Development and internal validation of a nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery in older adults: a retrospective cohort study

Development and internal validation of a nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery in older adults: a retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and internal validation of a nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery in older adults: a retrospective cohort study Xiaopeng Chen, Lili Zou, Chuying Chen, Ling Li, Hongjian Li, Jiusheng He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8482635/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Postoperative pneumonia is one of the most common and serious complications following hip fracture surgery in older adults, substantially increasing morbidity, length of hospital stay, and mortality. Early identification of patients at high risk for postoperative pneumonia is essential for targeted preventive strategies. This study aimed to develop and internally validate a clinically applicable nomogram to predict the risk of pneumonia within 7 days after hip fracture surgery in elderly patients. Methods We conducted a single-center retrospective cohort study including consecutive patients aged ≥ 65 years who underwent surgical treatment for femoral neck or intertrochanteric fractures between January 2021 and December 2024. Patients with preoperative pneumonia were excluded. The primary outcome was postoperative pneumonia occurring within 7 days after surgery. Candidate predictors were prespecified based on clinical relevance and previous literature. A multivariable logistic regression model was developed, and internal validation was performed using bootstrap resampling (1000 iterations). Model performance was assessed using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). A nomogram was constructed to facilitate individualized risk prediction. Results A total of 760 patients were included in the final analysis, among whom 65 (8.6%) developed postoperative pneumonia within 7 days. Multivariable logistic regression identified age (OR 1.181, 95% CI 1.103–1.265, P < 0.001), chronic pulmonary disease (OR 2.585, 95% CI 1.266–5.276, P = 0.009), dementia (OR 3.138, 95% CI 1.445–6.812, P = 0.004), minimum oxygen saturation within 24 hours (OR 0.795, 95% CI 0.694–0.910, P < 0.001), serum albumin (OR 0.656, 95% CI 0.586–0.733, P < 0.001), and log-transformed C-reactive protein (OR 4.729, 95% CI 2.308–9.686, P < 0.001) as independent predictors. The nomogram demonstrated good discriminative ability, with an optimism-corrected C-index of 0.949. Calibration curves showed good agreement between predicted and observed risks. Decision curve analysis indicated that the nomogram provided a positive net clinical benefit across a wide range of threshold probabilities. Conclusions We developed and internally validated a nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery in older adults. This tool may assist clinicians in early risk stratification and implementation of targeted preventive interventions. Hip fracture postoperative pneumonia nomogram elderly prediction model internal validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hip fractures represent a major public health challenge in the aging population, with steadily increasing incidence worldwide. It is estimated that the annual number of hip fractures will exceed 4.5 million globally by 2050, largely driven by population aging and osteoporosis prevalence[ 1 – 3 ]. Elderly patients with hip fractures often present with multiple comorbidities and limited physiological reserves, making them particularly vulnerable to perioperative complications[ 3 – 6 ]. Postoperative pneumonia is one of the most frequent and severe complications following hip fracture surgery. Previous studies have reported pneumonia incidence rates ranging from 5% to 15% in the early postoperative period, with even higher rates observed during longer follow-up. The occurrence of pneumonia is associated with prolonged hospitalization, increased healthcare costs, delayed functional recovery, and significantly elevated short- and long-term mortality[ 7 – 10 ]. Importantly, pneumonia often develops early after surgery, suggesting that timely identification of high-risk patients may allow preventive strategies to be implemented before irreversible deterioration occurs. Several risk factors for postoperative pneumonia in hip fracture patients have been reported, including advanced age, pre-existing pulmonary disease, cognitive impairment, poor nutritional status, inflammatory burden, and perioperative hypoxemia[ 10 – 15 ]. However, in routine clinical practice, risk assessment often relies on subjective judgment rather than quantitative tools. Although various scoring systems have been proposed for postoperative complications, few are specifically designed for early postoperative pneumonia in elderly hip fracture patients, and many lack adequate validation. Nomograms have emerged as intuitive and user-friendly tools for individualized risk prediction. By integrating multiple predictors into a graphical scoring system, nomograms facilitate bedside estimation of outcome probabilities and support clinical decision-making. In recent years, nomogram-based prediction models have been increasingly applied in orthopedic and geriatric research, including the assessment of perioperative delirium, cardiac complications and mortality in hip fracture patients[ 16 – 21 ]. However, nomograms specifically targeting early postoperative pneumonia in elderly hip fracture patients remain limited. Hence, the objective of this study was to develop and internally validate a nomogram to predict the risk of pneumonia within 7 days after hip fracture surgery in older adults using routinely available clinical and laboratory variables. Materials and methods Study population This retrospective cohort study included elderly patients admitted to the orthopedic department of Shunyi District Hospital between January 2021 and December 2024. Eligible patients were aged ≥ 65 years and underwent surgical treatment for femoral neck or intertrochanteric fractures. Patients were excluded if they had evidence of pneumonia before surgery, did not undergo surgical intervention, or had missing key clinical data required for outcome assessment. All data were extracted from the electronic medical record system. Patient identifiers were removed before analysis to ensure confidentiality. This study was approved by the institutional ethics committee of Shunyi District Hospital, and the requirement for informed consent was waived due to the retrospective nature of the study. Clinical trial number: not applicable. Definition of postoperative pneumonia Postoperative pneumonia was defined as pneumonia occurring within 7 days after surgery, diagnosed based on a combination of radiological findings (new pulmonary infiltrates on chest radiography or computed tomography), compatible clinical features (such as fever, cough, purulent sputum, or auscultatory findings), and initiation or escalation of antibiotic therapy[ 22 – 24 ]. The first documented date of pneumonia diagnosis was recorded. Candidate predictors Based on clinical relevance and prior literature, the following candidate predictors were prespecified and measured at admission or within the first 24 hours of hospitalization: age, sex, fracture type, history of chronic pulmonary disease (including chronic obstructive pulmonary disease or asthma), dementia, minimum oxygen saturation within 24 hours, serum albumin concentration, hemoglobin level, and C-reactive protein (CRP)[ 23 , 25 – 27 ]. CRP was log-transformed to reduce skewness. Statistical analysis Continuous variables were summarized as means with standard deviations or medians with interquartile ranges, depending on distribution. Categorical variables were presented as counts and percentages. Comparisons between patients with and without postoperative pneumonia were performed using appropriate statistical tests. A multivariable logistic regression model was developed to estimate the probability of postoperative pneumonia within 7 days. To reduce overfitting, the number of predictors was limited according to the number of outcome events. Continuous variables were retained as continuous predictors without categorization. Internal validation was performed using bootstrap resampling with 1000 iterations. Model discrimination was evaluated using the C-index and ROC curve. Calibration was assessed using calibration plots, calibration slope, and intercept. Clinical utility was evaluated using decision curve analysis by calculating net benefit across a range of threshold probabilities. A nomogram was constructed based on the final model, which was developed to provide individualized risk estimates rather than to define mandatory clinical actions. All analyses were conducted using R software (version 4.5.1). Statistical significance was set at P < 0.05. Results Patient selection and baseline characteristics During the study period, 1,863 patients with hip fractures were initially screened. After sequential exclusion of patients without surgical treatment (n = 163), those with multiple injuries (n = 191), age younger than 65 years (n = 350), pathologic fractures (n = 36), old fractures (n = 30), preoperative pneumonia (n = 156), and incomplete clinical data (n = 177), a total of 760 patients were included in the final analysis (Fig. 1 ). Flowchart of patient selection. During the study period, patients with hip fracture were screened according to predefined inclusion and exclusion criteria. After exclusions, a total of 760 older adults undergoing hip fracture surgery were included in the final analysis. The mean age of the study population was 80.2 ± 6.8 years, and 61.2% were female. Postoperative pneumonia within 7 days occurred in 65 patients, yielding an incidence of 8.6%. Baseline characteristics stratified by postoperative pneumonia status are summarized in Table 1 . Compared with patients without pneumonia, those who developed pneumonia were generally older and more likely to have chronic pulmonary disease and dementia. They also exhibited lower minimum oxygen saturation within the first 24 hours, lower serum albumin levels, and higher CRP levels at admission. Table 1 Baseline characteristics of hip fracture patients classified by pneumonia within 7 days after hip fracture surgery in older adults Variables Total ( N = 760) Non-pneumonia ( N = 695) Pneumonia ( N = 65) P-value Age, mean ± SD (years) 80.2 ± 6.8 79.6 ± 6.6 87.1 ± 5.1 < 0.001 Gender,N(%) 0.546 Male 295(38.8) 267(38.4) 28(43.1) Female 465(61.2) 428(61.6) 37(56.9) Fracture type, N(%) 0.373 Femur neck fracture 317(41.7) 286(41.2) 31(47.7) Intertrochanteric fracture 443(58.3) 409(58.8) 34(52.3) Time_to_surgery, mean ± SD (hours) 42.5 ± 25.4 41.2 ± 25.0 55.8 ± 26.3 < 0.001 COPD_asthma, N(%) < 0.001 NO 618(81.3) 584(81.2) 34(52.3) YES 142(18.7) 111(16.0) 31(47.7) Stroke history, N(%) 0.951 NO 616(81.1) 564(81.2) 52(80.0) YES 144(18.9) 131(18.8) 13(20.0) Dementia, N(%) < 0.001 NO 605(79.6) 572(82.3) 33(50.8) YES 155(20.4) 123(17.7) 32(49.2) Diabetes, N(%) 0.212 NO 552(72.6) 500(71.9) 52(80.0) YES 208(27.4) 195(28.1) 13(20.0) Chronic_kidney_disease, N(%) 0.856 NO 666(87.6) 610(87.8) 56(86.2) YES 94(12.4) 85(12.2) 9(13.8) SpO2_min_24h, mean ± SD 94.8 ± 2.5 94.9 ± 2.4 93.1 ± 3.1 < 0.001 Oxygen_therapy_24h, N(%) 0.001 NO 515(67.8) 483(69.5) 32(49.2) YES 245(32.2) 212(30.5) 33(50.8) Albumin mean ± SD (g/L) 35.5 ± 4.5 35.9 ± 4.3 30.5 ± 3.5 < 0.001 Hemoglobin, mean ± SD (g/L) 112.1 ± 14.6 112.3 ± 14.5 110.3 ± 15.8 0.292 CRP, mean ± SD (mg/L) 15.6 ± 11.0 15.2 ± 11.1 20.3 ± 8.6 < 0.001 Construction of the predictive model Multivariable logistic regression analysis identified age, chronic pulmonary disease, dementia, minimum oxygen saturation within the first 24 hours, serum albumin level, and log-transformed CRP as independent predictors of postoperative pneumonia within 7 days (Table 2 , Fig. 2 ). No evidence of multicollinearity was observed among the included predictors, with all variance inflation factors below 2 (Supplementary Table S2). Table 2 Prediction factors of postoperative pneumonia within 7 days in geriatric patients with hip fractures B SE Wald P value Odds ratio(95%CI) Age 0.166 0.035 22.662 < 0.001 1.181(1.103–1.265) COPD_asthma 0.950 0.364 6.803 0.009 2.585(1.266–5.276) Dementia 1.143 0.396 8.357 0.004 3.138(1.445–6.812) SpO2_min_24h -0.230 0.069 11.113 < 0.001 0.795(0.694–0.910) Albumin -0.422 0.057 54.909 < 0.001 0.656(0.586–0.733) log_crp 1.554 0.366 18.033 < 0.001 4.729(2.308–9.686) Forest plot showing odds ratios (ORs) and 95% confidence intervals (CIs) for independent predictors of postoperative pneumonia within 7 days after hip fracture surgery, derived from multivariable logistic regression analysis. Based on these predictors, a nomogram was constructed to provide an individualized estimate of the probability of developing postoperative pneumonia within 7 days after hip fracture surgery (Fig. 3 ). Discussion In this study, we constructed and internally validated a nomogram to estimate the risk of pneumonia within the first 7 days following hip fracture surgery in older adults. The model integrates clinical characteristics and laboratory parameters that are routinely obtained during early hospitalization. Overall, it demonstrated satisfactory discrimination, stable calibration, and potential clinical usefulness. Importantly, this nomogram was not designed as an automated decision-making tool. Instead, it aims to assist clinicians in identifying patients who may benefit from closer surveillance and early preventive strategies in routine practice. Age emerged as one of the most influential predictors in the model. However, the association between age and postoperative pneumonia likely reflects more than chronological aging alone. With advancing age, several physiological changes converge to increase pulmonary vulnerability. These include age-related immune dysfunction, reduced respiratory muscle strength, and a less effective cough reflex, all of which impair airway defense. In addition, older patients are more likely to have multiple comorbidities and to experience prolonged postoperative immobilization, which further promotes atelectasis and secretion retention. Previous studies have consistently reported a higher incidence of postoperative pneumonia among elderly orthopedic patients[ 6 , 28 ]. Clinically, these findings highlight the importance of early respiratory assessment and proactive monitoring, particularly in very elderly individuals [ 29 , 30 ]. Chronic pulmonary disease was another independent risk factor for postoperative pneumonia. Patients with chronic obstructive pulmonary disease often present with a substantial burden of comorbid conditions, reflecting a state of chronic systemic vulnerability rather than isolated lung pathology. Persistent inflammation, recurrent hypoxemia, and cardiopulmonary interactions together reduce physiological reserve and increase susceptibility to infection. Prior research has consistently linked chronic pulmonary disease with postoperative respiratory infections [ 23 , 31 ]. Skajaa et al [ 9 ] reported that 43% of patients with COPD died within five years of follow-up, underscoring the severity of this condition. In a clinician’s view, COPD should be viewed as a prototypical multimorbidity syndrome. Effective management therefore requires a broader, integrated approach that extends beyond respiratory care alone, particularly in elderly patients undergoing major orthopedic surgery. Dementia was also independently associated with early postoperative pneumonia. This relationship likely reflects a combination of behavioral and physiological mechanisms[ 32 , 33 ]. Cognitive impairment increases aspiration risk through impaired swallowing coordination and reduced protective reflexes, while limited cooperation with postoperative mobilization and breathing exercises further exacerbates pulmonary vulnerability. Funayama et al[ 34 ] reported that pneumonia development in patients with dementia was closely related to poor oral hygiene, dysphagia, and impaired consciousness. These observations suggest that patients with dementia require tailored perioperative management, including early swallowing assessment and enhanced nursing support. Lower minimum oxygen saturation within the first 24 hours was strongly associated with subsequent pneumonia. Clinically, early hypoxemia tends to serve as an early indicator of respiratory compromise before overt signs of infection become apparent. Mechanistically, insufficient oxygenation can impair pulmonary immune defenses, weaken cough effectiveness, and reduce the clearance of aspirated material. Previous studies have shown that hypoxemia at hospital admission is an independent risk factor for postoperative pneumonia after hip fracture surgery[ 35 , 36 ]. Notably, this association is not limited to patients with preexisting lung disease, suggesting that hypoxemia itself represents a high-risk physiological state. In practice, early detection of hypoxemia should prompt intensified monitoring and timely respiratory support. Hypoalbuminemia was another important predictor in the nomogram. Low serum albumin levels are common in elderly patients with hip fracture and reflect a complex interplay of malnutrition, systemic inflammation, and reduced physiological reserve. These factors collectively impair immune competence and tissue repair, increasing susceptibility to infection. However, Not all of these mechanisms are necessarily present in every patient. Numerous studies have reported that hypoalbuminemia is associated with postoperative infections and poorer outcomes across surgical populations [ 23 , 37 – 40 ]. Clinically, this finding underscores the importance of early nutritional assessment and timely nutritional intervention as part of comprehensive perioperative care. Elevated CRP levels were independently associated with postoperative pneumonia. As a marker of systemic inflammation, CRP was likely to reflect surgical stress, occult infection, or chronic inflammatory conditions. A heightened inflammatory burden can disrupt immune homeostasis and predispose patients to subsequent infectious complications. Although evidence regarding CRP in acute respiratory infections is heterogeneous, markedly elevated CRP levels have been associated with bacterial pneumonia, whereas lower values tends to suggest non-bacterial etiologies[ 26 , 27 ]. In clinical practice, CRP can serve as a pragmatic adjunct for risk stratification, particularly in settings where microbiological testing is limited. Elevated CRP levels should therefore prompt closer postoperative surveillance, especially in patients with multiple comorbidities. Beyond individual predictors, the overall performance of the nomogram was supported by decision curve analysis. The model consistently provided a higher net benefit than both treat-all and treat-none strategies across a range of low to moderate threshold probabilities. This finding is clinically relevant, as many preventive interventions for postoperative pneumonia—such as respiratory physiotherapy, nutritional optimization, early mobilization, and closer monitoring—are generally low risk and low cost. In such settings, initiating preventive measures at relatively low predicted risk levels is likely to be reasonable. Importantly, decision curve analysis supports individualized risk stratification rather than routine intervention for all patients. Internal validation using bootstrap resampling demonstrated stable discrimination and calibration after bias correction. The calibration slope was close to 1.0 and the intercept was near zero, indicating minimal overall miscalibration. The bootstrap-corrected calibration curve further supported good agreement between predicted and observed risks. Although these findings suggest that model optimism was adequately controlled, external validation in independent cohorts remains necessary. The inclusion of time from admission to surgery in a supplementary model did not materially change the performance or the main conclusions of the primary model, supporting the robustness of our findings. Taken together, our results suggest that postoperative pneumonia following hip fracture surgery arises from the interaction of vulnerabilities across respiratory, nutritional, inflammatory, and cognitive domains. Effective prevention therefore requires an integrated approach that extends beyond single-organ assessment. Early identification of high-risk patients using a multivariable nomogram may facilitate coordinated multidisciplinary management and closer monitoring of multiple physiological systems during the critical postoperative period. Limitations Despite the strengths of the present work, several limitations warrant consideration. This study was conducted as a single-center retrospective analysis, which may restrict the generalizability of the findings beyond the study setting. Variations in patient characteristics—such as frailty status and comorbidity burden—as well as differences in perioperative care pathways and healthcare systems, particularly in older adult populations, could influence model performance. For this reason, validation in independent, multi-center cohorts remains an important next step before broader clinical use. In designing the model, we deliberately focused on predictors that are routinely available early during hospital admission to enhance feasibility in real-world geriatric practice. As a consequence, some potentially relevant perioperative factors, including anesthesia technique, analgesic management, and detailed respiratory support, were not consistently captured in this retrospective dataset. Although these factors may contribute to postoperative pneumonia risk in older surgical patients, they could not be evaluated systematically in the present study. Prospective studies with standardized data collection may help clarify their additional predictive value. The diagnosis of postoperative pneumonia was based on a combination of radiological findings, clinical features, and initiation or escalation of antibiotic therapy, reflecting routine clinical practice in elderly patients. While this approach improves clinical relevance, it may also introduce diagnostic variability and treatment-related bias that cannot be fully eliminated in retrospective analyses. Finally, although the model demonstrated high apparent discrimination, internal validation using bootstrap resampling was applied to account for potential overfitting. The optimism-corrected calibration suggested stable model performance; however, internal validation alone cannot replace external evaluation. Accordingly, the predictive accuracy of the nomogram should be interpreted with appropriate caution until it has been tested in diverse older adult populations. Conclusions In our study, a nomogram was developed and internally validated to predict the risk of postoperative pneumonia occurring within 7 days after hip fracture surgery in older adults. The model was constructed using routinely available clinical and laboratory variables obtained during early hospitalization, allowing for timely risk assessment in real-world clinical settings. By integrating multiple patient-specific factors, the nomogram provides individualized risk estimates rather than a uniform risk classification. The predictive performance of the model demonstrated satisfactory discrimination and calibration, suggesting that it can reliably reflect the early postoperative pneumonia risk in this vulnerable population. Importantly, the variables incorporated into the nomogram are easy to obtain and commonly assessed in routine perioperative care, which enhances the feasibility of its application without adding additional clinical burden. From a clinical standpoint, this tool is not intended to replace physician judgment or function as an automated decision-making system. Instead, it may assist clinicians in identifying patients at higher risk of postoperative pneumonia who could benefit from closer monitoring, early preventive interventions, and more individualized perioperative management strategies. Early recognition of high-risk patients may facilitate targeted respiratory care, nutritional support, and multidisciplinary coordination, with the potential to reduce the incidence of postoperative pneumonia and improve short-term outcomes in older patients undergoing hip fracture surgery. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Shunyi District Hospital (Approval No. 2022-K-004-02). The requirement for informed consent was waived due to the retrospective nature of the study and use of de-identified data. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Authors' information 1Department of Orthopedic Trauma, Shunyi District Hosptial, Beijing. 2Department of Radiology, Shunyi District Hospital, Beijing. No.3 Guangming South Street, Shunyi District, Beijing, China. Funding This work was supported by the Capital’s Funds for Health Improvement and Research (2022-1-2071) Author Contribution XPC conceived of the study and drafted the manuscript. LLZ, CYC, LL and HJL gathered and processed the data. JSH supervision, and revised the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgements The authors would like to thank all patients and clinical staff who contributed to this study. We also acknowledge the support of the orthopedic and geriatric teams involved in patient care and data collection. 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Pneumonia risk increased by dementia-related daily living difficulties: poor oral hygiene and dysphagia as contributing factors. Am J Geriatr Psychiatry: Off J Am Assoc Geriatr Psychiatry. 2023;31:877–85. https://doi.org/10.1016/j.jagp.2023.05.007 . Ji Y, Li X, Wang Y, Cheng L, Tian H, Li N, et al. Partial pressure of oxygen level at admission as a predictor of postoperative pneumonia after hip fracture surgery in a geriatric population: a retrospective cohort study. BMJ Open. 2021;11. https://doi.org/10.1136/bmjopen-2020-048272 . Han S-B, Kim S-B, Shin K-H. Risk factors for postoperative pneumonia in patients undergoing hip fracture surgery: a systematic review and meta-analysis. BMC musculoskelet disord. 2022;23:553. https://doi.org/10.1186/s12891-022-05497-1 . Chen Z, Wu H, Jiang J, Xu K, Gao S, Chen L, et al. Nutritional risk screening score as an independent predictor of nonventilator hospital-acquired pneumonia: a cohort study of 67,280 patients. BMC Infect Dis. 2021;21:313. https://doi.org/10.1186/s12879-021-06014-w . Yao W, Tang W, Wang W, Lv Q, Ding W. Correlation between admission hypoalbuminemia and postoperative urinary tract infections in elderly hip fracture patients. J Orthop Surg Res. 2023;18:774. https://doi.org/10.1186/s13018-023-04274-7 . Xie J, Liu H, Deng S, Niu T, Wang J, Wang H, et al. Association between immediate postoperative hypoalbuminemia and surgical site infection after posterior lumbar fusion surgery. Eur Spine J: Off Publ Eur Spine Soc Eur Spinal Deform Soc Eur Sect Cerv Spine Res Soc. 2023;32:2012–9. https://doi.org/10.1007/s00586-023-07682-9 . Llombart R, Mariscal G, Barrios C, de la Rubia Ortí JE, Llombart-Ais R. The impact of hypoalbuminemia on postoperative complications in patients undergoing shoulder arthroplasty: a meta-analysis. J Nutr Health Aging. 2023;27:1248–54. https://doi.org/10.1007/s12603-023-2050-6 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 07 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 16 Jan, 2026 Editor assigned by journal 02 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 30 Dec, 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-8482635","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587784206,"identity":"bd731af3-f2f5-401a-a351-e26e2d9e727b","order_by":0,"name":"Xiaopeng Chen","email":"","orcid":"","institution":"Shunyi District Hosptial","correspondingAuthor":false,"prefix":"","firstName":"Xiaopeng","middleName":"","lastName":"Chen","suffix":""},{"id":587784208,"identity":"fa8deb8d-6fab-40bb-b0a4-6be0fd20b597","order_by":1,"name":"Lili Zou","email":"","orcid":"","institution":"Shunyi District Hosptial","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zou","suffix":""},{"id":587784209,"identity":"aeae9c90-a86b-4eb7-88cf-0b02b67214fd","order_by":2,"name":"Chuying Chen","email":"","orcid":"","institution":"Shunyi District Hosptial","correspondingAuthor":false,"prefix":"","firstName":"Chuying","middleName":"","lastName":"Chen","suffix":""},{"id":587784210,"identity":"4b7f8ca0-6765-4925-9ea8-ff88aa96499e","order_by":3,"name":"Ling Li","email":"","orcid":"","institution":"Shunyi District Hosptial","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""},{"id":587784211,"identity":"9de25495-1aab-4180-8d27-c43423a57de8","order_by":4,"name":"Hongjian Li","email":"","orcid":"","institution":"Shunyi District Hosptial","correspondingAuthor":false,"prefix":"","firstName":"Hongjian","middleName":"","lastName":"Li","suffix":""},{"id":587784212,"identity":"5bc700b8-e1f3-4c4e-a397-276680582c71","order_by":5,"name":"Jiusheng He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACA4YDIMqGh5+/gTQtaTKSMw4QrQUMDtsYNCQQqcWc8fgDxp9t53mA1jF++JhDhBbLhjMGzLxtt3nMmRuYJWduI8ZhB84wMDMCtVg2HGBj5iVOC9hh53gMDiQQreWAAQNv2wGStAD9wnMumUdyxsFmIv1yA+iwH2V29vz8zQc/fCRGC4PEAfYfjGwgFmMDMeqBAJxO/hCpeBSMglEwCkYmAACPizi1djG28gAAAABJRU5ErkJggg==","orcid":"","institution":"Shunyi District Hosptial","correspondingAuthor":true,"prefix":"","firstName":"Jiusheng","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-12-30 15:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8482635/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8482635/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102745707,"identity":"c0b852d3-c62d-4fea-9094-3a2cb710903b","added_by":"auto","created_at":"2026-02-16 08:53:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94747,"visible":true,"origin":"","legend":"\u003cp\u003eThe patient flow chart in our study\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8482635/v1/6757e1319ca6a3f3853f4d0d.jpg"},{"id":102439677,"identity":"9c49282c-1db1-40ae-8b5a-fc72bada7cfe","added_by":"auto","created_at":"2026-02-11 16:42:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68785,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of independent predictors\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8482635/v1/a2abbe2505a7cc087685394a.jpg"},{"id":102439675,"identity":"db21b3e9-f19f-491a-949d-9ad6fe99b41c","added_by":"auto","created_at":"2026-02-11 16:42:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67164,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram model for predicting the occurrence of postoperative pneumonia within 7 days in geriatric patients with hip fractures\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8482635/v1/30254b732f6ab774095441b2.jpg"},{"id":102746002,"identity":"6fc240a9-4d50-44f6-8390-24452a7d8341","added_by":"auto","created_at":"2026-02-16 08:55:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33619,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Receiver operating characteristic (ROC) curve for the prediction model of postoperative pneumonia.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8482635/v1/e4c325133b9e728bcf59a425.jpg"},{"id":102745784,"identity":"a9fdb3ec-b686-4c7e-8fcd-2f14d4473073","added_by":"auto","created_at":"2026-02-16 08:53:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36974,"visible":true,"origin":"","legend":"\u003cp\u003eBootstrap-corrected calibration curve of the nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery. The dashed line represents ideal calibration, the dotted line indicates apparent performance, and the solid line shows bias-corrected estimates. The calibration slope was close to 1.0 with an intercept near zero, indicating good agreement between predicted and observed risks.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8482635/v1/14ce093e091ee606d8ab77d0.jpg"},{"id":102439678,"identity":"cbf974ec-0877-4427-8424-4ff0adf134ab","added_by":"auto","created_at":"2026-02-11 16:42:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31152,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) for the prediction model of postoperative pneumonia.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8482635/v1/c90c2a719859219546b77f30.jpg"},{"id":102751710,"identity":"286abb0f-ea40-48cf-9d7f-f2437bf71554","added_by":"auto","created_at":"2026-02-16 09:27:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1101885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8482635/v1/58f11da9-e429-4161-a578-473f71970694.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and internal validation of a nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery in older adults: a retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHip fractures represent a major public health challenge in the aging population, with steadily increasing incidence worldwide. It is estimated that the annual number of hip fractures will exceed 4.5\u0026nbsp;million globally by 2050, largely driven by population aging and osteoporosis prevalence[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Elderly patients with hip fractures often present with multiple comorbidities and limited physiological reserves, making them particularly vulnerable to perioperative complications[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePostoperative pneumonia is one of the most frequent and severe complications following hip fracture surgery. Previous studies have reported pneumonia incidence rates ranging from 5% to 15% in the early postoperative period, with even higher rates observed during longer follow-up. The occurrence of pneumonia is associated with prolonged hospitalization, increased healthcare costs, delayed functional recovery, and significantly elevated short- and long-term mortality[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Importantly, pneumonia often develops early after surgery, suggesting that timely identification of high-risk patients may allow preventive strategies to be implemented before irreversible deterioration occurs.\u003c/p\u003e \u003cp\u003eSeveral risk factors for postoperative pneumonia in hip fracture patients have been reported, including advanced age, pre-existing pulmonary disease, cognitive impairment, poor nutritional status, inflammatory burden, and perioperative hypoxemia[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, in routine clinical practice, risk assessment often relies on subjective judgment rather than quantitative tools. Although various scoring systems have been proposed for postoperative complications, few are specifically designed for early postoperative pneumonia in elderly hip fracture patients, and many lack adequate validation.\u003c/p\u003e \u003cp\u003eNomograms have emerged as intuitive and user-friendly tools for individualized risk prediction. By integrating multiple predictors into a graphical scoring system, nomograms facilitate bedside estimation of outcome probabilities and support clinical decision-making. In recent years, nomogram-based prediction models have been increasingly applied in orthopedic and geriatric research, including the assessment of perioperative delirium, cardiac complications and mortality in hip fracture patients[\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, nomograms specifically targeting early postoperative pneumonia in elderly hip fracture patients remain limited. Hence, the objective of this study was to develop and internally validate a nomogram to predict the risk of pneumonia within 7 days after hip fracture surgery in older adults using routinely available clinical and laboratory variables.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included elderly patients admitted to the orthopedic department of Shunyi District Hospital between January 2021 and December 2024. Eligible patients were aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years and underwent surgical treatment for femoral neck or intertrochanteric fractures. Patients were excluded if they had evidence of pneumonia before surgery, did not undergo surgical intervention, or had missing key clinical data required for outcome assessment.\u003c/p\u003e \u003cp\u003eAll data were extracted from the electronic medical record system. Patient identifiers were removed before analysis to ensure confidentiality. This study was approved by the institutional ethics committee of Shunyi District Hospital, and the requirement for informed consent was waived due to the retrospective nature of the study. Clinical trial number: not applicable.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of postoperative pneumonia\u003c/h3\u003e\n\u003cp\u003ePostoperative pneumonia was defined as pneumonia occurring within 7 days after surgery, diagnosed based on a combination of radiological findings (new pulmonary infiltrates on chest radiography or computed tomography), compatible clinical features (such as fever, cough, purulent sputum, or auscultatory findings), and initiation or escalation of antibiotic therapy[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The first documented date of pneumonia diagnosis was recorded.\u003c/p\u003e\n\u003ch3\u003eCandidate predictors\u003c/h3\u003e\n\u003cp\u003eBased on clinical relevance and prior literature, the following candidate predictors were prespecified and measured at admission or within the first 24 hours of hospitalization: age, sex, fracture type, history of chronic pulmonary disease (including chronic obstructive pulmonary disease or asthma), dementia, minimum oxygen saturation within 24 hours, serum albumin concentration, hemoglobin level, and C-reactive protein (CRP)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. CRP was log-transformed to reduce skewness.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized as means with standard deviations or medians with interquartile ranges, depending on distribution. Categorical variables were presented as counts and percentages. Comparisons between patients with and without postoperative pneumonia were performed using appropriate statistical tests.\u003c/p\u003e \u003cp\u003eA multivariable logistic regression model was developed to estimate the probability of postoperative pneumonia within 7 days. To reduce overfitting, the number of predictors was limited according to the number of outcome events. Continuous variables were retained as continuous predictors without categorization.\u003c/p\u003e \u003cp\u003eInternal validation was performed using bootstrap resampling with 1000 iterations. Model discrimination was evaluated using the C-index and ROC curve. Calibration was assessed using calibration plots, calibration slope, and intercept. Clinical utility was evaluated using decision curve analysis by calculating net benefit across a range of threshold probabilities. A nomogram was constructed based on the final model, which was developed to provide individualized risk estimates rather than to define mandatory clinical actions.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R software (version 4.5.1). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection and baseline characteristics\u003c/h2\u003e \u003cp\u003eDuring the study period, 1,863 patients with hip fractures were initially screened. After sequential exclusion of patients without surgical treatment (n\u0026thinsp;=\u0026thinsp;163), those with multiple injuries (n\u0026thinsp;=\u0026thinsp;191), age younger than 65 years (n\u0026thinsp;=\u0026thinsp;350), pathologic fractures (n\u0026thinsp;=\u0026thinsp;36), old fractures (n\u0026thinsp;=\u0026thinsp;30), preoperative pneumonia (n\u0026thinsp;=\u0026thinsp;156), and incomplete clinical data (n\u0026thinsp;=\u0026thinsp;177), a total of 760 patients were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFlowchart of patient selection. During the study period, patients with hip fracture were screened according to predefined inclusion and exclusion criteria. After exclusions, a total of 760 older adults undergoing hip fracture surgery were included in the final analysis.\u003c/p\u003e \u003cp\u003eThe mean age of the study population was 80.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 years, and 61.2% were female. Postoperative pneumonia within 7 days occurred in 65 patients, yielding an incidence of 8.6%. Baseline characteristics stratified by postoperative pneumonia status are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with patients without pneumonia, those who developed pneumonia were generally older and more likely to have chronic pulmonary disease and dementia. They also exhibited lower minimum oxygen saturation within the first 24 hours, lower serum albumin levels, and higher CRP levels at admission.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of hip fracture patients classified by pneumonia within 7 days after hip fracture surgery in older adults\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;760)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-pneumonia\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;695)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePneumonia\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender,N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295(38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267(38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465(61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428(61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture type, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemur neck fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317(41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntertrochanteric fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443(58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e409(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime_to_surgery,\u003c/p\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u0026thinsp;\u0026plusmn;\u0026thinsp;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD_asthma, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618(81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e584(81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke history, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e616(81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e564(81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131(18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e605(79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572(82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e552(72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500(71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195(28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic_kidney_disease, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e666(87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e610(87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2_min_24h, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen_therapy_24h, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e515(67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e483(69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245(32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction of the predictive model\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression analysis identified age, chronic pulmonary disease, dementia, minimum oxygen saturation within the first 24 hours, serum albumin level, and log-transformed CRP as independent predictors of postoperative pneumonia within 7 days (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No evidence of multicollinearity was observed among the included predictors, with all variance inflation factors below 2 (Supplementary Table S2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction factors of postoperative pneumonia within 7 days in geriatric patients with hip fractures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds ratio(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.181(1.103\u0026ndash;1.265)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD_asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.585(1.266\u0026ndash;5.276)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.138(1.445\u0026ndash;6.812)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2_min_24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.795(0.694\u0026ndash;0.910)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.656(0.586\u0026ndash;0.733)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog_crp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.729(2.308\u0026ndash;9.686)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eForest plot showing odds ratios (ORs) and 95% confidence intervals (CIs) for independent predictors of postoperative pneumonia within 7 days after hip fracture surgery, derived from multivariable logistic regression analysis.\u003c/p\u003e \u003cp\u003eBased on these predictors, a nomogram was constructed to provide an individualized estimate of the probability of developing postoperative pneumonia within 7 days after hip fracture surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we constructed and internally validated a nomogram to estimate the risk of pneumonia within the first 7 days following hip fracture surgery in older adults. The model integrates clinical characteristics and laboratory parameters that are routinely obtained during early hospitalization. Overall, it demonstrated satisfactory discrimination, stable calibration, and potential clinical usefulness. Importantly, this nomogram was not designed as an automated decision-making tool. Instead, it aims to assist clinicians in identifying patients who may benefit from closer surveillance and early preventive strategies in routine practice.\u003c/p\u003e \u003cp\u003eAge emerged as one of the most influential predictors in the model. However, the association between age and postoperative pneumonia likely reflects more than chronological aging alone. With advancing age, several physiological changes converge to increase pulmonary vulnerability. These include age-related immune dysfunction, reduced respiratory muscle strength, and a less effective cough reflex, all of which impair airway defense. In addition, older patients are more likely to have multiple comorbidities and to experience prolonged postoperative immobilization, which further promotes atelectasis and secretion retention. Previous studies have consistently reported a higher incidence of postoperative pneumonia among elderly orthopedic patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Clinically, these findings highlight the importance of early respiratory assessment and proactive monitoring, particularly in very elderly individuals [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic pulmonary disease was another independent risk factor for postoperative pneumonia. Patients with chronic obstructive pulmonary disease often present with a substantial burden of comorbid conditions, reflecting a state of chronic systemic vulnerability rather than isolated lung pathology. Persistent inflammation, recurrent hypoxemia, and cardiopulmonary interactions together reduce physiological reserve and increase susceptibility to infection. Prior research has consistently linked chronic pulmonary disease with postoperative respiratory infections [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Skajaa et al [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] reported that 43% of patients with COPD died within five years of follow-up, underscoring the severity of this condition. In a clinician\u0026rsquo;s view, COPD should be viewed as a prototypical multimorbidity syndrome. Effective management therefore requires a broader, integrated approach that extends beyond respiratory care alone, particularly in elderly patients undergoing major orthopedic surgery.\u003c/p\u003e \u003cp\u003eDementia was also independently associated with early postoperative pneumonia. This relationship likely reflects a combination of behavioral and physiological mechanisms[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Cognitive impairment increases aspiration risk through impaired swallowing coordination and reduced protective reflexes, while limited cooperation with postoperative mobilization and breathing exercises further exacerbates pulmonary vulnerability. Funayama et al[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] reported that pneumonia development in patients with dementia was closely related to poor oral hygiene, dysphagia, and impaired consciousness. These observations suggest that patients with dementia require tailored perioperative management, including early swallowing assessment and enhanced nursing support.\u003c/p\u003e \u003cp\u003eLower minimum oxygen saturation within the first 24 hours was strongly associated with subsequent pneumonia. Clinically, early hypoxemia tends to serve as an early indicator of respiratory compromise before overt signs of infection become apparent. Mechanistically, insufficient oxygenation can impair pulmonary immune defenses, weaken cough effectiveness, and reduce the clearance of aspirated material. Previous studies have shown that hypoxemia at hospital admission is an independent risk factor for postoperative pneumonia after hip fracture surgery[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Notably, this association is not limited to patients with preexisting lung disease, suggesting that hypoxemia itself represents a high-risk physiological state. In practice, early detection of hypoxemia should prompt intensified monitoring and timely respiratory support.\u003c/p\u003e \u003cp\u003eHypoalbuminemia was another important predictor in the nomogram. Low serum albumin levels are common in elderly patients with hip fracture and reflect a complex interplay of malnutrition, systemic inflammation, and reduced physiological reserve. These factors collectively impair immune competence and tissue repair, increasing susceptibility to infection. However, Not all of these mechanisms are necessarily present in every patient. Numerous studies have reported that hypoalbuminemia is associated with postoperative infections and poorer outcomes across surgical populations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Clinically, this finding underscores the importance of early nutritional assessment and timely nutritional intervention as part of comprehensive perioperative care.\u003c/p\u003e \u003cp\u003eElevated CRP levels were independently associated with postoperative pneumonia. As a marker of systemic inflammation, CRP was likely to reflect surgical stress, occult infection, or chronic inflammatory conditions. A heightened inflammatory burden can disrupt immune homeostasis and predispose patients to subsequent infectious complications. Although evidence regarding CRP in acute respiratory infections is heterogeneous, markedly elevated CRP levels have been associated with bacterial pneumonia, whereas lower values tends to suggest non-bacterial etiologies[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In clinical practice, CRP can serve as a pragmatic adjunct for risk stratification, particularly in settings where microbiological testing is limited. Elevated CRP levels should therefore prompt closer postoperative surveillance, especially in patients with multiple comorbidities.\u003c/p\u003e \u003cp\u003eBeyond individual predictors, the overall performance of the nomogram was supported by decision curve analysis. The model consistently provided a higher net benefit than both treat-all and treat-none strategies across a range of low to moderate threshold probabilities. This finding is clinically relevant, as many preventive interventions for postoperative pneumonia\u0026mdash;such as respiratory physiotherapy, nutritional optimization, early mobilization, and closer monitoring\u0026mdash;are generally low risk and low cost. In such settings, initiating preventive measures at relatively low predicted risk levels is likely to be reasonable. Importantly, decision curve analysis supports individualized risk stratification rather than routine intervention for all patients.\u003c/p\u003e \u003cp\u003eInternal validation using bootstrap resampling demonstrated stable discrimination and calibration after bias correction. The calibration slope was close to 1.0 and the intercept was near zero, indicating minimal overall miscalibration. The bootstrap-corrected calibration curve further supported good agreement between predicted and observed risks. Although these findings suggest that model optimism was adequately controlled, external validation in independent cohorts remains necessary. The inclusion of time from admission to surgery in a supplementary model did not materially change the performance or the main conclusions of the primary model, supporting the robustness of our findings.\u003c/p\u003e \u003cp\u003eTaken together, our results suggest that postoperative pneumonia following hip fracture surgery arises from the interaction of vulnerabilities across respiratory, nutritional, inflammatory, and cognitive domains. Effective prevention therefore requires an integrated approach that extends beyond single-organ assessment. Early identification of high-risk patients using a multivariable nomogram may facilitate coordinated multidisciplinary management and closer monitoring of multiple physiological systems during the critical postoperative period.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite the strengths of the present work, several limitations warrant consideration. This study was conducted as a single-center retrospective analysis, which may restrict the generalizability of the findings beyond the study setting. Variations in patient characteristics\u0026mdash;such as frailty status and comorbidity burden\u0026mdash;as well as differences in perioperative care pathways and healthcare systems, particularly in older adult populations, could influence model performance. For this reason, validation in independent, multi-center cohorts remains an important next step before broader clinical use.\u003c/p\u003e \u003cp\u003eIn designing the model, we deliberately focused on predictors that are routinely available early during hospital admission to enhance feasibility in real-world geriatric practice. As a consequence, some potentially relevant perioperative factors, including anesthesia technique, analgesic management, and detailed respiratory support, were not consistently captured in this retrospective dataset. Although these factors may contribute to postoperative pneumonia risk in older surgical patients, they could not be evaluated systematically in the present study. Prospective studies with standardized data collection may help clarify their additional predictive value.\u003c/p\u003e \u003cp\u003eThe diagnosis of postoperative pneumonia was based on a combination of radiological findings, clinical features, and initiation or escalation of antibiotic therapy, reflecting routine clinical practice in elderly patients. While this approach improves clinical relevance, it may also introduce diagnostic variability and treatment-related bias that cannot be fully eliminated in retrospective analyses.\u003c/p\u003e \u003cp\u003eFinally, although the model demonstrated high apparent discrimination, internal validation using bootstrap resampling was applied to account for potential overfitting. The optimism-corrected calibration suggested stable model performance; however, internal validation alone cannot replace external evaluation. Accordingly, the predictive accuracy of the nomogram should be interpreted with appropriate caution until it has been tested in diverse older adult populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn our study, a nomogram was developed and internally validated to predict the risk of postoperative pneumonia occurring within 7 days after hip fracture surgery in older adults. The model was constructed using routinely available clinical and laboratory variables obtained during early hospitalization, allowing for timely risk assessment in real-world clinical settings. By integrating multiple patient-specific factors, the nomogram provides individualized risk estimates rather than a uniform risk classification.\u003c/p\u003e \u003cp\u003eThe predictive performance of the model demonstrated satisfactory discrimination and calibration, suggesting that it can reliably reflect the early postoperative pneumonia risk in this vulnerable population. Importantly, the variables incorporated into the nomogram are easy to obtain and commonly assessed in routine perioperative care, which enhances the feasibility of its application without adding additional clinical burden.\u003c/p\u003e \u003cp\u003eFrom a clinical standpoint, this tool is not intended to replace physician judgment or function as an automated decision-making system. Instead, it may assist clinicians in identifying patients at higher risk of postoperative pneumonia who could benefit from closer monitoring, early preventive interventions, and more individualized perioperative management strategies. Early recognition of high-risk patients may facilitate targeted respiratory care, nutritional support, and multidisciplinary coordination, with the potential to reduce the incidence of postoperative pneumonia and improve short-term outcomes in older patients undergoing hip fracture surgery.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was approved by the Ethics Committee of Shunyi District Hospital (Approval No. 2022-K-004-02). The requirement for informed consent was waived due to the retrospective nature of the study and use of de-identified data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors' information\u003c/h2\u003e \u003cp\u003e1Department of Orthopedic Trauma, Shunyi District Hosptial, Beijing. 2Department of Radiology, Shunyi District Hospital, Beijing. No.3 Guangming South Street, Shunyi District, Beijing, China.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Capital\u0026rsquo;s Funds for Health Improvement and Research (2022-1-2071)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXPC conceived of the study and drafted the manuscript. LLZ, CYC, LL and HJL gathered and processed the data. JSH supervision, and revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank all patients and clinical staff who contributed to this study. We also acknowledge the support of the orthopedic and geriatric teams involved in patient care and data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eda Silva ARB, Martinez LC, de Medeiros Pinheiro M, Szejnfeld VL. Secular trends in hip fractures in adults over 50 years old: a retrospective analysis of hospital admissions to the brazilian public health system from 2004 to 2013. 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J Nutr Health Aging. 2023;27:1248\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12603-023-2050-6\u003c/span\u003e\u003cspan address=\"10.1007/s12603-023-2050-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hip fracture, postoperative pneumonia, nomogram, elderly, prediction model, internal validation","lastPublishedDoi":"10.21203/rs.3.rs-8482635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8482635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePostoperative pneumonia is one of the most common and serious complications following hip fracture surgery in older adults, substantially increasing morbidity, length of hospital stay, and mortality. Early identification of patients at high risk for postoperative pneumonia is essential for targeted preventive strategies. This study aimed to develop and internally validate a clinically applicable nomogram to predict the risk of pneumonia within 7 days after hip fracture surgery in elderly patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a single-center retrospective cohort study including consecutive patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years who underwent surgical treatment for femoral neck or intertrochanteric fractures between January 2021 and December 2024. Patients with preoperative pneumonia were excluded. The primary outcome was postoperative pneumonia occurring within 7 days after surgery. Candidate predictors were prespecified based on clinical relevance and previous literature. A multivariable logistic regression model was developed, and internal validation was performed using bootstrap resampling (1000 iterations). Model performance was assessed using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). A nomogram was constructed to facilitate individualized risk prediction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 760 patients were included in the final analysis, among whom 65 (8.6%) developed postoperative pneumonia within 7 days. Multivariable logistic regression identified age (OR 1.181, 95% CI 1.103\u0026ndash;1.265, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chronic pulmonary disease (OR 2.585, 95% CI 1.266\u0026ndash;5.276, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), dementia (OR 3.138, 95% CI 1.445\u0026ndash;6.812, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), minimum oxygen saturation within 24 hours (OR 0.795, 95% CI 0.694\u0026ndash;0.910, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum albumin (OR 0.656, 95% CI 0.586\u0026ndash;0.733, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and log-transformed C-reactive protein (OR 4.729, 95% CI 2.308\u0026ndash;9.686, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as independent predictors. The nomogram demonstrated good discriminative ability, with an optimism-corrected C-index of 0.949. Calibration curves showed good agreement between predicted and observed risks. Decision curve analysis indicated that the nomogram provided a positive net clinical benefit across a wide range of threshold probabilities.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe developed and internally validated a nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery in older adults. This tool may assist clinicians in early risk stratification and implementation of targeted preventive interventions.\u003c/p\u003e","manuscriptTitle":"Development and internal validation of a nomogram for predicting postoperative pneumonia within 7 days after hip fracture surgery in older adults: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 16:42:40","doi":"10.21203/rs.3.rs-8482635/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-07T13:27:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12586356119237039937815707522440951931","date":"2026-02-07T12:48:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T11:27:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T07:00:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-02T07:44:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T07:41:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-12-30T15:10:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17b549c9-74a1-45ab-9fcc-9c83b6ba0fa2","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T16:42:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 16:42:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8482635","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8482635","identity":"rs-8482635","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}