Combining Systemic Inflammation Biomarkers with Traditional Prognostic Factors to Predict Surgical Site Infections in Elderly Hip Fracture Patients: A Risk Factor Analysis and Dynamic Nomogram Development

Combining Systemic Inflammation Biomarkers with Traditional Prognostic Factors to Predict Surgical Site Infections in Elderly Hip Fracture Patients: A Risk Factor Analysis and Dynamic Nomogram Development | 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 Combining Systemic Inflammation Biomarkers with Traditional Prognostic Factors to Predict Surgical Site Infections in Elderly Hip Fracture Patients: A Risk Factor Analysis and Dynamic Nomogram Development Yuhui Guo, Chengsi Li, Haichuan Guo, Peiyuan Wang, Xuebin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5040943/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Journal of Orthopaedic Surgery and Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background Systemic inflammation biomarkers have been widely shown to be associated with infection. This study aimed to construct a nomogram based on systemic inflammation biomarkers and traditional prognostic factors to assess the risk of surgical site infection (SSI) after hip fracture in the elderly. Methods Data were retrospectively collected from patients over 60 with acute hip fractures who underwent surgery and were followed for more than 12 months between June 2017 and June 2022 at a tertiary referral hospital. Biomarkers were calculated from peripheral venous blood collected on admission. The CDC definition of SSI was applied, with SSI identified through medical and pathogen culture records during hospitalization and routine postoperative telephone follow-ups. Multivariable logistic regression identified independent risk factors for SSI and developed predictive nomograms. Model stability was validated using an external set of patients treated from July 2022 to June 2023. Results A total of 1430 patients were included in model development, with 41 cases (2.87%) of superficial SSI and 6 cases (0.42%) of deep SSI. Multivariable analysis identified traditional prognostic factors older age (OR = 1.08, 95% CI 1.04–1.12), ASA class III-IV (OR = 2.46, 95% CI 1.32–4.56), surgical delay ≥ 6 days (OR = 3.59, 95% CI 1.36–9.47), surgical duration > 180 minutes (OR = 2.72, 95% CI 1.17–6.35), and systemic inflammation biomarkers PAR ≥ 6.6 (OR = 2.25, 95% CI 1.17–4.33) and SII ≥ 541.1 (OR = 2.24, 95% CI 1.14–4.40) as independent predictors of SSI. Model’s stability was proved by internal validation, and external validation with 307 patients, and an online dynamic nomogram ( https://brooklyn99.shinyapps.io/DynNomapp/ ) was generated. Conclusions This study combined systemic inflammatory biomarkers and developed an online dynamic nomogram to predict SSI in elderly hip fracture patients, which could be used to guide early screening of patients with high risk of SSI and provide a reference tool for perioperative management. hip fracture surgical site infection systemic inflammation biomarkers risk factors nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Elderly hip fractures, often deemed the "last fracture in life", result in up to 30% mortality within the first year[ 1 – 6 ]. As the population ages, projections indicate that global hip fractures could rise to 4.5 million by 2050[ 3 , 4 ]. Postoperative surgical site infections (SSI) following hip fractures significantly burden healthcare, with a 1-year mortality rate of up to 50%, accounting for 17% of all hospital-acquired infections and costing $ 1–10 billion annually[ 7 – 9 ]. Therefore, effective prediction and prevention of postoperative SSIs in elderly patients with hip fractures is paramount. In the perioperative period, despite strict adherence to antibiotic guidelines, SSI can still be induced by underlying conditions in elderly patients, such as diabetes and malnutrition, which decrease the resistance to body infection and impact wound healing and immune function[ 10 , 11 ]. Incorporating hematologic parameters that reflect the physical condition of patients into the risk assessment of SSI could improve the speed and objectivity of prediction. Studies have attempted to assess several parameters (high-sensitivity C-reactive protein, serum albumin, blood glucose, etc.), but predictive reliability was limited[ 12 – 15 ]. For example, Pfitzner et al. found that preoperative CRP concentrations were significantly higher in the infection group of patients undergoing primary arthroplasty; however, many confounding factors, such as advanced age and trauma, can affect CRP levels in addition to infection[ 15 ]. Therefore, it is imperative to explore more specific diagnostic hematologic parameters for SSI prediction in elderly patients with hip fracture. Systemic inflammation biomarkers measured at admission may offer new predictive insights. Several systemic inflammation biomarkers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NPR), platelet-to-albumin ratio (PAR), systemic immune inflammation index (SII) and systemic inflammation response index (SIRI) have been widely shown to be associated with infection[ 16 – 19 ]. For example, Guo et al. identified that SII > 423.62 was independently associated with the occurrence of SSI after open wedge high tibial osteotomy in a retrospective study of 1294 patients[ 16 ]. These biomarkers are derived from routine laboratory tests and can provide a reliable, unbiased measure of a patient's susceptibility to infection. Therefore, this study aims to evaluate the comprehensive predictive power of systemic inflammation biomarkers for SSI after hip fracture in the elderly. It included a relatively large sample size and combined systemic inflammation biomarkers with traditional prognostic factors into an online dynamic nomogram, thus visually quantifying the risk of SSI for clinicians. Materials and Methods Study Design and Populations This was a retrospective single-center study, and patients with acute hip fractures over 60 years of age undergoing surgery between June 2017 to June 2022 in a tertiary referral hospital were considered eligible for inclusion. Exclusion criteria were old fracture (surgical delay ≥ 21 days), pathologic fracture, death for any cause within one month after surgery, multiple fractures or polytrauma, incomplete or missing data, or lost to follow-up. All Patients were administered preoperative antibiotic prophylaxis within 24 hours prior to surgery. Validation cohort were screened based on the same criteria from July 2022 to June 2023. This present study was conducted following the consensus of the Declaration of Helsinki and was based on the Strengthening the Reporting of Cohort Studies in Surgery (STROCSS) guidelines, which were approved by the Institution’s Clinical Research Ethics Board. All patients and/or their family members were informed that their medical data were used for scientific research and have signed an informed consent document. All data were analyzed with anonymization. General Information All data for this study were obtained from medical records and follow-up visits. Preoperative baseline characteristics comprised demographic features and injury-related data, including age, sex, calculated body mass index (BMI), living place, current smoking, alcohol consumption, American Society of Anesthesiologists (ASA) class, surgical history, surgical delay (days), fracture type (femoral-neck or intertrochanteric fracture) and preoperative comorbidities, including hypertension, diabetes mellitus, cardiovascular disease, heart disease, liver disease, renal disease, chronic respiratory disease, and anemia. Perioperative data comprised antibiotics type, postoperative antibiotic use (days), surgical duration (minutes), operation type (osteosynthesis or arthroplasty), and anesthesia type (regional or general anesthesia). Collection and Calculation of the Systemic Inflammation Biomarkers The collection of standardized serum biomarkers from peripheral venous blood was performed at the time of admission of each patient. These laboratory variables were evaluated using the methods recommended by the manufacturer. Biochemical tests were performed using a Beckman Colter AU5800 chemistry analyzer, while blood cell count tests were performed on a UniCel DXI 800 (Beckman Colter). Reported predictive biomarkers, including white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM), erythrocyte sedimentation rate (ESR), and high-sensitivity C-reactive protein (HCRP), were included for assessment. Potential systemic inflammation biomarkers were calculated using the following equations: NLR = neutrophil count/lymphocyte count, PLR = platelet count/lymphocyte count, LMR = lymphocyte count/monocyte count, NPR = neutrophil count/platelet count, PAR = platelet count/ albumin, SII = platelet count × neutrophil count/lymphocyte count, SIRI = neutrophil count × monocyte count/ lymphocyte count. Diagnosis of SSI The Centers for Disease Control (CDC) definition of SSI was adopted for this study. Superficial SSI was defined as erythema, swelling, fever, and tenderness upon palpation of the skin and subcutaneous tissue at the wound site within 1 month after surgery, which usually resolved with frequent disinfection and dressing change and empirical oral antibiotics. Deep SSI was defined as infections that affect deep soft tissue, muscle, or fascia within 12 months after the surgery, with persistent wound bleeding, dehiscence, visible abscesses, or gangrene, which can require surgical debridement, application of systemic antibiotics, and implant replacement or removal[ 20 ]. We reviewed all medical records and pathogen culture records during the patient’s hospitalization, and routine postoperative telephone follow-ups of the patient for more than 12 months to identify SSI cases. These data were retrospectively and independently reviewed from the electronic medical records by two orthopedic expert examiners and mutually checked for accuracy, disagreements resolved by discussion with the senior chief physician. Statistical Analysis The normality of continuous variables was assessed following the results of the Kolmogorov–Smirnov test, and normally distributed data were expressed as mean ± standard deviation (SD) using the Student’s t-test. Otherwise, the Mann–Whitney test was used and expressed as the median and interquartile range (IQR). Categorical variables were assessed using the chi-square test or Fisher’s exact test. Taking into account the internationally accepted and guidelines recommended 48 h as the cut-off point for early surgery, we employed both 48 h and 5 days as the cut-off points for surgical delay[ 21 ]. The Youden index was applied to determine systemic inflammation biomarkers’ optimal cutoff values so as to better guide clinical practice after the t-test was performed. Potential predictors screened for P < 0.05 in univariate analyses were included in multivariable analyses, and backward stepwise logistic regression was used to determine independent predictors of SSI. Finally, nomogram prognostic models that predict the SSI were constructed using the screened systemic inflammation biomarkers and traditional prognostic factors. The receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA) to visually evaluate the model’s predictive power. The C-index and the area under the curve (AUC) are closer to 1, and the better the model’s discrimination ability. The calibration curve was presented to illustrate the consistency between the predicted probability of the model and the actual probability, and was further evaluated using the Hosmer-Lemeshow goodness-of-fit test. Brier score is an expansion of the Hosmer-Lemeshow test, and the closer it is to 0, the higher the ability of the model calibration. The net clinical benefit of the model was assessed by DCA. Internal validation was performed using the Bootstrap method to obtain the corrected C-index and corrected Brier scores after 1000 replicate samples. Finally, the model is evaluated externally[ 22 ]. All analyzes were performed with R software (R Foundation for Statistical Computing version 4.3.2), and P < 0.05 suggested a significant statistical difference. Results Clinical Characteristics Based on inclusion and exclusion criteria, 1430 patients (mean age 72.5 ± 7.4 years) were included for model development (Fig. 1 ), with 758 (53.0%) female and 1012 (70.8%) overweight. Of these, 917 (64.1%) underwent osteosynthesis and 513 (35.9%) underwent hip arthroplasty. Postoperative follow-up lasted at least 12 months. If a patient experienced multiple SSI events, only the most severe was counted. SSI occurred in 47 cases (3.29%), including 41 (2.87%) superficial and 6 (0.42%) deep infections. The validation cohort included 307 patients, with 8 (2.61%) superficial and 1 (0.33%) deep SSI. Univariate and Multivariable Analysis Univariate analysis showed age, ASA class, surgical delay, surgical duration, anesthesia type, ESR, HCRP and four systemic inflammation biomarkers (NLR, PLR, PAR and SII) were significantly correlated with SSI (P < 0.05). The above variables were subjected to multivariable analysis, and the best model was determined using stepwise backward regression with minimum AIC values. Older age, ASA class III-IV, surgical delay ≥ 6 days, surgical duration > 180 minutes, PAR ≥ 6.6 and SII ≥ 541.1 were ultimately identified as independent risk factors for SSI (Table 2 ). Construction and Validation of a Dynamic Nomogram The six predictors finally selected by the multivariable analysis were transformed into a simple nomogram and a dynamic nomogram. The use of the simple nomogram (Fig. 2a) is not conditioned, it allows the surgeon to draw a vertical line on the score axis of each variable, sum the scores to obtain the total score, and the predicted probability of SSI can be obtained by making a vertical line downward. The dynamic nomogram (Fig. 2b) requires surgeons to input the results of each covariate while connected to the network to display the predicted probability of SSI with 95% confidence intervals. The AUC for the combination of the above predictors was 0.794 (95% confidence interval (CI): 0.721–0. 866), with the specificity of 63.8% and sensitivity of 83.9%, which indicates good model discrimination (Fig. 3 a). The C-index and Brier score were 0.794 and 0.029, respectively, and after Bootstrap validation (B = 1000 replicates), the correction values were 0.774 and 0.030, respectively, indicating that the model performed well overall. The P-values of Hosmer–Lemeshow χ2 statistics of the calibration curve (Fig. 4 ) in the development and validation datasets were 0.396 and 0.235 (> 0.05), which illustrated a favorable consistency between the probability of predicting SSI and the actual probability of occurrence. DCA shows that the application of the nomogram provides a net positive benefit when the threshold probability is in the range of 2%-56% (Fig. 5 a). Furthermore, the discriminatory power of the model performs equally well in the external validation dataset, with an AUC of 0.804. Discission The prevalence of SSI in elderly patients with hip fractures varies significantly between different institutions and regions (ranging from 2–9.5%)[ 12 – 14 , 23 ]. Screening and prevention of SSIs that occur despite strict application of antibiotics according to published clinical practice guidelines perioperatively remains a challenge for surgeons. Previous studies attempted to predict SSI by combining serum biomarkers (high-sensitivity C-reactive protein, serum albumin, and blood glucose, etc.), but the sensitivity and specificity of the prediction varied and were limited by confounding factors (e.g., advanced age and trauma)[ 12 – 14 ]. Systemic inflammation biomarkers that integrate multiple leukocyte subsets and/or other inflammatory markers have demonstrated significant predictive capacity to predict postoperative infections in orthopedic surgery[ 16 – 19 ]. For example, Yao et al. retrospectively analyzed NLR, PLR, and SII collected upon admission in 1199 patients and found that NLR ≥ 5.84 had a significant predictive value for the presence of postoperative pneumonia in geriatric patients with hip fracture (OR = 2.24, 95% CI 1.43–3.51)[ 18 ]. However, to our knowledge, there are no existing predictive models for SSI after hip fracture that integrate systemic inflammation biomarkers. In this retrospective study, systemic inflammatory biomarkers (PAR ≥ 6.6 and SII ≥ 541.1) and traditional prognostic factors (older age, ASA class III-IV, surgical delay ≥ 6 days, surgical duration > 180 minutes) were identified as independently associated with a high risk of SSI. The nomogram was established based on these predictors, and the C-index, AUC values, and calibration curves validated the discriminatory power and precision of the models. The DCA showed that the model has clinical application and can help early assessment of the SSI risk. The model is equally robust in external validation. The precise mechanism of the association between systemic inflammation biomarkers and SSI may involve several aspects. Elevated levels of SII and PAR upon admission reflect dynamic changes in four subsets: neutrophils, lymphocytes, platelets, and albumin. The systemic inflammatory response contributes to the secondary injury associated with hip fractures, and the diminished physiological reserve of elderly patients makes them more susceptible to the release of inflammatory cytokines after injury while lacking sufficient anti-inflammatory mediators to counterbalance them[ 24 , 25 ]. Elevated levels of cytokines (e.g., tumor necrosis factor-α, interleukin-6, and interleukin-1β) can prime the body to a hyper-inflammatory state, making it more susceptible to infections after surgery, as normal immune defenses are already engaged and may not respond adequately to new pathogens[ 26 ]. Besides, changes in leukocyte function and distribution due to chronic systemic inflammation somewhat weaken the body's ability to resist infection after surgery[ 27 ]. In addition, endothelial dysfunction caused by platelet aggregation and adhesion can affect vascular permeability. This state facilitates the translocation of bacteria, making it easier for microbes to enter the surgical site, thus increasing the risk of deep SSI[ 28 ]. Furthermore, patients with nutritional deficiencies reflected by hypoalbuminemia in hematologic tests are more likely to induce a chronic inflammatory state in the body, which can affect wound healing and immune function, further increasing the risk of postoperative infection[ 29 ]. The traditional prognostic factors identified in this study (older age, ASA class III-IV, surgical delay ≥ 6 days, surgical duration > 180 minutes) have previously been widely reported[ 12 – 14 , 30 , 31 ]. The independent predictability of these predictors combined with systemic inflammatory biomarkers in multifactorial analysis suggested that, with the exception of inappropriate antibiotic use, the presence of SSI might be due to a composite of poor physical status. The presence of comorbidities in older patients with a higher ASA class tends to result in individuals with much lower baseline exercise tolerance compared to those in good general condition[ 13 , 31 ]. For this population of patients, surgery is delayed in large part because more time is needed to optimize poor presurgical medical conditions such as weakness, poor soft tissue status, and insulin dependence[ 14 ]. And while waiting for surgery, depletion and imbalance of complement cascade components and systemic inflammatory response syndromes secondary to acute trauma are more likely to increase the risk of infection[ 32 ]. Prolonged surgical times are also believed to reflect poor physiologic conditions and more severe injuries, while more extensive soft tissue debridement and extensive wound exposure also carry a higher risk of infection. The prediction model integrating systemic inflammatory biomarkers could complement existing knowledge in the following aspects. First, the parameters of systemic inflammatory biomarkers can be easily obtained from admission hematologic tests without the need for a real-time physical examination, which is particularly useful in referral centers. Second, as an independent evaluation of baseline characteristics, systemic inflammatory biomarkers prevent the effect of recall bias and other confounding on the precision of the assessment metrics and provide a more objective prediction of the risk of SSI. Third, because systemic inflammatory biomarkers combine different subsets of parameters, they can provide surgeons with broader guidance on perioperative management, such as the management of patient chronic disease status, adjustment of antibiotic dose and early nutritional intervention. The main strengths of this study are the large sample size and the translation of independent risk factors into a predictive nomogram model that allows surgeons to personalize risk assessment for hip fracture patients. All predictors can be quickly obtained from routine perioperative laboratory results and admission reports from patients. On this basis, the predicted probability of SSI can be obtained in a few minutes by entering the results into a web page or by drawing vertical lines on the corresponding axes. Several limitations should also be discussed. First, this study enrolled patients from a tertiary orthopedic referral medical center, thus some patients were transferred from relatively remote hospitals. Patients with surgical delays longer than 6 days may have compromised levels of systemic inflammatory biomarkers upon admission due to systemic inflammatory response syndromes secondary to acute trauma[ 32 ]. However, this bias was somewhat balanced by including the duration of surgical delay as an independent risk factor in the model as well. Second, all data were collected retrospectively with some information reported by patients and their families, so there is an inherent selection bias and recall bias. Third, these retrospective data failed to obtain surgeon-specific information, such as surgical experience, details that could potentially influence the results. Fourth, despite the good internal and external validation results, this single-center study requires a large prospective study with multicenter data to confirm the applicability of the model. Conclusion This study constructed a risk prediction online dynamic model for the postoperative SSI in elderly hip fracture patients based on systemic inflammatory biomarkers (PAR and SII) and traditional prognostic factors (age, ASA class, surgical delay and surgical duration, which can be used to guide early screening of patients with high risk of SSI and provide a reference tool for perioperative management. Declarations Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local ethical committee (The 3rd Hospital of Hebei Medical University, Shijiazhuang, China; Ke2014-015-1). Consent to participate All subjects gave their written informed consent to take part in the study. Consent for publication Not applicable. Availability of data and material The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Conflict of interest All authors declared that they have no conflict of interest. Funding This research was supported by the 2024 Government-funded Clinical Medicine Excellence Training Program Leader Project (No. ZF2024093). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Author’s contributions XBZ designed the study; YHG, CSL, HCG and PYW searched for relevant studies and abstracted the data; CSL and HCG analyzed and interpreted the data; YHG and CSL wrote the manuscript, and XBZ approved the final version of the manuscript. All authors reviewed the manuscript before submitting it. Acknowledgements We sincerely thank all the patients in this study. References Sattui SE, Saag KG. Fracture mortality: associations with epidemiology and osteoporosis treatment. Nat Rev Endocrinol. 2014;10(10):592–602. Lund CA, Møller AM, Wetterslev J, Lundstrøm LH. Organizational factors and long-term mortality after hip fracture surgery. A cohort study of 6143 consecutive patients undergoing hip fracture surgery. PLoS ONE. 2014;9(6):e99308. Gehrig L, Lane J, O'Connor MI. Osteoporosis: management and treatment strategies for orthopaedic surgeons. J Bone Joint Surg Am Volume. 2008;90(6):1362–74. Kanis JA, Odén A, McCloskey EV, Johansson H, Wahl DA, Cooper C. 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Supplementary Files Table.docx Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Journal of Orthopaedic Surgery and Research → Version 1 posted Editorial decision: Revision requested 05 Oct, 2024 Reviews received at journal 04 Oct, 2024 Reviewers agreed at journal 24 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviewers invited by journal 11 Sep, 2024 Editor assigned by journal 11 Sep, 2024 Submission checks completed at journal 10 Sep, 2024 First submitted to journal 05 Sep, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5040943","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":362528923,"identity":"52befd56-13cc-46f6-82de-3cceac6bf596","order_by":0,"name":"Yuhui Guo","email":"","orcid":"","institution":"the 3rd Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhui","middleName":"","lastName":"Guo","suffix":""},{"id":362528925,"identity":"a158f9f9-2026-4dfe-9da4-7130edb346b3","order_by":1,"name":"Chengsi Li","email":"","orcid":"","institution":"the 3rd Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengsi","middleName":"","lastName":"Li","suffix":""},{"id":362528926,"identity":"a3f28a41-e08b-4a61-b0d5-116f57f3cdf0","order_by":2,"name":"Haichuan Guo","email":"","orcid":"","institution":"the 3rd Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haichuan","middleName":"","lastName":"Guo","suffix":""},{"id":362528929,"identity":"88d9f9db-f737-4c00-acd2-79d2a0a28608","order_by":3,"name":"Peiyuan Wang","email":"","orcid":"","institution":"the 3rd Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peiyuan","middleName":"","lastName":"Wang","suffix":""},{"id":362528930,"identity":"9f1eaad6-0be3-4c89-bc49-6f9e937d7d26","order_by":4,"name":"Xuebin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3QIQvCQBTA8QfCLIdnvAPxM7w0y8Cv8sZglolGoyDM4MTqxzAap8IsZ79g2Mr6imBRp4hxWzTcL114fx73AAzjP1lAAIy3F4eUZk7zpCejxMNU+Q2TkoM6sGUWHuvnUXtJmu2BoYrtGVkx8OWKKhO59UfoKmDyPPc1sSsIddlVJlwEtnBDYB0VJ5pEDijG1YklJrdPAtoNp4Sn+qTcYn2SrvbK01GDREb5AN9JeeSWoNhntX/Bs5dn9xCGvL0pivvD6fPlujr5ev5erMm4YRiGUeMFsK9Jij0+gCIAAAAASUVORK5CYII=","orcid":"","institution":"the 3rd Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xuebin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-09-06 02:06:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5040943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5040943/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13018-024-05446-9","type":"published","date":"2025-01-13T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69961814,"identity":"e20385cd-e3ac-4f45-a3db-0c43e7ccb2ac","added_by":"auto","created_at":"2024-11-27 04:39:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157633,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for screening eligible patients. SSI, surgical site infection; ROC, receiver-operating characteristic; DCA, decision curve analysis.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5040943/v1/8114447ed163631f0b667679.jpg"},{"id":69961799,"identity":"0e2a0029-d907-4fb9-bb16-479cea8ec339","added_by":"auto","created_at":"2024-11-27 04:39:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":272931,"visible":true,"origin":"","legend":"\u003cp\u003eThe simple nomogram (a) and dynamic nomogram (b) integrated with systemic inflammation biomarkers and traditional prognostic factors for predicting the risk of prospective SSI in old patients with hip fracture. p values are indicated as one star (*) if p \u0026lt; 0.05, two stars (**) if p \u0026lt; 0.01, and three stars (***) if p \u0026lt; 0.001. (Access to dynamic nomogram: https://brooklyn99.shinyapps.io/DynNomapp/).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5040943/v1/58cb09a1372bc94efd7f771d.jpg"},{"id":69961808,"identity":"4bfb41bc-ba93-4174-a029-68fcf5f197a8","added_by":"auto","created_at":"2024-11-27 04:39:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113400,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver-operating characteristic (ROC) curves for the nomogram in the development (a) and validation sets (b). The predictive accuracy of the nomogram was positively correlated with the area under the curve (AUC). The AUC of the nomogram was 0.794 and 0.804 in the development and validation sets, respectively, indicating that the model had good discriminative ability.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5040943/v1/32e7c4ec97ab1020dd172a41.jpg"},{"id":69961813,"identity":"558eddf9-9df4-4f8a-8176-2219909da6db","added_by":"auto","created_at":"2024-11-27 04:39:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126764,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of nomogram in the development set (a, P = 0.396) and validation set (b, P = 0.235). X-axis represents the predicted probability of the model and y-axis represents the actual prob ability. The closer the red and green curves fit the ideal dashed line, the better the predictive consistency of the nomogram.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5040943/v1/d4699709b6facd3d96ffd823.jpg"},{"id":69961817,"identity":"8a7c5841-8c99-42a4-9bc6-36bf82f35508","added_by":"auto","created_at":"2024-11-27 04:39:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":97099,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) of nomogram in the training set (a) and validation set (b). DCA illustrated that the net benefit of the training model is higher in the threshold probability interval of 2–56%, and the net benefit of the validation model is higher in the threshold probability interval of 1–44%.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5040943/v1/1f3589ae200fc248a2e85011.jpg"},{"id":74285748,"identity":"3a43f16d-b6b0-4122-91c8-9942400e3316","added_by":"auto","created_at":"2025-01-20 16:14:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1379589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5040943/v1/5ccd9c41-8785-45ee-80f7-2a5c0c2e0745.pdf"},{"id":69961805,"identity":"2144e461-0929-4c7e-b466-7f41eedb726d","added_by":"auto","created_at":"2024-11-27 04:39:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29187,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-5040943/v1/4203a13166bd851d0885adc8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining Systemic Inflammation Biomarkers with Traditional Prognostic Factors to Predict Surgical Site Infections in Elderly Hip Fracture Patients: A Risk Factor Analysis and Dynamic Nomogram Development","fulltext":[{"header":"Introduction","content":"\u003cp\u003eElderly hip fractures, often deemed the \"last fracture in life\", result in up to 30% mortality within the first year[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As the population ages, projections indicate that global hip fractures could rise to 4.5\u0026nbsp;million by 2050[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Postoperative surgical site infections (SSI) following hip fractures significantly burden healthcare, with a 1-year mortality rate of up to 50%, accounting for 17% of all hospital-acquired infections and costing \u003cspan\u003e$\u003c/span\u003e1\u0026ndash;10\u0026nbsp;billion annually[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, effective prediction and prevention of postoperative SSIs in elderly patients with hip fractures is paramount.\u003c/p\u003e \u003cp\u003eIn the perioperative period, despite strict adherence to antibiotic guidelines, SSI can still be induced by underlying conditions in elderly patients, such as diabetes and malnutrition, which decrease the resistance to body infection and impact wound healing and immune function[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Incorporating hematologic parameters that reflect the physical condition of patients into the risk assessment of SSI could improve the speed and objectivity of prediction. Studies have attempted to assess several parameters (high-sensitivity C-reactive protein, serum albumin, blood glucose, etc.), but predictive reliability was limited[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, Pfitzner et al. found that preoperative CRP concentrations were significantly higher in the infection group of patients undergoing primary arthroplasty; however, many confounding factors, such as advanced age and trauma, can affect CRP levels in addition to infection[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, it is imperative to explore more specific diagnostic hematologic parameters for SSI prediction in elderly patients with hip fracture.\u003c/p\u003e \u003cp\u003eSystemic inflammation biomarkers measured at admission may offer new predictive insights. Several systemic inflammation biomarkers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NPR), platelet-to-albumin ratio (PAR), systemic immune inflammation index (SII) and systemic inflammation response index (SIRI) have been widely shown to be associated with infection[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, Guo et al. identified that SII\u0026thinsp;\u0026gt;\u0026thinsp;423.62 was independently associated with the occurrence of SSI after open wedge high tibial osteotomy in a retrospective study of 1294 patients[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These biomarkers are derived from routine laboratory tests and can provide a reliable, unbiased measure of a patient's susceptibility to infection. Therefore, this study aims to evaluate the comprehensive predictive power of systemic inflammation biomarkers for SSI after hip fracture in the elderly. It included a relatively large sample size and combined systemic inflammation biomarkers with traditional prognostic factors into an online dynamic nomogram, thus visually quantifying the risk of SSI for clinicians.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Populations\u003c/h2\u003e \u003cp\u003eThis was a retrospective single-center study, and patients with acute hip fractures over 60 years of age undergoing surgery between June 2017 to June 2022 in a tertiary referral hospital were considered eligible for inclusion. Exclusion criteria were old fracture (surgical delay\u0026thinsp;\u0026ge;\u0026thinsp;21 days), pathologic fracture, death for any cause within one month after surgery, multiple fractures or polytrauma, incomplete or missing data, or lost to follow-up. All Patients were administered preoperative antibiotic prophylaxis within 24 hours prior to surgery. Validation cohort were screened based on the same criteria from July 2022 to June 2023. This present study was conducted following the consensus of the Declaration of Helsinki and was based on the Strengthening the Reporting of Cohort Studies in Surgery (STROCSS) guidelines, which were approved by the Institution\u0026rsquo;s Clinical Research Ethics Board. All patients and/or their family members were informed that their medical data were used for scientific research and have signed an informed consent document. All data were analyzed with anonymization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Information\u003c/h2\u003e \u003cp\u003eAll data for this study were obtained from medical records and follow-up visits. Preoperative baseline characteristics comprised demographic features and injury-related data, including age, sex, calculated body mass index (BMI), living place, current smoking, alcohol consumption, American Society of Anesthesiologists (ASA) class, surgical history, surgical delay (days), fracture type (femoral-neck or intertrochanteric fracture) and preoperative comorbidities, including hypertension, diabetes mellitus, cardiovascular disease, heart disease, liver disease, renal disease, chronic respiratory disease, and anemia. Perioperative data comprised antibiotics type, postoperative antibiotic use (days), surgical duration (minutes), operation type (osteosynthesis or arthroplasty), and anesthesia type (regional or general anesthesia).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCollection and Calculation of the Systemic Inflammation Biomarkers\u003c/h2\u003e \u003cp\u003eThe collection of standardized serum biomarkers from peripheral venous blood was performed at the time of admission of each patient. These laboratory variables were evaluated using the methods recommended by the manufacturer. Biochemical tests were performed using a Beckman Colter AU5800 chemistry analyzer, while blood cell count tests were performed on a UniCel DXI 800 (Beckman Colter). Reported predictive biomarkers, including white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM), erythrocyte sedimentation rate (ESR), and high-sensitivity C-reactive protein (HCRP), were included for assessment. Potential systemic inflammation biomarkers were calculated using the following equations: NLR\u0026thinsp;=\u0026thinsp;neutrophil count/lymphocyte count, PLR\u0026thinsp;=\u0026thinsp;platelet count/lymphocyte count, LMR\u0026thinsp;=\u0026thinsp;lymphocyte count/monocyte count, NPR\u0026thinsp;=\u0026thinsp;neutrophil count/platelet count, PAR\u0026thinsp;=\u0026thinsp;platelet count/ albumin, SII\u0026thinsp;=\u0026thinsp;platelet count \u0026times; neutrophil count/lymphocyte count, SIRI\u0026thinsp;=\u0026thinsp;neutrophil count \u0026times; monocyte count/ lymphocyte count.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiagnosis of SSI\u003c/h2\u003e \u003cp\u003eThe Centers for Disease Control (CDC) definition of SSI was adopted for this study. Superficial SSI was defined as erythema, swelling, fever, and tenderness upon palpation of the skin and subcutaneous tissue at the wound site within 1 month after surgery, which usually resolved with frequent disinfection and dressing change and empirical oral antibiotics. Deep SSI was defined as infections that affect deep soft tissue, muscle, or fascia within 12 months after the surgery, with persistent wound bleeding, dehiscence, visible abscesses, or gangrene, which can require surgical debridement, application of systemic antibiotics, and implant replacement or removal[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We reviewed all medical records and pathogen culture records during the patient\u0026rsquo;s hospitalization, and routine postoperative telephone follow-ups of the patient for more than 12 months to identify SSI cases. These data were retrospectively and independently reviewed from the electronic medical records by two orthopedic expert examiners and mutually checked for accuracy, disagreements resolved by discussion with the senior chief physician.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe normality of continuous variables was assessed following the results of the Kolmogorov\u0026ndash;Smirnov test, and normally distributed data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) using the Student\u0026rsquo;s t-test. Otherwise, the Mann\u0026ndash;Whitney test was used and expressed as the median and interquartile range (IQR). Categorical variables were assessed using the chi-square test or Fisher\u0026rsquo;s exact test. Taking into account the internationally accepted and guidelines recommended 48 h as the cut-off point for early surgery, we employed both 48 h and 5 days as the cut-off points for surgical delay[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The Youden index was applied to determine systemic inflammation biomarkers\u0026rsquo; optimal cutoff values so as to better guide clinical practice after the t-test was performed. Potential predictors screened for P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analyses were included in multivariable analyses, and backward stepwise logistic regression was used to determine independent predictors of SSI.\u003c/p\u003e \u003cp\u003eFinally, nomogram prognostic models that predict the SSI were constructed using the screened systemic inflammation biomarkers and traditional prognostic factors. The receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA) to visually evaluate the model\u0026rsquo;s predictive power. The C-index and the area under the curve (AUC) are closer to 1, and the better the model\u0026rsquo;s discrimination ability. The calibration curve was presented to illustrate the consistency between the predicted probability of the model and the actual probability, and was further evaluated using the Hosmer-Lemeshow goodness-of-fit test. Brier score is an expansion of the Hosmer-Lemeshow test, and the closer it is to 0, the higher the ability of the model calibration. The net clinical benefit of the model was assessed by DCA. Internal validation was performed using the Bootstrap method to obtain the corrected C-index and corrected Brier scores after 1000 replicate samples. Finally, the model is evaluated externally[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. All analyzes were performed with R software (R Foundation for Statistical Computing version 4.3.2), and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 suggested a significant statistical difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics\u003c/h2\u003e \u003cp\u003eBased on inclusion and exclusion criteria, 1430 patients (mean age 72.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 years) were included for model development (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with 758 (53.0%) female and 1012 (70.8%) overweight. Of these, 917 (64.1%) underwent osteosynthesis and 513 (35.9%) underwent hip arthroplasty. Postoperative follow-up lasted at least 12 months. If a patient experienced multiple SSI events, only the most severe was counted. SSI occurred in 47 cases (3.29%), including 41 (2.87%) superficial and 6 (0.42%) deep infections. The validation cohort included 307 patients, with 8 (2.61%) superficial and 1 (0.33%) deep SSI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and Multivariable Analysis\u003c/h2\u003e \u003cp\u003eUnivariate analysis showed age, ASA class, surgical delay, surgical duration, anesthesia type, ESR, HCRP and four systemic inflammation biomarkers (NLR, PLR, PAR and SII) were significantly correlated with SSI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The above variables were subjected to multivariable analysis, and the best model was determined using stepwise backward regression with minimum AIC values. Older age, ASA class III-IV, surgical delay\u0026thinsp;\u0026ge;\u0026thinsp;6 days, surgical duration\u0026thinsp;\u0026gt;\u0026thinsp;180 minutes, PAR\u0026thinsp;\u0026ge;\u0026thinsp;6.6 and SII\u0026thinsp;\u0026ge;\u0026thinsp;541.1 were ultimately identified as independent risk factors for SSI (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and Validation of a Dynamic Nomogram\u003c/h2\u003e \u003cp\u003eThe six predictors finally selected by the multivariable analysis were transformed into a simple nomogram and a dynamic nomogram. The use of the simple nomogram (Fig.\u0026nbsp;2a) is not conditioned, it allows the surgeon to draw a vertical line on the score axis of each variable, sum the scores to obtain the total score, and the predicted probability of SSI can be obtained by making a vertical line downward. The dynamic nomogram (Fig.\u0026nbsp;2b) requires surgeons to input the results of each covariate while connected to the network to display the predicted probability of SSI with 95% confidence intervals.\u003c/p\u003e \u003cp\u003eThe AUC for the combination of the above predictors was 0.794 (95% confidence interval (CI): 0.721\u0026ndash;0. 866), with the specificity of 63.8% and sensitivity of 83.9%, which indicates good model discrimination (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The C-index and Brier score were 0.794 and 0.029, respectively, and after Bootstrap validation (B\u0026thinsp;=\u0026thinsp;1000 replicates), the correction values were 0.774 and 0.030, respectively, indicating that the model performed well overall. The P-values of Hosmer\u0026ndash;Lemeshow χ2 statistics of the calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) in the development and validation datasets were 0.396 and 0.235 (\u0026gt;\u0026thinsp;0.05), which illustrated a favorable consistency between the probability of predicting SSI and the actual probability of occurrence. DCA shows that the application of the nomogram provides a net positive benefit when the threshold probability is in the range of 2%-56% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Furthermore, the discriminatory power of the model performs equally well in the external validation dataset, with an AUC of 0.804.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discission","content":"\u003cp\u003eThe prevalence of SSI in elderly patients with hip fractures varies significantly between different institutions and regions (ranging from 2\u0026ndash;9.5%)[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Screening and prevention of SSIs that occur despite strict application of antibiotics according to published clinical practice guidelines perioperatively remains a challenge for surgeons.\u003c/p\u003e \u003cp\u003ePrevious studies attempted to predict SSI by combining serum biomarkers (high-sensitivity C-reactive protein, serum albumin, and blood glucose, etc.), but the sensitivity and specificity of the prediction varied and were limited by confounding factors (e.g., advanced age and trauma)[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Systemic inflammation biomarkers that integrate multiple leukocyte subsets and/or other inflammatory markers have demonstrated significant predictive capacity to predict postoperative infections in orthopedic surgery[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, Yao et al. retrospectively analyzed NLR, PLR, and SII collected upon admission in 1199 patients and found that NLR\u0026thinsp;\u0026ge;\u0026thinsp;5.84 had a significant predictive value for the presence of postoperative pneumonia in geriatric patients with hip fracture (OR\u0026thinsp;=\u0026thinsp;2.24, 95% CI 1.43\u0026ndash;3.51)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, to our knowledge, there are no existing predictive models for SSI after hip fracture that integrate systemic inflammation biomarkers. In this retrospective study, systemic inflammatory biomarkers (PAR\u0026thinsp;\u0026ge;\u0026thinsp;6.6 and SII\u0026thinsp;\u0026ge;\u0026thinsp;541.1) and traditional prognostic factors (older age, ASA class III-IV, surgical delay\u0026thinsp;\u0026ge;\u0026thinsp;6 days, surgical duration\u0026thinsp;\u0026gt;\u0026thinsp;180 minutes) were identified as independently associated with a high risk of SSI. The nomogram was established based on these predictors, and the C-index, AUC values, and calibration curves validated the discriminatory power and precision of the models. The DCA showed that the model has clinical application and can help early assessment of the SSI risk. The model is equally robust in external validation.\u003c/p\u003e \u003cp\u003eThe precise mechanism of the association between systemic inflammation biomarkers and SSI may involve several aspects. Elevated levels of SII and PAR upon admission reflect dynamic changes in four subsets: neutrophils, lymphocytes, platelets, and albumin. The systemic inflammatory response contributes to the secondary injury associated with hip fractures, and the diminished physiological reserve of elderly patients makes them more susceptible to the release of inflammatory cytokines after injury while lacking sufficient anti-inflammatory mediators to counterbalance them[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Elevated levels of cytokines (e.g., tumor necrosis factor-α, interleukin-6, and interleukin-1β) can prime the body to a hyper-inflammatory state, making it more susceptible to infections after surgery, as normal immune defenses are already engaged and may not respond adequately to new pathogens[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Besides, changes in leukocyte function and distribution due to chronic systemic inflammation somewhat weaken the body's ability to resist infection after surgery[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, endothelial dysfunction caused by platelet aggregation and adhesion can affect vascular permeability. This state facilitates the translocation of bacteria, making it easier for microbes to enter the surgical site, thus increasing the risk of deep SSI[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, patients with nutritional deficiencies reflected by hypoalbuminemia in hematologic tests are more likely to induce a chronic inflammatory state in the body, which can affect wound healing and immune function, further increasing the risk of postoperative infection[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe traditional prognostic factors identified in this study (older age, ASA class III-IV, surgical delay\u0026thinsp;\u0026ge;\u0026thinsp;6 days, surgical duration\u0026thinsp;\u0026gt;\u0026thinsp;180 minutes) have previously been widely reported[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The independent predictability of these predictors combined with systemic inflammatory biomarkers in multifactorial analysis suggested that, with the exception of inappropriate antibiotic use, the presence of SSI might be due to a composite of poor physical status. The presence of comorbidities in older patients with a higher ASA class tends to result in individuals with much lower baseline exercise tolerance compared to those in good general condition[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For this population of patients, surgery is delayed in large part because more time is needed to optimize poor presurgical medical conditions such as weakness, poor soft tissue status, and insulin dependence[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. And while waiting for surgery, depletion and imbalance of complement cascade components and systemic inflammatory response syndromes secondary to acute trauma are more likely to increase the risk of infection[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Prolonged surgical times are also believed to reflect poor physiologic conditions and more severe injuries, while more extensive soft tissue debridement and extensive wound exposure also carry a higher risk of infection.\u003c/p\u003e \u003cp\u003eThe prediction model integrating systemic inflammatory biomarkers could complement existing knowledge in the following aspects. First, the parameters of systemic inflammatory biomarkers can be easily obtained from admission hematologic tests without the need for a real-time physical examination, which is particularly useful in referral centers. Second, as an independent evaluation of baseline characteristics, systemic inflammatory biomarkers prevent the effect of recall bias and other confounding on the precision of the assessment metrics and provide a more objective prediction of the risk of SSI. Third, because systemic inflammatory biomarkers combine different subsets of parameters, they can provide surgeons with broader guidance on perioperative management, such as the management of patient chronic disease status, adjustment of antibiotic dose and early nutritional intervention.\u003c/p\u003e \u003cp\u003eThe main strengths of this study are the large sample size and the translation of independent risk factors into a predictive nomogram model that allows surgeons to personalize risk assessment for hip fracture patients. All predictors can be quickly obtained from routine perioperative laboratory results and admission reports from patients. On this basis, the predicted probability of SSI can be obtained in a few minutes by entering the results into a web page or by drawing vertical lines on the corresponding axes. Several limitations should also be discussed. First, this study enrolled patients from a tertiary orthopedic referral medical center, thus some patients were transferred from relatively remote hospitals. Patients with surgical delays longer than 6 days may have compromised levels of systemic inflammatory biomarkers upon admission due to systemic inflammatory response syndromes secondary to acute trauma[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, this bias was somewhat balanced by including the duration of surgical delay as an independent risk factor in the model as well. Second, all data were collected retrospectively with some information reported by patients and their families, so there is an inherent selection bias and recall bias. Third, these retrospective data failed to obtain surgeon-specific information, such as surgical experience, details that could potentially influence the results. Fourth, despite the good internal and external validation results, this single-center study requires a large prospective study with multicenter data to confirm the applicability of the model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study constructed a risk prediction online dynamic model for the postoperative SSI in elderly hip fracture patients based on systemic inflammatory biomarkers (PAR and SII) and traditional prognostic factors (age, ASA class, surgical delay and surgical duration, which can be used to guide early screening of patients with high risk of SSI and provide a reference tool for perioperative management.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local ethical committee (The 3rd Hospital of Hebei Medical University, Shijiazhuang, China; Ke2014-015-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects gave their written informed consent to take part in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declared that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the 2024 Government-funded Clinical Medicine Excellence Training Program Leader Project (No. ZF2024093). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXBZ designed the study; YHG, CSL, HCG and PYW searched for relevant studies and abstracted the data; CSL and HCG analyzed and interpreted the data; YHG and CSL wrote the manuscript, and XBZ approved the final version of the manuscript. All authors reviewed the manuscript before submitting it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the patients in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSattui SE, Saag KG. Fracture mortality: associations with epidemiology and osteoporosis treatment. 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JAMA Surg. 2017;152(8):784\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoja L, Piatti A, Pecoraro V, Ricci C, Virgili G, Salanti G, Germagnoli L, Liberati A, Banfi G. Timing matters in hip fracture surgery: patients operated within 48 hours have better outcomes. A meta-analysis and meta-regression of over 190,000 patients. PLoS ONE. 2012;7(10):e46175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ (Clinical Research ed). 2015; 350:g7594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdwards C, Counsell A, Boulton C, Moran CG. Early infection after hip fracture surgery: risk factors, costs and outcome. J Bone Joint Surg Br. 2008;90(6):770\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGan L, Chen X, Sun T, Li Q, Zhang R, Zhang J, Zhong J. Significance of Serum mtDNA Concentration in Lung Injury Induced by Hip Fracture. Shock. 2015;44(1):52\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaillet P, Klemm S, Ducher M, Aussem A, Schott AM. Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks. PLoS ONE. 2015;10(3):e0120125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDessouki O, Mahomed NN, Gandhi R. Metabolic abnormality and the proinflammatory state following hip joint surgery. Int J Clin Rheumatol. 2011;6(3):347\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoche JJ, Wenn RT, Sahota O, Moran CG. Effect of comorbidities and postoperative complications on mortality after hip fracture in elderly people: prospective observational cohort study. BMJ. 2005;331(7529):1374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuda Y, Yasunaga H, Horiguchi H, Ogawa S, Kawano H, Tanaka S. Association between dementia and postoperative complications after hip fracture surgery in the elderly: analysis of 87,654 patients using a national administrative database. Arch Orthop Trauma Surg. 2015;135(11):1511\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulut G, Yasmin D, Ak D, Y\u0026yacute;ld\u0026yacute;z M. Value of procalcitonin in differentiating post-surgical systemic and infectious complications from inflammatory reaction caused by surgical trauma in fracture surgery: preliminary report. Eur J Orthop Surg Traumatol. 2009;20(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim BD, Hsu WK, De Oliveira GS Jr., Saha S, Kim JY. Operative duration as an independent risk factor for postoperative complications in single-level lumbar fusion: an analysis of 4588 surgical cases. Spine (Phila Pa 1976). 2014;39(6):510\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoailles T, Brulefert K, Chalopin A, Longis PM, Gouin F. What are the risk factors for post-operative infection after hip hemiarthroplasty? Systematic review of literature. Int Orthop. 2016;40(9):1843\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLord JM, Midwinter MJ, Chen YF, Belli A, Brohi K, Kovacs EJ, Koenderman L, Kubes P, Lilford RJ. The systemic immune response to trauma: an overview of pathophysiology and treatment. Lancet. 2014;384(9952):1455\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-orthopaedic-surgery-and-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"josr","sideBox":"Learn more about [Journal of Orthopaedic Surgery and Research](http://josr-online.biomedcentral.com)","snPcode":"13018","submissionUrl":"https://submission.nature.com/new-submission/13018/3","title":"Journal of Orthopaedic Surgery and Research","twitterHandle":"@MSKmedBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hip fracture, surgical site infection, systemic inflammation biomarkers, risk factors, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-5040943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5040943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSystemic inflammation biomarkers have been widely shown to be associated with infection. This study aimed to construct a nomogram based on systemic inflammation biomarkers and traditional prognostic factors to assess the risk of surgical site infection (SSI) after hip fracture in the elderly.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were retrospectively collected from patients over 60 with acute hip fractures who underwent surgery and were followed for more than 12 months between June 2017 and June 2022 at a tertiary referral hospital. Biomarkers were calculated from peripheral venous blood collected on admission. The CDC definition of SSI was applied, with SSI identified through medical and pathogen culture records during hospitalization and routine postoperative telephone follow-ups. Multivariable logistic regression identified independent risk factors for SSI and developed predictive nomograms. Model stability was validated using an external set of patients treated from July 2022 to June 2023.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1430 patients were included in model development, with 41 cases (2.87%) of superficial SSI and 6 cases (0.42%) of deep SSI. Multivariable analysis identified traditional prognostic factors older age (OR\u0026thinsp;=\u0026thinsp;1.08, 95% CI 1.04\u0026ndash;1.12), ASA class III-IV (OR\u0026thinsp;=\u0026thinsp;2.46, 95% CI 1.32\u0026ndash;4.56), surgical delay\u0026thinsp;\u0026ge;\u0026thinsp;6 days (OR\u0026thinsp;=\u0026thinsp;3.59, 95% CI 1.36\u0026ndash;9.47), surgical duration\u0026thinsp;\u0026gt;\u0026thinsp;180 minutes (OR\u0026thinsp;=\u0026thinsp;2.72, 95% CI 1.17\u0026ndash;6.35), and systemic inflammation biomarkers PAR\u0026thinsp;\u0026ge;\u0026thinsp;6.6 (OR\u0026thinsp;=\u0026thinsp;2.25, 95% CI 1.17\u0026ndash;4.33) and SII\u0026thinsp;\u0026ge;\u0026thinsp;541.1 (OR\u0026thinsp;=\u0026thinsp;2.24, 95% CI 1.14\u0026ndash;4.40) as independent predictors of SSI. Model\u0026rsquo;s stability was proved by internal validation, and external validation with 307 patients, and an online dynamic nomogram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://brooklyn99.shinyapps.io/DynNomapp/\u003c/span\u003e\u003cspan address=\"https://brooklyn99.shinyapps.io/DynNomapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was generated.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study combined systemic inflammatory biomarkers and developed an online dynamic nomogram to predict SSI in elderly hip fracture patients, which could be used to guide early screening of patients with high risk of SSI and provide a reference tool for perioperative management.\u003c/p\u003e","manuscriptTitle":"Combining Systemic Inflammation Biomarkers with Traditional Prognostic Factors to Predict Surgical Site Infections in Elderly Hip Fracture Patients: A Risk Factor Analysis and Dynamic Nomogram Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 04:38:32","doi":"10.21203/rs.3.rs-5040943/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-05T08:03:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T23:25:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176608896849448840858784581208685308589","date":"2024-09-24T15:10:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156672404986461577048850120518130095623","date":"2024-09-23T07:02:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314968689503063314176732221585887966167","date":"2024-09-11T18:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-11T04:17:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-11T04:16:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-10T21:32:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Orthopaedic Surgery and Research","date":"2024-09-06T02:05:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-orthopaedic-surgery-and-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"josr","sideBox":"Learn more about [Journal of Orthopaedic Surgery and Research](http://josr-online.biomedcentral.com)","snPcode":"13018","submissionUrl":"https://submission.nature.com/new-submission/13018/3","title":"Journal of Orthopaedic Surgery and Research","twitterHandle":"@MSKmedBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ec6335b-e9cb-4207-8630-e9c6b2e03504","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:11:44+00:00","versionOfRecord":{"articleIdentity":"rs-5040943","link":"https://doi.org/10.1186/s13018-024-05446-9","journal":{"identity":"journal-of-orthopaedic-surgery-and-research","isVorOnly":false,"title":"Journal of Orthopaedic Surgery and Research"},"publishedOn":"2025-01-13 15:57:02","publishedOnDateReadable":"January 13th, 2025"},"versionCreatedAt":"2024-11-27 04:38:32","video":"","vorDoi":"10.1186/s13018-024-05446-9","vorDoiUrl":"https://doi.org/10.1186/s13018-024-05446-9","workflowStages":[]},"version":"v1","identity":"rs-5040943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5040943","identity":"rs-5040943","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}