Development and Validation of a Predictive Model for Complicated Appendicitis in the Elderly: A Retrospective Comparative Study

Development and Validation of a Predictive Model for Complicated Appendicitis in the Elderly: A Retrospective Comparative 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 Development and Validation of a Predictive Model for Complicated Appendicitis in the Elderly: A Retrospective Comparative Study Weihuan Luo, Chi Liang, Yu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8536710/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Objective The clinical manifestations of acute appendicitis in the elderly population frequently present as atypical, resulting in delayed diagnosis and a significantly increased risk of progression to complicated appendicitis. This progression complicates treatment strategies and adversely affects prognostic outcomes. Presently, there exists a deficiency of simple and reliable early prediction tools for complicated appendicitis in this demographic. The objective of this study was to investigate independent risk factors associated with complicated appendicitis in the elderly and to develop a clinical prediction model. Methods Clinical data from 288 elderly patients with acute appendicitis admitted to the General Surgery Department of Jiangsu Province (Suqian)Hospital, between January 2016 and December 2024 were retrospectively analyzed. Patients were categorized into a complicated appendicitis (CA) group (n = 117) and an uncomplicated appendicitis (UCA) group (n = 171) based on postoperative pathology. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and to develop a predictive model. Model performance was evaluated using the Hosmer-Lemeshow (H-L) test, calibration curves, and receiver operating characteristic (ROC) curves. Results Multivariate analysis identified white blood cell count (WBC) ≥ 12.81×10⁹/L (OR = 2.78), lymphocyte count ≤ 0.955×10⁹/L (OR = 0.27), and C-reactive protein (CRP) ≥ 56.085 mg/L (OR = 20.42) as independent predictors of CA. The constructed predictive model exhibited an area under the curve (AUC) of 0.85, with a sensitivity of 81.2%, and a specificity of 84.8%. The H-L test indicated satisfactory model calibration (P = 0.933). Conclusion The predictive model based on WBC, lymphocyte count, and CRP exhibits strong predictive performance for complicated appendicitis in the elderly population. This model enhances the early identification of high-risk patients and serves as a valuable reference for clinical decision-making. Geriatric appendicitis Complicated appendicitis Risk factors Predictive model C-reactive protein Figures Figure 1 Figure 2 Introduction Acute appendicitis represents a prevalent form of surgical acute abdomen, and its clinical diagnosis poses greater challenges in the elderly compared to younger individuals [ 1 ]. Due to physiological decline and atypical clinical manifestations in the elderly, diagnosis is often delayed [ 2 ]. Currently, acute appendicitis is classified into complicated (e.g., perforation, gangrene, or abscess formation) and uncomplicated (simple or suppurative) forms [ 3 ]. In comparison to other age groups, elderly patients demonstrate a markedly elevated risk of developing complicated appendicitis. This condition is correlated with increased surgical challenges, a higher incidence of postoperative complications, significantly prolonged hospitalizations, and an augmented burden on healthcare resources [ 4 ]. Early clinical differentiation of complicated appendicitis remains challenging. Although imaging studies offer some value, a systematic review found that no imaging modality can reliably rule out complicated cases [ 5 ]. The primary scoring systems for assessing appendicitis severity are AIR and Alvarado scores; however, studies suggest these scores have limited ability to distinguish simple from complicated AA [ 6 ]. No simple, reliable predictive model currently exists for the elderly population. Therefore, it is crucial to explore straightforward and reliable clinical indicators that can be used to construct predictive models for the early identification and intervention of elderly patients with complicated appendicitis. This highlights the importance of developing such models. This retrospective analysis examines clinical data from 288 elderly patients diagnosed with appendicitis. The objective is to identify independent risk factors for complicated appendicitis, construct a multivariate predictive model, and validate it internally. This work aims to provide clinicians with an effective risk assessment tool. Methods Study Population This study retrospectively enrolled elderly patients diagnosed with acute appendicitis undergoing surgical treatment in the General Surgery Department of Jiangsu Province (Suqian)Hospital between January 2016 and December 2024. Elderly patients were those aged ≥ 65 years according to guidelines [ 4 ]. The exclusion criteria were: incomplete clinical data, non-surgical treatment, chronic appendicitis, or concomitant severe intra-abdominal infection or malignant tumors. A total of 288 cases were finally enrolled and assigned into the complicated appendicitis group (CA group, n = 117) and uncomplicated appendicitis group (UCA group, n = 171) with reference to the postoperative pathological findings. This study was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Suqian Hospital, Jiangsu Provincial People's Hospital (Approval No.: 2025-SR-0398). Considering the retrospective and non-interventional nature of the research, the Ethics Committee of Jiangsu Province (Suqian) Hospital, granted exemption from obtaining patient participation consent. Data Collection Patients' demographic data were collected time from symptom onset to presentation, fever status (T ≥ 37.3°C), presence of fecaliths, and preoperative laboratory parameters (complete blood count, inflammatory markers, liver function, electrolytes, coagulation function, etc.). Statistical Analysis Statistical analysis was conducted utilizing R software version 4.4.3. All tests employed a two-tailed approach, with P < 0.05 considered statistically significant. Initially, normality was assessed for quantitative data. Normally distributed variables were reported as mean ± standard deviation (SD), with intergroup differences evaluated utilizing the independent samples t-test. Conversely, non-normally distributed variables were presented as median (interquartile range), and intergroup differences were analyzed through the Mann–Whitney U test. Categorical data were expressed in terms of frequency and percentage, with intergroup comparisons executed using the chi-square test or Fisher's exact test. All continuous variables underwent IQR-based outlier handling prior to inclusion in logistic regression or ROC analysis (excluding samples exceeding Q3 + 1.5 × IQR or falling below Q1–1.5 × IQR), followed by binary classification using the optimal cutoff value determined by ROC analysis (low values assigned as 0, high values assigned as 1). Univariate ROC analysis was utilized to assess the predictive capabilities of each potential risk factor associated with complicated appendicitis. This analysis involved the calculation of AUC and its 95% confidence interval (CI), optimal cutoff values, sensitivity, specificity, and Youden's index. Subsequently, multivariate logistic regression analysis was conducted using stepwise regression with the backward elimination method, guided by the Bayesian Information Criterion (BIC). This approach aimed to identify independent risk factors. All continuous variables were dichotomized according to the optimal ROC cutoff values. The predictive capability of the model was evaluated through ROC curves, which included the reporting of the AUC with 95% CI, optimal cutoff values, sensitivity, and specificity. Internal validation of the model was performed to assess calibration by plotting calibration curves and performing Hosmer–Lemeshow goodness-of-fit tests. Furthermore, collinearity among variables was examined using variance inflation factors (VIF), with a VIF > 5 indicating the potential presence of multicollinearity. Results Comparison of Baseline Characteristics This study encompassed a cohort of 288 elderly patients diagnosed with appendicitis, which included 171 individuals classified within the uncomplicated appendicitis group and 117 individuals categorized in the complicated appendicitis group. A comparison of baseline characteristics indicated a significantly greater median age in the complicated appendicitis group, recorded at 72.00 years (IQR 68.00–79.00), in contrast to 70.00 years (IQR 66.50–75.00) in the uncomplicated group. This difference was statistically significant (Z = 3.092, P = 0.002). Conversely, the gender distribution did not reveal a significant difference between the two groups (χ² = 1.569, P = 0.210). Body mass index (BMI) also exhibited no significant difference (24.24 ± 3.41 vs 24.02 ± 3.05, t =–0.559, P = 0.569). Patients with longer onset times were more likely to develop complicated appendicitis, with median durations of 48.00 h (IQR 24.00–72.00) and 24.00 h (IQR 21.00–48.00), respectively (Z = 3.014, P = 0.003). The presence of fecaliths was significantly higher in the complicated appendicitis group (37.6% vs. 25.7%, χ² = 4.075, P = 0.044). The incidence of fever was also markedly elevated in the complicated appendicitis group (28.2% vs. 6.4%, χ² = 23.787, P < 0.001). Analysis of the complete blood count and inflammatory markers revealed that the median white blood cell count in the complicated appendicitis group was 12.72 × 10⁹/L (IQR 9.36–15.23), which was significantly higher compared with that in the uncomplicated appendicitis group (9.25 × 10⁹/L, IQR 6.28–11.77, Z = 6.156, P < 0.001). Moreover, the neutrophil percentage and absolute count were also significantly elevated (88.40% vs 83.00%, Z = 5.976; 10.92 × 10⁹/L vs 7.63 × 10⁹/L, Z = 6.591, both P < 0.001), while lymphocyte counts decreased (0.82 × 10⁹/L vs 1.06 × 10⁹/L, Z = − 4.154, P < 0.001), and an elevated NLR (13.77 vs 7.14, Z = 6.724, P < 0.001). Further analysis showed that CRP levels were markedly elevated in the complicated appendicitis group, with a median of 119.70 mg/L (IQR 69.40–139.96), markedly higher than in the uncomplicated appendicitis group (12.45 mg/L, IQR 8.81–33.43, Z = 10.433, P < 0.001) . Liver function indicators demonstrated elevated levels of total bilirubin, direct bilirubin, and indirect bilirubin in the cohort with complicated appendicitis (total bilirubin 19.50 vs 16.00 µmol/L, Z = 3.370; direct bilirubin 4.30 vs 2.60 µmol/L, Z = 4.208; indirect bilirubin 16.20 vs 13.40 µmol/L, Z = 2.183; P < 0.05 for all comparisons). Furthermore, serum sodium levels were significantly reduced in the complicated appendicitis group (136.32 ± 3.29 mmol/L vs 137.41 ± 3.09 mmol/L, t = 2.839, P = 0.004). Fibrinogen levels were markedly increased in the complicated appendicitis group, with a median of 5.07 g/L (IQR 3.91–6.95), in contrast to the uncomplicated appendicitis group (3.86 g/L, IQR 2.88–4.97; Z = 5.480, P < 0.001). In light of these findings, advanced age, prolonged onset time, the presence of fecaliths, fever and inflammation, as well as abnormal liver function indicators, are significantly associated with the occurrence of complicated appendicitis in the elderly population. These indicators may serve as potential risk factors for the development of subsequent predictive models (As shown in Table 1 ). Table 1 Comparison of Clinical Characteristics between Uncomplicated and Complicated Appendicitis in Elderly Patients Variable Uncomplicated appendicitis (N = 171) Complicated appendicitis (N = 117) χ²/t/Z P value Age (years) 70.00(66.50, 75.00) 72.00(68.00, 79.00) 3.092 0.002 Sex 1.569 0.21 Male 78(45.6%) 63(53.8%) Female 93(54.4%) 54(46.2%) BMI (kg/m²) 24.02 ± 3.05 24.24 ± 3.41 -0.559 0.569 Time from onset (h) 24.00(21.00, 48.00) 48.00(24.00, 72.00) 3.014 0.003 Fecalith presence 4.075 0.044 No 127(74.3%) 73(62.4%) Yes 44(25.7%) 44(37.6%) Fever 23.787 < .001 No 160(93.6%) 84(71.8%) Yes 11(6.4%) 33(28.2%) WBC (10⁹/L) 9.25(6.28, 11.77) 12.72(9.36, 15.23) 6.156 < .001 Neutrophil % 83.00(71.60, 88.80) 88.40(84.20, 91.50) 5.976 < .001 Neutrophil (10⁹/L) 7.63(4.41, 10.10) 10.92(7.96, 13.68) 6.591 < .001 Lymphocyte (10⁹/L) 1.06(0.70, 1.48) 0.82(0.55, 1.11) -4.154 < .001 NLR 7.14 (3.40 to 12.76) 13.77 (9.24 to 18.93) 6.724 < .001 CRP (mg/L) 12.45(8.81, 33.43) 119.70(69.40, 139.96) 10.433 < .001 Total bilirubin (µmol/L) 16.00(12.30, 21.30) 19.50(14.60, 27.60) 3.37 < .001 Direct bilirubin (µmol/L) 2.60(1.60, 4.60) 4.30(2.60, 6.30) 4.208 < .001 Indirect bilirubin (µmol/L) 13.40(9.15, 19.15) 16.20(10.60, 21.40) 2.183 0.029 Na⁺ (mmol/L) 137.41 ± 3.09 136.32 ± 3.29 2.839 0.004 Fibrinogen (g/L) 3.86(2.88, 4.97) 5.07(3.91, 6.95) 5.48 < .001 Univariate ROC Analysis ROC analysis was utilized to assess the predictive capacity of various potential risk factors in elderly patients diagnosed with complicated appendicitis. The findings revealed that CRP demonstrated the highest predictive value, achieving an AUC of 0.862 (95% CI 0.816–0.908), with an optimal cutoff value of 56.085 mg/L. This cutoff yielded a sensitivity of 0.812 and a specificity of 0.848, yielding a Youden's index of 0.660, which indicates a strong diagnostic performance of CRP in identifying cases of complicated appendicitis. The AUCs for white blood cell count (WBC) and neutrophil percentage were recorded at 0.714 and 0.707, respectively, with optimal cutoff values of 12.81 × 10⁹/L and 83.25%. The corresponding Youden's indices were 0.332 and 0.353, respectively, suggesting that these inflammatory markers also possess significant predictive value for complicated appendicitis. The NLR demonstrated an AUC of 0.733, an optimal cutoff value of 9.008, and a Youden index of 0.404, further affirming its predictive capability. In addition to the aforementioned markers, other laboratory and clinical indicators such as age (AUC 0.607), time of onset (AUC 0.602), total bilirubin (AUC 0.617), direct bilirubin (AUC 0.646), indirect bilirubin (AUC 0.576), serum sodium (AUC 0.585), and fibrinogen (AUC 0.690) demonstrated comparatively weaker predictive abilities for complicated appendicitis; however, they may still serve as supplementary assessment indicators. The lymphocyte count presented an AUC of 0.644, indicating that a decrease in lymphocyte levels is also associated with complicated appendicitis. Overall, inflammation-related markers (CRP, WBC, neutrophil percentage, and NLR) demonstrated superior predictive value, while age and baseline biochemical indicators provided additional insights into the risk of complications (As shown in Table 2 ). Table 2 ROC Analysis of Risk Factors for Complicated Appendicitis in Elderly Patients. Variable AUC (95% CI) Optimal Cutoff Sensitivity Specificity Youden Index Age (years) 0.607 (0.541–0.673) 70.500 0.607 0.550 0.157 Time from onset (h) 0.602 (0.537–0.667) 32.000 0.573 0.585 0.157 WBC (10⁹/L) 0.714 (0.654–0.773) 12.810 0.496 0.836 0.332 Neutrophil % 0.707 (0.648–0.767) 83.250 0.821 0.532 0.353 Lymphocyte (10⁹/L) 0.644 (0.58–0.708) 0.955 0.675 0.591 0.266 NLR 0.733 (0.676 ~ 0.791) 9.008 0.778 0.626 0.404 CRP (mg/L) 0.862 (0.816–0.908) 56.085 0.812 0.848 0.660 Total bilirubin (µmol/L) 0.617 (0.55–0.684) 17.350 0.658 0.561 0.220 Direct bilirubin (µmol/L) 0.646 (0.58–0.712) 2.850 0.709 0.550 0.259 Indirect bilirubin (µmol/L) 0.576 (0.508–0.643) 16.150 0.504 0.673 0.177 Na⁺ (mmol/L) 0.585 (0.518–0.653) 135.450 0.393 0.754 0.148 Fibrinogen (g/L) 0.690 (0.627–0.753) 3.900 0.752 0.520 0.273 Multivariate Logistic Regression Analysis Multivariate logistic regression analysis was utilized to assess independent risk factors associated with complicated appendicitis in the elderly population. All continuous variables were dichotomized based on optimal cutoff values determined from ROC analysis, wherein low values were assigned a score of 0 and high values were assigned a score of 1. Specifically: age ≥ 70.5 years was coded as 1, onset duration ≥ 32 hours was coded as 1, white blood cell count ≥ 12.81×10⁹/L was coded as 1, lymphocyte count ≤ 0.955×10⁹/L was coded as 1, NLR ≥ 9.008 was coded as 1, CRP ≥ 56.085 mg/L was assigned a value of 1, total bilirubin ≥ 17.35 µmol/L was assigned a value of 1, direct bilirubin ≥ 2.85 µmol/L was assigned a value of 1, indirect bilirubin ≥ 16.15 µmol/L was assigned a value of 1, serum sodium ≤ 135.45 mmol/L was assigned a value of 1, and fibrinogen ≥ 3.9 g/L was assigned a value of 1. Given the significant correlation between neutrophil percentage and neutrophil count with the NLR, which led to multicollinearity in the model, both neutrophil percentage and neutrophil count were excluded from further analysis. In the final backward stepwise regression model based on the BIC, three variables—white blood cell count, lymphocyte count, and CRP—were identified as independent risk factors. Patients with a white blood cell count ≥ 12.81×10⁹/L had markedly elevated risk of complicated appendicitis (OR 2.78, 95% CI 1.38–5.62, P = 0.004); Lymphocyte count ≤ 0.955 × 10⁹/L significantly increased risk (OR 0.27, 95% CI 0.14–0.54, P < 0.001), indicating lymphopenia as a risk marker; CRP ≥ 56.085 mg/L was the strongest predictor (OR 20.42, 95% CI 10.41–40.07, P < 0.001), suggesting that significantly high levels of inflammatory correlates with complicated appendicitis. The intercept term β was − 1.551 (P < 0.001), reflecting the baseline risk level. Overall, inflammatory markers, specifically WBC and CRP, along with indicators of immune function, such as lymphocyte count, exhibit independent predictive value for the development of complicated appendicitis in the elderly. These markers serve as important references for clinical risk assessment and early intervention (As shown in Table 3 ). Table 3 Multivariable Logistic Regression Analysis of Risk Factors for Complicated Appendicitis. Variable β Std. Error z value OR (95% CI) Pr(>|z|) (Intercept) -1.551 0.274 -5.669 0.21 (0.12, 0.36) < .001 WBC (10⁹/L) 1.023 0.359 2.846 2.78 (1.38, 5.62) 0.004 Lymphocyte (10⁹/L) -1.302 0.348 -3.746 0.27 (0.14, 0.54) < .001 CRP (mg/L) 3.017 0.344 8.772 20.42 (10.41, 40.07) < .001 Predictive Model Performance and Validation A ROC curve was plotted based on the final multifactorial logistic regression model to assess its predictive performance. The results indicated an AUC of 0.885 (95% CI 0.846–0.925), indicating that the model exhibits robust discriminatory ability. The optimal cutoff value was determined to be 0.456, corresponding to a sensitivity of 0.812 and a specificity of 0.848, yielding a Youden's index of 0.660. These findings underscore the model's high accuracy and clinical utility in identifying elderly patients with complicated appendicitis. Compared to individual inflammatory markers, the multifactorial model that integrates white blood cells, lymphocytes, and CRP demonstrated significant advantages in predicting complicated appendicitis. This suggests that composite indicators substantially enhance the ability to identify high-risk patients at an early stage (As shown in Fig. 1 ). To evaluate the predictive calibration of the multivariate logistic regression model, an internal validation calibration curve was generated, and a Hosmer–Lemeshow (HL) goodness-of-fit test was conducted. The calibration curve illustrated a strong alignment between the model's predicted probabilities and the actual occurrence probabilities, with the fitted curve approaching the ideal diagonal line, thereby indicating robust model predictions. The results of the HL test yielded χ² = 0.843, df = 4, P = 0.933, indicating no significant deviation and indicating a good model fit. These findings demonstrate that the multifactorial model exhibits excellent calibration within the internal sample, accurately indicating the actual risk of complex appendicitis in the elderly and providing reliable evidence for clinical prediction (As shown in Fig. 2 ). Discussion This retrospective analysis of clinical data involving 288 elderly patients diagnosed with acute appendicitis aimed to identify independent risk factors for complicated appendicitis (CA) and to construct a predictive model. The principal findings of this study are as follows: First, univariate analysis revealed that advanced age, prolonged illness duration, fecalith formation, fever, and multiple inflammatory as well as liver function indicators were significantly associated with CA. Second, multivariate analysis identified elevated white blood cell count (WBC), decreased lymphocyte count, and elevated C-reactive protein (CRP) as independent predictors of CA. Third, the predictive model constructed based on these indicators exhibited excellent discriminatory ability (AUC = 0.885) and demonstrated good calibration, thereby providing a robust tool for the early identification of CA in the elderly population. In this study, CRP emerged as the strongest independent predictor of complicated appendicitis (CA) (OR > 20). As an acute-phase reactant protein, CRP levels increase rapidly following tissue injury or infection. Previous studies have demonstrated its superiority over both ultrasound and the Alvarado score in predicting appendicitis [ 7 ], and its significant association with CA severity [ 8 – 14 ]. In this study, the median CRP level in the CA group was significantly higher than that in the uncomplicated appendicitis (UCA) group, thereby reinforcing the role of CRP as a sensitive biomarker indicative of appendiceal necrosis or perforation. The underlying mechanism is likely attributable to the frequent association of CA with uncontrolled local inflammation, which elicits a more robust systemic inflammatory response, thereby stimulating extensive CRP synthesis. Additionally, a reduced lymphocyte count was identified as an independent risk factor for CA (OR = 0.27). This finding is consistent with the conclusions drawn from a multicenter study involving adult patients [ 15 ]. Lymphocytes are essential in the regulation of immune responses and the elimination of pathogens; a reduction in their levels may impair infection control mechanisms and facilitate the progression of inflammation towards complications. The observation of this phenomenon in the elderly cohort may be related to age-related immune decline (immunosenescence) and immunosuppression under stress conditions [ 16 ], adding the dimension of “immune status” to the pathophysiological mechanisms of CA in the elderly. Elevated white blood cell (WBC) count, a well-established indicator of acute infection, demonstrates predictive value in this study. However, its discriminatory ability (AUC = 0.714) is inferior to that of C-reactive protein (CRP). Furthermore, in the context of the multivariate model, the significance of WBC count is partially superseded by CRP. This suggests that although WBC count may indicate the presence of infection, CRP provides a more precise measure of inflammatory intensity and the degree of tissue damage. Comparison with previous studies: Previous research has proposed multiple predictors for appendicitis, such as WBC, total bilirubin, CRP, NLR [ 17 ]; as well as duration of abdominal pain, signs of peritonitis, and appendiceal stones [ 18 , 19 ]. Several studies have indicated that appendiceal stones may serve as an independent risk factor for complicated appendicitis. Research has notably highlighted that both the diameter of appendiceal stones and their location at the base of the appendix significantly elevate the risk of developing complicated appendicitis [ 20 ]. In this study, univariate analysis revealed associations between total bilirubin, appendiceal calculi, and NLR with CA; however, these factors were excluded from the final multivariate model. In contrast to existing literature, this research specifically addresses the elderly population—a demographic frequently characterized by atypical clinical manifestations, multiple comorbidities, and diagnostic challenges. Notably, it is the first study to incorporate lymphocyte count into a predictive model for CA in the elderly, underscoring the potential significance of immune status in the infection process in this age group. Furthermore, the study bolstered the statistical robustness of its findings through multivariate regression analysis and internal validation. Advantages and Clinical Significance of the Predictive Model : The combined predictive model (WBC + lymphocytes + CRP) developed in this study integrates three pathophysiological dimensions: myeloid immune mobilization (WBC), adaptive immune status (lymphocytes), and systemic inflammation intensity (CRP). Its discriminatory efficacy significantly outperforms that of any single indicator. This model exhibits strong potential for clinical application, as risk probability can be rapidly calculated using routine blood test results upon patient admission. If the calculated probability exceeds the optimal cutoff value of 0.456, acute appendicitis should be strongly suspected, thereby warranting prioritized imaging studies (e.g., computed tomography), expedited surgical evaluation, and optimized surgical planning. In resource-limited settings, the model additionally serves as an adjunct tool for referral decisions, potentially shortening diagnostic timelines, reducing the risk of perforation, and ultimately enhancing patient outcomes. This study acknowledges several limitations: First, as a single-center retrospective study, it may be susceptible to selection and information biases. Second, while the sample size (n = 288) fulfills basic analytical requirements, a larger sample could enhance the model's stability and the identification of additional predictive factors. Third, although internal validation has been conducted, external validation remains lacking; thus, the model's generalizability across diverse populations and healthcare settings remains unclear. Finally, to improve clinical applicability, continuous variables were dichotomized, which may have resulted in the loss of valuable information. Future research may explore the following avenues: First, conducting multicenter prospective cohort studies to further validate and optimize this predictive model; second, integrating imaging features (such as appendix diameter, surrounding fat infiltration, and fluid accumulation) with laboratory indicators to establish a comprehensive imaging-clinical prediction system; third, thoroughly investigating the mechanisms underlying lymphopenia in elderly patients with appendicitis and its association with prognosis, thereby providing theoretical support for immunomodulatory adjuvant therapy. Conclusion This study demonstrates an independent predictive value of CRP, lymphocyte count, and WBC in elderly patients with complicated appendicitis. The predictive model developed based on these three parameters demonstrates high discriminatory efficacy and clinical applicability. This model facilitates early risk stratification for appendicitis in the elderly, thereby serving as a reference for timely intervention and improved prognosis, particularly for elderly patients exhibiting atypical clinical presentations. Declarations Acknowledgments Not applicable. Author Contributions Chen Yu conceived and designed the study. Liang Chi prepared materials, collected data, and performed analyses. The initial draft was written by Luo Weihuan. All authors read and approved the final version. Funding This work was supported by the Suqian Municipal Science and Technology Bureau Project (SY202403). Data and Materials Availability Data used and analyzed during the current study are available upon reasonable request from the corresponding author. Ethical Approval and Informed Consent This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Jiangsu Province (Suqian) Hospital (Approval No.: 2025-SR-0398). Given the retrospective and non-interventional nature of this study, the need for informed patient consent was waived by the Ethics Committee of Jiangsu Province (Suqian) Hospital. Publication Consent Not applicable. Conflict of Interest The authors declare no conflicts of interest. References Emektar E, Dağar S, Günsay RH, Uzunosmanoğlu H, Buluş H. Determination of factors associated with perforation in patients with geriatric acute appendicitis. Ulus Travma Acil Cerrahi Derg. 2022;28(1):33–8. https://doi.org/10.14744/tjtes.2020.25741 . Kumar SJ, Shukla S, Kumar S, Mishra P. 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Uludağ SS, Akıncı O, Güreş N, Tunç E, Erginöz E, Şanlı AN, et al. Effectiveness of pre-operative routine blood tests in predicting complicated acute appendicitis. Ulus Travma Acil Cerrahi Derg. 2022;28(11):1590–6. https://doi.org/10.14744/tjtes.2021.13472 . Kenan NK, Doğan E, Ertekin B, Tekin FC. Immature granulocyte and neutrophil-to-lymphocyte ratio in acute appendicitis: New markers to strengthen diagnosis. Rev Assoc Med Bras (1992). 2025;71(5):e20242063. https://doi.org/10.1590/1806-9282.20242063 Liang Y, Sailai M, Ding R, Yimamu B, Kazi T, He M, et al. Predictive model for identification of gangrenous or perforated appendicitis in adults: A multicenter retrospective study. BMC Gastroenterol. 2024;24(1):355. https://doi.org/10.1186/s12876-024-03445-y . Antar R, Farag C, Xu V, Drouaud A, Gordon O, Whalen MJ. Evaluating the baseline hemoglobin, albumin, lymphocyte, and platelet (HALP) score in the United States adult population and comorbidities: An analysis of the NHANES. Front Nutr. 2023;10:1206958. https://doi.org/10.3389/fnut.2023.1206958 . Patmano M, Çetin DA, Gümüş T. Laboratory markers used in the prediction of perforation in acute appendicitis. Ulus Travma Acil Cerrahi Derg. 2022;28(7):960–6. https://doi.org/10.14744/tjtes.2021.83364 . Feng H, Yu QS, Wang JX, Yuan YY, Yu SS, Wei FS, et al. Development and validation of a clinical prediction model for complicated appendicitis in the elderly. Front Surg. 2022;9:905075. https://doi.org/10.3389/fsurg.2022.905075 . Nikkolo C, Muuli M, Kirsimägi Ü, Lepner U. Appendicolith as a sign of complicated appendicitis: A myth or reality? A retrospective study. Eur Surg Res. 2025;66(1):1–8. https://doi.org/10.1159/000543683 . Sula S, Paananen T, Tammilehto V, Hurme S, Mattila A, Rantanen T, et al. Impact of an appendicolith and its characteristics on the severity of acute appendicitis. BJS Open. 2024;8(5):zrae093. https://doi.org/10.1093/bjsopen/zrae093 . Additional Declarations No competing interests reported. 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Suqian","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"Liang","suffix":""},{"id":576477424,"identity":"a0ed60e9-9d0b-4e33-9489-842527e0b4a1","order_by":2,"name":"Yu Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDACCQiVwMDe2PjwA2laeA43G0uQpkUivU2Ahxgd/LObDz6ubLPLM7j5sA2o305Ot4GQJXeOJRuebUsuNrid2PaggCHZ2OwAAS0GEjlmko1tBxI33E5sN5BgOJC4jbCW/O8/wVpuHmyT4CFOSw4bI1jLDUYitUjcSDOWbDiXnDjzTCIwkA2I8Av/jOSHHxvK7BL7jh9/+PBDhZ0cQS1gwMjGwKAAVmlAjHIw+MPAIN9AtOpRMApGwSgYaQAA0W5Ilj+xQUUAAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu Province (Suqian) Hospital Suqian","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-07 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1","display":"","copyAsset":false,"role":"figure","size":43856,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve of the Multivariable Logistic Regression Model for Predicting Complicated Appendicitis in Elderly Patients.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8536710/v1/c4df170e61f612da5bf6db56.png"},{"id":100750365,"identity":"37dcae46-bf38-4c36-8b6c-1f721e9d7c1a","added_by":"auto","created_at":"2026-01-21 04:31:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56137,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration Curve for Internal Validation of the Multivariable Logistic Regression Model.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8536710/v1/049f176a6a303627dd4b553e.png"},{"id":100750401,"identity":"e8aa0d7c-3a1e-4e36-94d0-413acef539c1","added_by":"auto","created_at":"2026-01-21 04:31:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":810395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8536710/v1/4a929530-0054-49d9-9984-39faa0dd213d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Predictive Model for Complicated Appendicitis in the Elderly: A Retrospective Comparative Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute appendicitis represents a prevalent form of surgical acute abdomen, and its clinical diagnosis poses greater challenges in the elderly compared to younger individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to physiological decline and atypical clinical manifestations in the elderly, diagnosis is often delayed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, acute appendicitis is classified into complicated (e.g., perforation, gangrene, or abscess formation) and uncomplicated (simple or suppurative) forms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In comparison to other age groups, elderly patients demonstrate a markedly elevated risk of developing complicated appendicitis. This condition is correlated with increased surgical challenges, a higher incidence of postoperative complications, significantly prolonged hospitalizations, and an augmented burden on healthcare resources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Early clinical differentiation of complicated appendicitis remains challenging. Although imaging studies offer some value, a systematic review found that no imaging modality can reliably rule out complicated cases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The primary scoring systems for assessing appendicitis severity are AIR and Alvarado scores; however, studies suggest these scores have limited ability to distinguish simple from complicated AA [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. No simple, reliable predictive model currently exists for the elderly population. Therefore, it is crucial to explore straightforward and reliable clinical indicators that can be used to construct predictive models for the early identification and intervention of elderly patients with complicated appendicitis. This highlights the importance of developing such models.\u003c/p\u003e \u003cp\u003eThis retrospective analysis examines clinical data from 288 elderly patients diagnosed with appendicitis. The objective is to identify independent risk factors for complicated appendicitis, construct a multivariate predictive model, and validate it internally. This work aims to provide clinicians with an effective risk assessment tool.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis study retrospectively enrolled elderly patients diagnosed with acute appendicitis undergoing surgical treatment in the General Surgery Department of Jiangsu Province (Suqian)Hospital between January 2016 and December 2024. Elderly patients were those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years according to guidelines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The exclusion criteria were: incomplete clinical data, non-surgical treatment, chronic appendicitis, or concomitant severe intra-abdominal infection or malignant tumors. A total of 288 cases were finally enrolled and assigned into the complicated appendicitis group (CA group, n\u0026thinsp;=\u0026thinsp;117) and uncomplicated appendicitis group (UCA group, n\u0026thinsp;=\u0026thinsp;171) with reference to the postoperative pathological findings. This study was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Suqian Hospital, Jiangsu Provincial People's Hospital (Approval No.: 2025-SR-0398). Considering the retrospective and non-interventional nature of the research, the Ethics Committee of Jiangsu Province (Suqian) Hospital, granted exemption from obtaining patient participation consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003ePatients' demographic data were collected time from symptom onset to presentation, fever status (T\u0026thinsp;\u0026ge;\u0026thinsp;37.3\u0026deg;C), presence of fecaliths, and preoperative laboratory parameters (complete blood count, inflammatory markers, liver function, electrolytes, coagulation function, etc.).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted utilizing R software version 4.4.3. All tests employed a two-tailed approach, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Initially, normality was assessed for quantitative data. Normally distributed variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), with intergroup differences evaluated utilizing the independent samples t-test. Conversely, non-normally distributed variables were presented as median (interquartile range), and intergroup differences were analyzed through the Mann\u0026ndash;Whitney U test. Categorical data were expressed in terms of frequency and percentage, with intergroup comparisons executed using the chi-square test or Fisher's exact test.\u003c/p\u003e \u003cp\u003eAll continuous variables underwent IQR-based outlier handling prior to inclusion in logistic regression or ROC analysis (excluding samples exceeding Q3\u0026thinsp;+\u0026thinsp;1.5 \u0026times; IQR or falling below Q1\u0026ndash;1.5 \u0026times; IQR), followed by binary classification using the optimal cutoff value determined by ROC analysis (low values assigned as 0, high values assigned as 1). Univariate ROC analysis was utilized to assess the predictive capabilities of each potential risk factor associated with complicated appendicitis. This analysis involved the calculation of AUC and its 95% confidence interval (CI), optimal cutoff values, sensitivity, specificity, and Youden's index.\u003c/p\u003e \u003cp\u003eSubsequently, multivariate logistic regression analysis was conducted using stepwise regression with the backward elimination method, guided by the Bayesian Information Criterion (BIC). This approach aimed to identify independent risk factors. All continuous variables were dichotomized according to the optimal ROC cutoff values. The predictive capability of the model was evaluated through ROC curves, which included the reporting of the AUC with 95% CI, optimal cutoff values, sensitivity, and specificity. Internal validation of the model was performed to assess calibration by plotting calibration curves and performing Hosmer\u0026ndash;Lemeshow goodness-of-fit tests. Furthermore, collinearity among variables was examined using variance inflation factors (VIF), with a VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 indicating the potential presence of multicollinearity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Baseline Characteristics\u003c/h2\u003e \u003cp\u003eThis study encompassed a cohort of 288 elderly patients diagnosed with appendicitis, which included 171 individuals classified within the uncomplicated appendicitis group and 117 individuals categorized in the complicated appendicitis group. A comparison of baseline characteristics indicated a significantly greater median age in the complicated appendicitis group, recorded at 72.00 years (IQR 68.00\u0026ndash;79.00), in contrast to 70.00 years (IQR 66.50\u0026ndash;75.00) in the uncomplicated group. This difference was statistically significant (Z\u0026thinsp;=\u0026thinsp;3.092, P\u0026thinsp;=\u0026thinsp;0.002). Conversely, the gender distribution did not reveal a significant difference between the two groups (χ\u0026sup2; = 1.569, P\u0026thinsp;=\u0026thinsp;0.210). Body mass index (BMI) also exhibited no significant difference (24.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41 vs 24.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05, t =\u0026ndash;0.559, P\u0026thinsp;=\u0026thinsp;0.569). Patients with longer onset times were more likely to develop complicated appendicitis, with median durations of 48.00 h (IQR 24.00\u0026ndash;72.00) and 24.00 h (IQR 21.00\u0026ndash;48.00), respectively (Z\u0026thinsp;=\u0026thinsp;3.014, P\u0026thinsp;=\u0026thinsp;0.003). The presence of fecaliths was significantly higher in the complicated appendicitis group (37.6% vs. 25.7%, χ\u0026sup2; = 4.075, P\u0026thinsp;=\u0026thinsp;0.044). The incidence of fever was also markedly elevated in the complicated appendicitis group (28.2% vs. 6.4%, χ\u0026sup2; = 23.787, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAnalysis of the complete blood count and inflammatory markers revealed that the median white blood cell count in the complicated appendicitis group was 12.72 \u0026times; 10⁹/L (IQR 9.36\u0026ndash;15.23), which was significantly higher compared with that in the uncomplicated appendicitis group (9.25 \u0026times; 10⁹/L, IQR 6.28\u0026ndash;11.77, Z\u0026thinsp;=\u0026thinsp;6.156, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the neutrophil percentage and absolute count were also significantly elevated (88.40% vs 83.00%, Z\u0026thinsp;=\u0026thinsp;5.976; 10.92 \u0026times; 10⁹/L vs 7.63 \u0026times; 10⁹/L, Z\u0026thinsp;=\u0026thinsp;6.591, both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while lymphocyte counts decreased (0.82 \u0026times; 10⁹/L vs 1.06 \u0026times; 10⁹/L, Z = \u0026minus;\u0026thinsp;4.154, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and an elevated NLR (13.77 vs 7.14, Z\u0026thinsp;=\u0026thinsp;6.724, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further analysis showed that CRP levels were markedly elevated in the complicated appendicitis group, with a median of 119.70 mg/L (IQR 69.40\u0026ndash;139.96), markedly higher than in the uncomplicated appendicitis group (12.45 mg/L, IQR 8.81\u0026ndash;33.43, Z\u0026thinsp;=\u0026thinsp;10.433, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) .\u003c/p\u003e \u003cp\u003eLiver function indicators demonstrated elevated levels of total bilirubin, direct bilirubin, and indirect bilirubin in the cohort with complicated appendicitis (total bilirubin 19.50 vs 16.00 \u0026micro;mol/L, Z\u0026thinsp;=\u0026thinsp;3.370; direct bilirubin 4.30 vs 2.60 \u0026micro;mol/L, Z\u0026thinsp;=\u0026thinsp;4.208; indirect bilirubin 16.20 vs 13.40 \u0026micro;mol/L, Z\u0026thinsp;=\u0026thinsp;2.183; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all comparisons). Furthermore, serum sodium levels were significantly reduced in the complicated appendicitis group (136.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29 mmol/L vs 137.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09 mmol/L, t\u0026thinsp;=\u0026thinsp;2.839, P\u0026thinsp;=\u0026thinsp;0.004). Fibrinogen levels were markedly increased in the complicated appendicitis group, with a median of 5.07 g/L (IQR 3.91\u0026ndash;6.95), in contrast to the uncomplicated appendicitis group (3.86 g/L, IQR 2.88\u0026ndash;4.97; Z\u0026thinsp;=\u0026thinsp;5.480, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn light of these findings, advanced age, prolonged onset time, the presence of fecaliths, fever and inflammation, as well as abnormal liver function indicators, are significantly associated with the occurrence of complicated appendicitis in the elderly population. These indicators may serve as potential risk factors for the development of subsequent predictive models (As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eComparison of Clinical Characteristics between Uncomplicated and Complicated Appendicitis in Elderly Patients\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUncomplicated appendicitis (N\u0026thinsp;=\u0026thinsp;171)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplicated appendicitis (N\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;/t/Z\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.00(66.50, 75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.00(68.00, 79.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(53.8%)\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(46.2%)\u003c/p\u003e \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\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from onset (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.00(21.00, 48.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.00(24.00, 72.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFecalith presence\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.075\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(62.4%)\u003c/p\u003e \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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(37.6%)\u003c/p\u003e \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\u003eFever\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160(93.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(71.8%)\u003c/p\u003e \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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(28.2%)\u003c/p\u003e \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\u003eWBC (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.25(6.28, 11.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.72(9.36, 15.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.00(71.60, 88.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.40(84.20, 91.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.63(4.41, 10.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.92(7.96, 13.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06(0.70, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82(0.55, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.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\u003e7.14 (3.40 to 12.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.77 (9.24 to 18.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.45(8.81, 33.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.70(69.40, 139.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.00(12.30, 21.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.50(14.60, 27.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.60(1.60, 4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.30(2.60, 6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.40(9.15, 19.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.20(10.60, 21.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa⁺ (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.86(2.88, 4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.07(3.91, 6.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate ROC Analysis\u003c/h2\u003e \u003cp\u003eROC analysis was utilized to assess the predictive capacity of various potential risk factors in elderly patients diagnosed with complicated appendicitis. The findings revealed that CRP demonstrated the highest predictive value, achieving an AUC of 0.862 (95% CI 0.816\u0026ndash;0.908), with an optimal cutoff value of 56.085 mg/L. This cutoff yielded a sensitivity of 0.812 and a specificity of 0.848, yielding a Youden's index of 0.660, which indicates a strong diagnostic performance of CRP in identifying cases of complicated appendicitis. The AUCs for white blood cell count (WBC) and neutrophil percentage were recorded at 0.714 and 0.707, respectively, with optimal cutoff values of 12.81 \u0026times; 10⁹/L and 83.25%. The corresponding Youden's indices were 0.332 and 0.353, respectively, suggesting that these inflammatory markers also possess significant predictive value for complicated appendicitis. The NLR demonstrated an AUC of 0.733, an optimal cutoff value of 9.008, and a Youden index of 0.404, further affirming its predictive capability.\u003c/p\u003e \u003cp\u003eIn addition to the aforementioned markers, other laboratory and clinical indicators such as age (AUC 0.607), time of onset (AUC 0.602), total bilirubin (AUC 0.617), direct bilirubin (AUC 0.646), indirect bilirubin (AUC 0.576), serum sodium (AUC 0.585), and fibrinogen (AUC 0.690) demonstrated comparatively weaker predictive abilities for complicated appendicitis; however, they may still serve as supplementary assessment indicators. The lymphocyte count presented an AUC of 0.644, indicating that a decrease in lymphocyte levels is also associated with complicated appendicitis. Overall, inflammation-related markers (CRP, WBC, neutrophil percentage, and NLR) demonstrated superior predictive value, while age and baseline biochemical indicators provided additional insights into the risk of complications (As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eROC Analysis of Risk Factors for Complicated Appendicitis in Elderly Patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\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\u003eOptimal Cutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607 (0.541\u0026ndash;0.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from onset (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.602 (0.537\u0026ndash;0.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.714 (0.654\u0026ndash;0.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.707 (0.648\u0026ndash;0.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.644 (0.58\u0026ndash;0.708)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.266\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.733 (0.676\u0026thinsp;~\u0026thinsp;0.791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.862 (0.816\u0026ndash;0.908)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003cp\u003e(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.617 (0.55\u0026ndash;0.684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003cp\u003e(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.646 (0.58\u0026ndash;0.712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin\u003c/p\u003e \u003cp\u003e(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.576 (0.508\u0026ndash;0.643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa⁺ (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.585 (0.518\u0026ndash;0.653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.690 (0.627\u0026ndash;0.753)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate Logistic Regression Analysis\u003c/h3\u003e\n\u003cp\u003eMultivariate logistic regression analysis was utilized to assess independent risk factors associated with complicated appendicitis in the elderly population. All continuous variables were dichotomized based on optimal cutoff values determined from ROC analysis, wherein low values were assigned a score of 0 and high values were assigned a score of 1. Specifically: age\u0026thinsp;\u0026ge;\u0026thinsp;70.5 years was coded as 1, onset duration\u0026thinsp;\u0026ge;\u0026thinsp;32 hours was coded as 1, white blood cell count\u0026thinsp;\u0026ge;\u0026thinsp;12.81\u0026times;10⁹/L was coded as 1, lymphocyte count\u0026thinsp;\u0026le;\u0026thinsp;0.955\u0026times;10⁹/L was coded as 1, NLR\u0026thinsp;\u0026ge;\u0026thinsp;9.008 was coded as 1, CRP\u0026thinsp;\u0026ge;\u0026thinsp;56.085 mg/L was assigned a value of 1, total bilirubin\u0026thinsp;\u0026ge;\u0026thinsp;17.35 \u0026micro;mol/L was assigned a value of 1, direct bilirubin\u0026thinsp;\u0026ge;\u0026thinsp;2.85 \u0026micro;mol/L was assigned a value of 1, indirect bilirubin\u0026thinsp;\u0026ge;\u0026thinsp;16.15 \u0026micro;mol/L was assigned a value of 1, serum sodium\u0026thinsp;\u0026le;\u0026thinsp;135.45 mmol/L was assigned a value of 1, and fibrinogen\u0026thinsp;\u0026ge;\u0026thinsp;3.9 g/L was assigned a value of 1. Given the significant correlation between neutrophil percentage and neutrophil count with the NLR, which led to multicollinearity in the model, both neutrophil percentage and neutrophil count were excluded from further analysis.\u003c/p\u003e \u003cp\u003eIn the final backward stepwise regression model based on the BIC, three variables\u0026mdash;white blood cell count, lymphocyte count, and CRP\u0026mdash;were identified as independent risk factors.\u003c/p\u003e \u003cp\u003ePatients with a white blood cell count\u0026thinsp;\u0026ge;\u0026thinsp;12.81\u0026times;10⁹/L had markedly elevated risk of complicated appendicitis (OR 2.78, 95% CI 1.38\u0026ndash;5.62, P\u0026thinsp;=\u0026thinsp;0.004); Lymphocyte count\u0026thinsp;\u0026le;\u0026thinsp;0.955 \u0026times; 10⁹/L significantly increased risk (OR 0.27, 95% CI 0.14\u0026ndash;0.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating lymphopenia as a risk marker; CRP\u0026thinsp;\u0026ge;\u0026thinsp;56.085 mg/L was the strongest predictor (OR 20.42, 95% CI 10.41\u0026ndash;40.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that significantly high levels of inflammatory correlates with complicated appendicitis. The intercept term β was \u0026minus;\u0026thinsp;1.551 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reflecting the baseline risk level.\u003c/p\u003e \u003cp\u003eOverall, inflammatory markers, specifically WBC and CRP, along with indicators of immune function, such as lymphocyte count, exhibit independent predictive value for the development of complicated appendicitis in the elderly. These markers serve as important references for clinical risk assessment and early intervention (As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eMultivariable Logistic Regression Analysis of Risk Factors for Complicated Appendicitis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21 (0.12, 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.78 (1.38, 5.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27 (0.14, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.42 (10.41, 40.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePredictive Model Performance and Validation\u003c/h3\u003e\n\u003cp\u003eA ROC curve was plotted based on the final multifactorial logistic regression model to assess its predictive performance. The results indicated an AUC of 0.885 (95% CI 0.846\u0026ndash;0.925), indicating that the model exhibits robust discriminatory ability. The optimal cutoff value was determined to be 0.456, corresponding to a sensitivity of 0.812 and a specificity of 0.848, yielding a Youden's index of 0.660. These findings underscore the model's high accuracy and clinical utility in identifying elderly patients with complicated appendicitis. Compared to individual inflammatory markers, the multifactorial model that integrates white blood cells, lymphocytes, and CRP demonstrated significant advantages in predicting complicated appendicitis. This suggests that composite indicators substantially enhance the ability to identify high-risk patients at an early stage (As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the predictive calibration of the multivariate logistic regression model, an internal validation calibration curve was generated, and a Hosmer\u0026ndash;Lemeshow (HL) goodness-of-fit test was conducted. The calibration curve illustrated a strong alignment between the model's predicted probabilities and the actual occurrence probabilities, with the fitted curve approaching the ideal diagonal line, thereby indicating robust model predictions. The results of the HL test yielded χ\u0026sup2; = 0.843, df\u0026thinsp;=\u0026thinsp;4, P\u0026thinsp;=\u0026thinsp;0.933, indicating no significant deviation and indicating a good model fit. These findings demonstrate that the multifactorial model exhibits excellent calibration within the internal sample, accurately indicating the actual risk of complex appendicitis in the elderly and providing reliable evidence for clinical prediction (As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective analysis of clinical data involving 288 elderly patients diagnosed with acute appendicitis aimed to identify independent risk factors for complicated appendicitis (CA) and to construct a predictive model. The principal findings of this study are as follows: First, univariate analysis revealed that advanced age, prolonged illness duration, fecalith formation, fever, and multiple inflammatory as well as liver function indicators were significantly associated with CA. Second, multivariate analysis identified elevated white blood cell count (WBC), decreased lymphocyte count, and elevated C-reactive protein (CRP) as independent predictors of CA. Third, the predictive model constructed based on these indicators exhibited excellent discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.885) and demonstrated good calibration, thereby providing a robust tool for the early identification of CA in the elderly population.\u003c/p\u003e \u003cp\u003eIn this study, CRP emerged as the strongest independent predictor of complicated appendicitis (CA) (OR\u0026thinsp;\u0026gt;\u0026thinsp;20). As an acute-phase reactant protein, CRP levels increase rapidly following tissue injury or infection. Previous studies have demonstrated its superiority over both ultrasound and the Alvarado score in predicting appendicitis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and its significant association with CA severity [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, the median CRP level in the CA group was significantly higher than that in the uncomplicated appendicitis (UCA) group, thereby reinforcing the role of CRP as a sensitive biomarker indicative of appendiceal necrosis or perforation. The underlying mechanism is likely attributable to the frequent association of CA with uncontrolled local inflammation, which elicits a more robust systemic inflammatory response, thereby stimulating extensive CRP synthesis.\u003c/p\u003e \u003cp\u003eAdditionally, a reduced lymphocyte count was identified as an independent risk factor for CA (OR\u0026thinsp;=\u0026thinsp;0.27). This finding is consistent with the conclusions drawn from a multicenter study involving adult patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Lymphocytes are essential in the regulation of immune responses and the elimination of pathogens; a reduction in their levels may impair infection control mechanisms and facilitate the progression of inflammation towards complications. The observation of this phenomenon in the elderly cohort may be related to age-related immune decline (immunosenescence) and immunosuppression under stress conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], adding the dimension of \u0026ldquo;immune status\u0026rdquo; to the pathophysiological mechanisms of CA in the elderly.\u003c/p\u003e \u003cp\u003eElevated white blood cell (WBC) count, a well-established indicator of acute infection, demonstrates predictive value in this study. However, its discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.714) is inferior to that of C-reactive protein (CRP). Furthermore, in the context of the multivariate model, the significance of WBC count is partially superseded by CRP. This suggests that although WBC count may indicate the presence of infection, CRP provides a more precise measure of inflammatory intensity and the degree of tissue damage.\u003c/p\u003e \u003cp\u003eComparison with previous studies: Previous research has proposed multiple predictors for appendicitis, such as WBC, total bilirubin, CRP, NLR [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; as well as duration of abdominal pain, signs of peritonitis, and appendiceal stones [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Several studies have indicated that appendiceal stones may serve as an independent risk factor for complicated appendicitis. Research has notably highlighted that both the diameter of appendiceal stones and their location at the base of the appendix significantly elevate the risk of developing complicated appendicitis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, univariate analysis revealed associations between total bilirubin, appendiceal calculi, and NLR with CA; however, these factors were excluded from the final multivariate model. In contrast to existing literature, this research specifically addresses the elderly population\u0026mdash;a demographic frequently characterized by atypical clinical manifestations, multiple comorbidities, and diagnostic challenges. Notably, it is the first study to incorporate lymphocyte count into a predictive model for CA in the elderly, underscoring the potential significance of immune status in the infection process in this age group. Furthermore, the study bolstered the statistical robustness of its findings through multivariate regression analysis and internal validation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdvantages and Clinical Significance of the Predictive Model\u003c/b\u003e: The combined predictive model (WBC\u0026thinsp;+\u0026thinsp;lymphocytes\u0026thinsp;+\u0026thinsp;CRP) developed in this study integrates three pathophysiological dimensions: myeloid immune mobilization (WBC), adaptive immune status (lymphocytes), and systemic inflammation intensity (CRP). Its discriminatory efficacy significantly outperforms that of any single indicator. This model exhibits strong potential for clinical application, as risk probability can be rapidly calculated using routine blood test results upon patient admission. If the calculated probability exceeds the optimal cutoff value of 0.456, acute appendicitis should be strongly suspected, thereby warranting prioritized imaging studies (e.g., computed tomography), expedited surgical evaluation, and optimized surgical planning. In resource-limited settings, the model additionally serves as an adjunct tool for referral decisions, potentially shortening diagnostic timelines, reducing the risk of perforation, and ultimately enhancing patient outcomes.\u003c/p\u003e \u003cp\u003eThis study acknowledges several limitations: First, as a single-center retrospective study, it may be susceptible to selection and information biases. Second, while the sample size (n\u0026thinsp;=\u0026thinsp;288) fulfills basic analytical requirements, a larger sample could enhance the model's stability and the identification of additional predictive factors. Third, although internal validation has been conducted, external validation remains lacking; thus, the model's generalizability across diverse populations and healthcare settings remains unclear. Finally, to improve clinical applicability, continuous variables were dichotomized, which may have resulted in the loss of valuable information.\u003c/p\u003e \u003cp\u003eFuture research may explore the following avenues: First, conducting multicenter prospective cohort studies to further validate and optimize this predictive model; second, integrating imaging features (such as appendix diameter, surrounding fat infiltration, and fluid accumulation) with laboratory indicators to establish a comprehensive imaging-clinical prediction system; third, thoroughly investigating the mechanisms underlying lymphopenia in elderly patients with appendicitis and its association with prognosis, thereby providing theoretical support for immunomodulatory adjuvant therapy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates an independent predictive value of CRP, lymphocyte count, and WBC in elderly patients with complicated appendicitis. The predictive model developed based on these three parameters demonstrates high discriminatory efficacy and clinical applicability. This model facilitates early risk stratification for appendicitis in the elderly, thereby serving as a reference for timely intervention and improved prognosis, particularly for elderly patients exhibiting atypical clinical presentations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChen Yu conceived and designed the study. Liang Chi prepared materials, collected data, and performed analyses. The initial draft was written by Luo Weihuan. All authors read and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Suqian Municipal Science and Technology Bureau Project (SY202403).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Materials Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used and analyzed during the current study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthical Approval and Informed Consent\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Jiangsu Province (Suqian) Hospital (Approval No.: 2025-SR-0398). Given the retrospective and non-interventional nature of this study, the need for informed patient consent was waived by the Ethics Committee of Jiangsu Province (Suqian) Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePublication Consent\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEmektar E, Dağar S, G\u0026uuml;nsay RH, Uzunosmanoğlu H, Buluş H. Determination of factors associated with perforation in patients with geriatric acute appendicitis. 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BJS Open. 2024;8(5):zrae093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bjsopen/zrae093\u003c/span\u003e\u003cspan address=\"10.1093/bjsopen/zrae093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Geriatric appendicitis, Complicated appendicitis, Risk factors, Predictive model, C-reactive protein","lastPublishedDoi":"10.21203/rs.3.rs-8536710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8536710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe clinical manifestations of acute appendicitis in the elderly population frequently present as atypical, resulting in delayed diagnosis and a significantly increased risk of progression to complicated appendicitis. This progression complicates treatment strategies and adversely affects prognostic outcomes. Presently, there exists a deficiency of simple and reliable early prediction tools for complicated appendicitis in this demographic. The objective of this study was to investigate independent risk factors associated with complicated appendicitis in the elderly and to develop a clinical prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical data from 288 elderly patients with acute appendicitis admitted to the General Surgery Department of Jiangsu Province (Suqian)Hospital, between January 2016 and December 2024 were retrospectively analyzed. Patients were categorized into a complicated appendicitis (CA) group (n\u0026thinsp;=\u0026thinsp;117) and an uncomplicated appendicitis (UCA) group (n\u0026thinsp;=\u0026thinsp;171) based on postoperative pathology. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and to develop a predictive model. Model performance was evaluated using the Hosmer-Lemeshow (H-L) test, calibration curves, and receiver operating characteristic (ROC) curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultivariate analysis identified white blood cell count (WBC)\u0026thinsp;\u0026ge;\u0026thinsp;12.81\u0026times;10⁹/L (OR\u0026thinsp;=\u0026thinsp;2.78), lymphocyte count\u0026thinsp;\u0026le;\u0026thinsp;0.955\u0026times;10⁹/L (OR\u0026thinsp;=\u0026thinsp;0.27), and C-reactive protein (CRP)\u0026thinsp;\u0026ge;\u0026thinsp;56.085 mg/L (OR\u0026thinsp;=\u0026thinsp;20.42) as independent predictors of CA. The constructed predictive model exhibited an area under the curve (AUC) of 0.85, with a sensitivity of 81.2%, and a specificity of 84.8%. The H-L test indicated satisfactory model calibration (P\u0026thinsp;=\u0026thinsp;0.933).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe predictive model based on WBC, lymphocyte count, and CRP exhibits strong predictive performance for complicated appendicitis in the elderly population. This model enhances the early identification of high-risk patients and serves as a valuable reference for clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Predictive Model for Complicated Appendicitis in the Elderly: A Retrospective Comparative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 04:30:41","doi":"10.21203/rs.3.rs-8536710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-29T06:32:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T15:46:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236082342172982031559681583914886762154","date":"2026-01-26T15:07:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150588853261864130419235526330322083400","date":"2026-01-23T04:04:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T15:57:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147320211028271090785281188446868221825","date":"2026-01-22T15:34:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T02:57:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241045826299146761967454394467577392633","date":"2026-01-16T14:09:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T13:22:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-08T08:34:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T14:06:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T14:05:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2026-01-07T04:27:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"031fd0bf-59ae-46b3-8afd-8f1ce321753e","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T06:24:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 04:30:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8536710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8536710","identity":"rs-8536710","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}