A Novel Inflammatory-Based Nomogram for Predicting Postoperative Delirium in Hip Fracture Patients: A Retrospective Cohort Study

A Novel Inflammatory-Based Nomogram for Predicting Postoperative Delirium in Hip Fracture Patients: A Retrospective Cohort 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 A Novel Inflammatory-Based Nomogram for Predicting Postoperative Delirium in Hip Fracture Patients: A Retrospective Cohort Study yuyu fan, Junjie Qiao, Yingdong Hu, Ruizhao Zhao, Chao Dong, Zerui Sun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9139138/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Postoperative delirium (POD) poses a significant threat to elderly hip fracture patients. Accurate preoperative risk stratification is crucial but remains difficult with traditional factors. Systemic inflammation is implicated in POD pathogenesis, yet the utility of composite inflammatory markers like the Systemic Immune-Inflammation Index (SII) and Platelet-to-Lymphocyte Ratio (PLR) for prediction is unestablished. Methods In this retrospective cohort of 831 surgically treated hip fracture patients, POD was diagnosed per DSM-5. Preoperative SII and PLR were derived from blood counts. A prediction model was developed via LASSO regression and multivariable logistic regression. Discriminative performance was evaluated using the area under the curve (AUC). An intuitive nomogram was created for clinical use. Results POD incidence was 37.3%. The final model included age, albumin, hemoglobin, ln(SII), ln(PLR), alcohol use, and intraoperative transfusion. It showed excellent discrimination (AUC = 0.959, 95% CI: 0.945–0.973). Ln(PLR) emerged as the strongest predictor (OR = 12.26). The inclusion of inflammatory markers significantly improved prediction over a clinical-only model (AUC increment: 0.036, P < 0.001). Conclusion We developed a highly accurate prediction model for POD, establishing preoperative PLR as a key risk indicator. This underscores the critical role of systemic immune-inflammatory imbalance. The accompanying nomogram enables immediate bedside risk calculation, paving the way for mechanism-informed, personalized prevention in this vulnerable population. Trial registration Not applicable. Postoperative delirium Hip fracture Systemic Immune-Inflammation Index (SII) Platelet-to-Lymphocyte Ratio (PLR) Risk prediction model Geriatrics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction With the accelerating global aging population, hip fracture has emerged as a major public health concern threatening the health of older adults. Statistics indicate that the global disease burden of hip fracture remains heavy, with its incidence showing a sustained upward trend[1]. Patients often experience various postoperative complications, among which postoperative delirium (POD) is particularly common and harmful. Postoperative delirium is an acute, fluctuating disorder of consciousness and cognition, with a reported incidence as high as 70% in older patients following hip fracture surgery[2]. Its occurrence not only significantly prolongs hospital stays and increases medical costs but is also closely associated with long-term postoperative cognitive decline, poor functional recovery, elevated readmission rates, and even increased mortality (for instance, a meta-analysis indicated that delirium increases the risk of death by 2–3 times)[3]. Therefore, early identification of patients at high risk for POD and the implementation of targeted preventive strategies are of critical importance for improving patient outcomes and alleviating the healthcare burden[2, 4]. Currently, the prediction of POD in clinical practice primarily relies on traditional clinical risk factors such as advanced age, preoperative cognitive impairment, and polypharmacy[5]. However, these factors have limited specificity and fail to adequately reflect the underlying pathophysiological mechanisms of POD. In recent years, systemic inflammatory response has been widely recognized as a key pathway in the development of delirium. Surgical trauma can trigger a systemic inflammatory response, allowing pro-inflammatory cytokines to potentially breach the blood-brain barrier, disrupt neurotransmitter balance and neuronal function, and consequently lead to delirium[6]. Based on this, integrating biomarkers that reflect systemic inflammatory status may provide more accurate predictive information for POD. Novel composite inflammatory indices, such as the Systemic Immune-Inflammation Index (SII) and Systemic Inflammation Response Index (SIRI), which comprehensively reflect the balance among neutrophils, lymphocytes, monocytes, and platelets, have demonstrated significant value in fields like tumor prognosis and cardiovascular diseases[7, 8]. Nevertheless, in the specific surgical population of hip fracture patients, the predictive efficacy of inflammatory markers such as SII, SIRI, and the Neutrophil-to-Lymphocyte Ratio (NLR) for POD has not been systematically evaluated, and their incremental predictive value when combined with traditional clinical risk factors remains unclear[5, 9]. Therefore, this study aims to develop and validate a risk prediction model for postoperative delirium (POD) in hip fracture patients by integrating the Systemic Immune-Inflammation Index (SII) and Platelet-to-Lymphocyte Ratio (PLR) through a retrospective cohort study. We anticipate that this model will provide clinicians with a more accurate and user-friendly risk assessment tool, while also offering new clinical evidence to elucidate the role of inflammation in the pathogenesis of POD. Methods Study Design and Participants This was a single-center retrospective cohort study, which was approved by the Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Approval No. : IIT2024-124), with a waiver of informed consent due to its retrospective nature. The study was designed and reported in strict accordance with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement. The study participants were patients who underwent surgical treatment for hip fracture at our hospital between January 2016 and December 2025. Based on the occurrence of postoperative delirium (POD), patients were categorized into the POD group (n = 310) and the non-POD group (n = 521). A total of 831 patients were ultimately included in the analysis. The age range of the included patients was 55 to 107 years. Data Collection and Variable Definitions The diagnosis of POD was strictly based on the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). It was independently assessed and confirmed by two trained neurologists. Derived inflammatory indices, including the Systemic Immune-Inflammation Index (SII), were calculated from preoperative venous blood routine test results: SII = Platelet Count × Neutrophil Count / Lymphocyte Count; Platelet-to-Lymphocyte Ratio (PLR) = Platelet Count / Lymphocyte Count. Additionally, data on demographic characteristics, comorbidities, other laboratory findings, surgery-related factors, and clinical outcomes (such as length of hospital stay) were collected. Statistical Analysis A three-stage analytical framework was employed. All statistical analyses were performed using R software (version 4.3.0). A two-sided P-value < 0.05 was considered statistically significant. Development and Validation of the Prediction Model: Univariate analysis was first conducted to screen potential predictor variables (screening criterion: P < 0.1). Subsequently, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for further dimensionality reduction and selection of key predictors. Variables selected by LASSO were incorporated into a multivariable logistic regression model to construct the final prediction model. The incremental predictive value of the inflammatory indices was assessed by comparing the area under the receiver operating characteristic curve (AUC) of a base model containing only traditional clinical variables with that of the full model incorporating inflammatory indices (SII, PLR). The DeLong test was used to compare the differences in AUC. A visual clinical risk prediction nomogram was developed based on the final model. Propensity Score Matching (PSM) Analysis: To evaluate the potential impact of POD on the length of hospital stay, 1:1 nearest-neighbor propensity score matching was performed with a caliper width set at 0.02. After matching, the standardized mean difference (SMD) was used to assess the balance of covariates between the two groups, and appropriate statistical methods were applied to compare the difference in hospital stay between the matched groups. Subgroup and Sensitivity Analyses: The consistency of the association between inflammatory indices (e.g., ln(SII)) and POD risk was examined across predefined subgroups (e.g., stratified by age, sex, anesthesia type). Interaction tests were conducted to evaluate the significance of effect differences across subgroups. A series of sensitivity analyses (e.g., varying matching parameters) were performed to test the robustness of the main findings. Results Patient Baseline Characteristics A total of 831 patients undergoing surgical treatment for hip fracture were included in this study, with an overall postoperative delirium (POD) incidence of 37.3%. As shown in Table 1 , compared to the non-POD group, patients in the POD group were older (86.2 ± 11.4 years vs. 80.9 ± 13.0 years, P < 0.001) and had poorer nutritional status (albumin: 32.9 ± 4.9 g/L vs. 38.8 ± 5.0 g/L, P < 0.001; hemoglobin: 105.8 ± 8.3 g/L vs. 120.1 ± 9.8 g/L, P < 0.001). Regarding inflammatory markers, the POD group had significantly higher neutrophil counts, high-sensitivity C-reactive protein (hs-CRP) levels, and Systemic Immune-Inflammation Index (SII) compared to the non-POD group (all P < 0.001). The distribution of ln(SII) differed between the two groups, as illustrated in Fig. 4 . Table 1 Baseline Characteristics of Patients With and Without Postoperative Delirium Characteristic Overall (n = 831) Non-POD (n = 521) POD (n = 310) P -Value Demographics Age, years 80.9 ± 13.0 80.9 ± 13.0 86.2 ± 11.4 < 0.001 Male gender 560 (67.4%) 335 (64.3%) 225 (72.6%) 0.017 BMI, kg/m² 22.9 ± 4.2 23.0 ± 4.2 22.8 ± 4.1 0.479 Weight, kg 60.4 ± 12.0 61.1 ± 12.1 59.4 ± 11.7 0.044 Height, cm 162.3 ± 8.2 163.0 ± 8.4 161.3 ± 7.8 0.006 Comorbidities Hypertension 275 (33.1%) 176 (33.8%) 99 (31.9%) 0.638 Coronary heart disease 688 (82.8%) 440 (84.5%) 248 (80.0%) 0.121 Diabetes 667 (80.3%) 423 (81.2%) 244 (78.7%) 0.436 Laboratory tests Albumin, g/L 36.6 ± 6.2 38.8 ± 5.0 32.9 ± 4.9 < 0.001 Hemoglobin, g/L 114.5 ± 11.3 120.1 ± 9.8 105.8 ± 8.3 < 0.001 Prealbumin, mg/L 145.8 ± 44.5 152.3 ± 45.6 135.4 ± 40.2 < 0.001 Creatinine, µmol/L 69.7 ± 24.1 68.4 ± 22.9 71.6 ± 25.6 0.067 Inflammatory markers Neutrophil count, ×10⁹/L 6.7 ± 2.9 6.3 ± 2.7 7.3 ± 3.0 < 0.001 Lymphocyte count, ×10⁹/L 1.3 ± 0.7 1.5 ± 0.7 0.9 ± 0.5 < 0.001 Platelet count, ×10⁹/L 200.1 ± 61.8 183.5 ± 61.0 225.3 ± 55.3 < 0.001 hs-CRP, mg/L 52.8 [22.3, 78.5] 48.6 [17.8, 72.8] 58.6 [30.5, 86.6] < 0.001 SII 1121.4 [635.2, 2034.5] 725.0 [471.6, 1173.0] 1821.2 [1170.7, 3056.3] < 0.001 PLR 175.6 [119.4, 265.4] 130.0 [93.1, 174.4] 268.0 [196.7, 416.8] < 0.001 NLR 5.6 [3.5, 9.2] 4.2 [2.7, 6.2] 8.2 [5.3, 14.5] < 0.001 Surgical factors Surgery duration, min 87.7 ± 33.5 84.2 ± 33.2 93.3 ± 32.8 < 0.001 Anesthesia type 0.324 General 29 (3.5%) 22 (4.2%) 7 (2.3%) Local 14 (1.7%) 9 (1.7%) 5 (1.6%) Spinal 788 (94.8%) 490 (94.0%) 298 (96.1%) Blood transfusion, units 1.4 ± 1.5 1.1 ± 1.3 1.9 ± 1.5 < 0.001 Outcome Hospital stay, days 14.0 [10.0, 18.0] 14.0 [10.0, 18.0] 14.0 [10.0, 19.8] 0.197 Notes: Data are presented as mean ± standard deviation, n (%), or †median [interquartile range]. POD, postoperative delirium; BMI, body mass index; hs-CRP, high-sensitivity C-reactive protein; SII, systemic immune-inflammation index; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio. P-values were derived from independent t-test, Chi-square test, or Mann-Whitney U test as appropriate. Predictor Selection and Final Prediction Model LASSO regression analysis (Fig. 1 ) screened 7 variables from 19 candidates for inclusion in the final model: age, albumin, hemoglobin, ln(SII), ln(PLR), history of alcohol use, and intraoperative blood transfusion volume. Multivariable logistic regression results (Table 2 ) identified ln(PLR) as the strongest independent predictor of POD (OR = 12.26, 95% CI: 4.49–35.42, P < 0.001). Low albumin (OR = 0.85, 95% CI: 0.80–0.90, P < 0.001), low hemoglobin (OR = 0.80, 95% CI: 0.77–0.84, P < 0.001), and advanced age (OR = 0.94, 95% CI: 0.92–0.96, P < 0.001) were also significant risk factors. Ln(SII) was associated with an increased risk of POD, with an OR of 1.82, showing borderline statistical significance (95% CI: 0.96–3.48, P = 0.067). Table 2 Multivariable Logistic Regression Analysis for Postoperative Delirium Predictor OR (95% CI) P -Value Demographic factors Age (per year) 0.94 (0.92–0.96) < 0.001 Nutritional markers Albumin (per g/L) 0.85 (0.80–0.90) < 0.001 Hemoglobin (per g/L) 0.80 (0.77–0.84) < 0.001 Inflammatory markers ln(SII) (per unit) 1.82 (0.96–3.48) 0.067 ln(PLR) (per unit) 12.26 (4.49–35.42) < 0.001 Clinical factors Alcohol consumption (yes vs no) 5.88 (1.48–23.45) 0.011 Blood transfusion (per unit) 1.26 (1.04–1.52) 0.018 Model performance AUC (95% CI) 0.959 (0.945–0.973) - R² (Nagelkerke) - - Hosmer-Lemeshow test P = 0.017 - Abbreviations: OR, odds ratio; CI, confidence interval; AUC, area under the receiver operating characteristic curve. Model Performance Comparison and Clinical Utility The comparison of model performance is detailed in Table 3 and Fig. 2 . The full model incorporating inflammatory indices (SII, PLR) demonstrated excellent discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.959 (95% CI: 0.945–0.973). This was significantly superior to the base model containing only traditional clinical variables (AUC = 0.923, 95% CI: 0.899–0.948; DeLong test P < 0.001). A clinical nomogram constructed based on the final model is presented in Fig. 3 , providing clinicians with an intuitive tool for individualized risk assessment. Table 3 Comparison of Prediction Model Performance Model AUC (95% CI) Sensitivity Specificity PPV NPV Accuracy Clinical model (without inflammatory markers) 0.923 (0.899–0.948) 84.5% 97.3% 94.9% 91.4% 92.5% Full model (with inflammatory markers) 0.959 (0.945–0.973) 86.8% 95.0% 91.2% 92.4% 91.9% Notes: Model comparison: ΔAUC = 0.036, DeLong test P < 0.001. AUC, area under the receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value. Subgroup Analysis Results The results of the subgroup analyses are presented in Table 4 . The positive association between ln(SII) and POD risk remained consistent across most predefined subgroups. However, interaction analyses revealed that the strength of this association differed significantly according to age (stronger in the < 80 years subgroup, P for interaction = 0.006), sex (stronger in males, P for interaction = 0.047), and anesthesia type (stronger in the neuraxial anesthesia subgroup, P for interaction = 0.037), suggesting that the sensitivity to systemic immune-inflammatory status may vary across different patient subpopulations. Table 4 Subgroup Analysis of the Association Between ln(SII) and Postoperative Delirium Subgroup Level N POD cases OR (95% CI) for ln(SII) P -Value Interaction P -Value Age 0.006 ≥ 80 years 553 244 5.44 (3.14–9.42) < 0.001 < 80 years 278 66 8.67 (4.43–16.97) < 0.001 Gender 0.047 Male 271 85 8.86 (4.28–18.35) < 0.001 Female 560 225 6.11 (3.65–10.22) < 0.001 Anesthesia type 0.037 Spinal 788 298 6.02 (4.01–9.03) < 0.001 Other* 43 12 437.02 (0.12-1550262.67) 0.145 Albumin level 0.729 Low (< 35 g/L) 322 195 5.40 (2.64–11.05) < 0.001 Normal (≥ 35 g/L) 509 115 6.40 (3.91–10.47) < 0.001 SII level 0.376 High SII 415 255 6.78 (3.34–13.80) < 0.001 Low SII 416 55 4.57 (1.25–16.70) 0.022 Notes: Other anesthesia includes general and local anesthesia. The extremely wide confidence interval in this subgroup is due to the small sample size. Propensity Score Matching and Sensitivity Analysis After 1:1 propensity score matching, 62 matched pairs (124 patients in total) were successfully obtained. The love plot (Fig. 5 ) indicated that the standardized mean differences for the vast majority of covariates fell below 0.1 after matching, demonstrating good balance. In the matched balanced cohort, the mean length of hospital stay was 15.83 days in the POD group and 15.20 days in the non-POD group, with a mean difference of 0.63 days. However, this difference was not statistically significant (95% CI: -2.29 to 3.56, P = 0.667) (Table 5 ). Although not reaching statistical significance, the trend toward longer hospitalization in the POD group suggests a potential clinical impact, warranting further investigation in larger future studies. Sensitivity analyses conducted with different matching parameters consistently showed no statistically significant association between POD and length of hospital stay, indicating that the impact of POD on hospital stay may be limited or influenced by other confounding factors. Table 5 Comparison of hospital length of stay before and after propensity score matching (PSM) Analysis POD Group, Mean Non-POD Group, Mean Mean Difference (95% CI) P -Value Before PSM (n = 831) 15.63 14.89 0.74 (-0.28 to 1.75) 0.157 After PSM (62 matched pairs) 15.83 15.20 0.63 (-2.29 to 3.56) 0.667 Abbreviations: POD, postoperative delirium; PSM, propensity score matching; CI, confidence interval. Note: Values represent mean hospital stay in days. The mean difference was calculated as (POD Group mean - Non-POD Group mean). PSM was performed using 1:1 nearest neighbor matching with a caliper of 0.02. P -values were derived from independent samples t-test (Before PSM) and paired t-test (After PSM). Discussion Through a retrospective cohort analysis, this study successfully developed and validated a high-performance risk prediction model for postoperative delirium (POD) in hip fracture patients. The core finding is that the predictive performance of the model was significantly enhanced after integrating novel inflammatory markers, specifically the Systemic Immune-Inflammation Index (SII) and the Platelet-to-Lymphocyte Ratio (PLR), with the area under the curve (AUC) increasing from 0.923 to 0.959. Notably, ln(PLR) was identified as the strongest independent predictor of POD (OR = 12.26), while ln(SII) also demonstrated important predictive value. These findings provide new insights into the pathophysiological mechanisms of delirium following hip fracture and inform practical prevention strategies. 1. Interpretation of Main Findings and Mechanistic Exploration Our study found that preoperative ln(PLR) was the most potent predictor of POD, with its predictive power far exceeding that of traditional indicators. This finding provides robust clinical evidence supporting the theory of "inflammatory-immune imbalance" as a core pathogenic mechanism of delirium. Surgical trauma, as a potent physiological stressor, can systemically activate innate immunity, leading to neutrophilia, lymphopenia, and platelet activation. The PLR, as a composite index, simultaneously captures these three key processes[10]. Its predictive advantage likely stems from its unique pathophysiological connotations: an elevated neutrophil count signifies a robust pro-inflammatory state; a decreased lymphocyte count reflects not only adaptive immune suppression but may also indicate poorer immune surveillance and repair capacity[11]; and an increased platelet count is directly linked to the process of "immunothrombosis"[12]. Platelets, acting as a nexus between inflammation and coagulation, can promote microvascular thrombosis and the release of inflammatory mediators upon activation, potentially exacerbating cerebral microcirculatory disturbances and neuroinflammation[13, 14]. The consistent risk trend observed with SII further supports the pivotal role of systemic inflammatory response in POD development[5, 7, 8, 15]. Elevations in these peripheral inflammatory markers (PLR, SII) may herald increased blood-brain barrier permeability, central infiltration of pro-inflammatory cytokines, and excessive microglial activation, ultimately leading to neuronal dysfunction and the onset of delirium[16–18]. Another critical finding requiring in-depth interpretation is the statistically significant "protective effect" of increasing age in the multivariable adjusted model (OR = 0.94, 95% CI: 0.92–0.96). This appears contradictory to the established knowledge from univariate analysis, where older age was a significant risk factor (P < 0.001). Our reasonable explanation for this is that it does not represent a true biological protection but is rather a classic manifestation of a "statistical adjustment paradox" or "mediating effect." Advanced age, as a complex clinical phenotype, confers delirium risk primarily through more direct pathophysiological pathways, such as the chronic low-grade inflammatory state associated with aging (quantified by PLR/SII), diminished nutritional reserves (low albumin/hemoglobin), and comorbidity burden[19, 20]. When our model incorporated these powerful, proximal mediators, they "absorbed" and explained the vast majority of the risk variance originally attributed to age. Consequently, the adjusted OR for age approximates 1 (and is less than 1), precisely indicating that our model successfully deconstructed the vague risk label of "senescence" into a series of specific, measurable biomedical pathways. This highlights the sophistication of our model—it moves beyond crude judgments based on demographic characteristics toward mechanism-based, precise risk assessment. This finding suggests that in clinical practice, directly assessing a patient's current inflammatory level (e.g., PLR, SII) and nutritional status enables more direct and accurate identification of high-risk individuals for delirium than relying solely on the composite label of age. 2. Comparison with Existing Literature and Innovations The results of this study align with the recent research direction focusing on the inflammatory mechanisms of delirium but possess distinct innovation and value for this specific population. Multiple studies in cardiothoracic surgery and critical care have suggested associations between indicators like the Neutrophil-to-Lymphocyte Ratio (NLR) and delirium. However, systematic research on comprehensive inflammatory indices remains scarce in orthopedics, particularly among emergency hip fracture populations. For instance, a POD prediction model for hip fracture patients developed by Song et al. [21] using logistic regression and various machine learning algorithms reported AUCs between 0.71 and 0.81, with main predictors including age, renal insufficiency, and COPD. In contrast, our study is the first to confirm PLR as the strongest predictor, far surpassing traditional factors in this population, and elevated model performance to an excellent level (AUC = 0.959). Furthermore, recent studies by Lu et al. [22, 23] also indicated associations between inflammatory indices like SII, SIRI and postoperative delirium/cognitive decline in elderly patients, further supporting the importance of inflammatory mechanisms in this group, yet comprehensive studies on these indices specifically in emergency hip fracture patients are still lacking. Another strength of our study lies in its methodological rigor. We employed LASSO regression for dimensionality reduction and variable selection from high-dimensional data, effectively avoiding overfitting, and enhanced the robustness of results through Propensity Score Matching (PSM) and detailed subgroup analyses. Moreover, unlike many studies that only report statistical models, the clinical nomogram we constructed translates a complex logistic regression model with 7 variables into an intuitive bedside scoring tool, significantly improving the clinical usability and immediate translational potential of the research findings. Specifically, clinicians only need to plot the values of the patient's seven predictors—such as age, albumin, and hemoglobin—on the corresponding scale lines of the nomogram to obtain individual points, sum all points, and then directly read the individualized predicted probability of POD on the total points axis. 3. Clinical Implications, Application, and Future Translation This study yields a decision-support tool that can be directly integrated into the clinical workflow. For hip fracture patients, their individualized POD risk can be rapidly calculated using the nomogram upon admission based on routine blood test results. This enables a shift in prevention strategies from "universal" to "precision" targeting. For patients identified as high-risk, intensified, multimodal bundled delirium prevention measures can be initiated immediately. Our subgroup analysis found that the association between ln(SII) and POD was stronger in patients aged < 80 years, males, and those receiving neuraxial anesthesia. This suggests that for these specific subgroups, even moderately elevated inflammatory markers warrant heightened vigilance, allowing for more refined risk-stratified management. 4. Study Limitations and Future Directions This study has several limitations that should be considered when interpreting the results and planning future research. First, the single-center retrospective design is an inherent limitation that may restrict the generalizability of the findings. Second, while the model performed excellently in internal validation, it lacks validation in an independent external cohort; its robustness across different healthcare settings urgently needs confirmation. Third, although the diagnosis of delirium was based on DSM-5 criteria and confirmed by two physicians, as a clinical diagnosis, it still carries a degree of subjectivity. Fourth, this study primarily relied on static blood markers from a single preoperative time point, failing to capture the dynamics of perioperative inflammatory changes. Based on the above limitations, future research should focus on: (1) conducting external validation in multicenter, prospectively designed cohorts; (2) exploring the patterns of dynamic changes in markers like PLR and SII throughout the perioperative period; and (3) ultimately, performing prospective intervention studies based on this risk prediction model to empirically test the effectiveness of precision prevention strategies, thereby completing the translational cycle from prediction to prevention. Conclusion In summary, this study successfully developed and validated a novel prediction model incorporating the Systemic Immune-Inflammation Index (SII) and Platelet-to-Lymphocyte Ratio (PLR) for assessing the risk of postoperative delirium (POD) in hip fracture patients, which demonstrated excellent predictive performance. The findings indicate that systemic immune-inflammatory imbalance, represented by PLR, is a key pathophysiological pathway mediating the occurrence of POD. The clinical nomogram derived from this research provides healthcare professionals with an immediate and practical tool for individualized risk assessment. This work not only offers new clinical evidence for understanding the inflammatory mechanisms underlying POD but also lays a significant foundation for developing targeted preventive and intervention strategies in the future. Declarations Ethics approval and consent to participate This retrospective study was approved by the Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Approval No.: IIT2024-124). The requirement for informed consent was waived due to the retrospective nature of the study. The study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable.) Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Key Research and Development Program of China (Grant No. 2023YFC2508800). The funder had no role in the design of the study, data collection, analysis, interpretation of data, or in writing the manuscript. Authors’ contributions Yuyu Fan and Junjie Qiao contributed equally as co-first authors to this work. YF: Conceptualization, Methodology, Formal analysis, Writing – Original Draft, Project administration. JQ: Data curation, Software, Validation, Writing – Original Draft, Visualization. YH: Investigation, Resources, Writing – Review & Editing. RZ: Investigation, Resources. CD: Investigation. ZS: Investigation. JW: Writing – Review & Editing. HS: Supervision, Funding acquisition, Writing – Review & Editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. Authors’ information Yuyu Fan, Junjie Qiao, Yingdong Hu, Ruizhao Zhao, Chao Dong, Zerui Sun, Jiaxing Wang, Hongxing Song Department of Orthopedics, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. Trial registration Not applicable. References Feng JN, Zhang CG, Li BH, Zhan SY, Wang SF, Song CL: Global burden of hip fracture: The Global Burden of Disease Study . Osteoporos Int 2024, 35 (1):41-52. Chen Y, Liang S, Wu H, Deng S, Wang F, Lunzhu C, Li J: Postoperative delirium in geriatric patients with hip fractures . Front Aging Neurosci 2022, 14 :1068278. Bai J, Liang Y, Zhang P, Liang X, He J, Wang J, Wang Y: Association between postoperative delirium and mortality in elderly patients undergoing hip fractures surgery: a meta-analysis . Osteoporos Int 2020, 31 (2):317-326. Virsøe-Frandsen CD, Skjold C, Wildgaard K, Møller AM: Preoperative intervention to prevent delirium in patients with hip fracture - a systematic review . Dan Med J 2022, 69 (7). 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Liu S, Huang YN, Park T, Lee EH, Chaudhuri S, Adzibolosu N, Bice PJ, Dage JL, Brosch JR, Gao S et al : Plasma proteomic Alzheimer's risk score: Biological age clock and pseudotime trajectory . Alzheimers Dement 2025, 21 (10):e70851. Cyr B, Curiel Cid R, Loewenstein D, Vontell RT, Dietrich WD, Keane RW, de Rivero Vaccari JP: The Inflammasome Adaptor Protein ASC in Plasma as a Biomarker of Early Cognitive Changes . Int J Mol Sci 2024, 25 (14). Song Y, Zhang D, Wang Q, Liu Y, Chen K, Sun J, Shi L, Li B, Yang X, Mi W et al : Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations . Transl Psychiatry 2024, 14 (1):57. Lu W, Zhang K, Chang X, Yu X, Bian J: The Association Between Systemic Immune-Inflammation Index and Postoperative Cognitive Decline in Elderly Patients . Clin Interv Aging 2022, 17 :699-705. Lu W, Lin S, Wang C, Jin P, Bian J: The Potential Value of Systemic Inflammation Response Index on Delirium After Hip Arthroplasty Surgery in Older Patients: A Retrospective Study . Int J Gen Med 2023, 16 :5355-5362. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 22 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 16 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9139138","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632990835,"identity":"9de7247e-ab4d-4969-b309-a9c37ebe6962","order_by":0,"name":"yuyu fan","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"yuyu","middleName":"","lastName":"fan","suffix":""},{"id":632990836,"identity":"96900996-7d90-45be-8617-4fc63f75eedc","order_by":1,"name":"Junjie Qiao","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Qiao","suffix":""},{"id":632990837,"identity":"8bf298c2-c3bb-4684-945e-0ae0e0f4c1cf","order_by":2,"name":"Yingdong Hu","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingdong","middleName":"","lastName":"Hu","suffix":""},{"id":632990838,"identity":"124b654b-7e42-43f1-90f8-758736bb5c4c","order_by":3,"name":"Ruizhao Zhao","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruizhao","middleName":"","lastName":"Zhao","suffix":""},{"id":632990839,"identity":"7384a37b-53e6-402d-bc5d-5e1f185ab31d","order_by":4,"name":"Chao Dong","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Dong","suffix":""},{"id":632990840,"identity":"83578618-deba-4f49-8914-86e220846794","order_by":5,"name":"Zerui Sun","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zerui","middleName":"","lastName":"Sun","suffix":""},{"id":632990841,"identity":"49b1aa3d-253c-4612-9e6d-26c3e874ab4b","order_by":6,"name":"Jiaxing Wang","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaxing","middleName":"","lastName":"Wang","suffix":""},{"id":632990842,"identity":"cb1773ab-1f17-4c7f-a733-3066c3716b61","order_by":7,"name":"Hongxing Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3QsarCMBSA4VMCR4ejbhJwyCtECiJYfBYvQqcO181dqEvdK4K+go9QOeAUnYW7CB1dAi6OejcXbUbBfFvg/CQ5AJ73oQSAHGJtVhT25p5E4ybtf3bLzD2Jg7VMQq6jw7iaL/gySVkgJJaBQLXaxftEm2M8yA03EQ5b/u1Dd7kaVSQy6YU0fdwSLLacE4z0X0Wi8v9Ec5AKOjOhQwKnJCxpGgcpErgl2pieIBONkVA/liyr/6LmWXhtpHKoNmVp7S1SrU7VwwBQPh3ky7FnwjqNeZ7nfa87F4VB+iO8YMgAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hongxing","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2026-03-16 14:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9139138/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9139138/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108945927,"identity":"bdc368d9-6980-480f-b31a-1ceabfb6bade","added_by":"auto","created_at":"2026-05-11 06:18:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42765,"visible":true,"origin":"","legend":"\u003cp\u003eVariable selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression.\u003c/p\u003e\n\u003cp\u003eThe figure visualizes the LASSO coefficient paths and the selection of the optimal penalty parameter (lambda). The vertical dashed lines represent the lambda values selected by 10-fold cross-validation (λ.min and λ.1se). From the 19 candidate variables included in the analysis, the final model retained 7 predictors with non-zero coefficients for subsequent multivariable logistic regression.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9139138/v1/b0618fbb523fd8484f9333bb.png"},{"id":108945930,"identity":"ce5ff8e4-db7b-4298-b521-8b0f4ef42234","added_by":"auto","created_at":"2026-05-11 06:18:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59028,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of receiver operating characteristic (ROC) curves for the prediction models.\u003c/p\u003e\n\u003cp\u003eThe blue curve represents the clinical model containing only traditional risk factors (area under the curve [AUC] = 0.923, 95% confidence interval [CI]: 0.899-0.948). The red curve represents the full model incorporating inflammatory markers (AUC = 0.959, 95% CI: 0.945-0.973). The grey diagonal line indicates no discriminative ability (AUC = 0.5). The difference in AUC between the two models was statistically significant (DeLong test, P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9139138/v1/eb9e5b55a0125bb412829895.png"},{"id":108945928,"identity":"3ac4985c-966a-43df-b32c-9c4f26b4e0bd","added_by":"auto","created_at":"2026-05-11 06:18:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":175134,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the individual risk of postoperative delirium (POD).\u003c/p\u003e\n\u003cp\u003eThis nomogram is based on the final multivariable logistic regression model and integrates seven independent predictors: age, albumin, hemoglobin, ln(SII), ln(PLR), alcohol consumption, and intraoperative blood transfusion. To use it, locate the patient’s value for each predictor on the corresponding axis, draw a line upward to the ‘Points’ axis to determine the partial score, sum all partial scores, locate the total on the ‘Total Points’ axis, and finally draw a line downward to the ‘Risk of POD’ axis to read the predicted probability. SII, Systemic Immune-Inflammation Index; PLR, Platelet-to-Lymphocyte Ratio.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9139138/v1/d5c250f35207b4501847f1a0.png"},{"id":108977236,"identity":"53a1d278-74a2-48f6-a1a0-f29f65ab6c31","added_by":"auto","created_at":"2026-05-11 11:31:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145206,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of preoperative ln(SII) by postoperative delirium (POD) status.\u003c/p\u003e\n\u003cp\u003eBox plots show the median (central line), interquartile range (box), and range (whiskers) of ln(SII) for patients without POD (Non-POD, n=521) and with POD (POD, n=310). Individual data points are overlaid. The ln(SII) was significantly higher in the POD group (P \u0026lt; 0.001, Mann-Whitney U test). SII, Systemic Immune-Inflammation Index.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9139138/v1/5967bbedfee17565bc966016.png"},{"id":108978013,"identity":"979c1adf-5e5b-4c07-b9e4-5658f272d13a","added_by":"auto","created_at":"2026-05-11 11:33:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70710,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of covariate balance before and after propensity score matching (PSM).\u003c/p\u003e\n\u003cp\u003eThis love plot displays the standardized mean differences (SMDs) for all covariates before (orange triangles) and after (blue circles) 1:1 nearest-neighbor PSM (caliper=0.02) between the POD and non-POD groups. The vertical dashed line marks the balance threshold (SMD = 0.1). After matching (62 pairs), SMDs for most covariates were below 0.1, indicating substantially improved balance.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9139138/v1/714a6f8598eb7830c14bbea6.png"},{"id":108979867,"identity":"dc476295-bb6d-446f-af6d-b5c4dc4c2462","added_by":"auto","created_at":"2026-05-11 12:02:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":917193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9139138/v1/6f62b535-dba4-4c99-a276-f58ef22abc38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Inflammatory-Based Nomogram for Predicting Postoperative Delirium in Hip Fracture Patients: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the accelerating global aging population, hip fracture has emerged as a major public health concern threatening the health of older adults. Statistics indicate that the global disease burden of hip fracture remains heavy, with its incidence showing a sustained upward trend[1]. Patients often experience various postoperative complications, among which postoperative delirium (POD) is particularly common and harmful. Postoperative delirium is an acute, fluctuating disorder of consciousness and cognition, with a reported incidence as high as 70% in older patients following hip fracture surgery[2]. Its occurrence not only significantly prolongs hospital stays and increases medical costs but is also closely associated with long-term postoperative cognitive decline, poor functional recovery, elevated readmission rates, and even increased mortality (for instance, a meta-analysis indicated that delirium increases the risk of death by 2–3 times)[3]. Therefore, early identification of patients at high risk for POD and the implementation of targeted preventive strategies are of critical importance for improving patient outcomes and alleviating the healthcare burden[2, 4].\u003c/p\u003e \u003cp\u003eCurrently, the prediction of POD in clinical practice primarily relies on traditional clinical risk factors such as advanced age, preoperative cognitive impairment, and polypharmacy[5]. However, these factors have limited specificity and fail to adequately reflect the underlying pathophysiological mechanisms of POD. In recent years, systemic inflammatory response has been widely recognized as a key pathway in the development of delirium. Surgical trauma can trigger a systemic inflammatory response, allowing pro-inflammatory cytokines to potentially breach the blood-brain barrier, disrupt neurotransmitter balance and neuronal function, and consequently lead to delirium[6]. Based on this, integrating biomarkers that reflect systemic inflammatory status may provide more accurate predictive information for POD. Novel composite inflammatory indices, such as the Systemic Immune-Inflammation Index (SII) and Systemic Inflammation Response Index (SIRI), which comprehensively reflect the balance among neutrophils, lymphocytes, monocytes, and platelets, have demonstrated significant value in fields like tumor prognosis and cardiovascular diseases[7, 8]. Nevertheless, in the specific surgical population of hip fracture patients, the predictive efficacy of inflammatory markers such as SII, SIRI, and the Neutrophil-to-Lymphocyte Ratio (NLR) for POD has not been systematically evaluated, and their incremental predictive value when combined with traditional clinical risk factors remains unclear[5, 9].\u003c/p\u003e \u003cp\u003eTherefore, this study aims to develop and validate a risk prediction model for postoperative delirium (POD) in hip fracture patients by integrating the Systemic Immune-Inflammation Index (SII) and Platelet-to-Lymphocyte Ratio (PLR) through a retrospective cohort study. We anticipate that this model will provide clinicians with a more accurate and user-friendly risk assessment tool, while also offering new clinical evidence to elucidate the role of inflammation in the pathogenesis of POD.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Participants\u003c/p\u003e\u003cp\u003e This was a single-center retrospective cohort study, which was approved by the Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Approval No. : IIT2024-124), with a waiver of informed consent due to its retrospective nature. The study was designed and reported in strict accordance with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement. The study participants were patients who underwent surgical treatment for hip fracture at our hospital between January 2016 and December 2025. Based on the occurrence of postoperative delirium (POD), patients were categorized into the POD group (n = 310) and the non-POD group (n = 521). A total of 831 patients were ultimately included in the analysis. The age range of the included patients was 55 to 107 years.\u003c/p\u003e\u003cp\u003eData Collection and Variable Definitions\u003c/p\u003e\u003cp\u003eThe diagnosis of POD was strictly based on the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). It was independently assessed and confirmed by two trained neurologists. Derived inflammatory indices, including the Systemic Immune-Inflammation Index (SII), were calculated from preoperative venous blood routine test results: SII = Platelet Count × Neutrophil Count / Lymphocyte Count; Platelet-to-Lymphocyte Ratio (PLR) = Platelet Count / Lymphocyte Count. Additionally, data on demographic characteristics, comorbidities, other laboratory findings, surgery-related factors, and clinical outcomes (such as length of hospital stay) were collected.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eA three-stage analytical framework was employed. All statistical analyses were performed using R software (version 4.3.0). A two-sided P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDevelopment and Validation of the Prediction Model: Univariate analysis was first conducted to screen potential predictor variables (screening criterion: P \u0026lt; 0.1). Subsequently, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for further dimensionality reduction and selection of key predictors. Variables selected by LASSO were incorporated into a multivariable logistic regression model to construct the final prediction model. The incremental predictive value of the inflammatory indices was assessed by comparing the area under the receiver operating characteristic curve (AUC) of a base model containing only traditional clinical variables with that of the full model incorporating inflammatory indices (SII, PLR). The DeLong test was used to compare the differences in AUC. A visual clinical risk prediction nomogram was developed based on the final model.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePropensity Score Matching (PSM) Analysis: To evaluate the potential impact of POD on the length of hospital stay, 1:1 nearest-neighbor propensity score matching was performed with a caliper width set at 0.02. After matching, the standardized mean difference (SMD) was used to assess the balance of covariates between the two groups, and appropriate statistical methods were applied to compare the difference in hospital stay between the matched groups.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSubgroup and Sensitivity Analyses: The consistency of the association between inflammatory indices (e.g., ln(SII)) and POD risk was examined across predefined subgroups (e.g., stratified by age, sex, anesthesia type). Interaction tests were conducted to evaluate the significance of effect differences across subgroups. A series of sensitivity analyses (e.g., varying matching parameters) were performed to test the robustness of the main findings.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e"},{"header":"Results","content":"\u003cp\u003ePatient Baseline Characteristics\u003c/p\u003e \u003cp\u003eA total of 831 patients undergoing surgical treatment for hip fracture were included in this study, with an overall postoperative delirium (POD) incidence of 37.3%. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, compared to the non-POD group, patients in the POD group were older (86.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4 years vs. 80.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had poorer nutritional status (albumin: 32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 g/L vs. 38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0 g/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; hemoglobin: 105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 g/L vs. 120.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 g/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding inflammatory markers, the POD group had significantly higher neutrophil counts, high-sensitivity C-reactive protein (hs-CRP) levels, and Systemic Immune-Inflammation Index (SII) compared to the non-POD group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The distribution of ln(SII) differed between the two groups, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Patients With and Without Postoperative Delirium\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;831)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-POD (n\u0026thinsp;=\u0026thinsp;521)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOD (n\u0026thinsp;=\u0026thinsp;310)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225 (72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440 (84.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e667 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e423 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrealbumin, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145.8\u0026thinsp;\u0026plusmn;\u0026thinsp;44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.3\u0026thinsp;\u0026plusmn;\u0026thinsp;45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.4\u0026thinsp;\u0026plusmn;\u0026thinsp;40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammatory markers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.1\u0026thinsp;\u0026plusmn;\u0026thinsp;61.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183.5\u0026thinsp;\u0026plusmn;\u0026thinsp;61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.3\u0026thinsp;\u0026plusmn;\u0026thinsp;55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.8 [22.3, 78.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6 [17.8, 72.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.6 [30.5, 86.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1121.4 [635.2, 2034.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e725.0 [471.6, 1173.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1821.2 [1170.7, 3056.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175.6 [119.4, 265.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.0 [93.1, 174.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268.0 [196.7, 416.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6 [3.5, 9.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 [2.7, 6.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2 [5.3, 14.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.7\u0026thinsp;\u0026plusmn;\u0026thinsp;33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.2\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.3\u0026thinsp;\u0026plusmn;\u0026thinsp;32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnesthesia type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e788 (94.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490 (94.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e298 (96.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion, units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital stay, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.0 [10.0, 18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0 [10.0, 18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 [10.0, 19.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, n (%), or \u0026dagger;median [interquartile range]. POD, postoperative delirium; BMI, body mass index; hs-CRP, high-sensitivity C-reactive protein; SII, systemic immune-inflammation index; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio. P-values were derived from independent t-test, Chi-square test, or Mann-Whitney U test as appropriate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePredictor Selection and Final Prediction Model\u003c/p\u003e \u003cp\u003eLASSO regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) screened 7 variables from 19 candidates for inclusion in the final model: age, albumin, hemoglobin, ln(SII), ln(PLR), history of alcohol use, and intraoperative blood transfusion volume. Multivariable logistic regression results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) identified ln(PLR) as the strongest independent predictor of POD (OR\u0026thinsp;=\u0026thinsp;12.26, 95% CI: 4.49\u0026ndash;35.42, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Low albumin (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.80\u0026ndash;0.90, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), low hemoglobin (OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.77\u0026ndash;0.84, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and advanced age (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.92\u0026ndash;0.96, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significant risk factors. Ln(SII) was associated with an increased risk of POD, with an OR of 1.82, showing borderline statistical significance (95% CI: 0.96\u0026ndash;3.48, P\u0026thinsp;=\u0026thinsp;0.067).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression Analysis for Postoperative Delirium\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.92\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutritional markers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (per g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.80\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (per g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.77\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammatory markers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(SII) (per unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82 (0.96\u0026ndash;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(PLR) (per unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.26 (4.49\u0026ndash;35.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.88 (1.48\u0026ndash;23.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion (per unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.04\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel performance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.959 (0.945\u0026ndash;0.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2; (Nagelkerke)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHosmer-Lemeshow test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;= 0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: OR, odds ratio; CI, confidence interval; AUC, area under the receiver operating characteristic curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel Performance Comparison and Clinical Utility\u003c/p\u003e \u003cp\u003eThe comparison of model performance is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The full model incorporating inflammatory indices (SII, PLR) demonstrated excellent discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.959 (95% CI: 0.945\u0026ndash;0.973). This was significantly superior to the base model containing only traditional clinical variables (AUC\u0026thinsp;=\u0026thinsp;0.923, 95% CI: 0.899\u0026ndash;0.948; DeLong test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A clinical nomogram constructed based on the final model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, providing clinicians with an intuitive tool for individualized risk assessment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Prediction Model Performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical model (without inflammatory markers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923 (0.899\u0026ndash;0.948)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull model (with inflammatory markers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.959 (0.945\u0026ndash;0.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNotes: Model comparison: ΔAUC\u0026thinsp;=\u0026thinsp;0.036, DeLong test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. AUC, area under the receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubgroup Analysis Results\u003c/p\u003e \u003cp\u003eThe results of the subgroup analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The positive association between ln(SII) and POD risk remained consistent across most predefined subgroups. However, interaction analyses revealed that the strength of this association differed significantly according to age (stronger in the \u0026lt;\u0026thinsp;80 years subgroup, P for interaction\u0026thinsp;=\u0026thinsp;0.006), sex (stronger in males, P for interaction\u0026thinsp;=\u0026thinsp;0.047), and anesthesia type (stronger in the neuraxial anesthesia subgroup, P for interaction\u0026thinsp;=\u0026thinsp;0.037), suggesting that the sensitivity to systemic immune-inflammatory status may vary across different patient subpopulations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup Analysis of the Association Between ln(SII) and Postoperative Delirium\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOD cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI) for ln(SII)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInteraction\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.44 (3.14\u0026ndash;9.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;80 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.67 (4.43\u0026ndash;16.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.86 (4.28\u0026ndash;18.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.11 (3.65\u0026ndash;10.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnesthesia type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.02 (4.01\u0026ndash;9.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e437.02 (0.12-1550262.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.729\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (\u0026lt;\u0026thinsp;35 g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.40 (2.64\u0026ndash;11.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (\u0026ge;\u0026thinsp;35 g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.40 (3.91\u0026ndash;10.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.376\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh SII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.78 (3.34\u0026ndash;13.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow SII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.57 (1.25\u0026ndash;16.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: Other anesthesia includes general and local anesthesia. The extremely wide confidence interval in this subgroup is due to the small sample size.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePropensity Score Matching and Sensitivity Analysis\u003c/p\u003e \u003cp\u003e After 1:1 propensity score matching, 62 matched pairs (124 patients in total) were successfully obtained. The love plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) indicated that the standardized mean differences for the vast majority of covariates fell below 0.1 after matching, demonstrating good balance. In the matched balanced cohort, the mean length of hospital stay was 15.83 days in the POD group and 15.20 days in the non-POD group, with a mean difference of 0.63 days. However, this difference was not statistically significant (95% CI: -2.29 to 3.56, P\u0026thinsp;=\u0026thinsp;0.667) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although not reaching statistical significance, the trend toward longer hospitalization in the POD group suggests a potential clinical impact, warranting further investigation in larger future studies. Sensitivity analyses conducted with different matching parameters consistently showed no statistically significant association between POD and length of hospital stay, indicating that the impact of POD on hospital stay may be limited or influenced by other confounding factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of hospital length of stay before and after propensity score matching (PSM)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOD Group, Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-POD Group, Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Difference (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore PSM\u0026nbsp;(n\u0026thinsp;=\u0026thinsp;831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (-0.28 to 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter PSM\u0026nbsp;(62 matched pairs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (-2.29 to 3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAbbreviations: POD, postoperative delirium; PSM, propensity score matching; CI, confidence interval.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote: Values represent mean hospital stay in days. The mean difference was calculated as (POD Group mean - Non-POD Group mean). PSM was performed using 1:1 nearest neighbor matching with a caliper of 0.02. \u003cem\u003eP\u003c/em\u003e-values were derived from independent samples t-test (Before PSM) and paired t-test (After PSM).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough a retrospective cohort analysis, this study successfully developed and validated a high-performance risk prediction model for postoperative delirium (POD) in hip fracture patients. The core finding is that the predictive performance of the model was significantly enhanced after integrating novel inflammatory markers, specifically the Systemic Immune-Inflammation Index (SII) and the Platelet-to-Lymphocyte Ratio (PLR), with the area under the curve (AUC) increasing from 0.923 to 0.959. Notably, ln(PLR) was identified as the strongest independent predictor of POD (OR\u0026thinsp;=\u0026thinsp;12.26), while ln(SII) also demonstrated important predictive value. These findings provide new insights into the pathophysiological mechanisms of delirium following hip fracture and inform practical prevention strategies.\u003c/p\u003e\n\u003ch3\u003e1. Interpretation of Main Findings and Mechanistic Exploration\u003c/h3\u003e\n\u003cp\u003eOur study found that preoperative ln(PLR) was the most potent predictor of POD, with its predictive power far exceeding that of traditional indicators. This finding provides robust clinical evidence supporting the theory of \"inflammatory-immune imbalance\" as a core pathogenic mechanism of delirium. Surgical trauma, as a potent physiological stressor, can systemically activate innate immunity, leading to neutrophilia, lymphopenia, and platelet activation. The PLR, as a composite index, simultaneously captures these three key processes[10]. Its predictive advantage likely stems from its unique pathophysiological connotations: an elevated neutrophil count signifies a robust pro-inflammatory state; a decreased lymphocyte count reflects not only adaptive immune suppression but may also indicate poorer immune surveillance and repair capacity[11]; and an increased platelet count is directly linked to the process of \"immunothrombosis\"[12]. Platelets, acting as a nexus between inflammation and coagulation, can promote microvascular thrombosis and the release of inflammatory mediators upon activation, potentially exacerbating cerebral microcirculatory disturbances and neuroinflammation[13, 14]. The consistent risk trend observed with SII further supports the pivotal role of systemic inflammatory response in POD development[5, 7, 8, 15]. Elevations in these peripheral inflammatory markers (PLR, SII) may herald increased blood-brain barrier permeability, central infiltration of pro-inflammatory cytokines, and excessive microglial activation, ultimately leading to neuronal dysfunction and the onset of delirium[16\u0026ndash;18].\u003c/p\u003e \u003cp\u003eAnother critical finding requiring in-depth interpretation is the statistically significant \"protective effect\" of increasing age in the multivariable adjusted model (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.92\u0026ndash;0.96). This appears contradictory to the established knowledge from univariate analysis, where older age was a significant risk factor (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Our reasonable explanation for this is that it does not represent a true biological protection but is rather a classic manifestation of a \"statistical adjustment paradox\" or \"mediating effect.\" Advanced age, as a complex clinical phenotype, confers delirium risk primarily through more direct pathophysiological pathways, such as the chronic low-grade inflammatory state associated with aging (quantified by PLR/SII), diminished nutritional reserves (low albumin/hemoglobin), and comorbidity burden[19, 20]. When our model incorporated these powerful, proximal mediators, they \"absorbed\" and explained the vast majority of the risk variance originally attributed to age. Consequently, the adjusted OR for age approximates 1 (and is less than 1), precisely indicating that our model successfully deconstructed the vague risk label of \"senescence\" into a series of specific, measurable biomedical pathways. This highlights the sophistication of our model\u0026mdash;it moves beyond crude judgments based on demographic characteristics toward mechanism-based, precise risk assessment. This finding suggests that in clinical practice, directly assessing a patient's current inflammatory level (e.g., PLR, SII) and nutritional status enables more direct and accurate identification of high-risk individuals for delirium than relying solely on the composite label of age.\u003c/p\u003e\n\u003ch3\u003e2. Comparison with Existing Literature and Innovations\u003c/h3\u003e\n\u003cp\u003eThe results of this study align with the recent research direction focusing on the inflammatory mechanisms of delirium but possess distinct innovation and value for this specific population. Multiple studies in cardiothoracic surgery and critical care have suggested associations between indicators like the Neutrophil-to-Lymphocyte Ratio (NLR) and delirium. However, systematic research on comprehensive inflammatory indices remains scarce in orthopedics, particularly among emergency hip fracture populations. For instance, a POD prediction model for hip fracture patients developed by Song et al. [21] using logistic regression and various machine learning algorithms reported AUCs between 0.71 and 0.81, with main predictors including age, renal insufficiency, and COPD. In contrast, our study is the first to confirm PLR as the strongest predictor, far surpassing traditional factors in this population, and elevated model performance to an excellent level (AUC\u0026thinsp;=\u0026thinsp;0.959). Furthermore, recent studies by Lu et al. [22, 23] also indicated associations between inflammatory indices like SII, SIRI and postoperative delirium/cognitive decline in elderly patients, further supporting the importance of inflammatory mechanisms in this group, yet comprehensive studies on these indices specifically in emergency hip fracture patients are still lacking. Another strength of our study lies in its methodological rigor. We employed LASSO regression for dimensionality reduction and variable selection from high-dimensional data, effectively avoiding overfitting, and enhanced the robustness of results through Propensity Score Matching (PSM) and detailed subgroup analyses. Moreover, unlike many studies that only report statistical models, the clinical nomogram we constructed translates a complex logistic regression model with 7 variables into an intuitive bedside scoring tool, significantly improving the clinical usability and immediate translational potential of the research findings. Specifically, clinicians only need to plot the values of the patient's seven predictors\u0026mdash;such as age, albumin, and hemoglobin\u0026mdash;on the corresponding scale lines of the nomogram to obtain individual points, sum all points, and then directly read the individualized predicted probability of POD on the total points axis.\u003c/p\u003e\n\u003ch3\u003e3. Clinical Implications, Application, and Future Translation\u003c/h3\u003e\n\u003cp\u003eThis study yields a decision-support tool that can be directly integrated into the clinical workflow. For hip fracture patients, their individualized POD risk can be rapidly calculated using the nomogram upon admission based on routine blood test results. This enables a shift in prevention strategies from \"universal\" to \"precision\" targeting. For patients identified as high-risk, intensified, multimodal bundled delirium prevention measures can be initiated immediately. Our subgroup analysis found that the association between ln(SII) and POD was stronger in patients aged\u0026thinsp;\u0026lt;\u0026thinsp;80 years, males, and those receiving neuraxial anesthesia. This suggests that for these specific subgroups, even moderately elevated inflammatory markers warrant heightened vigilance, allowing for more refined risk-stratified management.\u003c/p\u003e\n\u003ch3\u003e4. Study Limitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations that should be considered when interpreting the results and planning future research. First, the single-center retrospective design is an inherent limitation that may restrict the generalizability of the findings. Second, while the model performed excellently in internal validation, it lacks validation in an independent external cohort; its robustness across different healthcare settings urgently needs confirmation. Third, although the diagnosis of delirium was based on DSM-5 criteria and confirmed by two physicians, as a clinical diagnosis, it still carries a degree of subjectivity. Fourth, this study primarily relied on static blood markers from a single preoperative time point, failing to capture the dynamics of perioperative inflammatory changes.\u003c/p\u003e \u003cp\u003eBased on the above limitations, future research should focus on: (1) conducting external validation in multicenter, prospectively designed cohorts; (2) exploring the patterns of dynamic changes in markers like PLR and SII throughout the perioperative period; and (3) ultimately, performing prospective intervention studies based on this risk prediction model to empirically test the effectiveness of precision prevention strategies, thereby completing the translational cycle from prediction to prevention.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study successfully developed and validated a novel prediction model incorporating the Systemic Immune-Inflammation Index (SII) and Platelet-to-Lymphocyte Ratio (PLR) for assessing the risk of postoperative delirium (POD) in hip fracture patients, which demonstrated excellent predictive performance. The findings indicate that systemic immune-inflammatory imbalance, represented by PLR, is a key pathophysiological pathway mediating the occurrence of POD. The clinical nomogram derived from this research provides healthcare professionals with an immediate and practical tool for individualized risk assessment. This work not only offers new clinical evidence for understanding the inflammatory mechanisms underlying POD but also lays a significant foundation for developing targeted preventive and intervention strategies in the future.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Approval No.: IIT2024-124). The requirement for informed consent was waived due to the retrospective nature of the study. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.)\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (Grant No. 2023YFC2508800). The funder had no role in the design of the study, data collection, analysis, interpretation of data, or in writing the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuyu Fan and Junjie Qiao contributed equally as co-first authors to this work. YF: Conceptualization, Methodology, Formal analysis, Writing \u0026ndash; Original Draft, Project administration. JQ: Data curation, Software, Validation, Writing \u0026ndash; Original Draft, Visualization. YH: Investigation, Resources, Writing \u0026ndash; Review \u0026amp; Editing. RZ: Investigation, Resources. CD: Investigation. ZS: Investigation. JW: Writing \u0026ndash; Review \u0026amp; Editing. HS: Supervision, Funding acquisition, Writing \u0026ndash; Review \u0026amp; Editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuyu Fan, Junjie Qiao, Yingdong Hu, Ruizhao Zhao, Chao Dong, Zerui Sun, Jiaxing Wang, Hongxing Song\u003c/p\u003e\n\u003cp\u003eDepartment of Orthopedics, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeng JN, Zhang CG, Li BH, Zhan SY, Wang SF, Song CL: \u003cstrong\u003eGlobal burden of hip fracture: The Global Burden of Disease Study\u003c/strong\u003e. \u003cem\u003eOsteoporos Int \u003c/em\u003e2024, 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Index and Postoperative Cognitive Decline in Elderly Patients\u003c/strong\u003e. \u003cem\u003eClin Interv Aging \u003c/em\u003e2022, \u003cstrong\u003e17\u003c/strong\u003e:699-705.\u003c/li\u003e\n\u003cli\u003eLu W, Lin S, Wang C, Jin P, Bian J: \u003cstrong\u003eThe Potential Value of Systemic Inflammation Response Index on Delirium After Hip Arthroplasty Surgery in Older Patients: A Retrospective Study\u003c/strong\u003e. \u003cem\u003eInt J Gen Med \u003c/em\u003e2023, \u003cstrong\u003e16\u003c/strong\u003e:5355-5362.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Postoperative delirium, Hip fracture, Systemic Immune-Inflammation Index (SII), Platelet-to-Lymphocyte Ratio (PLR), Risk prediction model, Geriatrics","lastPublishedDoi":"10.21203/rs.3.rs-9139138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9139138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePostoperative delirium (POD) poses a significant threat to elderly hip fracture patients. Accurate preoperative risk stratification is crucial but remains difficult with traditional factors. Systemic inflammation is implicated in POD pathogenesis, yet the utility of composite inflammatory markers like the Systemic Immune-Inflammation Index (SII) and Platelet-to-Lymphocyte Ratio (PLR) for prediction is unestablished.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this retrospective cohort of 831 surgically treated hip fracture patients, POD was diagnosed per DSM-5. Preoperative SII and PLR were derived from blood counts. A prediction model was developed via LASSO regression and multivariable logistic regression. Discriminative performance was evaluated using the area under the curve (AUC). An intuitive nomogram was created for clinical use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePOD incidence was 37.3%. The final model included age, albumin, hemoglobin, ln(SII), ln(PLR), alcohol use, and intraoperative transfusion. It showed excellent discrimination (AUC = 0.959, 95% CI: 0.945–0.973). Ln(PLR) emerged as the strongest predictor (OR = 12.26). The inclusion of inflammatory markers significantly improved prediction over a clinical-only model (AUC increment: 0.036, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a highly accurate prediction model for POD, establishing preoperative PLR as a key risk indicator. This underscores the critical role of systemic immune-inflammatory imbalance. The accompanying nomogram enables immediate bedside risk calculation, paving the way for mechanism-informed, personalized prevention in this vulnerable population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"A Novel Inflammatory-Based Nomogram for Predicting Postoperative Delirium in Hip Fracture Patients: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:18:28","doi":"10.21203/rs.3.rs-9139138/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-29T06:22:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-22T13:11:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T03:57:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Langenbeck's Archives of Surgery","date":"2026-03-16T14:24:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"36e5e44f-74a6-4644-9367-4cad6c058286","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T06:18:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:18:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9139138","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9139138","identity":"rs-9139138","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}