Construction and Validation of a Predictive Nomogram for Prolonged Hospitalization in Elderly Patients with Hip Fractures: A Retrospective Cohort Study Incorporating Modifiable and Non-Modifiable Risk Factors

Construction and Validation of a Predictive Nomogram for Prolonged Hospitalization in Elderly Patients with Hip Fractures: A Retrospective Cohort Study Incorporating Modifiable and Non-Modifiable Risk Factors | 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 Construction and Validation of a Predictive Nomogram for Prolonged Hospitalization in Elderly Patients with Hip Fractures: A Retrospective Cohort Study Incorporating Modifiable and Non-Modifiable Risk Factors Guangxu Fu, Yong Wang, Zhen Zhang, Dengwu Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6538550/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: the global aging population has intensified the public health burden of hip fractures, characterized by high morbidity, mortality, and healthcare costs. Prolonged hospitalization (PLH) in elderly hip fracture patients is associated with adverse outcomes, yet existing predictive models often overlook the multifactorial interplay of clinical, surgical, and laboratory variables. This study aimed to develop and validate a nomogram integrating modifiable and non-modifiable risk factors to predict PLOS and optimize clinical decision-making. Methods: a retrospective observational cohort of 1,674 elderly hip fracture patients (2012-2025) from the People’s Hospital of Lichuan City was analyzed. Patients were categorized into Non-PLH (≤12.5 days) and PLH (>12.5 days) groups based on the 75th percentile of hospitalization duration. Variables spanning demographics, fracture characteristics, perioperative management, and comorbidities were collected. Multivariable logistic regression identified independent predictors, and a nomogram was developed using R software. Model performance was assessed via AUC, calibration curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Results: five independent predictors were identified: time from admission to surgery >48 hours (OR=16.62), hemiplegia (OR=3.48), heart failure (OR=2.66), lower extremity vascular disease (OR=2.31), and preoperative hemoglobin (protective, OR=0.96). The nomogram demonstrated robust discrimination (training AUC: 0.89, 95% CI: 0.87–0.91; testing AUC: 0.87, 95% CI: 0.83-0.91) and calibration (Brier scores: 0.18-0.21). DCA revealed significant clinical utility across thresholds of 0-97%, with a maximum net benefit of 0.25. Conclusion: this study presents a validated nomogram for predicting PLH in elderly hip fracture patients, integrating both modifiable and non-modifiable risk factors. The model’s high accuracy, interpretable risk stratification, and actionable thresholds enhance personalized resource allocation and preoperative optimization, advancing precision medicine in geriatric orthopedics. Hip fracture Prolonged hospitalization Nomogram Risk prediction model Elderly patients Clinical decision-making Figures Figure 1 1. Introduction The global aging population is intensifying the public health challenge of hip fractures among older adults. United Nations projections estimate that by 2050, the population aged 60 and over will reach 2.1 billion, with hip fractures—a major cause of disability in this group—continuing to rise in incidence [1–3] . The global age-standardized incidence rate of hip fractures increased from 781.56 per 100,000 in 1990 to 948.81 per 100,000 in 2021, with cases expected to increase by 2.6- to 4.0-fold by 2050 compared to 1990. Over half of these cases are anticipated in Asia, posing a significant challenge for China [1,4] . Despite representing only 14% of osteoporotic fractures, hip fractures consume 72% of healthcare resources. In the U.S., costs are projected to surpass $18.2 billion by 2025, underscoring the economic impact [5,6] . Hip fractures are linked to poor outcomes, including a one-year mortality rate of up to 30% and lasting functional impairments in over 40% of survivors, resulting in long-term care dependency [7,8] . Prolonged hospitalization (PLH) is a critical determinant of patient outcomes. Empirical studies indicate a strong correlation between PLH and various adverse events, including postoperative complications, readmission rates, and in-hospital mortality [9–13] . However, the definitions and risk factors associated with PLH demonstrate considerable regional variability, attributable to differences in healthcare systems, surgical delays, the complexity of comorbid conditions, and postoperative management protocols. This variability poses significant challenges to the formulation of universal predictive models. Current literature has identified potential associations between PLH and factors such as gender, the Charlson Comorbidity Index (CCI), preoperative waiting time, anemia, hypoalbuminemia, and elevated D-dimer levels; however, these associations exhibit inconsistencies across different patient cohorts [14–16] . The development of precise predictive tools represents a significant advancement in optimizing clinical decision-making processes. Nomograms, which function as interactive human-computer visualization tools, integrate multivariate regression parameters to quantify individualized risks and guide early interventions [17,18] . In the context of elderly patients with hip fractures, establishing a localized prediction model for PLH is of considerable clinical importance. By stratifying preoperative risks, clinicians can effectively identify high-risk patients, develop personalized treatment and rehabilitation plans, reduce hospital stays, decrease complication rates, and alleviate pressure on healthcare resources. This study utilized clinical cohort data and applied logistic regression analysis to identify independent predictors of PLH, leading to the construction and validation of a nomogram model. The model's performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis. The objective is to provide clinicians with a dynamic risk assessment tool that facilitates precise resource allocation, ultimately enhancing patient outcomes and the sustainability of the healthcare system. 2. Materials and methods 2.1 Study design and population This research utilized a retrospective observational cohort design. Participants were identified through electronic health records (HIS) at the People's Hospital of Lichuan City from December 2012 to March 2025. The study protocol received approval from the hospital’s Medical Ethics Committee (Approval No. 2025002) in compliance with the Declaration of Helsinki. All data were anonymized before analysis, and informed consent was waived due to the retrospective nature of the study. The inclusion criteria were: (1) patients aged 60 years or older; (2) patients undergoing Internal fixation or hip arthroplasty. The exclusion criteria included: (1) old fractures; (2) pathological fractures; (3) non-disease-related rapid discharge; (4) in-hospital mortality; (5) patients choosing discharge for hospice care; (6) patients transferred to other departments for continued treatment. A total of 1,674 patients were enrolled and randomly assigned to either a training set (70%) or a test set (30%). The 75th percentile of the total cohort's hospitalization duration was 12.5 days, which was used to categorize patients into two groups: Non-PLH (hospitalization ≤12.5 days) and PLH (prolonged hospitalization >12.5 days). 2.2 Collection of relevant variables Data were systematically gathered across four distinct domains: demographics, fracture characteristics, perioperative management, and comorbidities. The demographic variables encompassed sex, age, and body mass index (BMI). Fracture characteristics included fracture type (femoral neck, intertrochanteric, subtrochanteric), injury mechanism (low-energy or high-energy trauma), multiple traumas, and the time from injury to admission. Perioperative variables comprised the surgical approach (Internal fixation or hip arthroplasty), the time from admission to surgery, operative time, intraoperative blood loss, blood transfusion, anesthesia_method, ASA classification, and ICU transfer. Comorbidities and laboratory parameters were assessed, including smoking history, alcohol consumption, hypertension, coronary artery disease, heart failure, arrhythmia, metabolic disorders, cerebrovascular disease, Alzheimer’s disease, Parkinson’s disease, paraplegia, chronic obstructive pulmonary disease and/or pulmonary fibrosis, malignant_tumor, delirium, and lower extremity vascular disease (venous thrombosis). Preoperative and initial postoperative laboratory test results, as well as admission echocardiography parameters (aortic valve velocity, ejection fraction), were also recorded. 2.3 Statistical ananlysis Continuous variables were initially evaluated for normality using the Shapiro-Wilk test. Variables that were normally distributed were represented as mean±standard deviation (SD) and compared across groups using independent Studen’s t tests. Non-normally distributed variables were presented as median with interquartile range [M(IQR)] and analyzed using Mann-Whitney U tests. Categorical variables were expressed as frequencies and percentages (n, %), with group comparisons conducted via Pearson’s chi-square test or Fisher’s exact test. To identify independent predictors of prolonged hospitalization (PH), a two-stage variable selection strategy was implemented. Variables with a P value less than 0.1 in univariate analyses were included in a multivariate logistic regression model, employing backward stepwise elimination with a removal threshold of P≥0.05. Statistically significant final predictors (P<0.05) were reported as adjusted odds ratios (OR) with 95% confidence intervals (95% CI). A clinical nomogram was constructed utilizing the "rms" package within R software (version 4.3.1). The model's performance was rigorously evaluated through a comprehensive multidimensional framework. Discrimination was measured using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) and 95% confidence intervals (CI) calculated, and differences in AUC between the training and testing sets were tested using DeLong’s test. Calibration was assessed through calibration curves, the Hosmer-Lemeshow test, and 1,000 bootstrap resampling iterations. Classification performance metrics were obtained from confusion matrices, while clinical utility was evaluated using decision curve analysis (DCA). Data processing and analysis were performed using R version 4.4.0, along with Zstats 1.0 (www.zstats.net). 3. Results 3.1 Baseline demographics and characteristics The baseline characteristics of the entire patient cohort are detailed in Table 1. Of the 1,674 participants, 1,255 (75.0%) were categorized as Non-PLH, while 419 (25.0%) were identified as PLH. The PLH group demonstrated significantly lower median values for red blood cell count, preoperative hemoglobin, and preoperative albumin, alongside elevated D-dimer levels and ejection fraction. Postoperative albumin levels also remained lower in the PLH group. Additionally, the PLH group exhibited a higher prevalence of high-energy injuries, heart failure, hemiplegia, chronic obstructive pulmonary disease or pulmonary fibrosis, lower extremity vascular disease, and more than four comorbidities. Surgical delays exceeding 48 hours post-admission were more common among PLH patients. No significant differences were found between the groups regarding age, operative time, intraoperative blood loss, or most laboratory and clinical parameters (P>0.05), including comorbidities such as hypertension, diabetes, and malignancy. The equivalence of the Non-PLH and PLH groups is confirmed in Table S1. Table S2 presents the baseline characteristics of the training set. 3.2 Independent risk factors for prolonged hospitalization in elderly hip fracture patients Univariate analysis in the training set identified 7 potential risk factors for prolonged hospitalization in elderly hip fracture patients: time from admission to surgery, heart failure, hemiplegia, lower extremity vascular disease, number of comorbidities, preoperative hemoglobin (Pre Hb), and preoperative sodium (Na) (Table 2). Multivariable logistic regression analysis further confirmed 5 independent predictors: time from admission to surgery (OR = 16.618, 95% CI: 11.595 - 23.817), heart failure (OR = 2.660, 95% CI: 1.822 - 3.882), hemiplegia (OR = 3.484, 95% CI: 2.229 - 5.445), lower extremity vascular disease (OR = 2.314, 95% CI: 1.489 - 3.595), and preoperative hemoglobin level (OR = 0.962, 95% CI: 0.952 - 0.971) (Table 3). 3.3 Nomogram construction and validation Utilizing multivariable logistic regression analysis, a nomogram model was developed to identify five independent predictors of prolonged hospital stay (Figure 1A). The analysis revealed that the time from admission to surgery was the most significant risk factor, with patients in the PLH group exhibiting a 16.618-fold increased risk compared to those in the Non-PLH group (OR = 16.618, 95% CI: 11.595–23.817; β = 2.810, P < 0.001). Hemiplegia was identified as the second most significant risk factor (OR = 3.484, 95% CI: 2.229–5.445; β = 1.248, P < 0.001). Additionally, heart failure (OR = 2.660, 95% CI: 1.822–3.882; β = 0.978) and lower extremity vascular disease (OR = 2.314, 95% CI: 1.489–3.595; β = 0.839) were found to significantly elevate the risk (all P < 0.001). Importantly, preoperative hemoglobin level was identified as a crucial protective factor, with each 1 g/dL increase associated with a 3.8% reduction in the odds of prolonged hospitalization (OR = 0.962, 95% CI: 0.952–0.971; β = -0.039, P < 0.001). The β coefficients and effect sizes provided quantitative measures of the independent contributions of these variables to the risk of prolonged hospital stay. The nomogram model exhibited outstanding discriminative capability, as evidenced by area under the curve (AUC) values of 0.89 (95% confidence interval [CI]: 0.87–0.91) in the training set (Figure 1B) and 0.87 (95% CI: 0.83–0.91) in the testing set (Figure 1C). Sensitivity was recorded at 87% (95% CI: 85–90% in training; 87%, 95% CI: 84–91% in testing), and specificity was 77% (95% CI: 72–82% in training; 75%, 95% CI: 68–83% in testing), demonstrating consistency across both sets. DeLong’s test indicated no statistically significant difference in AUC (P = 0.12). Calibration analysis showed strong agreement between predicted and observed probabilities, corroborated by Brier scores of 0.18 for the training set and 0.21 for the testing set. The Hosmer-Lemeshow test results (training: χ² = 5.2, df = 7, P = 0.634; testing: χ² = 6.8, df = 7, P = 0.448) suggested a minimal risk of overfitting. The mean absolute error (MAE) was 0.006 for the training set and 0.014 for the testing set, indicating a prediction deviation of less than 1.5% (Figure 1D,E). Classification performance metrics were as follows: for the training set, accuracy was 85% (95% CI: 83–87%), positive predictive value (PPV) was 92% (95% CI: 90–94%), negative predictive value (NPV) was 67% (95% CI: 62–72%), and the F1-score was 0.89 (95% CI: 0.88–0.91). For the testing set, accuracy was 84% (95% CI: 81–87%), PPV was 91% (95% CI: 89–94%), NPV was 66% (95% CI: 58–74%), and the F1-score was 0.89 (95% CI: 0.87–0.92) (Table 6). DCA demonstrated significant clinical utility over a wide range of risk thresholds. The model exhibited a superior net benefit relative to the "treat-all" and "treat-none" strategies within thresholds of 0–96% for the training set and 0–97% for the testing set, achieving a peak standardized net benefit of 0.25 in both sets (Figure 1F,G). 4. Discussion Hip fractures in elderly patients epitomize a significant convergence of age-associated physiological decline, multimorbidity, and the strain on healthcare resources. Although previous research has identified individual predictors of PLH [19–21] , this study advances the field by integrating demographic, clinical, surgical, and laboratory variables into a comprehensive predictive framework. The nomogram addresses the multifactorial nature of PLH by incorporating both modifiable factors (such as the time form admission to surgery and preoperative hemoglobin levels) and non-modifiable factors (such as hemiplegia), representing a substantial improvement over existing tools that often fail to account for this complexity. By quantifying these interactions, the model enables clinicians to prioritize interventions for high-risk patients, with targeted strategies such as early correction of anemia or expedited surgical procedures potentially reducing the duration of hospitalization. The study identifies five independent predictors that influence PLH through distinct yet interconnected pathophysiological pathways. The time from admission to surgery exceeding 48 hours initiate systemic inflammation, as evidenced by elevated interleukin-6 (IL-6) and C-reactive protein (CRP) levels, and contribute to complications associated with immobilization, such as pressure ulcers, pneumonia, and deep vein thrombosis (DVT), thereby extending the duration of hospitalization [22–24] . The underlying mechanisms involve delayed tissue repair, increased immunosuppression, and accelerated muscle atrophy [25–27] . Additionally, prolonged immobilization results in muscle wasting and joint stiffness, further hindering functional recovery. Hemiplegia exacerbates dependency on nursing care, postpones the commencement of rehabilitation, and heightens the risk of DVT [28–32] . Neurogenic bladder and bowel dysfunction significantly increase the incidence of urinary tract infections (UTIs) (32% compared to 14%) [33] , necessitating an additional 5–7 days of antibiotic treatment [34] . Patients with hip fractures who have a history of stroke experience hospital stays that are 40% longer than those of non-hemiplegic patients [35] . Heart failure exacerbates perioperative fluid imbalance as a result of diminished cardiac reserve, thereby increasing the risk of pulmonary edema and the rate of ICU admissions (28% compared to 7% in patients without heart failure), with ICU stays extended by an average of 2.5 days [36–38] . Vascular disease in the lower extremities, particularly chronic ischemia, impedes wound healing by 40% due to compromised oxygen and nutrient delivery [39,40] . Peripheral artery disease is associated with higher 30-day reoperation rates (12% compared to 3%) due to infections or the necessity for revascularization [41] , while inadequate circulation increases the risk of deep vein thrombosis threefold [42,43] . Preoperative anemia, defined as hemoglobin levels below 10 g/dL, elevates the risk of postoperative delirium by 67% and increases transfusion rates to 58% (compared to 12% in non-anemic patients) [44,45] . Each unit of red blood cell transfusion is associated with an extension of hospitalization by 1.8 days, likely attributable to transfusion-related immunomodulation (TRIM) and increased infection risk [46–48] . This study presents an integrated framework that combines biomechanical impairments (such as mobility deficits), inflammatory cascades (including complications related to surgical delays), and metabolic dysregulation (such as anemia) to substantially improve the accuracy of predicting PLH. Unlike traditional models like POSSUM, which primarily focus on surgical risks and overlook modifiable biomarkers such as hemoglobin that are closely linked to postoperative complications [49–51] , our model incorporates time-sensitive variables (such as surgical delays) and preoperative laboratory parameters. This approach accounts for 22% of the variance not explained by previous models. DCA establishes actionable risk thresholds (10-55%), effectively addressing the "gray zone" issue inherent in traditional scoring systems. The quantified exponential risk associated with surgical delays exceeding 48 hours underscores the need to prioritize surgical scheduling, thereby enhancing the model's discriminative performance (AUC: 0.87–0.89 compared to 0.78–0.85 for models based solely on comorbidities) and expanding its clinical applicability. This study is subject to limitations typical of retrospective designs, such as potential selection bias in the classification of surgical delay and the presence of unmeasured confounders, including adherence to rehabilitation protocols. The use of single-center data may restrict the generalizability of the findings, especially in regions with varying surgical protocols or social care systems. The omission of socioeconomic factors, such as caregiver availability and access to home rehabilitation, may lead to an overestimation of the model's optimism. Furthermore, the definition of PLH based on the 75th percentile (12.5 days) lacks international consensus; therefore, future research should establish thresholds using multinational registries. Prospective multicenter studies that incorporate dynamic postoperative biomarkers, such as serial measurements of hemoglobin and albumin, along with machine learning algorithms, are necessary to refine the model. Additionally, incorporating comprehensive geriatric assessments (CGA) to evaluate cognitive function and social support, as well as investigating the association of PLH with long-term outcomes, such as one-year mortality and secondary fracture risk, will enhance the clinical applicability of the findings. 5. Conclusion This study established a novel nomogram incorporating five clinically validated predictors—surgical delay, hemiplegia, heart failure, lower extremity vascular disease, and preoperative hemoglobin—to predict prolonged hospitalization in elderly hip fracture patients. The model demonstrated robust discriminative accuracy (AUC >0.85), substantial clinical utility (net benefit of 0.25 across actionable thresholds via decision curve analysis), and interpretable risk stratification, providing a reliable tool for personalized resource allocation and targeted preoperative optimization. By integrating modifiable and non-modifiable risk factors into a unified framework, this tool not only enhances clinical decision-making but also holds promise for advancing precision medicine in orthopedics, particularly in optimizing care pathways for high-risk geriatric populations. Abbreviations PLH Prolonged hospitalization AUC Area under the curve DCA Decision curve analysis BMI Body mass index ASA American Society of Anesthesiologist ICU Intensive care unit COPD Chronic obstructive pulmonary disease RBC: Red blood cel WBC White blood cell Pre Hb Preoperative hemoglobin PLT Platelets N Neutrophile granulocyte HCT Hematocrit K Kalium Ca Calcium Na Natrium Pre Alb Preoperative albumin ALT Alanine aminotransferase AST Aspartate aminotransferase LDH Lactate dehydrogenase BUN Blood urea nitrogen Cr Creatinine PT Prothrombin time APTT Activated partial thromboplastin time INR International normalized ratio FIB Fibrinogen AV Aortic velocity EF Ejection fraction Declarations Acknowledgements The authors express their gratitude to Professor Zheng and his team at Zhejiang University of Traditional Chinese Medicine for developing the platform for statistical analysis of the data. Author contributions All authors participated in the development of the article and provided approval for the submitted version. Guangxu Fu and Dengwu Tan primarily oversaw the article's design, data analysis, and manuscript preparation. The literature review was conducted by Guangxu Fu and Yong Wang. Data analysis and results visualization were carried out by Guangxu Fu, and Zhen Zhang. Funding This study was funded by the Enshi Tujia and Miao Autonomous Prefecture Health Commission. Data availability The authors confirm that all pertinent data supporting the study's findings are included in the article and its Supplementary Information files, or can be obtained from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate In accordance with the Declaration of Helsinki, this study was approved by the Medical Ethics Committee of the People's Hospital of Lichuan City (approval number: 2025002). 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The Journal of Urology, 2020, 203(3): 579-584. 203. doi:10.1097/JU.0000000000000555. Ross S S, Forster C S, Borawski K. Urinary Tract Infection and Neuropathic Bladder[J]. The Urologic Clinics of North America, 2024, 51(4): 551-559. 51. doi:10.1016/j.ucl.2024.06.009. Feng M, Zhang J, Shen H, et al. Predictors of Prognosis for Elderly Patients with Poststroke Hemiplegia Experiencing Hip Fractures[J]. Clinical Orthopaedics and Related Research, 2009, 467(11): 2970-2978. 467. doi:10.1007/s11999-009-0835-5. Rourke E M, Kuttab H I, Lykins J D, et al. Fluid Resuscitation in Septic Patients with Comorbid Heart Failure[J]. Critical Care Medicine, 2021, 49(2): e201-e204. 49. doi:10.1097/CCM.0000000000004730. Shay D, Ng P Y, Dudzinski D M, et al. Preoperative Heart Failure Treatment Prevents Postoperative Cardiac Complications in Patients with Lower Risk: A Retrospective Cohort Study[J]. Annals of Surgery, 2023, 277(1): e33-e39. 277. doi:10.1097/SLA.0000000000004779. Sae-Phua V, Tanasittiboon S, Sangtongjaraskul S. The Effect of Goal-Directed Fluid Management Based on Stroke Volume Variation on ICU Length of Stay in Elderly Patients Undergoing Elective Craniotomy: A Randomized Controlled Trial[J]. Indian Journal of Critical Care Medicine: Peer-Reviewed, Official Publication of Indian Society of Critical Care Medicine, 2023, 27(10): 709-716. 27. doi:10.5005/jp-journals-10071-24551. Hutchings G, Kruszyna Ł, Nawrocki M J, et al. Molecular Mechanisms Associated with ROS-Dependent Angiogenesis in Lower Extremity Artery Disease[J]. Antioxidants (Basel, Switzerland), 2021, 10(5): 735. 10. doi:10.3390/antiox10050735. Behrendt C A, Koncar I. Treatment of Lower Extremity Peripheral Arterial Disease[J]. European Journal of Vascular and Endovascular Surgery: The Official Journal of the European Society for Vascular Surgery, 2024, 68(3): 288-289. 68. doi:10.1016/j.ejvs.2024.06.005. Secemsky E A, Schermerhorn M, Carroll B J, et al. Readmissions after Revascularization Procedures for Peripheral Arterial Disease: A Nationwide Cohort Study[J]. Annals of Internal Medicine, 2018, 168(2): 93-99. 168. doi:10.7326/M17-1058. Mufti Y, Qiu A, Chmielecki J, et al. The Association between Pulmonary Embolism and Deep Vein Thrombosis in the Upper or Lower Extremities in Neurocritical Care Patients[J]. World Neurosurgery, 2025, 195: 123683. 195. doi:10.1016/j.wneu.2025.123683. Callori S, Wysokinsk W, Vlazny D, et al. Impact of Coincident Lower Extremity Deep Vein Thrombosis on Symptomatic and Incidental Pulmonary Embolism Outcomes. A Single-Center Prospective Cohort Study[J]. Journal of thrombosis and haemostasis: JTH, 2025, 23(4): 1260-1268. 23. doi:10.1016/j.jtha.2024.12.025. Mufti H, Alsharm F, Bahawi M, et al. The Association between Preoperative Anemia, Blood Transfusion Need, and Postoperative Complications in Adult Cardiac Surgery, a Single Center Contemporary Experience[J]. Journal of Cardiothoracic Surgery, 2023, 18(1): 10. 18. doi:10.1186/s13019-023-02132-5. Telang S S, Palmer R C, Yoshida B, et al. Preoperative Anemia as a Predictor of Periprosthetic Joint Infection Following Total Knee Arthroplasty: A Continuous Variable Analysis[J]. The Journal of Arthroplasty, 2025: S0883-5403(25)00186-X. doi:10.1016/j.arth.2025.02.058. Deeb A P, Aquina C T, Monson J R T, et al. Allogeneic Leukocyte-Reduced Red Blood Cell Transfusion Is Associated with Postoperative Infectious Complications and Cancer Recurrence after Colon Cancer Resection[J]. Digestive Surgery, 2020, 37(2): 163-170. 37. doi:10.1159/000498865. Obonyo N G, Sela D P, White N, et al. Effects of Transfusing Older Red Blood Cells on Patient Outcomes in Critical Illness: A Retrospective Cohort Study[J]. Vox Sanguinis, 2025. doi:10.1111/vox.70007. Xu X, Zhang Y, Gan J, et al. Association between Perioperative Allogeneic Red Blood Cell Transfusion and Infection after Clean-Contaminated Surgery: A Retrospective Cohort Study[J]. British Journal of Anaesthesia, 2021, 127(3): 405-414. 127. doi:10.1016/j.bja.2021.05.031. Saeidimehr S, Ebadi A, Kalantari F, et al. A Validation Study for POSSUM and EuroSCORE as a Predictor of Mortality after Selective Cardiac Surgery[J]. Acta Medica Indonesiana, 2015, 47(1): 38-44. 47. Charalampakis V, Wiglesworth A, Formela L, et al. POSSUM and P-POSSUM Overestimate Morbidity and Mortality in Laparoscopic Bariatric Surgery[J]. Surgery for Obesity and Related Diseases: Official Journal of the American Society for Bariatric Surgery, 2014, 10(6): 1147-1153. 10. doi:10.1016/j.soard.2014.04.023. Richards C H, Leitch F E, Horgan P G, et al. A Systematic Review of POSSUM and Its Related Models as Predictors of Post-Operative Mortality and Morbidity in Patients Undergoing Surgery for Colorectal Cancer[J]. Journal of Gastrointestinal Surgery: Official Journal of the Society for Surgery of the Alimentary Tract, 2010, 14(10): 1511-1520. 14. doi:10.1007/s11605-010-1333-5. Tables Tables 1 to 3 are available in the Supplementary Files section Table 6 is not available with this version. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.docx TableS3.docx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6538550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463277959,"identity":"665df862-fb4b-486d-9dae-ca95ea8788f3","order_by":0,"name":"Guangxu Fu","email":"","orcid":"","institution":"The People’s Hospital of Lichuan City, Enshi Tujia\u0026miao Autonomous Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Guangxu","middleName":"","lastName":"Fu","suffix":""},{"id":463277960,"identity":"7d9d80b6-38b4-4fc5-a29d-95c998fda1ce","order_by":1,"name":"Yong Wang","email":"","orcid":"","institution":"The People’s Hospital of Lichuan City, Enshi Tujia\u0026miao Autonomous Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""},{"id":463277961,"identity":"6ca6601a-136d-4b3d-a806-b20da11ec2b2","order_by":2,"name":"Zhen Zhang","email":"","orcid":"","institution":"The People’s Hospital of Lichuan City, Enshi Tujia\u0026miao Autonomous Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Zhang","suffix":""},{"id":463277962,"identity":"4e9f8552-bcd3-4dea-99e2-8d89840845da","order_by":3,"name":"Dengwu Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3RrwvCQBTA8SeDsxxaT5CtmSeCyT/mPQZLE4wXDBvKFvyR/TOMxo3BWc6+ODGYbYuiVdlmM9wn35d37w7AMP4Qc+7nCqXgTneVlSiXzUkPAhSlntnjrfLcUqvmxAZ0B9fYn0ARjAbXtdXiYpCiizqnKAyYpJBBP9lgfdKJUkSZUwIXVdBpCEJfjvWJBZi+p0R7vyDNwBXzhoTBOKQ4pzDn0wXFVouEwwTotb7iU2iXCO4Dvh+ZeQK14o27OIeu6lSvr3Ru2aOSS7uf7OqTD/y344ZhGMZXTxhETUxzXxeYAAAAAElFTkSuQmCC","orcid":"","institution":"The People’s Hospital of Lichuan City, Enshi Tujia\u0026miao Autonomous Prefecture","correspondingAuthor":true,"prefix":"","firstName":"Dengwu","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2025-04-27 06:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6538550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6538550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83836326,"identity":"ee7dea1b-8340-4259-9429-5c55c3925ac1","added_by":"auto","created_at":"2025-06-03 13:16:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1022927,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of the nomogram for PLH. (A) Nomogram. (B) ROC curve of the nomogram in the training set. (C) ROC curve of the nomogram in the testing set. (D) Calibration curve of the nomogram in the training set. (E) Calibration curve of the nomogram in the testing set. (F) DCA curve of nomogram in the training set. (G) DCA curve of the nomogram in the testing set.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6538550/v1/4a5be1dba161a637ea66be5e.png"},{"id":93051253,"identity":"ff5f5e31-12e9-4406-b608-044e354fea96","added_by":"auto","created_at":"2025-10-08 14:17:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1620647,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6538550/v1/4dc84a84-1fef-4266-b4fa-2b9fe773d7bb.pdf"},{"id":83837524,"identity":"e30a4fb1-dc71-42b6-9d38-05e5d8101124","added_by":"auto","created_at":"2025-06-03 13:24:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50922,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6538550/v1/4ab01a317cd694427b0a7ce5.docx"},{"id":83836324,"identity":"5271ee0f-3d6f-4c59-9519-b4fb0e334d08","added_by":"auto","created_at":"2025-06-03 13:16:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":50675,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6538550/v1/b236738e1c70175805a20746.docx"},{"id":83837525,"identity":"e14e4c20-2a93-419b-bf0b-f7be5322a86c","added_by":"auto","created_at":"2025-06-03 13:24:17","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15181,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6538550/v1/ea47b06fab22ae1ac6354e25.docx"},{"id":83836322,"identity":"701510b6-0f2f-4f25-8b28-0ce9b25496ec","added_by":"auto","created_at":"2025-06-03 13:16:17","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":54340,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6538550/v1/3cd11870fe9ef835c7fb3659.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and Validation of a Predictive Nomogram for Prolonged Hospitalization in Elderly Patients with Hip Fractures: A Retrospective Cohort Study Incorporating Modifiable and Non-Modifiable Risk Factors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global aging population is intensifying the public health challenge of hip fractures among older adults. United Nations projections estimate that by 2050, the population aged 60 and over will reach 2.1 billion, with hip fractures\u0026mdash;a major cause of disability in this group\u0026mdash;continuing to rise in incidence\u003csup\u003e[1\u0026ndash;3]\u003c/sup\u003e. The global age-standardized incidence rate of hip fractures increased from 781.56 per 100,000 in 1990 to 948.81 per 100,000 in 2021, with cases expected to increase by 2.6- to 4.0-fold by 2050 compared to 1990. Over half of these cases are anticipated in Asia, posing a significant challenge for China\u003csup\u003e[1,4]\u003c/sup\u003e. Despite representing only 14% of osteoporotic fractures, hip fractures consume 72% of healthcare resources. In the U.S., costs are projected to surpass $18.2 billion by 2025, underscoring the economic impact\u003csup\u003e[5,6]\u003c/sup\u003e. Hip fractures are linked to poor outcomes, including a one-year mortality rate of up to 30% and lasting functional impairments in over 40% of survivors, resulting in long-term care dependency\u003csup\u003e[7,8]\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProlonged hospitalization (PLH) is a critical determinant of patient outcomes. Empirical studies indicate a strong correlation between PLH and various adverse events, including postoperative complications, readmission rates, and in-hospital mortality\u0026nbsp;\u003csup\u003e[9\u0026ndash;13]\u003c/sup\u003e. However, the definitions and risk factors associated with PLH demonstrate considerable regional variability, attributable to differences in healthcare systems, surgical delays, the complexity of comorbid conditions, and postoperative management protocols. This variability poses significant challenges to the formulation of universal predictive models. Current literature has identified potential associations between PLH and factors such as gender, the Charlson Comorbidity Index (CCI), preoperative waiting time, anemia, hypoalbuminemia, and elevated D-dimer levels; however, these associations exhibit inconsistencies across different patient cohorts\u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe development of precise predictive tools represents a significant advancement in optimizing clinical decision-making processes. Nomograms, which function as interactive human-computer visualization tools, integrate multivariate regression parameters to quantify individualized risks and guide early interventions\u003csup\u003e[17,18]\u003c/sup\u003e. In the context of elderly patients with hip fractures, establishing a localized prediction model for PLH is of considerable clinical importance. By stratifying preoperative risks, clinicians can effectively identify high-risk patients, develop personalized treatment and rehabilitation plans, reduce hospital stays, decrease complication rates, and alleviate pressure on healthcare resources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study utilized clinical cohort data and applied logistic regression analysis to identify independent predictors of PLH, leading to the construction and validation of a nomogram model. The model\u0026apos;s performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis. The objective is to provide clinicians with a dynamic risk assessment tool that facilitates precise resource allocation, ultimately enhancing patient outcomes and the sustainability of the healthcare system.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e2.1 Study design and population\u003c/p\u003e\n\u003cp\u003eThis research utilized a retrospective observational cohort design. Participants were identified through electronic health records (HIS) at the People\u0026apos;s Hospital of Lichuan City from December 2012 to March 2025. The study protocol received approval from the hospital\u0026rsquo;s Medical Ethics Committee (Approval No. 2025002) in compliance with the Declaration of Helsinki. All data were anonymized before analysis, and informed consent was waived due to the retrospective nature of the study. The inclusion criteria were: (1) patients aged 60 years or older; (2) patients undergoing Internal fixation or hip arthroplasty. The exclusion criteria included: (1) old fractures; (2) pathological fractures; (3) non-disease-related rapid discharge; (4) in-hospital mortality; (5) patients choosing discharge for hospice care; (6) patients transferred to other departments for continued treatment. A total of 1,674 patients were enrolled and randomly assigned to either a training set (70%) or a test set (30%). The 75th percentile of the total cohort\u0026apos;s hospitalization duration was 12.5 days, which was used to categorize patients into two groups: Non-PLH (hospitalization\u0026nbsp;\u0026le;12.5 days) and PLH (prolonged hospitalization \u0026gt;12.5 days).\u003c/p\u003e\n\u003cp\u003e2.2 Collection of relevant variables\u003c/p\u003e\n\u003cp\u003eData were systematically gathered across four distinct domains: demographics, fracture characteristics, perioperative management, and comorbidities. The demographic variables encompassed sex, age, and body mass index (BMI). Fracture characteristics included fracture type (femoral neck, intertrochanteric, subtrochanteric), injury mechanism (low-energy or high-energy trauma), multiple traumas, and the time from injury to admission. Perioperative variables comprised the surgical approach (Internal fixation or hip arthroplasty), the time from admission to surgery, operative time, intraoperative blood loss, blood transfusion, anesthesia_method, ASA classification, and ICU transfer. Comorbidities and laboratory parameters were assessed, including smoking history, alcohol consumption, hypertension, coronary artery disease, heart failure, arrhythmia, metabolic disorders, cerebrovascular disease, Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, paraplegia, chronic obstructive pulmonary disease and/or pulmonary fibrosis, malignant_tumor, delirium, and lower extremity vascular disease (venous thrombosis). \u0026nbsp; Preoperative and initial postoperative laboratory test results, as well as admission echocardiography parameters (aortic valve velocity, ejection fraction), were also recorded.\u003c/p\u003e\n\u003cp\u003e2.3 Statistical ananlysis\u003c/p\u003e\n\u003cp\u003eContinuous variables were initially evaluated for normality using the Shapiro-Wilk test. Variables that were normally distributed were represented as mean\u0026plusmn;standard deviation (SD) and compared across groups using independent Studen\u0026rsquo;s t tests. Non-normally distributed variables were presented as median with interquartile range [M(IQR)] and analyzed using Mann-Whitney U tests. Categorical variables were expressed as frequencies and percentages (n, %), with group comparisons conducted via Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test. To identify independent predictors of prolonged hospitalization (PH), a two-stage variable selection strategy was implemented. Variables with a P value less than 0.1 in univariate analyses were included in a multivariate logistic regression model, employing backward stepwise elimination with a removal threshold of P\u0026ge;0.05. Statistically significant final predictors (P\u0026lt;0.05) were reported as adjusted odds ratios (OR) with 95% confidence intervals (95% CI).\u003c/p\u003e\n\u003cp\u003eA clinical nomogram was constructed utilizing the \u0026quot;rms\u0026quot; package within R software (version 4.3.1). The model\u0026apos;s performance was rigorously evaluated through a comprehensive multidimensional framework. Discrimination was measured using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) and 95% confidence intervals (CI) calculated, and differences in AUC between the training and testing sets were tested using DeLong\u0026rsquo;s test. Calibration was assessed through calibration curves, the Hosmer-Lemeshow test, and 1,000 bootstrap resampling iterations. Classification performance metrics were obtained from confusion matrices, while clinical utility was evaluated using decision curve analysis (DCA). Data processing and analysis were performed using R version 4.4.0, along with Zstats 1.0 (www.zstats.net).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Baseline demographics and characteristics\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the entire patient cohort are detailed in Table 1. Of the 1,674 participants, 1,255 (75.0%) were categorized as Non-PLH, while 419 (25.0%) were identified as PLH. The PLH group demonstrated significantly lower median values for red blood cell count, preoperative hemoglobin, and preoperative albumin, alongside elevated D-dimer levels and ejection fraction. Postoperative albumin levels also remained lower in the PLH group. Additionally, the PLH group exhibited a higher prevalence of high-energy injuries, heart failure, hemiplegia, chronic obstructive pulmonary disease or pulmonary fibrosis, lower extremity vascular disease, and more than four comorbidities. Surgical delays exceeding 48 hours post-admission were more common among PLH patients. No significant differences were found between the groups regarding age, operative time, intraoperative blood loss, or most laboratory and clinical parameters (P\u0026gt;0.05), including comorbidities such as hypertension, diabetes, and malignancy. The equivalence of the Non-PLH and PLH groups is confirmed in Table S1. Table S2 presents the baseline characteristics of the training set.\u003c/p\u003e\n\u003cp\u003e3.2 Independent risk factors for prolonged hospitalization in elderly hip fracture patients\u003c/p\u003e\n\u003cp\u003eUnivariate analysis in the training set identified 7 potential risk factors for prolonged hospitalization in elderly hip fracture patients: time from admission to surgery, heart failure, hemiplegia, lower extremity vascular disease, number of comorbidities, preoperative hemoglobin (Pre Hb), and preoperative sodium (Na) (Table 2). Multivariable logistic regression analysis further confirmed 5 independent predictors: time from admission to surgery (OR = 16.618, 95% CI: 11.595 - 23.817), heart failure (OR = 2.660, 95% CI: 1.822 - 3.882), hemiplegia (OR = 3.484, 95% CI: 2.229 - 5.445), lower extremity vascular disease (OR = 2.314, 95% CI: 1.489 - 3.595), and preoperative hemoglobin level (OR = 0.962, 95% CI: 0.952 - 0.971) (Table 3).\u003c/p\u003e\n\u003cp\u003e3.3 Nomogram construction and validation\u003c/p\u003e\n\u003cp\u003eUtilizing multivariable logistic regression analysis, a nomogram model was developed to identify five independent predictors of prolonged hospital stay (Figure 1A). The analysis revealed that the time from admission to surgery was the most significant risk factor, with patients in the PLH group exhibiting a 16.618-fold increased risk compared to those in the Non-PLH group (OR = 16.618, 95% CI: 11.595\u0026ndash;23.817; \u0026beta; = 2.810, P \u0026lt; 0.001). Hemiplegia was identified as the second most significant risk factor (OR = 3.484, 95% CI: 2.229\u0026ndash;5.445; \u0026beta; = 1.248, P \u0026lt; 0.001). Additionally, heart failure (OR = 2.660, 95% CI: 1.822\u0026ndash;3.882; \u0026beta; = 0.978) and lower extremity vascular disease (OR = 2.314, 95% CI: 1.489\u0026ndash;3.595; \u0026beta; = 0.839) were found to significantly elevate the risk (all P \u0026lt; 0.001). Importantly, preoperative hemoglobin level was identified as a crucial protective factor, with each 1 g/dL increase associated with a 3.8% reduction in the odds of prolonged hospitalization (OR = 0.962, 95% CI: 0.952\u0026ndash;0.971; \u0026beta; = -0.039, P \u0026lt; 0.001). The \u0026beta; coefficients and effect sizes provided quantitative measures of the independent contributions of these variables to the risk of prolonged hospital stay.\u003c/p\u003e\n\u003cp\u003eThe nomogram model exhibited outstanding discriminative capability, as evidenced by area under the curve (AUC) values of 0.89 (95% confidence interval [CI]: 0.87\u0026ndash;0.91) in the training set (Figure 1B) and 0.87 (95% CI: 0.83\u0026ndash;0.91) in the testing set (Figure 1C). Sensitivity was recorded at 87% (95% CI: 85\u0026ndash;90% in training; 87%, 95% CI: 84\u0026ndash;91% in testing), and specificity was 77% (95% CI: 72\u0026ndash;82% in training; 75%, 95% CI: 68\u0026ndash;83% in testing), demonstrating consistency across both sets. DeLong\u0026rsquo;s test indicated no statistically significant difference in AUC (P = 0.12). Calibration analysis showed strong agreement between predicted and observed probabilities, corroborated by Brier scores of 0.18 for the training set and 0.21 for the testing set. The Hosmer-Lemeshow test results (training: \u0026chi;\u0026sup2; = 5.2, df = 7, P = 0.634; testing: \u0026chi;\u0026sup2; = 6.8, df = 7, P = 0.448) suggested a minimal risk of overfitting. The mean absolute error (MAE) was 0.006 for the training set and 0.014 for the testing set, indicating a prediction deviation of less than 1.5% (Figure 1D,E). Classification performance metrics were as follows: for the training set, accuracy was 85% (95% CI: 83\u0026ndash;87%), positive predictive value (PPV) was 92% (95% CI: 90\u0026ndash;94%), negative predictive value (NPV) was 67% (95% CI: 62\u0026ndash;72%), and the F1-score was 0.89 (95% CI: 0.88\u0026ndash;0.91). For the testing set, accuracy was 84% (95% CI: 81\u0026ndash;87%), PPV was 91% (95% CI: 89\u0026ndash;94%), NPV was 66% (95% CI: 58\u0026ndash;74%), and the F1-score was 0.89 (95% CI: 0.87\u0026ndash;0.92) (Table 6). DCA demonstrated significant clinical utility over a wide range of risk thresholds. The model exhibited a superior net benefit relative to the \u0026quot;treat-all\u0026quot; and \u0026quot;treat-none\u0026quot; strategies within thresholds of 0\u0026ndash;96% for the training set and 0\u0026ndash;97% for the testing set, achieving a peak standardized net benefit of 0.25 in both sets (Figure 1F,G).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHip fractures in elderly patients epitomize a significant convergence of age-associated physiological decline, multimorbidity, and the strain on healthcare resources. Although previous research has identified individual predictors of PLH\u003csup\u003e[19\u0026ndash;21]\u003c/sup\u003e, this study advances the field by integrating demographic, clinical, surgical, and laboratory variables into a comprehensive predictive framework. The nomogram addresses the multifactorial nature of PLH by incorporating both modifiable factors (such as the time form admission to surgery and preoperative hemoglobin levels) and non-modifiable factors (such as hemiplegia), representing a substantial improvement over existing tools that often fail to account for this complexity. By quantifying these interactions, the model enables clinicians to prioritize interventions for high-risk patients, with targeted strategies such as early correction of anemia or expedited surgical procedures potentially reducing the duration of hospitalization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study identifies five independent predictors that influence PLH through distinct yet interconnected pathophysiological pathways. The time from admission to surgery exceeding 48 hours initiate systemic inflammation, as evidenced by elevated interleukin-6 (IL-6) and C-reactive protein (CRP) levels, and contribute to complications associated with immobilization, such as pressure ulcers, pneumonia, and deep vein thrombosis (DVT), thereby extending the duration of hospitalization\u003csup\u003e[22\u0026ndash;24]\u003c/sup\u003e. The underlying mechanisms involve delayed tissue repair, increased immunosuppression, and accelerated muscle atrophy\u003csup\u003e[25\u0026ndash;27]\u003c/sup\u003e. Additionally, prolonged immobilization results in muscle wasting and joint stiffness, further hindering functional recovery. Hemiplegia exacerbates dependency on nursing care, postpones the commencement of rehabilitation, and heightens the risk of DVT\u003csup\u003e[28\u0026ndash;32]\u003c/sup\u003e. Neurogenic bladder and bowel dysfunction significantly increase the incidence of urinary tract infections (UTIs) (32% compared to 14%)\u003csup\u003e[33]\u003c/sup\u003e, necessitating an additional 5\u0026ndash;7 days of antibiotic treatment\u003csup\u003e[34]\u003c/sup\u003e. Patients with hip fractures who have a history of stroke experience hospital stays that are 40% longer than those of non-hemiplegic patients\u003csup\u003e[35]\u003c/sup\u003e. Heart failure exacerbates perioperative fluid imbalance as a result of diminished cardiac reserve, thereby increasing the risk of pulmonary edema and the rate of ICU admissions (28% compared to 7% in patients without heart failure), with ICU stays extended by an average of 2.5 days\u003csup\u003e[36\u0026ndash;38]\u003c/sup\u003e. Vascular disease in the lower extremities, particularly chronic ischemia, impedes wound healing by 40% due to compromised oxygen and nutrient delivery\u003csup\u003e[39,40]\u003c/sup\u003e. Peripheral artery disease is associated with higher 30-day reoperation rates (12% compared to 3%) due to infections or the necessity for revascularization\u003csup\u003e[41]\u003c/sup\u003e, while inadequate circulation increases the risk of deep vein thrombosis threefold\u003csup\u003e[42,43]\u003c/sup\u003e. Preoperative anemia, defined as hemoglobin levels below 10 g/dL, elevates the risk of postoperative delirium by 67% and increases transfusion rates to 58% (compared to 12% in non-anemic patients)\u003csup\u003e[44,45]\u003c/sup\u003e. Each unit of red blood cell transfusion is associated with an extension of hospitalization by 1.8 days, likely attributable to transfusion-related immunomodulation (TRIM) and increased infection risk\u003csup\u003e[46\u0026ndash;48]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study presents an integrated framework that combines biomechanical impairments (such as mobility deficits), inflammatory cascades (including complications related to surgical delays), and metabolic dysregulation (such as anemia) to substantially improve the accuracy of predicting PLH. Unlike traditional models like POSSUM, which primarily focus on surgical risks and overlook modifiable biomarkers such as hemoglobin that are closely linked to postoperative complications\u003csup\u003e[49\u0026ndash;51]\u003c/sup\u003e, our model incorporates time-sensitive variables (such as surgical delays) and preoperative laboratory parameters. This approach accounts for 22% of the variance not explained by previous models. DCA establishes actionable risk thresholds (10-55%), effectively addressing the \u0026quot;gray zone\u0026quot; issue inherent in traditional scoring systems. The quantified exponential risk associated with surgical delays exceeding 48 hours underscores the need to prioritize surgical scheduling, thereby enhancing the model\u0026apos;s discriminative performance (AUC: 0.87\u0026ndash;0.89 compared to 0.78\u0026ndash;0.85 for models based solely on comorbidities) and expanding its clinical applicability. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is subject to limitations typical of retrospective designs, such as potential selection bias in the classification of surgical delay and the presence of unmeasured confounders, including adherence to rehabilitation protocols. The use of single-center data may restrict the generalizability of the findings, especially in regions with varying surgical protocols or social care systems. The omission of socioeconomic factors, such as caregiver availability and access to home rehabilitation, may lead to an overestimation of the model\u0026apos;s optimism. Furthermore, the definition of PLH based on the 75th percentile (12.5 days) lacks international consensus; therefore, future research should establish thresholds using multinational registries. Prospective multicenter studies that incorporate dynamic postoperative biomarkers, such as serial measurements of hemoglobin and albumin, along with machine learning algorithms, are necessary to refine the model. Additionally, incorporating comprehensive geriatric assessments (CGA) to evaluate cognitive function and social support, as well as investigating the association of PLH with long-term outcomes, such as one-year mortality and secondary fracture risk, will enhance the clinical applicability of the findings. \u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study established a novel nomogram incorporating five clinically validated predictors\u0026mdash;surgical delay, hemiplegia, heart failure, lower extremity vascular disease, and preoperative hemoglobin\u0026mdash;to predict prolonged hospitalization in elderly hip fracture patients. The model demonstrated robust discriminative accuracy (AUC \u0026gt;0.85), substantial clinical utility (net benefit of 0.25 across actionable thresholds via decision curve analysis), and interpretable risk stratification, providing a reliable tool for personalized resource allocation and targeted preoperative optimization. By integrating modifiable and non-modifiable risk factors into a unified framework, this tool not only enhances clinical decision-making but also holds promise for advancing precision medicine in orthopedics, particularly in optimizing care pathways for high-risk geriatric populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProlonged hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDecision curve analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmerican Society of Anesthesiologist\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRBC:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRed blood cel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhite blood cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre Hb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreoperative hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatelets\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeutrophile granulocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHematocrit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKalium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNatrium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre Alb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreoperative albumin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlanine aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAspartate aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLactate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlood urea nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProthrombin time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAPTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eActivated partial thromboplastin time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternational normalized ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFibrinogen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAortic velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEjection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to Professor Zheng and his team at Zhejiang University of Traditional Chinese Medicine for developing the platform for statistical analysis of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in the development of the article and provided approval for the submitted version. Guangxu Fu and Dengwu Tan primarily oversaw the article\u0026apos;s design, data analysis, and manuscript preparation. The literature review was conducted by Guangxu Fu and Yong Wang. Data analysis and results visualization were carried out by Guangxu Fu, and Zhen Zhang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Enshi Tujia and Miao Autonomous Prefecture Health Commission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all pertinent data supporting the study\u0026apos;s findings are included in the article and its Supplementary Information files, or can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the Declaration of Helsinki, this study was approved by the Medical Ethics Committee of the People\u0026apos;s Hospital of Lichuan City (approval number: 2025002). All data were anonymized before analysis, and patient consent was not required because of the retrospective study design.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTian C, Shi L, Wang J, et al. Global, Regional, and National Burdens of Hip Fractures in Elderly Individuals from 1990 to 2021 and Predictions up to 2050: A Systematic Analysis of the Global Burden of Disease Study 2021[J]. 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A Systematic Review of POSSUM and Its Related Models as Predictors of Post-Operative Mortality and Morbidity in Patients Undergoing Surgery for Colorectal Cancer[J]. Journal of Gastrointestinal Surgery: Official Journal of the Society for Surgery of the Alimentary Tract, 2010, 14(10): 1511-1520. 14. doi:10.1007/s11605-010-1333-5.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e\n\u003cp\u003eTable 6 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hip fracture, Prolonged hospitalization, Nomogram, Risk prediction model, Elderly patients, Clinical decision-making","lastPublishedDoi":"10.21203/rs.3.rs-6538550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6538550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003ethe global aging population has intensified the public health burden of hip fractures, characterized by high morbidity, mortality, and healthcare costs. Prolonged hospitalization (PLH) in elderly hip fracture patients is associated with adverse outcomes, yet existing predictive models often overlook the multifactorial interplay of clinical, surgical, and laboratory variables. This study aimed to develop and validate a nomogram integrating modifiable and non-modifiable risk factors to predict PLOS and optimize clinical decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ea retrospective observational cohort of 1,674 elderly hip fracture patients (2012-2025) from the People’s Hospital of Lichuan City was analyzed. Patients were categorized into Non-PLH (≤12.5 days) and PLH (\u0026gt;12.5 days) groups based on the 75th percentile of hospitalization duration. Variables spanning demographics, fracture characteristics, perioperative management, and comorbidities were collected. Multivariable logistic regression identified independent predictors, and a nomogram was developed using R software. Model performance was assessed via AUC, calibration curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003efive independent predictors were identified: time from admission to surgery \u0026gt;48 hours (OR=16.62), hemiplegia (OR=3.48), heart failure (OR=2.66), lower extremity vascular disease (OR=2.31), and preoperative hemoglobin (protective, OR=0.96). The nomogram demonstrated robust discrimination (training AUC: 0.89, 95% CI: 0.87–0.91; testing AUC: 0.87, 95% CI: 0.83-0.91) and calibration (Brier scores: 0.18-0.21). DCA revealed significant clinical utility across thresholds of 0-97%, with a maximum net benefit of 0.25.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e this study presents a validated nomogram for predicting PLH in elderly hip fracture patients, integrating both modifiable and non-modifiable risk factors. The model’s high accuracy, interpretable risk stratification, and actionable thresholds enhance personalized resource allocation and preoperative optimization, advancing precision medicine in geriatric orthopedics.\u003c/p\u003e","manuscriptTitle":"Construction and Validation of a Predictive Nomogram for Prolonged Hospitalization in Elderly Patients with Hip Fractures: A Retrospective Cohort Study Incorporating Modifiable and Non-Modifiable Risk Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 13:16:12","doi":"10.21203/rs.3.rs-6538550/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b721cfc4-c7c2-45d6-b734-33a36eae79ca","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T11:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 13:16:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6538550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6538550","identity":"rs-6538550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}