Development and Validation of a Clinical Prediction Model for Postoperative Pneumonia in Elderly Patients with Femoral Neck Fracture

Development and Validation of a Clinical Prediction Model for Postoperative Pneumonia in Elderly Patients with Femoral Neck Fracture | 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 Clinical Prediction Model for Postoperative Pneumonia in Elderly Patients with Femoral Neck Fracture Xuxing Sun, Yuhang Wang, Xinyong Chen, Chengwei Xu, Yucheng Tang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6384696/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective: This study aimed to investigate the risk factors associated with postoperative pneumonia in elderly patients undergoing hip replacement for femoral neck fracture and to develop and validate a risk prediction model for such complications. Methods: A retrospective cohort of elderly patients (January 2022–September 2024) who underwent hip replacement for femoral neck fracture at the Qingpu Branch of Zhongshan Hospital, Fudan University, was analyzed. A total of 27 parameters were evaluated, including age, Modified 5-Factor Frailty Index (MFI-5), and anesthesia scores. The cohort was randomly divided into a derivation cohort (75.8%) and a validation cohort (24.2%). LASSO regression was applied to select optimal predictors through 10-fold cross-validation. Logistic regression identified significant factors for postoperative pneumonia, and a nomogram was constructed. Model accuracy was assessed using calibration curves, while predictive performance was evaluated via area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results: Among 418 included patients, 317 in the derivation cohort were stratified into pneumonia (n=83) and non-pneumonia (n=234) groups. Multivariate logistic regression identified age >65 years, smoking history, hemiarthroplasty, American Society of Anesthesiologists (ASA) grade ≥3, and MFI-5 >3 as independent risk factors (all P<0.05). The nomogram demonstrated excellent consistency and accuracy. AUC values for the derivation and validation cohorts were 0.872 (95% CI: 0.82496–0.91904) and 0.895 (95% CI: 0.82444–0.96556), respectively. Calibration curves showed minimal deviation (mean absolute error: 0.022 vs. 0.039). ROC and DCA confirmed robust predictive efficacy. Conclusion: Advanced age (>65 years), smoking, hemiarthroplasty, ASA grade ≥3, and MFI-5 >3 are significant risk factors for postoperative pneumonia in elderly femoral neck fracture patients. The developed nomogram provides a practical tool for preoperative risk stratification, enabling clinicians to optimize perioperative management and improve patient outcomes. Femoral neck fracture Postoperative pneumonia Risk factors prediction model Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Femoral neck fracture, a prevalent traumatic injury among the elderly population, is closely associated with high disability and mortality rates, which are strongly linked to postoperative complications, among which pneumonia stands out as one of the most common occurrences. [1-3] .Studies indicate that the incidence of postoperative pneumonia in elderly patients with femoral neck fractures ranges from 5% to 10%. Pulmonary infections not only significantly prolong hospital stays and increase medical costs but may also lead to respiratory failure and multiple organ dysfunction, severely impacting patient prognosis. [4-6] .However, the risk of pneumonia remains persistently high in elderly patients due to age-related physiological decline, complex comorbidities (e.g., chronic obstructive pulmonary disease [COPD], congestive heart failure[CHF],hypoalbuminemia),and prolonged postoperative immobility. [7] .Therefore, early identification of high-risk patients and the development of targeted preventive strategies are critical for improving clinical outcomes. Daniel D. Bohl et al. conducted a multivariate regression analysis to explore risk factors for postoperative pneumonia. [8] ,identifying advanced age (>65 years), pre-existing respiratory diseases, hypoalbuminemia, and anemia as key contributors. However, existing predictive models have limitations, including inconsistent methodological quality, failure to adjust for confounding variables or incorporate biomarker predictive value, high risk of bias, and overreliance on traditional clinical variables while neglecting perioperative management details, thereby restricting clinical applicability. To address these gaps, this retrospective, multicenter study systematically integrates age, comorbidities, and laboratory parameters to construct a clinical prediction model for postoperative pneumonia in elderly femoral neck fracture patients. Machine learning algorithms will be employed to optimize variable selection, and internal validation will assess model discrimination, calibration, and clinical utility. The proposed model aims to provide clinicians with a personalized risk assessment tool for early identification of high-risk populations, enabling precise intervention strategies to reduce postoperative pneumonia incidence and enhance long-term patient outcomes. Materials and Methods Study Population This study was approved by the Medical Ethics Committee of the Qingpu Branch of Zhongshan Hospital affiliated with Fudan University (approval code: qingyi2021-35) .Elderly patients undergoing hip replacement surgery for femoral neck fractures at the Qingpu Branch of Zhongshan Hospital, Fudan University, between January 2022 and September 2024 were enrolled. A total of 418 eligible patients were included in the final analysis, with 75.8% (317 cases) allocated to the derivation cohort and 24.2% (101 cases) to the validation cohort. Postoperative outcomes were categorized into pneumonia (83 cases) and non-pneumonia groups (234 cases).Inclusion Criteria: (1)Diagnosis of unilateral femoral neck fracture requiring primary surgical treatment, confirmed by clinical symptoms, physical examination, and imaging findings.(2)Unstable femoral neck fracture (Garden type III or IV) based on Garden classification.(3)Age >65 years.Exclusion Criteria:(1)Preoperative diagnosis of pneumonia.(2)Presence of multiple fractures.(3)Postoperative mortality due to non-pulmonary infections.(4)Pneumonia Diagnosis.The diagnosis of pneumonia was confirmed according to previously published criteria and validated by consultation with a qualified pulmonologist. [9-11] .This retrospective study utilized the hospital’s digital medical record system through computer terminals to collect clinical data based on pre-designed questionnaires. The hospital employs standardized perioperative management protocols and data collection systems. Data were collected by two investigators with over five years of orthopedic experience. For individual missing values in clinical variables, mean imputation within each column was applied. Risk Factors Clinical data on postoperative pulmonary infection in elderly patients with femoral neck fractures were collected using the hospital’s digital medical record system. The following risk factors were analyzed for study participants:（1）Demographic information: Age, sex, body mass index (BMI), smoking history, fracture type, preoperative waiting time, number of preoperative comorbidities, American Society of Anesthesiologists (ASA) grade, and Modified 5-Factor Frailty Index (MFI-5).（2）Pre-existing medical conditions: Chronic obstructive pulmonary disease (COPD), asthma, congestive heart failure (CHF), obstructive sleep apnea syndrome, other respiratory disease history, diabetes, cognitive impairment, stroke history, and malignancy history.（3）Preoperative laboratory parameters: White blood cell count, hemoglobin, serum albumin, and serum creatinine.（4）Surgical factors: Surgical approach, anesthesia type, and operation duration. Statistical Analysis All statistical analyses in this study were performed using R software version 4.4.1 (https://www.example.com). Descriptive statistics (counts and percentages) were calculated for categorical variables. Comparisons between groups were conducted using the chi-square test or Fisher's exact test, depending on sample size adequacy. [12] Normally distributed continuous variables were expressed as mean ± standard deviation and compared using the independent t-test. Skewed variables were described using median and interquartile range (IQR). Univariate and multivariate logistic regression analyses were performed to identify risk factors influencing postoperative pneumonia. Variables with P < 0.05 in univariate analysis were included in subsequent multivariate logistic regression. Forward stepwise regression was applied to determine the final set of predictors for the prediction model. Statistical significance was defined as P < 0.05. [13] Finally, a clinical prediction model for postoperative pulmonary infection complications in elderly femoral neck fracture patients was established by deriving equations based on the partial regression coefficients of each variable. A nomogram was constructed to visually present the risk prediction. The predictive performance of the model was further validated using the area under the receiver operating characteristic curve (AUC). [14] The derivation cohort consisted of 317 cases, while the validation cohort comprised 101 cases. No statistically significant differences in baseline clinical characteristics were observed between the two groups (P > 0.05; see Table 1). Table 1. Baseline characteristics of modeling and validation sets. Characteristic Modeling group （ n=317 ） Validation group (n=101) P Age 0.6551 >75 143（45.1%） 43（42.6%） ＜=75 174（54.9%） 58（57.4%） Preoperative _ waiting _ time 0.8410 >3 169（53.3%） 55（54.5%） ＜=3 148（46.7%） 46（45.5%） Hypoproteinemia 0.2797 Yes 16 （5.0%） 8（7.9%） NO 301（95.0%） 93（92.1%） History_of_smoking 0.5359 Yes 152（47.9%） 52（51.5%） No 165（52.1%） 49（48.5%） MFI_5 0.6906 >3 156（49.2%） 52（51.5%） =3 130（41.0%） 46（45.5%） 1~2 187（59.0%） 55（54.5）） congestive_heart_failure 0.8141 Yes 34（10.7%） 10（9.9%） No 283（89.3%） 91（90.1%） Chronic_Obstructive Pulmonary _ Diseas 0.0930 Yes 92（29.0%） 33（32.7%） No 285（71.0%） 68（67.3%） Results Patient Characteristics A total of 317 patients were included in the derivation cohort, categorized into pneumonia (83 cases, 26.2%) and non-pneumonia groups (234 cases, 73.8%) based on postoperative pneumonia occurrence (Table 2). Additionally, 101 elderly patients from the Qingpu Branch of Zhongshan Hospital, Fudan University, were included in the validation cohort, among whom 31 cases (30.7%, 31/101) developed postoperative pneumonia. Table 2. Comparison of clinical data between the Pulmonary infection group and the Non-pulmonary infection group. Characteristic Pulmonary infection group (n=83) Non-pulmonary Infection group(n=234) P Age 0.0030 >75 49（59.0%） 94（40.2%） ＜=75 34（41.0%） 140（59.8%） Preoperative _ waiting _ time 0.0472 >3 52（62.7%） 117（50.0%） ＜=3 31（37.3%） 117（50.0%） Hypoproteinemia 0.0262 Yes 8（9.6%） 8（3.4%） NO 75（90.4%） 226（96.6%） History_of_smoking <0.0001 Yes 67（80.7%） 86（36.8%） No 16（19.3%） 148（63.2%） MFI_5 3 65（78.3%） 89（38.0%） ＜=3 18（21.7%） 145（62.0%） Surgical_method 0.0098 total hip arthroplasty 23（27.7%） 35（15.0%） hemiarthroplasty 60（72.3%） 199（85.0%） ASA <0.0001 ＞=3 55（66.3%） 75（32.1%） 1~2 28（33.7%） 159（67.9%） congestive_heart_failure 0.0118 Yes 15（18.1%） 19（8.1%） No 68（81.9%） 215（91.9%） Chronic_Obstructive Pulmonary _ Diseas 0.0121 Yes 33（39.8%） 59（25.2%） No 50（60.2%） 175（74.8%） Univariate Analysis of Postoperative Pneumonia Following Femoral Neck Fracture Surgery The results of univariate analysis are summarized in Table 2. Eighteen factors with P > 0.05 were excluded, leaving nine statistically significant variables: age, smoking history, surgical approach, preoperative waiting time, serum albumin, American Society of Anesthesiologists (ASA) grade, Modified 5-Factor Frailty Index (MFI-5), chronic obstructive pulmonary disease (COPD), and congestive heart failure (P < 0.05). Multivariate Analysis of Postoperative Pneumonia Following Femoral Neck Fracture Surgery The nine significant risk factors identified in univariate analysis were included in a forward stepwise multivariate logistic regression model, with likelihood ratio tests applied for variable screening. LASSO regression incorporated all nine variables (age, smoking history, surgical approach, preoperative waiting time, serum albumin, ASA grade, MFI-5, COPD history, and congestive heart failure) (Figure 2-A). LASSO compressed coefficients of most variables to zero, ultimately retaining five non-zero-coefficient predictors. Cross-validation optimized the regularization parameter (λ), finalizing five variables as independent risk factors for postoperative pneumonia (Figure 2-B). Multivariate logistic regression confirmed five independent predictors: age, smoking history, surgical approach, ASA grade, and MFI-5 (P < 0.05) (Table 3).A nomogram was constructed using these five risk factors to predict postoperative pneumonia risk (Figure 3). Points were assigned to each predictor, summed to calculate a total risk score, which corresponded to the probability of pneumonia. The ROC curve was plotted, and AUC values for the derivation and validation cohorts were 0.872 (95% CI: 0.82496–0.91904) and 0.895 (95% CI: 0.82444–0.96556), respectively, demonstrating excellent discriminative ability (Figure 4). Calibration curves revealed mean absolute errors (MAE) of 0.022 (model group, Figure 5-A) and 0.039 (validation group, Figure 5-B), indicating strong agreement between predicted and observed outcomes.A decision curve analysis evaluated the clinical net benefit of the nomogram. At thresholds ranging from 0.01 to 0.87, the model demonstrated positive net benefits, supporting its clinical applicability across a wide range of risk thresholds (Figure 6). Table 3. Results of the multifactor analysis of the risk of postoperative pulmonary infection in elderly patients with Femoral neck fracture after modification based on the log likelihood of the profile penalty. Factor Prediction model β Odd ratio(95% 区间) P值 AGE 0.8973700 2.453142770（1.277966648 4.81325705） 0.007733 History of smoking 1.7832884 5.949388386（3.022678336 12.34321497） <0.0001*** Surgical method 0.8607742 2.364990943（1.076469836 5.22517472） 0.031898 ASA 1.1519716 3.164425878（1.661097421 6.15143756） 0.000534 MFI-5 1.8149153 6.140556186（3.129737793 12.70104252） <0.0001*** Discussion Femoral neck fractures are a leading cause of hospitalization among elderly patients, and postoperative pneumonia stands as one of the most critical complications following surgical intervention for these fractures. These complications are associated with mortality rates as high as 30% to 44%, underscoring the severity of postoperative pulmonary infections in this vulnerable population. [13,15,16] This retrospective study constructed and validated a clinical prediction model for postoperative pneumonia in elderly patients with femoral neck fractures. [17,18] The model demonstrated superior performance in both discrimination (AUC = 0.872, 95% CI: 0.82496–0.91904) and calibration compared to previous studies, providing a robust evidence-based foundation for individualized risk assessment and precision interventions. [19] Through comprehensive evaluation of both univariate and multivariate analyses, age, smoking history, surgical approach, ASA grade, and MFI-5 were identified as independent predictors of postoperative pneumonia in elderly patients with femoral neck fractures. In the analysis of clinical characteristics, this study identified age, smoking history, surgical approach, and ASA grade as independent risk factors for postoperative pulmonary infections in elderly patients with femoral neck fractures. These four findings align with the conclusions of numerous prior studies. [11,16,20,21] With advancing age, age-related physiological decline—particularly reduced lung elasticity and weakened respiratory function—compromises the body’s homeostasis. Additionally, elderly patients exhibit poor tolerance to surgical stress and compromised postoperative recovery capacity, significantly increasing their susceptibility to postoperative complications. [6] Annette Erichsen Andersson and colleagues identified [22] ,Elderly patients often present with multiple chronic comorbidities, such as cardiovascular diseases and diabetes mellitus, which significantly increase the risk of postoperative pneumonia. Surgical approaches for femoral neck fractures primarily include internal fixation and arthroplasty. According to treatment guidelines for femoral neck fractures, patients younger than 65 years are generally defined as "younger patients," while those aged over 75 years are classified as "elderly patients." [23] For patients aged between 65 and 75 years, classification as "younger" or "elderly" should be determined based on their pre-injury physiological status. For younger patients, surgical goals prioritize femoral head preservation, prevention of avascular necrosis, and achievement of bony union, with closed or open reduction and internal fixation (ORIF) as the preferred approach. Anatomical reduction and effective fixation are critical for ensuring favorable prognoses and functional outcomes. In contrast, elderly patients with unstable femoral neck fractures (Garden type III or IV), those unable to tolerate prolonged bed rest, those with poor tolerance for reoperation, or extremely elderly individuals (aged >75 years) should undergo arthroplasty. [24,25] Total hip arthroplasty (THA) is recommended for elderly patients with femoral neck fractures who have longer life expectancy, higher pre-injury activity levels, or greater postoperative functional demands, particularly those with concurrent acetabular osteoarthritis, dysplasia, or other acetabular pathologies necessitating joint replacement. Hemiarthroplasty (HA), in contrast, is more suitable for extremely elderly patients with lower activity demands, poorer general health status, or limited tolerance for surgical complexity. [26,27] Total hip arthroplasty (THA) is recommended for elderly patients with femoral neck fractures who have longer life expectancy, higher pre-injury activity levels, or postoperative functional demands, particularly those with concurrent acetabular osteoarthritis, dysplasia, or other acetabular pathologies necessitating joint replacement. Hemiarthroplasty (HA) is more suitable for extremely elderly patients with lower activity demands, poorer general health status, or limited tolerance for surgical complexity. [28] This study identified [29] ,Smoking history emerged as an independent risk factor for postoperative pneumonia following joint replacement in elderly patients with femoral neck fractures (adjusted OR = 5.95, 95% CI: 3.02–12.34, P < 0.001), highlighting its critical impact on postoperative outcomes and establishing a key target for preoperative risk stratification and intervention strategies. [30] Tobacco smoke directly damages tracheal epithelial cilia, leading to mucociliary clearance dysfunction and an increased risk of postoperative sputum retention. [31] Smokers exhibit impaired phagocytic function of alveolar macrophages and Th1/Th2 cell ratio imbalance, which compromises pathogen clearance capacity. Among frail patients with Modified 5-Factor Frailty Index (MFI-5) ≥3, smoking history significantly increases the risk of pneumonia, demonstrating a synergistic biological interaction between frailty and smoking that amplifies risk. Consequently, smoking history markedly elevates the risk of postoperative pneumonia in elderly patients with femoral neck fractures. [32] The American Society of Anesthesiologists (ASA) grade reflects the number and severity of comorbidities in patients. In a study by ACMeyer and colleagues analyzing ASA grades, high ASA grades (defined as ASA grade ≥3) were associated with a significantly higher incidence of postoperative pneumonia (OR = 3.16, 95% CI: 1.66–6.15). This association may stem from multisystem interactions:Impaired respiratory reserve: High ASA-grade patients often have comorbidities such as COPD or interstitial lung disease, leading to weakened postoperative cough reflexes, impaired mucociliary clearance, and increased risks of aspiration and pathogen colonization.Immune-inflammatory imbalance: ASA grades correlate positively with systemic inflammatory markers (e.g., CRP, IL-6), suggesting exacerbated inflammatory responses postoperatively [33] ,Excessive inflammatory responses may disrupt the alveolar-capillary barrier, thereby promoting the development of pulmonary infections. It is particularly noteworthy that [34] ：When the ASA grade is ≥III, the risk of pneumonia exhibits a steep rise, suggesting that this threshold may serve as a critical decision point for initiating intensive respiratory management. Notably, unlike static indicators (e.g., age), the ASA grade integrates real-time changes in patients’ recent cardiopulmonary compensatory status. For instance, dynamic impacts such as acute decompensated heart failure may more sensitively reflect actual risk levels. In this study, the Modified 5-Factor Frailty Index (MFI-5) was incorporated into the predictive model for postoperative pneumonia in elderly patients with femoral neck fractures. [35] ,The Modified 5-Factor Frailty Index (MFI-5) evaluates nutritional status (weight loss), physical function (grip strength decline), exercise tolerance (independent walking ability), cognitive function, and comorbidity burden, revealing the multidimensional pathways linking frailty to pneumonia. This study found that MFI-5 ≥3 (defined as high frailty status) significantly increased pneumonia risk (adjusted OR = 3.13–12.70, 95% CI: 3.13–12.70, P < 0.001). These findings confirm the specific impact of preoperative frailty on postoperative pulmonary infections, establishing MFI-5 as a multidimensional assessment tool for risk stratification in elderly patients with hip fractures. [36] The biological mechanisms underlying MFI-5’s prediction of pneumonia are closely linked to immune-metabolic imbalance and impaired respiratory mechanics:Weight loss and hypoalbuminemia (components of MFI-5) collectively indicate protein-energy malnutrition, leading to suppressed lymphocyte function and reduced airway IgA secretion;Grip strength decline (another MFI-5 metric) correlates with diminished pulmonary function—a 5 kg decrease in grip strength corresponds to a 12% reduction in Forced Vital Capacity (FVC), impairing cough clearance capacity. [37] Therefore, the Modified 5-Factor Frailty Index (MFI-5) serves as a reliable predictive indicator for postoperative pneumonia following femoral neck fracture surgery. The predictive model and nomogram established in this study demonstrate broad applicability. For example, outpatient clinicians can assess postoperative pneumonia risk in elderly patients with femoral neck fractures to guide surgical decision-making, serving as a critical reference for surgeons in identifying high-risk populations and selecting optimal surgical strategies. In this study, elderly patients who developed postoperative pneumonia had significantly longer hospital stays compared to those without pneumonia. Implementing preventive measures for high-risk patients may improve prognostic outcomes and reduce healthcare costs. These interventions include maintaining an upright posture during meals to minimize aspiration risk, encouraging coughing and deep breathing exercises to facilitate airway clearance, elevating the head of the bed by at least 30 degrees to reduce gastric reflux and aspiration, and strict pain management protocols to promote early mobilization, thereby enhancing pulmonary function and minimizing complications. Additionally, daily oral hygiene practices (at least twice daily) are recommended to reduce oral bacterial load and prevent aspiration pneumonia. [38,39] Conclusion This study identified advanced age (>75 years), smoking history, hemiarthroplasty, American Society of Anesthesiologists (ASA) grade ≥3, and Modified 5-Factor Frailty Index (MFI-5) >3 as significant risk factors for postoperative pneumonia in elderly patients undergoing hip replacement for femoral neck fractures. The developed predictive model enables clinicians to perform individualized risk assessments, facilitating targeted perioperative interventions to improve patient outcomes. This aligns with the modern trend of precision medicine. By integrating current research advancements, our findings provide novel insights into the diagnosis and management of postoperative pneumonia following femoral neck fracture surgery. The established model demonstrates robust discrimination and calibration, with AUC values of 0.872 (95% CI: 0.82496–0.91904) and 0.895 (95% CI: 0.82444–0.96556) in the derivation and validation cohorts, respectively. These results validate its clinical utility, demonstrating that the model can be widely applied to guide clinical decision-making and therapeutic strategies for high-risk populations. Declarations Acknowledgements None. Author contributions Xuxing Sun: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing. Yuhang Wang: Formal analysis, Investigation, Methodology, Validation, Writing – original draft. Xinyong Chen: Data curation, Writing – review & editing. Chengwei Xu : Data curation, Writing – review & editing. Yucheng Tang: Data curation, Software, Validation. Haojie Zhang: Data curation, Software, Validation. Long Jia: Supervision, Methodology, Writing – review & editing. Yadong Qian : Supervision, Writing – review & editing. Dongliang Gong : Conceptualization, Formal analysis, Funding acquisition, Investigation, Supervision, Methodology, Project administration, Writing – review & editing.All authors read and approved the final manuscript. Funding This study was funded Shanghai Society of Integrated Chinese and Western Medicine community special fund.(Grant No. 2024-43）,Natural Science Foundation of Bengbu Medical University(Grant No. 2023byzd198）,Fifth Round of Discipline Development and Talent Cultivation Program (Rising Star Award) of Shanghai Qingpu District Healthcare System (Award No. YY2023-21) Data availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of the Qingpu Branch of Zhongshan Hospital affiliated with Fudan University (approval code: qingyi2021-35). All patients provided written informed consent. The study was performed in accordance with the ethical standards of the Declaration of Helsinki (1964) and its subsequent amendments. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author details a Department of Orthopedics, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700, China b School of Clinical Medicine, Bengbu Medical University , No.2600 Donghai Avenue, Bengbu 233030, China/p> References Accelerated surgery versus standard care in hip fracture (HIP ATTACK): an international, randomised, controlled trial. Lancet . Feb 29 2020;395(10225):698-708. doi:10.1016/s0140-6736(20)30058-1 Gao YC, Zhang YW, Shi L, et al. What are Risk Factors of Postoperative Pneumonia in Geriatric Individuals after Hip Fracture Surgery: A Systematic Review and Meta-Analysis. Orthop Surg . Jan 2023;15(1):38-52. doi:10.1111/os.13631 Jang SY, Cha Y, Yoo JI, et al. 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Incidence and risk factors for postoperative pneumonia following surgically treated hip fracture in geriatric patients: a retrospective cohort study. J Orthop Surg Res . Mar 24 2022;17(1):179. doi:10.1186/s13018-022-03071-y Kanis JA, Johnell O, Oden A, et al. Smoking and fracture risk: a meta-analysis. Osteoporos Int . Feb 2005;16(2):155-62. doi:10.1007/s00198-004-1640-3 Lugg ST, Scott A, Parekh D, Naidu B, Thickett DR. Cigarette smoke exposure and alveolar macrophages: mechanisms for lung disease. Thorax . Jan 2022;77(1):94-101. doi:10.1136/thoraxjnl-2020-216296 Huang Q, Wang Y, Zhang L, et al. Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD. Respir Res . Dec 20 2022;23(1):367. doi:10.1186/s12931-022-02293-2 Wang X, Hu J, Wu D. Risk factors for frailty in older adults. Medicine (Baltimore) . Aug 26 2022;101(34):e30169. doi:10.1097/md.0000000000030169 Kloepfer KM, Kennedy JL. Childhood respiratory viral infections and the microbiome. J Allergy Clin Immunol . Oct 2023;152(4):827-834. doi:10.1016/j.jaci.2023.08.008 Meyer AC, Eklund H, Hedström M, Modig K. The ASA score predicts infections, cardiovascular complications, and hospital readmissions after hip fracture - A nationwide cohort study. Osteoporos Int . Nov 2021;32(11):2185-2192. doi:10.1007/s00198-021-05956-w Camino-Willhuber G, Choi J, Holc F, et al. Utility of the Modified 5-Items Frailty Index to Predict Complications and Mortality After Elective Cervical, Thoracic and Lumbar Posterior Spine Fusion Surgery: Multicentric Analysis From ACS-NSQIP Database. Global Spine J . Apr 2024;14(3):839-845. doi:10.1177/21925682221124101 Zhao LH, Chen J, Zhu RX. The relationship between frailty and community-acquired pneumonia in older patients. Aging Clin Exp Res . Feb 2023;35(2):349-355. doi:10.1007/s40520-022-02301-x Hamahata N, Pinsky MR. Heart-Lung Interactions. Semin Respir Crit Care Med . Oct 2023;44(5):650-660. doi:10.1055/s-0043-1770062 de Jong L, van Rijckevorsel V, Raats JW, et al. Delirium after hip hemiarthroplasty for proximal femoral fractures in elderly patients: risk factors and clinical outcomes. Clin Interv Aging . 2019;14:427-435. doi:10.2147/cia.S189760 Chen X, Zhang J, Lin Y, et al. Risk factors for postoperative mortality at 30 days in elderly Chinese patients with hip fractures. Osteoporos Int . May 2022;33(5):1109-1116. doi:10.1007/s00198-021-06257-y Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 29 May, 2025 Editor invited by journal 06 May, 2025 Editor assigned by journal 14 Apr, 2025 Submission checks completed at journal 12 Apr, 2025 First submitted to journal 12 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6384696","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464688888,"identity":"11772a3f-ef4c-4d0a-b8f7-6571c70fb471","order_by":0,"name":"Xuxing Sun","email":"","orcid":"","institution":"Department of Orthopedics, Qingpu Branch, Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700,","correspondingAuthor":false,"prefix":"","firstName":"Xuxing","middleName":"","lastName":"Sun","suffix":""},{"id":464688889,"identity":"44dd4f58-a2bc-4928-a156-268abe1ca5c4","order_by":1,"name":"Yuhang Wang","email":"","orcid":"","institution":"Department of Orthopedics, Qingpu Branch, Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700,","correspondingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Wang","suffix":""},{"id":464688890,"identity":"de8d1dd8-d949-4539-828a-873ff9b2d240","order_by":2,"name":"Xinyong Chen","email":"","orcid":"","institution":"Department of Orthopedics, Qingpu Branch, Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700,","correspondingAuthor":false,"prefix":"","firstName":"Xinyong","middleName":"","lastName":"Chen","suffix":""},{"id":464688891,"identity":"ee2312dd-b4a4-4f41-899c-650157d5405a","order_by":3,"name":"Chengwei Xu","email":"","orcid":"","institution":"School of Clinical Medicine, Bengbu Medical University , No.2600 Donghai Avenue, Bengbu 233030","correspondingAuthor":false,"prefix":"","firstName":"Chengwei","middleName":"","lastName":"Xu","suffix":""},{"id":464688892,"identity":"650c475b-5ea1-4c6f-bff1-7ae21b868a4a","order_by":4,"name":"Yucheng Tang","email":"","orcid":"","institution":"School of Clinical Medicine, Bengbu Medical University , No.2600 Donghai Avenue, Bengbu 233030","correspondingAuthor":false,"prefix":"","firstName":"Yucheng","middleName":"","lastName":"Tang","suffix":""},{"id":464688893,"identity":"6c89dbf7-117c-4a29-8fa2-070ec800b7be","order_by":5,"name":"Haojie Zhang","email":"","orcid":"","institution":"School of Clinical Medicine, Bengbu Medical University , No.2600 Donghai Avenue, Bengbu 233030","correspondingAuthor":false,"prefix":"","firstName":"Haojie","middleName":"","lastName":"Zhang","suffix":""},{"id":464688895,"identity":"151bc74c-2861-4e44-b234-3da05a6dbc88","order_by":6,"name":"Long Jia","email":"","orcid":"","institution":"Department of Orthopedics, Qingpu Branch, Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700,","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Jia","suffix":""},{"id":464688897,"identity":"e2f8893d-5933-453a-bf94-2e6a19db1156","order_by":7,"name":"Yadong Qian","email":"","orcid":"","institution":"Department of Orthopedics, Qingpu Branch, Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700,","correspondingAuthor":false,"prefix":"","firstName":"Yadong","middleName":"","lastName":"Qian","suffix":""},{"id":464688902,"identity":"25951106-e533-41a5-a3c0-6f8cdafaaa55","order_by":8,"name":"Dongliang Gong","email":"data:image/png;base64,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","orcid":"","institution":"Department of Orthopedics, Qingpu Branch, Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700,","correspondingAuthor":true,"prefix":"","firstName":"Dongliang","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2025-04-06 04:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6384696/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6384696/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83836593,"identity":"fe2f42be-fa9b-42a2-89f2-e4c5b9b3e27d","added_by":"auto","created_at":"2025-06-03 13:18:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159327,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProcedure flowchart used in this study. The flow chart shows the entire research process from indicator acquisition, patient diagnosis, statistical analysis, and conclusions. LASSO, lead absolute contraction and selection operator; CLASS, classification error; AUC, area under the curve; MSE, mean square error; ASA grade, American Society of Anaesthesiologists classification; MFI-5, Modified Frailty Index-5.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6384696/v1/13d4034bb270dd1dd4a5d17b.png"},{"id":83837764,"identity":"9a903af5-abb4-4719-908a-8e969f9385a6","added_by":"auto","created_at":"2025-06-03 13:26:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimal parameter selection in the LASSO model. A Optimal parameter selection in the LASSO model; B LASSO coefficient distribution for all risk factors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6384696/v1/e73cf1041ea5f16f05e7dab2.png"},{"id":83836592,"identity":"f5f6a55f-500f-4724-b043-f14a05763ecc","added_by":"auto","created_at":"2025-06-03 13:18:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA nomogram was constructed to predict postoperative pulmonary infection in elderly patients with Femoral neck fracture, based on 9 independent predictors. Mark the values of these factors on the corresponding axis. Draw a vertical line from each value to the top lines to obtain the corresponding points. Then, sum the points from each variable. Locate the sum on the total points scale and project it vertically onto the bottom axis to determine the risk of postoperative pulmonary infection in elderly patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6384696/v1/ae0750902bece6c32ad0b027.png"},{"id":83836591,"identity":"65d10682-066b-4df0-a823-51326003f246","added_by":"auto","created_at":"2025-06-03 13:18:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curves of the nomogram model. (A) indicates the ROC curve of the training set, and (B) indicates the ROC curve of the validation set. ROC curve and AUC of training cohort model (A) = 0.872 and validation cohort model (B) = 0.895.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6384696/v1/13006d0b5b89217fe77bec51.png"},{"id":83836595,"identity":"7488d866-1efc-4743-8509-52fa1f7eeb10","added_by":"auto","created_at":"2025-06-03 13:18:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves for the scale. (A) depicts the calibration curve for the training set, while (B) represents the calibration curve for the validation set. The x-axis corresponds to the predicted probability of postoperative pulmonary infection in elderly patients with Femoral neck fracture, and the y-axis corresponds to the actual diagnosis of postoperative pneumonia following Femoral neck fracture. The dashed line signifies perfect prediction, where the predicted probability matches the actual probability. The fine dashed line illustrates the scale's performance, and the solid line represents the performance of the calibrated model. The closer the model's calibration curve aligns with the dashed line, the better the model's predictive performance.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6384696/v1/6bbdc5dceef0c7dfa1b0e89d.png"},{"id":83838242,"identity":"f8adfd7d-69ed-4a4a-b6bf-aba5cb270776","added_by":"auto","created_at":"2025-06-03 13:34:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":55416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe decision curve for the prediction model in the modeling and validation populations. The x-axis represents the threshold probability, while the y-axis measures the net benefit. The decision curves indicate that the model is clinically beneficial across a relatively wide range of threshold probabilities.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6384696/v1/11b74727d67d75e6be73e406.png"},{"id":83839068,"identity":"f0b60d32-0d19-4e99-8e9e-9e7574eebdf6","added_by":"auto","created_at":"2025-06-03 13:42:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2478323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6384696/v1/27bd1336-62c2-48ee-b841-69557fdcd462.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Clinical Prediction Model for Postoperative Pneumonia in Elderly Patients with Femoral Neck Fracture","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFemoral neck fracture, a prevalent traumatic injury among the elderly population, is closely associated with high disability and mortality rates, which are strongly linked to postoperative complications, among which pneumonia stands out as one of the most common occurrences.\u003csup\u003e[1-3]\u003c/sup\u003e.Studies indicate that the incidence of postoperative pneumonia in elderly patients with femoral neck fractures ranges from 5% to 10%. Pulmonary infections not only significantly prolong hospital stays and increase medical costs but may also lead to respiratory failure and multiple organ dysfunction, severely impacting patient prognosis.\u003csup\u003e[4-6]\u003c/sup\u003e.However, the risk of pneumonia remains persistently high in elderly patients due to age-related physiological decline, complex comorbidities (e.g., chronic obstructive pulmonary disease [COPD], congestive heart failure[CHF],hypoalbuminemia),and prolonged postoperative immobility.\u003csup\u003e[7]\u003c/sup\u003e.Therefore, early identification of high-risk patients and the development of targeted preventive strategies are critical for improving clinical outcomes.\u003c/p\u003e\n\u003cp\u003eDaniel D. Bohl et al. conducted a multivariate regression analysis to explore risk factors for postoperative pneumonia.\u003csup\u003e[8]\u003c/sup\u003e,identifying advanced age (\u0026gt;65 years), pre-existing respiratory diseases, hypoalbuminemia, and anemia as key contributors. However, existing predictive models have limitations, including inconsistent methodological quality, failure to adjust for confounding variables or incorporate biomarker predictive value, high risk of bias, and overreliance on traditional clinical variables while neglecting perioperative management details, thereby restricting clinical applicability. To address these gaps, this retrospective, multicenter study systematically integrates age, comorbidities, and laboratory parameters to construct a clinical prediction model for postoperative pneumonia in elderly femoral neck fracture patients. Machine learning algorithms will be employed to optimize variable selection, and internal validation will assess model discrimination, calibration, and clinical utility. The proposed model aims to provide clinicians with a personalized risk assessment tool for early identification of high-risk populations, enabling precise intervention strategies to reduce postoperative pneumonia incidence and enhance long-term patient outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of the Qingpu Branch of Zhongshan Hospital affiliated with Fudan University (approval code: qingyi2021-35) .Elderly patients undergoing hip replacement surgery for femoral neck fractures at the Qingpu Branch of Zhongshan Hospital, Fudan University, between January 2022 and September 2024 were enrolled. A total of 418 eligible patients were included in the final analysis, with 75.8% (317 cases) allocated to the derivation cohort and 24.2% (101 cases) to the validation cohort. Postoperative outcomes were categorized into pneumonia (83 cases) and non-pneumonia groups (234 cases).Inclusion Criteria:\u003c/p\u003e\n\u003cp\u003e(1)Diagnosis of unilateral femoral neck fracture requiring primary surgical treatment, confirmed by clinical symptoms, physical examination, and imaging findings.(2)Unstable femoral neck fracture (Garden type III or IV) based on Garden classification.(3)Age \u0026gt;65 years.Exclusion Criteria:(1)Preoperative diagnosis of pneumonia.(2)Presence of multiple fractures.(3)Postoperative mortality due to non-pulmonary infections.(4)Pneumonia Diagnosis.The diagnosis of pneumonia was confirmed according to previously published criteria and validated by consultation with a qualified pulmonologist.\u003csup\u003e[9-11]\u003c/sup\u003e .This retrospective study utilized the hospital\u0026rsquo;s digital medical record system through computer terminals to collect clinical data based on pre-designed questionnaires. The hospital employs standardized perioperative management protocols and data collection systems. Data were collected by two investigators with over five years of orthopedic experience. For individual missing values in clinical variables, mean imputation within each column was applied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data on postoperative pulmonary infection in elderly patients with femoral neck fractures were collected using the hospital\u0026rsquo;s digital medical record system. The following risk factors were analyzed for study participants:（1）Demographic information: Age, sex, body mass index (BMI), smoking history, fracture type, preoperative waiting time, number of preoperative comorbidities, American Society of Anesthesiologists (ASA) grade, and Modified 5-Factor Frailty Index (MFI-5).（2）Pre-existing medical conditions: Chronic obstructive pulmonary disease (COPD), asthma, congestive heart failure (CHF), obstructive sleep apnea syndrome, other respiratory disease history, diabetes, cognitive impairment, stroke history, and malignancy history.（3）Preoperative laboratory parameters: White blood cell count, hemoglobin, serum albumin, and serum creatinine.（4）Surgical factors: Surgical approach, anesthesia type, and operation duration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses in this study were performed using R software version 4.4.1 (https://www.example.com). Descriptive statistics (counts and percentages) were calculated for categorical variables. Comparisons between groups were conducted using the chi-square test or Fisher\u0026apos;s exact test, depending on sample size adequacy.\u003csup\u003e[12]\u003c/sup\u003eNormally distributed continuous variables were expressed as mean \u0026plusmn; standard deviation and compared using the independent t-test. Skewed variables were described using median and interquartile range (IQR). Univariate and multivariate logistic regression analyses were performed to identify risk factors influencing postoperative pneumonia. Variables with P \u0026lt; 0.05 in univariate analysis were included in subsequent multivariate logistic regression. Forward stepwise regression was applied to determine the final set of predictors for the prediction model. Statistical significance was defined as P \u0026lt; 0.05.\u003csup\u003e[13]\u003c/sup\u003eFinally, a clinical prediction model for postoperative pulmonary infection complications in elderly femoral neck fracture patients was established by deriving equations based on the partial regression coefficients of each variable. A nomogram was constructed to visually present the risk prediction. The predictive performance of the model was further validated using the area under the receiver operating characteristic curve (AUC).\u003csup\u003e[14]\u003c/sup\u003eThe derivation cohort consisted of 317 cases, while the validation cohort comprised 101 cases. No statistically significant differences in baseline clinical characteristics were observed between the two groups (P \u0026gt; 0.05; see Table 1).\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of modeling and validation sets.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModeling group\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=317\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation group (n=101)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.6551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026gt;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e143（45.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e43（42.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e＜=75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e174（54.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e58（57.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003cstrong\u003e_\u003c/strong\u003e\u003cstrong\u003ewaiting\u003c/strong\u003e\u003cstrong\u003e_\u003c/strong\u003e\u003cstrong\u003etime\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.8410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e169（53.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e55（54.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e＜=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e148（46.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e46（45.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypoproteinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.2797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e16 （5.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e8（7.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e301（95.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e93（92.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory_of_smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.5359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e152（47.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e52（51.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e165（52.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e49（48.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMFI_5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.6906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e156（49.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e52（51.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026lt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e161（50.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e49（48.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical_method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.3217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003etotal hip arthroplasty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e58（18.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e23（22.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ehemiarthroplasty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e259（81.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e78（77.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.4215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026gt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e130（41.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e46（45.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e1~2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e187（59.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e55（54.5））\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003econgestive_heart_failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.8141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e34（10.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e10（9.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e283（89.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e91（90.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic_Obstructive Pulmonary _ Diseas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.0930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e92（29.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e33（32.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e285（71.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e68（67.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 317 patients were included in the derivation cohort, categorized into pneumonia (83 cases, 26.2%) and non-pneumonia groups (234 cases, 73.8%) based on postoperative pneumonia occurrence (Table 2). Additionally, 101 elderly patients from the Qingpu Branch of Zhongshan Hospital, Fudan University, were included in the validation cohort, among whom 31 cases (30.7%, 31/101) developed postoperative pneumonia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Comparison of clinical data between the Pulmonary infection group and the Non-pulmonary infection group.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulmonary infection group (n=83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-pulmonary\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInfection group(n=234)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026gt;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e49（59.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e94（40.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e＜=75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e34（41.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e140（59.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003cstrong\u003e_\u003c/strong\u003e\u003cstrong\u003ewaiting\u003c/strong\u003e\u003cstrong\u003e_\u003c/strong\u003e\u003cstrong\u003etime\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0472\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e52（62.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e117（50.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e＜=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e31（37.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e117（50.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypoproteinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0262\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e8（9.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8（3.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e75（90.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e226（96.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory_of_smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e67（80.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e86（36.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e16（19.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e148（63.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMFI_5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e65（78.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e89（38.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e＜=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e18（21.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e145（62.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical_method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0098\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003etotal hip arthroplasty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e23（27.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e35（15.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ehemiarthroplasty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e60（72.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e199（85.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e＞=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e55（66.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e75（32.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e1~2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e28（33.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e159（67.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003econgestive_heart_failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e15（18.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e19（8.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e68（81.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e215（91.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic_Obstructive Pulmonary _ Diseas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0121\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e33（39.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e59（25.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e50（60.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e175（74.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis of Postoperative Pneumonia Following Femoral Neck Fracture Surgery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of univariate analysis are summarized in Table 2. Eighteen factors with P \u0026gt; 0.05 were excluded, leaving nine statistically significant variables: age, smoking history, surgical approach, preoperative waiting time, serum albumin, American Society of Anesthesiologists (ASA) grade, Modified 5-Factor Frailty Index (MFI-5), chronic obstructive pulmonary disease (COPD), and congestive heart failure (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis of Postoperative Pneumonia Following Femoral Neck Fracture Surgery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nine significant risk factors identified in univariate analysis were included in a forward stepwise multivariate logistic regression model, with likelihood ratio tests applied for variable screening. LASSO regression incorporated all nine variables (age, smoking history, surgical approach, preoperative waiting time, serum albumin, ASA grade, MFI-5, COPD history, and congestive heart failure) (Figure 2-A). LASSO compressed coefficients of most variables to zero, ultimately retaining five non-zero-coefficient predictors. Cross-validation optimized the regularization parameter (\u0026lambda;), finalizing five variables as independent risk factors for postoperative pneumonia (Figure 2-B). Multivariate logistic regression confirmed five independent predictors: age, smoking history, surgical approach, ASA grade, and MFI-5 (P \u0026lt; 0.05) (Table 3).A nomogram was constructed using these five risk factors to predict postoperative pneumonia risk (Figure 3). Points were assigned to each predictor, summed to calculate a total risk score, which corresponded to the probability of pneumonia. The ROC curve was plotted, and AUC values for the derivation and validation cohorts were 0.872 (95% CI: 0.82496\u0026ndash;0.91904) and 0.895 (95% CI: 0.82444\u0026ndash;0.96556), respectively, demonstrating excellent discriminative ability (Figure 4). Calibration curves revealed mean absolute errors (MAE) of 0.022 (model group, Figure 5-A) and 0.039 (validation group, Figure 5-B), indicating strong agreement between predicted and observed outcomes.A decision curve analysis evaluated the clinical net benefit of the nomogram. At thresholds ranging from 0.01 to 0.87, the model demonstrated positive net benefits, supporting its clinical applicability across a wide range of risk thresholds (Figure 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Results of the multifactor analysis of the risk of postoperative pulmonary infection in elderly patients with Femoral neck fracture after modification based on the log likelihood of the profile penalty.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 440px;\"\u003e\n \u003cp\u003ePrediction model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003eOdd ratio(95%\u0026nbsp;区间)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eP值\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.8973700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003e2.453142770（1.277966648 \u0026nbsp;4.81325705）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007733\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eHistory of smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.7832884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003e5.949388386（3.022678336 \u0026nbsp;12.34321497）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eSurgical method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.8607742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003e2.364990943（1.076469836 \u0026nbsp;5.22517472）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031898\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.1519716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003e3.164425878（1.661097421 \u0026nbsp;6.15143756）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000534\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eMFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.8149153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003e6.140556186（3.129737793 \u0026nbsp;12.70104252）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eFemoral neck fractures are a leading cause of hospitalization among elderly patients, and postoperative pneumonia stands as one of the most critical complications following surgical intervention for these fractures. These complications are associated with mortality rates as high as 30% to 44%, underscoring the severity of postoperative pulmonary infections in this vulnerable population.\u003csup\u003e[13,15,16]\u003c/sup\u003eThis retrospective study constructed and validated a clinical prediction model for postoperative pneumonia in elderly patients with femoral neck fractures.\u003csup\u003e[17,18]\u003c/sup\u003eThe model demonstrated superior performance in both discrimination (AUC = 0.872, 95% CI: 0.82496\u0026ndash;0.91904) and calibration compared to previous studies, providing a robust evidence-based foundation for individualized risk assessment and precision interventions.\u003csup\u003e[19]\u003c/sup\u003eThrough comprehensive evaluation of both univariate and multivariate analyses, age, smoking history, surgical approach, ASA grade, and MFI-5 were identified as independent predictors of postoperative pneumonia in elderly patients with femoral neck fractures.\u003c/p\u003e\n\u003cp\u003eIn the analysis of clinical characteristics, this study identified age, smoking history, surgical approach, and ASA grade as independent risk factors for postoperative pulmonary infections in elderly patients with femoral neck fractures. These four findings align with the conclusions of numerous prior studies.\u003csup\u003e[11,16,20,21]\u003c/sup\u003eWith advancing age, age-related physiological decline\u0026mdash;particularly reduced lung elasticity and weakened respiratory function\u0026mdash;compromises the body\u0026rsquo;s homeostasis. Additionally, elderly patients exhibit poor tolerance to surgical stress and compromised postoperative recovery capacity, significantly increasing their susceptibility to postoperative complications.\u003csup\u003e[6]\u003c/sup\u003eAnnette Erichsen Andersson and colleagues identified\u003csup\u003e[22]\u003c/sup\u003e,Elderly patients often present with multiple chronic comorbidities, such as cardiovascular diseases and diabetes mellitus, which significantly increase the risk of postoperative pneumonia.\u003c/p\u003e\n\u003cp\u003eSurgical approaches for femoral neck fractures primarily include internal fixation and arthroplasty. According to treatment guidelines for femoral neck fractures, patients younger than 65 years are generally defined as \u0026quot;younger patients,\u0026quot; while those aged over 75 years are classified as \u0026quot;elderly patients.\u0026quot;\u003csup\u003e[23]\u003c/sup\u003eFor patients aged between 65 and 75 years, classification as \u0026quot;younger\u0026quot; or \u0026quot;elderly\u0026quot; should be determined based on their pre-injury physiological status. For younger patients, surgical goals prioritize femoral head preservation, prevention of avascular necrosis, and achievement of bony union, with closed or open reduction and internal fixation (ORIF) as the preferred approach. Anatomical reduction and effective fixation are critical for ensuring favorable prognoses and functional outcomes. In contrast, elderly patients with unstable femoral neck fractures (Garden type III or IV), those unable to tolerate prolonged bed rest, those with poor tolerance for reoperation, or extremely elderly individuals (aged \u0026gt;75 years) should undergo arthroplasty.\u003csup\u003e[24,25]\u003c/sup\u003eTotal hip arthroplasty (THA) is recommended for elderly patients with femoral neck fractures who have longer life expectancy, higher pre-injury activity levels, or greater postoperative functional demands, particularly those with concurrent acetabular osteoarthritis, dysplasia, or other acetabular pathologies necessitating joint replacement. Hemiarthroplasty (HA), in contrast, is more suitable for extremely elderly patients with lower activity demands, poorer general health status, or limited tolerance for surgical complexity.\u003csup\u003e[26,27]\u003c/sup\u003eTotal hip arthroplasty (THA) is recommended for elderly patients with femoral neck fractures who have longer life expectancy, higher pre-injury activity levels, or postoperative functional demands, particularly those with concurrent acetabular osteoarthritis, dysplasia, or other acetabular pathologies necessitating joint replacement. Hemiarthroplasty (HA) is more suitable for extremely elderly patients with lower activity demands, poorer general health status, or limited tolerance for surgical complexity.\u003csup\u003e[28]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis study identified\u003csup\u003e[29]\u003c/sup\u003e,Smoking history emerged as an independent risk factor for postoperative pneumonia following joint replacement in elderly patients with femoral neck fractures (adjusted OR = 5.95, 95% CI: 3.02\u0026ndash;12.34, P \u0026lt; 0.001), highlighting its critical impact on postoperative outcomes and establishing a key target for preoperative risk stratification and intervention strategies.\u003csup\u003e[30]\u003c/sup\u003eTobacco smoke directly damages tracheal epithelial cilia, leading to mucociliary clearance dysfunction and an increased risk of postoperative sputum retention.\u003csup\u003e[31]\u003c/sup\u003eSmokers exhibit impaired phagocytic function of alveolar macrophages and Th1/Th2 cell ratio imbalance, which compromises pathogen clearance capacity. Among frail patients with Modified 5-Factor Frailty Index (MFI-5) \u0026ge;3, smoking history significantly increases the risk of pneumonia, demonstrating a synergistic biological interaction between frailty and smoking that amplifies risk. Consequently, smoking history markedly elevates the risk of postoperative pneumonia in elderly patients with femoral neck fractures.\u003csup\u003e[32]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe American Society of Anesthesiologists (ASA) grade reflects the number and severity of comorbidities in patients. In a study by ACMeyer and colleagues analyzing ASA grades, high ASA grades (defined as ASA grade \u0026ge;3) were associated with a significantly higher incidence of postoperative pneumonia (OR = 3.16, 95% CI: 1.66\u0026ndash;6.15). This association may stem from multisystem interactions:Impaired respiratory reserve: High ASA-grade patients often have comorbidities such as COPD or interstitial lung disease, leading to weakened postoperative cough reflexes, impaired mucociliary clearance, and increased risks of aspiration and pathogen colonization.Immune-inflammatory imbalance: ASA grades correlate positively with systemic inflammatory markers (e.g., CRP, IL-6), suggesting exacerbated inflammatory responses postoperatively\u003csup\u003e[33]\u003c/sup\u003e,Excessive inflammatory responses may disrupt the alveolar-capillary barrier, thereby promoting the development of pulmonary infections. It is particularly noteworthy that\u003csup\u003e[34]\u003c/sup\u003e：When the ASA grade is\u0026nbsp;\u0026ge;III, the risk of pneumonia exhibits a steep rise, suggesting that this threshold may serve as a critical decision point for initiating intensive respiratory management. Notably, unlike static indicators (e.g., age), the ASA grade integrates real-time changes in patients\u0026rsquo;\u0026nbsp;recent cardiopulmonary compensatory status. For instance, dynamic impacts such as acute decompensated heart failure may more sensitively reflect actual risk levels.\u003c/p\u003e\n\u003cp\u003eIn this study, the Modified 5-Factor Frailty Index (MFI-5) was incorporated into the predictive model for postoperative pneumonia in elderly patients with femoral neck fractures.\u003csup\u003e[35]\u003c/sup\u003e,The Modified 5-Factor Frailty Index (MFI-5) evaluates nutritional status (weight loss), physical function (grip strength decline), exercise tolerance (independent walking ability), cognitive function, and comorbidity burden, revealing the multidimensional pathways linking frailty to pneumonia. This study found that MFI-5 \u0026ge;3 (defined as high frailty status) significantly increased pneumonia risk (adjusted OR = 3.13\u0026ndash;12.70, 95% CI: 3.13\u0026ndash;12.70, P \u0026lt; 0.001). These findings confirm the specific impact of preoperative frailty on postoperative pulmonary infections, establishing MFI-5 as a multidimensional assessment tool for risk stratification in elderly patients with hip fractures.\u003csup\u003e[36]\u003c/sup\u003eThe biological mechanisms underlying MFI-5\u0026rsquo;s prediction of pneumonia are closely linked to immune-metabolic imbalance and impaired respiratory mechanics:Weight loss and hypoalbuminemia (components of MFI-5) collectively indicate protein-energy malnutrition, leading to suppressed lymphocyte function and reduced airway IgA secretion;Grip strength decline (another MFI-5 metric) correlates with diminished pulmonary function\u0026mdash;a 5 kg decrease in grip strength corresponds to a 12% reduction in Forced Vital Capacity (FVC), impairing cough clearance capacity.\u003csup\u003e[37]\u003c/sup\u003eTherefore, the Modified 5-Factor Frailty Index (MFI-5) serves as a reliable predictive indicator for postoperative pneumonia following femoral neck fracture surgery.\u003c/p\u003e\n\u003cp\u003eThe predictive model and nomogram established in this study demonstrate broad applicability. For example, outpatient clinicians can assess postoperative pneumonia risk in elderly patients with femoral neck fractures to guide surgical decision-making, serving as a critical reference for surgeons in identifying high-risk populations and selecting optimal surgical strategies. In this study, elderly patients who developed postoperative pneumonia had significantly longer hospital stays compared to those without pneumonia. Implementing preventive measures for high-risk patients may improve prognostic outcomes and reduce healthcare costs. These interventions include maintaining an upright posture during meals to minimize aspiration risk, encouraging coughing and deep breathing exercises to facilitate airway clearance, elevating the head of the bed by at least 30 degrees to reduce gastric reflux and aspiration, and strict pain management protocols to promote early mobilization, thereby enhancing pulmonary function and minimizing complications. Additionally, daily oral hygiene practices (at least twice daily) are recommended to reduce oral bacterial load and prevent aspiration pneumonia.\u003csup\u003e[38,39]\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified advanced age (\u0026gt;75 years), smoking history, hemiarthroplasty, American Society of Anesthesiologists (ASA) grade \u0026ge;3, and Modified 5-Factor Frailty Index (MFI-5) \u0026gt;3 as significant risk factors for postoperative pneumonia in elderly patients undergoing hip replacement for femoral neck fractures. The developed predictive model enables clinicians to perform individualized risk assessments, facilitating targeted perioperative interventions to improve patient outcomes. This aligns with the modern trend of precision medicine. By integrating current research advancements, our findings provide novel insights into the diagnosis and management of postoperative pneumonia following femoral neck fracture surgery. The established model demonstrates robust discrimination and calibration, with AUC values of 0.872 (95% CI: 0.82496\u0026ndash;0.91904) and 0.895 (95% CI: 0.82444\u0026ndash;0.96556) in the derivation and validation cohorts, respectively. These results validate its clinical utility, demonstrating that the model can be widely applied to guide clinical decision-making and therapeutic strategies for high-risk populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXuxing Sun:\u003c/strong\u003e Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;Yuhang Wang:\u003c/strong\u003e Formal analysis, Investigation, Methodology, Validation, Writing \u0026ndash; original draft. \u003cstrong\u003eXinyong Chen:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;Chengwei Xu\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYucheng Tang:\u003c/strong\u003e Data curation, Software, Validation.\u003cstrong\u003eHaojie Zhang:\u003c/strong\u003e Data curation, Software, Validation. \u003cstrong\u003eLong Jia:\u003c/strong\u003e Supervision, Methodology, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYadong Qian\u003c/strong\u003e: Supervision, Writing \u0026ndash; review \u0026amp; editing. \u0026nbsp;\u003cstrong\u003eDongliang Gong\u003c/strong\u003e: Conceptualization, Formal analysis, Funding acquisition, Investigation, Supervision, Methodology, Project administration, Writing \u0026ndash; review \u0026amp; editing.All authors read and approved the final manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded Shanghai Society of Integrated Chinese and Western Medicine community special fund.(Grant No.\u0026nbsp;2024-43）,Natural Science Foundation of Bengbu Medical University(Grant No.\u0026nbsp;2023byzd198）,Fifth Round of Discipline Development and Talent Cultivation Program (Rising Star Award) of Shanghai Qingpu District Healthcare System (Award No. YY2023-21)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of the Qingpu Branch of Zhongshan Hospital affiliated with Fudan University (approval code: qingyi2021-35). All patients provided written informed consent. The study was performed in accordance with the ethical standards of the Declaration of Helsinki (1964) and its subsequent amendments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any \u0026nbsp;commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea Department of Orthopedics, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, No. 1158 East Park Road,Shanghai 201700, China\u003c/p\u003e\n\u003cp\u003eb School of Clinical Medicine, Bengbu Medical University , No.2600 Donghai Avenue, Bengbu 233030, China/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAccelerated surgery versus standard care in hip fracture (HIP ATTACK): an international, randomised, controlled trial. \u003cem\u003eLancet\u003c/em\u003e. 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The sequential antifracturative treatment: a meta-analysis of randomized clinical trials. \u003cem\u003eTher Adv Musculoskelet Dis\u003c/em\u003e. 2024;16:1759720x241234584. doi:10.1177/1759720x241234584\u003c/li\u003e\n\u003cli\u003eBai F, Leng M, Zhang Y, Guo J, Wang Z. Effectiveness of intensive versus regular or no exercise in older adults after hip fracture surgery: A systematic review and meta-analysis. \u003cem\u003eBraz J Phys Ther\u003c/em\u003e. Jan-Feb 2023;27(1):100482. doi:10.1016/j.bjpt.2023.100482\u003c/li\u003e\n\u003cli\u003eKheir MM, Dilley JE, Speybroeck J, et al. The Influence of Dorr Type and Femoral Fixation on Outcomes Following Total Hip Arthroplasty for Acute Femoral Neck Fractures: A Multicenter Study. \u003cem\u003eJ Arthroplasty\u003c/em\u003e. Apr 2023;38(4):719-725. doi:10.1016/j.arth.2022.10.028\u003c/li\u003e\n\u003cli\u003eTian Y, Zhu Y, Zhang K, et al. 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Delirium after hip hemiarthroplasty for proximal femoral fractures in elderly patients: risk factors and clinical outcomes. \u003cem\u003eClin Interv Aging\u003c/em\u003e. 2019;14:427-435. doi:10.2147/cia.S189760\u003c/li\u003e\n\u003cli\u003eChen X, Zhang J, Lin Y, et al. Risk factors for postoperative mortality at 30 days in elderly Chinese patients with hip fractures. \u003cem\u003eOsteoporos Int\u003c/em\u003e. May 2022;33(5):1109-1116. doi:10.1007/s00198-021-06257-y\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Femoral neck fracture, Postoperative pneumonia, Risk factors prediction model, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6384696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6384696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to investigate the risk factors associated with postoperative pneumonia in elderly patients undergoing hip replacement for femoral neck fracture and to develop and validate a risk prediction model for such complications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective cohort of elderly patients (January 2022–September 2024) who underwent hip replacement for femoral neck fracture at the Qingpu Branch of Zhongshan Hospital, Fudan University, was analyzed. A total of 27 parameters were evaluated, including age, Modified 5-Factor Frailty Index (MFI-5), and anesthesia scores. The cohort was randomly divided into a derivation cohort (75.8%) and a validation cohort (24.2%). LASSO regression was applied to select optimal predictors through 10-fold cross-validation. Logistic regression identified significant factors for postoperative pneumonia, and a nomogram was constructed. Model accuracy was assessed using calibration curves, while predictive performance was evaluated via area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 418 included patients, 317 in the derivation cohort were stratified into pneumonia (n=83) and non-pneumonia (n=234) groups. Multivariate logistic regression identified age \u0026gt;65 years, smoking history, hemiarthroplasty, American Society of Anesthesiologists (ASA) grade ≥3, and MFI-5 \u0026gt;3 as independent risk factors (all P\u0026lt;0.05). The nomogram demonstrated excellent consistency and accuracy. AUC values for the derivation and validation cohorts were 0.872 (95% CI: 0.82496–0.91904) and 0.895 (95% CI: 0.82444–0.96556), respectively. Calibration curves showed minimal deviation (mean absolute error: 0.022 vs. 0.039). ROC and DCA confirmed robust predictive efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAdvanced age (\u0026gt;65 years), smoking, hemiarthroplasty, ASA grade ≥3, and MFI-5 \u0026gt;3 are significant risk factors for postoperative pneumonia in elderly femoral neck fracture patients. The developed nomogram provides a practical tool for preoperative risk stratification, enabling clinicians to optimize perioperative management and improve patient outcomes.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Clinical Prediction Model for Postoperative Pneumonia in Elderly Patients with Femoral Neck Fracture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 13:18:27","doi":"10.21203/rs.3.rs-6384696/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-05-29T13:16:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-06T12:01:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T18:29:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-12T15:24:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2025-04-12T15:23:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4451057-8c86-48bb-9c64-334d91e099ad","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-03T13:18:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 13:18:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6384696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6384696","identity":"rs-6384696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}