoperative anxiety and onset of delirium after cardiovascular surgery in elderly patients: focus on personality and coping process. Psychogeriatrics 22:453–459. https://doi.org/10.1111/psyg.12840 Freedman Z, Hudock N, Hallan DR, Kelleher J (2022) Anxiety as a Risk Factor for Postoperative Delirium in Elective Spine Deformity Surgeries: A National Database Study. Cureus 14:e28984. https://doi.org/10.7759/cureus.28984 Huang M, Liu K, Liang C, et al (2023) The relationship between living alone or not and depressive symptoms in older adults: a parallel mediation effect of sleep quality and anxiety. BMC Geriatr 23:506. https://doi.org/10.1186/s12877-023-04161-0 Nazemi AK, Gowd AK, Carmouche JJ, et al (2017) Prevention and Management of Postoperative Delirium in Elderly Patients Following Elective Spinal Surgery. Clin Spine Surg 30:112–119. https://doi.org/10.1097/BSD.0000000000000467 Ulsa MC, Xi Z, Li P, et al (2022) Association of Poor Sleep Burden in Middle Age and Older Adults With Risk for Delirium During Hospitalization. J Gerontol A Biol Sci Med Sci 77:507–516. https://doi.org/10.1093/gerona/glab272 Wang X, Hua D, Tang X, et al (2021) The Role of Perioperative Sleep Disturbance in Postoperative Neurocognitive Disorders. Nat Sci Sleep 13:1395–1410. https://doi.org/10.2147/NSS.S320745 Li R, Chen N, Wang E, Tang Z (2021) Correlation between preoperative sleep disorders and postoperative delayed neurocognitive recovery in elderly patients. Zhong Nan Da Xue Xue Bao Yi Xue Ban 46:1251–1259. https://doi.org/10.11817/j.issn.1672-7347.2021.210015 Khaing K, Nair BR (2021) Melatonin for delirium prevention in hospitalized patients: A systematic review and meta-analysis. J Psychiatr Res 133:181–190. https://doi.org/10.1016/j.jpsychires.2020.12.020 O’Gara BP, Gao L, Marcantonio ER, Subramaniam B (2021) Sleep, Pain, and Cognition: Modifiable Targets for Optimal Perioperative Brain Health. Anesthesiology 135:1132–1152. https://doi.org/10.1097/ALN.0000000000004046 Denny DL, Such TL (2018) Exploration of Relationships Between Postoperative Pain and Subsyndromal Delirium in Older Adults. Nurs Res 67:421–429. https://doi.org/10.1097/NNR.0000000000000305 Koyama T, Kawano T, Iwata H, et al (2019) Acute postoperative pain exacerbates neuroinflammation and related delirium-like cognitive dysfunction in rats. J Anesth 33:482–486. https://doi.org/10.1007/s00540-019-02635-3 Eckert SC, Spies CD, Mörgeli R, et al (2023) The association of chronic pain and postoperative delirium: a prospective observational cohort study. Minerva Anestesiol 89:377–386. https://doi.org/10.23736/S0375-9393.22.16858-6 Cortese GP, Burger C (2017) Neuroinflammatory challenges compromise neuronal function in the aging brain: Postoperative cognitive delirium and Alzheimer’s disease. Behav Brain Res 322:269–279. https://doi.org/10.1016/j.bbr.2016.08.027 Liu X, Yu Y, Zhu S (2018) Inflammatory markers in postoperative delirium (POD) and cognitive dysfunction (POCD): A meta-analysis of observational studies. PLoS One 13:e0195659. https://doi.org/10.1371/journal.pone.0195659 Taylor J, Parker M, Casey CP, et al (2022) Postoperative delirium and changes in the blood-brain barrier, neuroinflammation, and cerebrospinal fluid lactate: a prospective cohort study. Brit J Anaesth 129:219–230. https://doi.org/10.1016/j.bja.2022.01.005 Li K, Wang J, Chen L, et al (2021) Netrin-1 Ameliorates Postoperative Delirium-Like Behavior in Aged Mice by Suppressing Neuroinflammation and Restoring Impaired Blood-Brain Barrier Permeability. Front Mol Neurosci 14:751570. https://doi.org/10.3389/fnmol.2021.751570 Song Y, Luo Y, Zhang F, et al (2022) Systemic immune-inflammation index predicts postoperative delirium in elderly patients after surgery: a retrospective cohort study. BMC Geriatr 22:730. https://doi.org/10.1186/s12877-022-03418-4 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4065304","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279857949,"identity":"311164ab-5e64-4ee5-86f1-cafc2e9abd1e","order_by":0,"name":"Hao Wang","email":"","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":279857950,"identity":"db6d9ee0-ee27-4f68-8a7e-959be8908b83","order_by":1,"name":"Jie Chen","email":"","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Chen","suffix":""},{"id":279857951,"identity":"87d24fb8-4e1d-455b-a46d-21f573c76e87","order_by":2,"name":"Jing Chen","email":"","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""},{"id":279857952,"identity":"a22c2bf0-9f6f-452e-9aac-25e41f4f7ed2","order_by":3,"name":"Yanhua Chen","email":"","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanhua","middleName":"","lastName":"Chen","suffix":""},{"id":279857953,"identity":"97acd822-d880-4c80-ab50-4d1f214b9608","order_by":4,"name":"Yinying Qin","email":"","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yinying","middleName":"","lastName":"Qin","suffix":""},{"id":279857954,"identity":"5ca642cd-1b5b-49a0-bff0-e4be2f927d86","order_by":5,"name":"Tianxiao Liu","email":"","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tianxiao","middleName":"","lastName":"Liu","suffix":""},{"id":279857955,"identity":"b8d54491-5660-4910-8724-f185783c9d87","order_by":6,"name":"Sining Pan","email":"","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sining","middleName":"","lastName":"Pan","suffix":""},{"id":279857956,"identity":"5317501e-3653-48ff-a1c5-5298901be8ff","order_by":7,"name":"Yubo Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFACHgYJMM3e2PjwAwlaDID04WZjCdK0SKS3CfAQo8HgRu7BGx9q/siZz3zYBtRsJ6fbQECL5Iy8ZMsZxwyMZW4ntj0oYEg2NjtAQAu/RI6ZNG+DQeIM6cR2AwmGA4nbCGlhg2uRPNgmwUOMFoQtEoxEapHseQfyi7GxBE8iMJANiPCLwXFwiMnJSbAff/jwQ4WdHEEtDAIJKCYQUg4C/AQNHQWjYBSMghEPAPK8PRtJ7wY2AAAAAElFTkSuQmCC","orcid":"","institution":"Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yubo","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-03-10 13:18:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4065304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4065304/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00520-025-09289-w","type":"published","date":"2025-03-10T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53008850,"identity":"e22884eb-956c-4a89-8537-6f4aa4f2b3c6","added_by":"auto","created_at":"2024-03-19 15:20:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194667,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the models. All models performed well, and Model 1 showed the highest AUC in predicting the occurrence of POD\u003c/p\u003e","description":"","filename":"Figure1ROCcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-4065304/v1/5e8ab4c0189db4f1a2a31a68.png"},{"id":53008878,"identity":"1ddb8b99-4613-498e-8780-d6bedaaac07e","added_by":"auto","created_at":"2024-03-19 15:20:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159565,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of Model 1. The calibration curve showed good concordance between predicted probability and actual probability\u003c/p\u003e","description":"","filename":"Figure2Calibrationcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-4065304/v1/ed07edc426b8e535fe74a52e.png"},{"id":53008805,"identity":"87f2b7f1-38e0-468a-8665-7b88b238377b","added_by":"auto","created_at":"2024-03-19 15:20:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":240637,"visible":true,"origin":"","legend":"\u003cp\u003eDCA of the models. Decision curve analysis showed a greater net benefit for patients using Model 1\u003c/p\u003e","description":"","filename":"Figure3DCAcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-4065304/v1/33d1b530a12eb10733bb0833.png"},{"id":53008786,"identity":"15e4567f-5c36-465d-985b-904592af9956","added_by":"auto","created_at":"2024-03-19 15:20:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":146797,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram for predicting the risk of POD in elderly patients undergoing radical prostatectomy. Based on postoperative infection, ACCI, preoperative sleep disorder, and NRS\u003c/p\u003e","description":"","filename":"Figure4Nomogram.png","url":"https://assets-eu.researchsquare.com/files/rs-4065304/v1/1ea8088238e9fb5178739ea5.png"},{"id":78688993,"identity":"3d5efc31-dd26-4a3e-9492-ae50e1a59591","added_by":"auto","created_at":"2025-03-17 16:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1409419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4065304/v1/4982f68b-31d6-4812-ae7f-336d6237eabf.pdf"},{"id":53008853,"identity":"c6d042ba-b8ec-4584-b444-fd630c240dc1","added_by":"auto","created_at":"2024-03-19 15:20:12","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":70496,"visible":true,"origin":"","legend":"","description":"","filename":"Rawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4065304/v1/e34f73ada6ae2b70d9786109.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer ranks as the second most prevalent malignant neoplasm in males and stands as the fifth primary contributor to male mortality on a global scale [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Prostate cancer primarily affects older individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As China's population continues to age, the incidence and mortality rates of prostate cancer are on the rise. Radical prostatectomy stands as an effective treatment for local prostate cancer.\u003c/p\u003e \u003cp\u003eMost postoperative delirium (POD) occurs within 3 days after operation, with a higher prevalence among elderly patients. The main characteristics of POD include acute attention disorder and cognitive dysfunction [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A previous study has shown that the overall incidence of POD was 19% after elective surgery and 32% after emergency surgery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Researchers found that the incidence of POD in otorhinolaryngology was 12%, 13% in general surgery, 29% in aortic surgery, 50% in major abdominal surgery and 51% in cardiac surgery [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Delirium not only prolongs the length of hospital stay, but impacts the postoperative quality of life for patients. There is currently no perfect clinical treatment plan for POD, and the underlying mechanism remains unclear. The factors contributing to POD are multifaceted, involving preoperative, intraoperative, and postoperative elements. It is clinically significant to timely identify and manage the risk factors associated with POD in order to decrease its occurrence in elderly patients.\u003c/p\u003e \u003cp\u003ePredictive models have the potential to support physicians in implementing precision medicine, thus enabling the development of individualized and effective treatment strategies [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The objective of this investigation was to examine the present status and potential risk factors linked to POD in elderly individuals undergoing radical prostatectomy, as there has been little focus on them. Therefore, we aimed to gather and analyze clinical data in this prospective cohort analysis to establish a predictive model that can serve as a reference for identifying high-risk patients with POD and formulating preventive measures in future clinical practice.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This was a single-center, observational, prospective cohort study with the approval of the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (Ethics No.2024-E093-01) in accordance with the Declaration of Helsinki. Between November 2020 and January 2023, a total of 183 elderly patients who received radical prostatectomy were collected from the First Affiliated Hospital of Guangxi Medical University. The patient data was retrieved from the hospital's electronic health record system, and postoperative follow-up was conducted for 7 days. The inclusion criteria included: (1) radical prostatectomy under general anesthesia, (2) postoperative hospitalization for at least 3 days, (3) American Association of Anesthesiologists (ASA) grade I to III, (4) no language communication barriers, with certain language comprehension and reading abilities, (5) patient and family members provided consent to be involved in the research, (6) patient age\u0026thinsp;\u0026ge;\u0026thinsp;65 years old. The exclusion criteria were delineated as follows: (1) patient or their families refused to be involved in the research, (2) had a confirmed history of delirium or dementia, (3) emergency surgery, (4) entered ICU after surgery, (5) with severe visual or auditory impairments, (6) preoperative cognitive impairment is defined as having a MMSE score of 17 or lower for individuals who are illiterate, a score of 20 or lower for those with a primary school education, or a score of 24 or lower for individuals with a middle school education or higher. All patients provided signed informed consent forms.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eGeneral clinical data of patients such as age, height, weight, education, past medical history, smoking history, drinking history, medication history, ASA grade, preoperative urinary tract infection, hospitalization days, etc. were collected. The following laboratory findings were collected: hemoglobin, serum albumin, serum potassium, serum sodium and blood glucose. Intraoperative information was gathered by searching through the Anesthesia Clinical Information System: anesthetic drugs and dosage, type of surgical procedure, duration of surgery, length of anesthesia, duration of resuscitation, blood gas analysis, and blood loss.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment scales\u003c/h2\u003e \u003cp\u003ePrior to the surgery, the patients' baseline cognitive function was assessed using the Mini-Mental State Examination (MMSE) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The levels of anxiety were measured using the Generalized Anxiety Disorder-7 (GAD-7) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while the levels of depression were evaluated using the Patient Health Questionnaire (PHQ-9) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The frailty of the patients was assessed using the FRAIL Scale [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and their sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Preoperative comorbidities were determined by the age-adjusted Charlson comorbidity index (ACCI) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and physical status was assessed using the metablic equivalents (METs) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Following the surgery, pain levels were quantified using the numerical rating scale (NRS) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and delirium was assessed using the 3-minute Diagnostic Interview for the Confusion Assessment Method (3D-CAM) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Model\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses were utilized to assess potential risk factors linked to POD. Once independent risk factors had been identified, random combinations were employed to establish models. The stepwise regression approach was utilized to evaluate the models with strong differentiation. R software (version 4.3.0) was then used to evaluate and plot the ROC curve, C-index, calibration curve, and DCA curve for each model, with 5-fold cross-validation executed. The resulting data was used to generate a nomogram representing the optimal model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe study employed the Kolmogorov-Smirnov test to assess the normality of the data. Continuous variables exhibiting normal distributions were represented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) and compared using the independent samples t-test. Skewed continuous variables were presented as medians (interquartile ranges) and analyzed using the Mann-Whitney U test. Counting data was expressed as percentages [n (%)], and group comparisons were conducted using the Chi-square test. Statistical analysis was conducted using SPSS software (version 27.0), with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eDemographic and Clinical Characteristics of Patients\u003c/h2\u003e\n\u003cp\u003eOur study involved 183 patients in total, with 156 patients (85%) successfully completing the follow-up process. Among them, 24 patients (15.38%) developed POD. The demographic and clinical details of the patients can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. There were notable variations between the POD group and the non-POD group in age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety levels (P\u0026thinsp;=\u0026thinsp;0.013), frailty (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sleep disorder (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), blood glucose levels (P\u0026thinsp;=\u0026thinsp;0.02), ACCI scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and MET scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding the intraoperative risk factors outlined in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, there were no statistically significant differences identified in surgical method, benzodiazepine use, electrolyte disturbances, surgical time, anesthesia time, resuscitation time, and hypotension time between the two groups. Nonetheless, there was a significantly lower proportion of anticholinergic use (P\u0026thinsp;=\u0026thinsp;0.019) observed in the POD group compared to the non-POD group. Additionally, the intraoperative blood loss (P\u0026thinsp;=\u0026thinsp;0.015) was significantly higher in the POD group. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, which provides information on postoperative indicators, revealed significant differences in postoperative infections (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and postoperative pain (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the two cohorts. There were no statistically significant variations observed in the levels of serum sodium, serum potassium, and serum albumin between the two groups.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGeneral characteristics and preoperative factors of patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePOD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge [median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74 (7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -4.102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et = -0.625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.533\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASA, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 2.797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.247\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (33.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (66.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88 (88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (20.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (79.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnxiety, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 6.205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 0.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (21.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (78.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114 (85.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrailty, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 13.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (26.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (73.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSleep disorder, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 40.789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (40.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (59.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (98.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.638\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (13.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (86.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (16.3% )\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87 (83.7% )\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 0.541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.462\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (12.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (87.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (16.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (83.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrinary tract infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 2.781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 ( 27.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 ( 72.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (13.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116 (86.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum sodium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 0.175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.676\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (15.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (84.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbnormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum potassium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 0.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.485\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (14.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (85.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbnormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHemoglobin [g/L, median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (20.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133 (20.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -1.673\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood glucose [mmol/L, median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.47 (1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.41 (1.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -3.125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACCI scores\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -6.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMET values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -3.422\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\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\u003ePOD, postoperative delirium; BMI, body mass index; ASA, American Society of Anesthesiologists; ACCI, age-adjusted Charlson comorbidity index; MET, metabolic equivalent. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eIntraoperative factors of patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePOD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTypes of surgery, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 1.266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.542\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRobot-assisted laparoscopic radical prostatectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (16.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (83.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLaparoscopic radical prostatectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (12.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (87.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOpen radical prostatectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (33.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (66.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnticholinergics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 5.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (27.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (72.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (11.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106 (88.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenzodiazepines\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 0.355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.552\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (11.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (88.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (16.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109 (83.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectrolyte disturbance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2; = 0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.898\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (83.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (15.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106 (84.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuration of surgery (min)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e248.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuration of anesthesia (min)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e336.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e301.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.546\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.133\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuration of recovery [min, median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88 (33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -0.349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.727\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood loss [ml, median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200 (200)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (128)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -2.444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuration of hypotension [min, median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ = -0.432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.666\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003ePOD, postoperative delirium. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePostoperative factors of patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePOD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum sodium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2;=0.292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (15.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (84.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbnormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum potassium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2;=0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (15.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95 (84.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbnormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostoperative infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u0026sup2;=77.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (93.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (7.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131(92.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum albumin [mg/L, median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et=-1.317\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNRS scores [median (IQR)]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.14 (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (1.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ=-4.155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\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\u003ePOD, postoperative delirium; NRS, numerical rating scale. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eFeature Selection and Prediction Model Construction\u003c/h2\u003e\n\u003cp\u003eSubsequently, eleven possible risk factors were included in logistic regression analysis, as demonstrated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The results revealed that ACCI (OR: 2.608, 95%CI: 1.143\u0026ndash;5.950, P\u0026thinsp;=\u0026thinsp;0.023), preoperative sleep disorder (OR: 12.931, 95%CI: 1.191-140.351, P\u0026thinsp;=\u0026thinsp;0.035), postoperative pain (OR:4.033, 95%CI: 1.062\u0026ndash;15.324, P\u0026thinsp;=\u0026thinsp;0.041), and postoperative infection (OR:19.298, 95%CI: 2.53-147.202, P\u0026thinsp;=\u0026thinsp;0.04) were found to be independent risk factors for POD. These risk factors were randomly combined to establish models, and five models with good differentiation were selected for further analysis. These models include: Model 1 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS), Model 2 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD), Model 3 (PI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS), Model 4 (PI\u0026thinsp;+\u0026thinsp;PSD), Model 5 (ACCI\u0026thinsp;+\u0026thinsp;NRS).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate logistic regression analyses of POD\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.938 (0.626\u0026ndash;1.406)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.757\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnxiety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.442 (0.061\u0026ndash;3.191)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.418\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrailty\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.632 (0.054\u0026ndash;7.376)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.714\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSleep disorder\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.931 (1.191-140.351)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.035*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood glucose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.423 (0.704\u0026ndash;2.879)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.326\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACCI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.608 (1.143\u0026ndash;5.950)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMET\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.776 (0.162\u0026ndash;3.724)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.751\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnticholinergics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.103 (0.597\u0026ndash;16.143)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.178\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood loss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.001 (0.999\u0026ndash;1.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostoperative pain (NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.033 (1.062\u0026ndash;15.324)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.041*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostoperative infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.298 (2.53-147.202)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04*\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\u003ePOD, postoperative delirium; ACCI, age-adjusted Charlson comorbidity index; MET, metabolic equivalent; NRS, numerical rating scale. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eConstruction of POD Nomogram\u003c/h2\u003e\n\u003cp\u003eThe results of ROC curve for each model are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Model 1 in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e showed the highest AUC of 0.973 (95%CI: 0.949\u0026ndash;0.996) in predicting the occurrence of POD. Furthermore, all combined models exhibited better diagnostic efficacy compared to individual indicators. The C-index and the calibration curve for each model are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, highlighting their robust consistency both in theory and application. Following internal verification via five-fold cross-validation, a new C-index was obtained as presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Model 1 continued to exhibit the utmost diagnostic efficiency with a new C-index of 0.963 (95%CI: 0.944\u0026ndash;0.983), substantiating its exceptional innate repeatability. Furthermore, the decision curve analysis (DCA) indicated that the net benefit of using the Model 1 to predict POD was superior to other models, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on Model 1, a nomogram was constructed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e to provide a more precise prediction of individualized risk of POD.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAUC of fusion model and single variable\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\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\u003eACCI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.916\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.870\u0026ndash;0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePreoperative sleep disorder (PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.758\u0026ndash;0.917\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostoperative infection (PI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.640\u0026ndash;0.890\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.698\u0026ndash;0.867\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.949\u0026ndash;0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.924\u0026ndash;0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3 (PI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.956\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.926\u0026ndash;0.986\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 4 (PI\u0026thinsp;+\u0026thinsp;PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.918\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.876\u0026ndash;0.959\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 5 (ACCI\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.936\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.898\u0026ndash;0.973\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\u003eAUC, area under the curve; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eC-index of fusion model and single variable\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eC-index\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\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\u003eACCI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.916\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.870\u0026ndash;0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePreoperative sleep disorder (PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.758\u0026ndash;0.917\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostoperative infection (PI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.640\u0026ndash;0.890\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.698\u0026ndash;0.867\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.949\u0026ndash;0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.924\u0026ndash;0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3 (PI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.956\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.926\u0026ndash;0.986\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 4 (PI\u0026thinsp;+\u0026thinsp;PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.918\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.876\u0026ndash;0.959\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 5 (ACCI\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.936\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.898\u0026ndash;0.973\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\u003eC-index, concordance index; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eC-index of models after five-fold cross-validation\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eC-index\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\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\u003eModel 1 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.944\u0026ndash;0.983\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2 (PI\u0026thinsp;+\u0026thinsp;ACCI\u0026thinsp;+\u0026thinsp;PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.957\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.934\u0026ndash;0.981\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3 (PI\u0026thinsp;+\u0026thinsp;PSD\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.920\u0026ndash;0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 4 (PI\u0026thinsp;+\u0026thinsp;PSD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.915\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.890\u0026ndash;0.940\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 5 (ACCI\u0026thinsp;+\u0026thinsp;NRS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.938\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.915\u0026ndash;0.961\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\u003eC-index, concordance index; CI, confidence interval; ACCI, age-adjusted Charlson comorbidity index; NRS, numerical rating scale\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort study, the incidence of POD was 15.38%, a figure that similar to the previously reported 19% incidence in a retrospective study of elderly cancer patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our study identified ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection as factors that can predict the occurrence of POD. We constructed and validated a predictive model for POD and depicted the impact of different factors on POD through a nomogram.\u003c/p\u003e \u003cp\u003eAt present, most research on POD focuses on patients undergoing cardiac and orthopedic surgeries [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The prevalence of POD in cardiac surgery patients varies from 25\u0026ndash;50%[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while orthopedic surgery patients have a POD incidence of 17.3% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. There is a noticeable disparity in the occurrence rates of POD among different surgical procedures, indicating that the nature of the surgery itself may pose a considerable risk factor for POD. Therefore, developing a predictive model for POD in elderly patients after a specific type of surgery may be more feasible and valuable in clinical practice.\u003c/p\u003e \u003cp\u003eA recent study extracted 576 variables and concluded that older age was the most influential factor in the onset of POD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, studies found that older age and the presence of comorbidities were associated with an increased risk of POD [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Elderly patients typically have multiple comorbidities and tend to become more frail as they age [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. ACCI is a reliable predictor of overall survival and mortality across various surgeries, and it has the potential to accurately predict the onset of POD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Researchers observed that 25% of spinal surgery patients developed POD, and 24% were frail, with frail patients having a 6.6 times higher probability of developing POD compared to robust patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In the older population undergoing major planned noncardiac surgical procedures, the probability of developing POD was 2.7 times higher in frail patients compared to robust patients. However, frailty was not found to be related to postoperative cognitive decline [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to previous studies, preoperative anxiety has been recognized as a contributing factor for POD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. There is an evident association between preoperative anxiety and sleep disorder [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Postoperative sleep disorder was among one of the risk factors for POD in elderly patients who underwent elective spinal surgery [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Another study also determined that sleep disorder increased the probability of developing POD [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Perioperative individuals with sleep disorders demonstrated heightened neuroinflammation, increased oxidative stress, compromised blood-brain barrier integrity, and impaired glymphatic pathway function, and accumulation of amyloid-beta proteins, all of which were linked to postoperative neurocognitive dysfunction [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, patients who experienced sleep disturbances prior to surgery exhibited notably elevated occurrences of delayed neurocognitive improvement and postoperative infection compared to those without sleep disorders [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Melatonin, a hormone produced by the pineal gland, has been shown to regulate sleep disturbances. Studies demonstrated that melatonin can notably reduce the occurrence of delirium [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], suggesting that improving perioperative sleep may decrease the likelihood of POD and postoperative cognitive dysfunction (POCD). Our study solely concentrated on the influence of preoperative sleep disorder on POD and did not assess postoperative sleep quality. Subsequent research should further investigate the connection between postoperative sleep disorder and POD.\u003c/p\u003e \u003cp\u003eResearchers discovered that after surgery, the pain experienced by the patient may lead to an increased likelihood of POD. However, the effect of pain on other postoperative neurocognitive disorders remained uncertain [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Advanced age, smoking, cognitive impairment, and postoperative pain were identified as risk factors for subsyndromal delirium in a descriptive correlational study [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Postoperative acute pain could worsen neuroinflammation and associated cognitive functional impairments in an animal study [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, studies found that preoperative chronic pain was not an independent predictor of POD [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our research found that elevated postoperative pain levels in elderly individuals were linked to a higher likelihood of developing POD. The association between resting pain and POD was even more pronounced, with higher scores and longer duration of resting pain potentially elevating the risk of POD.\u003c/p\u003e \u003cp\u003eIn our study, we found that postoperative infection was the most prominent independent indicator for POD. Factors such as trauma, surgical manipulation, intraoperative bleeding, hypoxia, electrolyte disturbances, acid-base imbalance, and the use of specific anesthetic drugs can stimulate the body's immune system and peripheral inflammatory response, resulting in the liberation of a substantial quantity of inflammatory mediators. These factors can disrupt the blood-brain barrier, allowing the inflammatory factors to reach the brain parenchymal cells through various pathways and ultimately contributing to the occurrence of POD [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous research has shown an association between POD and POCD with the levels of inflammatory markers in the peripheral tissues. Certain markers, including IL-6 and CRP, have been associated with both POD and POCD [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, studies have linked the elevation of inflammatory markers CPAR and S100B to the occurrence of POD [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, it has been discovered that the axon guidance molecule Netrin-1 possesses properties that are anti-inflammatory and neuroprotective. Netrin-1 was found to decrease the levels of IL-6 and HMGB-1 in peripheral blood, prefrontal cortex, and hippocampus, while concurrently increasing the expression of IL-10. Additionally, Netrin-1 was observed to diminish the activation of microglial cells in the prefrontal cortex and hippocampus, and ameliorate behavior characteristic of POD in mice [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Another study observed a significant association between the increase in systemic immune-inflammation index and the presence of POD [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], although this finding was not validated in our study. The study has a limited sample size and did not encompass perioperative drug use that lacked statistical significance. Subsequent research should enlarge the sample size and include it. Moreover, the onset of POD varies, and may occur beyond the duration of our follow-up. Many assessment scales rely on subjective accounts from patients, potentially leading to biased outcomes. It is expected that more comprehensive research will be conducted to provide early intervention for high-risk patients with POD in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this research, we have emphasized that factors such as ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection independently contribute to the risk of POD in elderly patients with prostate cancer. Furthermore, we have developed a nomogram model based on these factors, which demonstrates an effective ability to predict the occurrence of POD in this patient population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Special Fund of Neurotoxicity of General Anesthetics and Its Prevention and Treatment Innovation Team of the First Affiliated Hospital of Guangxi Medical University (No. YYZS2022001), the Guangxi Clinical Research Center for Anesthesiology (No. GK AD22035214) and the Key Project of Natural Science Foundation of Guangxi (No. 2020GXNSFDA238025).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHao Wang, Yubo Xie, Jie Chen and Sining Pan designed the experiments; Hao Wang, Jie Chen and Sining Pan analyzed the data and drafted the work; Hao Wang, Jie Chen, Sining Pan, Yanhua Chen, Yinying Qin, Jing Chen and Tianxiao Liu performed the experiments and prepared the figures and tables.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Br J Anaesth 130:e254\u0026ndash;e262. https://doi.org/10.1016/j.bja.2022.05.032\u003c/li\u003e\n\u003cli\u003eYang Y, Zhao X, Dong T, et al (2017) Risk factors for postoperative delirium following hip fracture repair in elderly patients: a systematic review and meta-analysis. Aging Clin Exp Res 29:115\u0026ndash;126. https://doi.org/10.1007/s40520-016-0541-6\u003c/li\u003e\n\u003cli\u003ePapaconstantinou D, Frountzas M, Ruurda JP, et al (2023) Risk factors and consequences of post-esophagectomy delirium: a systematic review and meta-analysis. Dis Esophagus 36:doac103. https://doi.org/10.1093/dote/doac103\u003c/li\u003e\n\u003cli\u003eEsmaeeli S, Franco-Garcia E, Akeju O, et al (2022) Association of preoperative frailty with postoperative delirium in elderly orthopedic trauma patients. Aging Clin Exp Res 34:625\u0026ndash;631. https://doi.org/10.1007/s40520-021-01961-5\u003c/li\u003e\n\u003cli\u003eLiu J, Li J, He J, et al (2022) The Age-adjusted Charlson Comorbidity Index predicts post-operative delirium in the elderly following thoracic and abdominal surgery: A prospective observational cohort study. Front Aging Neurosci 14:979119. https://doi.org/10.3389/fnagi.2022.979119\u003c/li\u003e\n\u003cli\u003eSusano MJ, Grasfield RH, Friese M, et al (2020) Brief Preoperative Screening for Frailty and Cognitive Impairment Predicts Delirium after Spine Surgery. Anesthesiology 133:1184\u0026ndash;1191. https://doi.org/10.1097/ALN.0000000000003523\u003c/li\u003e\n\u003cli\u003eMahanna-Gabrielli E, Zhang K, Sieber FE, et al (2020) Frailty Is Associated With Postoperative Delirium But Not With Postoperative Cognitive Decline in Older Noncardiac Surgery Patients. Anesth Analg 130:1516\u0026ndash;1523. https://doi.org/10.1213/ANE.0000000000004773\u003c/li\u003e\n\u003cli\u003eFukunaga H, Sugawara H, Koyama A, et al (2022) Relationship between preoperative anxiety and onset of delirium after cardiovascular surgery in elderly patients: focus on personality and coping process. Psychogeriatrics 22:453\u0026ndash;459. https://doi.org/10.1111/psyg.12840\u003c/li\u003e\n\u003cli\u003eFreedman Z, Hudock N, Hallan DR, Kelleher J (2022) Anxiety as a Risk Factor for Postoperative Delirium in Elective Spine Deformity Surgeries: A National Database Study. Cureus 14:e28984. https://doi.org/10.7759/cureus.28984\u003c/li\u003e\n\u003cli\u003eHuang M, Liu K, Liang C, et al (2023) The relationship between living alone or not and depressive symptoms in older adults: a parallel mediation effect of sleep quality and anxiety. BMC Geriatr 23:506. https://doi.org/10.1186/s12877-023-04161-0\u003c/li\u003e\n\u003cli\u003eNazemi AK, Gowd AK, Carmouche JJ, et al (2017) Prevention and Management of Postoperative Delirium in Elderly Patients Following Elective Spinal Surgery. Clin Spine Surg 30:112\u0026ndash;119. https://doi.org/10.1097/BSD.0000000000000467\u003c/li\u003e\n\u003cli\u003eUlsa MC, Xi Z, Li P, et al (2022) Association of Poor Sleep Burden in Middle Age and Older Adults With Risk for Delirium During Hospitalization. J Gerontol A Biol Sci Med Sci 77:507\u0026ndash;516. https://doi.org/10.1093/gerona/glab272\u003c/li\u003e\n\u003cli\u003eWang X, Hua D, Tang X, et al (2021) The Role of Perioperative Sleep Disturbance in Postoperative Neurocognitive Disorders. 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Anesthesiology 135:1132\u0026ndash;1152. https://doi.org/10.1097/ALN.0000000000004046\u003c/li\u003e\n\u003cli\u003eDenny DL, Such TL (2018) Exploration of Relationships Between Postoperative Pain and Subsyndromal Delirium in Older Adults. Nurs Res 67:421\u0026ndash;429. https://doi.org/10.1097/NNR.0000000000000305\u003c/li\u003e\n\u003cli\u003eKoyama T, Kawano T, Iwata H, et al (2019) Acute postoperative pain exacerbates neuroinflammation and related delirium-like cognitive dysfunction in rats. J Anesth 33:482\u0026ndash;486. https://doi.org/10.1007/s00540-019-02635-3\u003c/li\u003e\n\u003cli\u003eEckert SC, Spies CD, M\u0026ouml;rgeli R, et al (2023) The association of chronic pain and postoperative delirium: a prospective observational cohort study. Minerva Anestesiol 89:377\u0026ndash;386. https://doi.org/10.23736/S0375-9393.22.16858-6\u003c/li\u003e\n\u003cli\u003eCortese GP, Burger C (2017) Neuroinflammatory challenges compromise neuronal function in the aging brain: Postoperative cognitive delirium and Alzheimer\u0026rsquo;s disease. Behav Brain Res 322:269\u0026ndash;279. https://doi.org/10.1016/j.bbr.2016.08.027\u003c/li\u003e\n\u003cli\u003eLiu X, Yu Y, Zhu S (2018) Inflammatory markers in postoperative delirium (POD) and cognitive dysfunction (POCD): A meta-analysis of observational studies. PLoS One 13:e0195659. https://doi.org/10.1371/journal.pone.0195659\u003c/li\u003e\n\u003cli\u003eTaylor J, Parker M, Casey CP, et al (2022) Postoperative delirium and changes in the blood-brain barrier, neuroinflammation, and cerebrospinal fluid lactate: a prospective cohort study. Brit J Anaesth 129:219\u0026ndash;230. https://doi.org/10.1016/j.bja.2022.01.005\u003c/li\u003e\n\u003cli\u003eLi K, Wang J, Chen L, et al (2021) Netrin-1 Ameliorates Postoperative Delirium-Like Behavior in Aged Mice by Suppressing Neuroinflammation and Restoring Impaired Blood-Brain Barrier Permeability. Front Mol Neurosci 14:751570. https://doi.org/10.3389/fnmol.2021.751570\u003c/li\u003e\n\u003cli\u003eSong Y, Luo Y, Zhang F, et al (2022) Systemic immune-inflammation index predicts postoperative delirium in elderly patients after surgery: a retrospective cohort study. BMC Geriatr 22:730. https://doi.org/10.1186/s12877-022-03418-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Prostate cancer, Elderly patients, Postoperative delirium, Risk factors, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-4065304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4065304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAnalyze the risk factors for postoperative delirium (POD) in elderly patients undergoing radical prostatectomy, built a predictive nomogram model for early identification of high-risk individuals and develop strategies for preventive interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 156 patients was recruited and categorized according to the development of POD within 7 days. After identifying independent risk factors through univariate and multivariate logistic regression analyses, predictive models were established. The discrimination and calibration were determined by C-index and calibration curve, with five-fold cross-validation executed. A nomogram model representing the optimal model was constructed based on the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePOD occurred in 24 (15.38%) patients. Significant differences were observed in age, anxiety, physical status, sleep disorders, blood glucose, age-adjusted Charlson comorbidity index (ACCI), anticholinergic, blood loss, postoperative infection, and numerical rating scale (NRS). Logistic regression analyses showed that sleep disorders (OR:12.931, 95% CI:1.191-140.351, P\u0026thinsp;=\u0026thinsp;0.035), ACCI (OR:2.608, 95% CI:1.143\u0026ndash;5.950, P\u0026thinsp;=\u0026thinsp;0.023), postoperative infection (OR:19.298, 95% CI:2.53-147.202, P\u0026thinsp;=\u0026thinsp;0.04), and NRS (OR:4.033, 95% CI:1.062\u0026ndash;15.324, P\u0026thinsp;=\u0026thinsp;0.041) were independent risk factors for POD. Model 1 (postoperative infection, ACCI, preoperative sleep disorder, NRS showed better diagnostic performance than the others, of which the area under the curve (AUC) was 0.973. The best diagnostic performance was found in model 1 through five-fold cross-validation, with a C-index of 0.963.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis prospective cohort study highlighted that ACCI, preoperative sleep disorder, postoperative pain, and postoperative infection were identified as independent risk factors for POD. Furthermore, the nomogram derived from model 1 proved to be effective in predicting POD in elderly patients undergoing radical prostatectomy.\u003c/p\u003e","manuscriptTitle":"Predictors of postoperative delirium in patients undergoing radical prostatectomy: a prospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 15:19:30","doi":"10.21203/rs.3.rs-4065304/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-25T15:14:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-25T14:43:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122804242337000114692374866144484361485","date":"2025-01-17T18:23:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-22T08:35:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206947240546008180033585232921202389403","date":"2024-05-22T08:32:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-23T00:56:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-21T13:25:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-15T05:48:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2024-03-10T12:59:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6c94279a-fc83-4f7d-bc3f-b4d1869b62ff","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:02:45+00:00","versionOfRecord":{"articleIdentity":"rs-4065304","link":"https://doi.org/10.1007/s00520-025-09289-w","journal":{"identity":"supportive-care-in-cancer","isVorOnly":false,"title":"Supportive Care in Cancer"},"publishedOn":"2025-03-10 15:57:09","publishedOnDateReadable":"March 10th, 2025"},"versionCreatedAt":"2024-03-19 15:19:30","video":"","vorDoi":"10.1007/s00520-025-09289-w","vorDoiUrl":"https://doi.org/10.1007/s00520-025-09289-w","workflowStages":[]},"version":"v1","identity":"rs-4065304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4065304","identity":"rs-4065304","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}