The association between different ejection fractions and all-cause mortality in elderly patients with hip fractures: a retrospective cohort study and the development of a predictive model

The association between different ejection fractions and all-cause mortality in elderly patients with hip fractures: a retrospective cohort study and the development of a predictive model | 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 The association between different ejection fractions and all-cause mortality in elderly patients with hip fractures: a retrospective cohort study and the development of a predictive model Xue Ge, Lixiang Ma, Lan Yao, Yan Liu, Yi Wang, Fang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7476617/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted 9 You are reading this latest preprint version Abstract Background Hip fractures in the elderly are a major public health concern due to high mortality and poor outcomes. While low ejection fraction (EF) is linked to increased mortality in many populations, its impact on elderly hip fracture patients is unclear. This study investigates the relationship between EF and all-cause mortality and develops a predictive model based on EF and other clinical factors. Methods A retrospective cohort study was conducted, including 1,500 elderly patients who suffered hip fractures and had EF data recorded. The patients were stratified into three groups based on their EF: normal (EF ≥ 50%), mildly reduced (EF 40%-49%), and severely reduced (EF < 40%). The primary outcome was all-cause mortality, and the secondary outcome was 1-year mortality. Statistical analysis was performed using Kaplan-Meier curves, Cox proportional hazards regression, and multivariate analysis to examine the relationship between EF and mortality. A predictive model for all-cause mortality was developed using multiple clinical factors, and its accuracy was evaluated with the C-index. Results A total of 1,526 elderly patients with hip fractures were included in the study, with a mean follow-up of 60 months. Multivariate Cox regression analysis identified nine key predictors for 5-year all-cause mortality: age, EF, triglycerides, coronary artery disease, total cholesterol, diabetes, the E/A ratio, stroke volume, and LVED. A nomogram incorporating these variables was developed, enabling individualized risk assessment for predicting 1-year, 3-year, and 5-year mortality. Additionally, a web-based dynamic nomogram was created to enhance accessibility, allowing clinicians to input patient-specific data and obtain real-time survival predictions. The nomogram demonstrated excellent predictive performance, with a C-index of 0.827 and AUCs of 0.840, 0.820, and 0.817 for 1-year, 3-year, and 5-year survival, respectively. Calibration curves showed strong agreement between predicted and observed survival probabilities, while decision curve analysis confirmed the model's clinical utility in guiding personalized risk management. Conclusion Low EF is a strong predictor of increased all-cause mortality in elderly patients with hip fractures. The predictive model based on EF and clinical characteristics provides valuable information for clinicians to identify high-risk patients and improve patient management. Hip fracture Ejection fraction Elderly All-cause mortality Predictive model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Hip fractures are a common medical condition worldwide, particularly among the elderly population[ 1 ]. With the increasing trend of global population aging, the incidence of hip fractures in older adults has been steadily rising, making it one of the most serious health challenges for this demographic[ 2 – 4 ]. Osteoporosis is commonly prevalent among the elderly, making their bones more fragile and prone to fractures. In addition, as people age, they often develop multiple chronic diseases, such as cardiovascular diseases, diabetes, and hypertension. These conditions increase the risk of falls and complicate postoperative recovery. Hip fractures in the elderly not only affect their independence and quality of life, but also remain one of the leading causes of death among elderly patients. Studies have shown that the all-cause mortality rate is 5%-10% within 30 days and 20%-30% within the first year post-surgery[ 5 – 7 ]. Mortality risk escalates dramatically with advancing age[ 8 ]. Ejection Fraction (EF) is a critical measure of the heart's pumping ability, assessed primarily through imaging techniques such as echocardiography. EF quantifies the percentage of blood ejected from the ventricle with each contraction relative to the total volume of blood in the ventricle. As a direct indicator of myocardial contractility, a reduced EF often signals impaired cardiac contractile function, adversely affecting overall cardiac output and systemic blood circulation[ 9 ]. Under normal conditions, EF typically ranges between 50% and 70%. However, an EF below 40% is generally considered indicative of significant cardiac dysfunction, often associated with clinical heart failure and other severe cardiovascular diseases[ 10 ]. In recent years, EF has gained increasing recognition for its prognostic value across a range of medical conditions. Research highlights its critical role in predicting outcomes in chronic heart failure, coronary artery disease, and myocardial infarction. A declining EF is not only associated with the severity of these conditions but also correlates closely with long-term survival outcomes[ 11 ]. Even in patients without overt cardiac disease, particularly older adults, EF serves as a valuable marker of overall health status and is increasingly utilized in prognostic evaluations[ 12 ]. The high mortality rate observed in elderly hip fracture patients is closely linked to multiple factors, with underlying comorbidities being among the most significant contributors. Older adults often have chronic conditions such as cardiovascular disease, diabetes, and hypertension, which increase surgical risks and prolong recovery. Yombi, Omer, and colleagues found that patients with an EF below 40% face significantly higher postoperative mortality risks, largely due to the elevated incidence of cardiac complications, such as heart failure and arrhythmias[ 13 , 14 ]. Reduced EF is often accompanied by dysfunction in the heart and other organ systems, further increasing the likelihood of complications. Common postoperative complications, such as pneumonia, deep vein thrombosis (DVT), and pressure ulcers, are frequent contributors to mortality[ 15 ]. Additionally, EF plays a crucial role in postoperative recovery. Research by Lerman and colleagues has shown that patients with normal EF typically experience faster functional recovery after surgery, whereas those with reduced EF are more likely to suffer from complications, experience slower recovery, and face an increased risk of mortality[ 16 ]. As a key marker of cardiac function, EF has critical prognostic value in elderly hip fracture patients. Monitoring EF levels can help assess postoperative mortality risks and guide clinical decision-making, particularly in tailoring personalized treatment plans and implementing early interventions. Existing research primarily focuses on the relationship between EF and cardiovascular diseases. However, the application of EF in hip fracture patients, particularly the relationship between EF stratification (normal, intermediate, and reduced groups) and mortality risk in elderly hip fracture patients, has not been fully explored. Therefore, this study aims to fill this gap by assessing the impact of different EF levels on the mortality risk of elderly hip fracture patients through EF stratification. Additionally, by incorporating other clinical data, such as underlying conditions and preoperative status, we aim to develop a mortality risk prediction model. This model will provide clinicians with a more precise risk assessment tool, ultimately improving the long-term survival rate and quality of life for elderly patients with hip fractures. Method Patients and Study Design This study was a retrospective cohort study that collected medical data of inpatients undergoing hip fracture surgery in the Department of Orthopedics at Qinhuangdao First Hospital from January 2015 to December 2019. Inclusion criteria for patients were as follows: (1) age ≥ 65 years; (2) a confirmed diagnosis of femoral neck fracture or intertrochanteric femur fracture; (3) having undergone hip fracture-related surgical treatment, including internal fixation with plates/screws, hemiarthroplasty, or total hip arthroplasty; (4) fracture resulting from low-energy trauma, primarily due to falls; and (5) availability of complete medical records, including echocardiographic data on EF and relevant laboratory test results. Exclusion criteria included the following: (1) hip fractures caused by pathological fractures; (2) patients treated conservatively; (3) incomplete medical records, laboratory test results, or echocardiographic data; (4) fractures caused by high-energy trauma, including but not limited to motor vehicle accidents or falls from significant heights; and (5) patients lost to follow-up. During the initial screening, a total of 2,247 cases diagnosed with hip fractures were identified in the hospital database. After excluding 324 cases for not meeting the age requirement, 227 cases for receiving conservative treatment, 58 duplicate cases, and 112 cases for loss to follow-up, 1,526 patients met the inclusion criteria and were ultimately included in the analysis (Fig. 1 ). This study was based on a retrospective analysis of pre-existing medical records. All patient data were strictly anonymized to ensure privacy protection. The study was approved by the Ethics Committee of Qinhuangdao First Hospital (Approval No. 202401A111) and complied with relevant medical ethical standards. Measurement of Ejection Fraction In this study, the assessment of Ejection Fraction (EF) was performed using high-precision two-dimensional echocardiography. All examinations were conducted with a Philips EPIC 7C echocardiographic system equipped with a 2.5 to 3.5 MHz phased-array transducer, operated by highly experienced sonographers. The EF measurement strictly adhered to the guidelines established by the American Society of Echocardiography (ASE). During the measurement process, images of the left ventricle at end-diastole and end-systole were obtained from the apical four-chamber long-axis view, with the modified biplane Simpson’s method used to automatically calculate the left ventricular ejection fraction. To ensure the accuracy of the measurements, particular attention was given to obtaining clear images with distinct chamber boundaries and minimal artifacts, ensuring that the procedure was performed while the patient remained in a stable breathing state. For each patient, left ventricular end-diastolic volume (LVEDV) and end-systolic volume (LVESV) were measured over two consecutive cardiac cycles, and the EF was automatically calculated using the formula: EF = [(LVEDV - LVESV)/LVEDV] × 100%. The EF value for each patient was independently assessed by two echocardiography specialists to evaluate measurement consistency, ensuring the reproducibility of the results. Data Extraction Patient perioperative demographic and clinical data were extracted from the hospital’s electronic medical record database and were standardized to ensure data completeness and consistency. EF stratification was performed according to the 2021 European Society of Cardiology (ESC) classification: normal EF (≥ 50%), mid-range EF (41%-49%), and reduced EF (≤ 40%). Demographic data included sex, age, body mass index (BMI), smoking history (current or former smoker), and alcohol consumption history (current or former drinker). Fracture-related variables included fracture type (femoral neck fracture or intertrochanteric femur fracture) and the time from injury to hospital admission (recorded in hours). Comorbidity data encompassed medical histories of coronary artery disease, atrial fibrillation, valvular heart disease, hypertension, prior stroke, chronic kidney disease, diabetes mellitus, chronic obstructive pulmonary disease (COPD), and liver dysfunction. Perioperative complications were documented, including acute myocardial infarction, pneumonia, ventricular arrhythmias, acute kidney injury, and stress ulcers. Cardiac function and echocardiographic parameters included preoperative ejection fraction, left ventricular end-diastolic (LVED), cardiac output (CO), stroke volume (SV), the ratio of early to late diastolic mitral inflow velocity (E/A ratio), and the ratio of left ventricular end-diastolic pressure to mitral inflow velocity (E/e’ ratio). Preoperative laboratory tests included platelet count, C-reactive protein (CRP), total cholesterol, triglycerides, glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and random blood glucose levels. Additionally, D-dimer levels and anemia status were recorded, with anemia defined as hemoglobin < 120 g/L in men and < 110 g/L in women. Electrolyte and biochemical abnormalities were also documented: hyponatremia was defined as serum sodium < 135 mmol/L, hypokalemia as serum potassium < 3.5 mmol/L, and hypoalbuminemia as serum albumin < 35 g/L. Endpoints and Follow-Up The primary endpoint of this study was 5-year all-cause mortality, defined as death from any cause within 5 years following surgery. Confirmation of all-cause mortality was achieved through review of hospital medical records, data from the national death registration system, or death certificates provided by the patient’s family. All patients underwent systematic follow-up after discharge. The follow-up schedule is as follows: the first follow-up will occur at 1 month, 3 months, 6 months, and 1 year after discharge, with subsequent follow-ups every six months. Data will be collected through telephone interviews or outpatient visits. After that, annual follow-ups will be conducted, during which survival status, hospitalization details, and relevant medical events will be recorded via telephone interviews or outpatient visits. All death events will be cross-verified using hospital records, family interviews, and official death registration databases to ensure the accuracy and reliability of the data. Statistical Analysis Continuous variables with a normal distribution were expressed as mean ± standard deviation (SD) and compared between groups using one-way analysis of variance (ANOVA). Continuous variables with a non-normal distribution were expressed as median and interquartile range (IQR) and compared between groups using the Kruskal-Wallis test. Categorical variables were presented as frequencies and percentages, and group comparisons were performed using the χ² test or Fisher’s exact test. The 5-year survival curves for patients with normal EF (≥ 50%), mid-range EF (41%-49%), and reduced EF (≤ 40%) were generated using the Kaplan-Meier method. The log-rank test was used to compare survival differences between groups. To optimize variable selection for the Cox proportional hazards regression model, the study incorporated the Boruta algorithm, a feature selection method based on random forests that effectively identifies statistically significant variables[ 17 ]. Using the selected variables, a Cox proportional hazards regression model was constructed to comprehensively evaluate the impact of each variable on mortality risk. Additionally, to explore the potential nonlinear relationship between ejection fraction and all-cause mortality, a restricted cubic spline (RCS) analysis was performed, providing an intuitive depiction of the nonlinear effects of the variable on the outcome. RCS is a flexible method that allows for the modeling of nonlinear relationships without assuming a specific functional form between the variable and the outcome. Based on the results of the multivariable Cox regression model, a nomogram was developed to predict individualized 1-year, 3-year, and 5-year survival probabilities. To assess the performance of the prediction model, the area under the receiver operating characteristic curve (AUC-ROC) was utilized to quantitatively measure the model’s discriminative ability. Calibration curves were also employed to evaluate the consistency between predicted and observed risks. After internal validation by bootstrapping, a calibration curve was used to assess the agreement between the actual and predicted survival probabilities, thereby enhancing the model’s internal reliability. Furthermore, decision curve analysis (DCA) was applied to evaluate the clinical utility and net benefit of the model, thereby validating its reliability and practical value from multiple perspectives. All statistical analyses were conducted using R software (version 4.3.1) and Python, ensuring the flexibility and rigor of the analytical tools. A p-value of < 0.05 was considered statistically significant, and all results were based on two-sided tests to ensure the scientific robustness and credibility of the conclusions. Results Baseline Characteristics A total of 1,526 elderly patients with hip fractures were included in this study. Patients were categorized into three groups based on their ejection fraction : the normal EF group (EF ≥ 50%, N = 726), the mid-range EF group (EF 41%-49%, N = 572), and the reduced EF group (EF ≤ 40%, N = 228). Baseline characteristics are shown in Table 1 . There were no statistically significant differences among the three groups in terms of sex, BMI, smoking history, alcohol consumption history, fracture type, fracture site, or time from injury to admission (all p > 0.05). Regarding comorbidities, patients in the reduced EF group had significantly higher rates of coronary artery disease (36.4%), atrial fibrillation (33.3%), valvular heart disease (21.0%), hypertension (55.7%), and prior stroke (38.6%) compared to the normal EF and mid-range EF groups (all p < 0.05). The prevalence of diabetes mellitus (33.8%, p = 0.002) and COPD (15.0%, p = 0.017) was also significantly higher in the reduced EF group. For perioperative complications, the reduced EF group had significantly higher incidences of acute myocardial infarction (26.3%), acute kidney injury (14.9%), hypokalemia (23.7%), and hypoalbuminemia (32.5%) compared to the other groups (all p < 0.05). Laboratory results revealed that patients in the reduced EF group had significantly higher levels of total cholesterol, triglycerides, HbA1c, and D-dimer compared to the other groups (all p < 0.05). In terms of cardiac function parameters, the reduced EF group exhibited significantly lower LVED and EF, while the E/A and E/e' ratios were significantly elevated (both p < 0.001). Table 1 Baseline clinical characteristics in older patients with hip fracture Variables Total(N = 1526) Normal EF group (N = 726) Mid-range EF group (N = 572) Reduced EF group (N = 228) p-value Gender, N (%) Male 644(42.2%) 304(41.9%) 245(42.8%) 95(41.7%) 0.927 Female 882(57.8%) 422(58.1%) 327(57.2%) 133(58.3%) Age 76.2 ± 6.9 74.7 ± 6.5 77.9 ± 7.1 76.6 ± 6.4 <0.001 BMI 24.5 ± 3.1 24.5 ± 3.1 24.5 ± 3.1 24.2 ± 3.0 0.503 Smoking history, N(%) 0.294 No 993(65.1%) 464(63.9%) 386(67.5%) 143(62.7%) Yes 533(34.9%) 262(36.1%) 186(32.5%) 85(37.3%) Drinking history, N(%) 0.571 No 1186(77.7%) 564(77.7%) 452(79.0%) 170(74.6%) Yes 340(22.3%) 162(22.3%) 120(21.0%) 58(25.4%) Type of fracture, N(%) 0.957 Femoral neck 683(44.8%) 400(55.1%) 257(44.9%) 128(56.1%) Intertrochanteric 883(55.2%) 326(44.9%) 315(55.1%) 100(43.9%) Fracture site, N(%) 0.979 Left 775(50.8%) 369(50.8%) 289(50.5%) 117(51.3%) Right 751(49.2%) 357(49.2%) 283(49.5%) 111(48.7%) The time from injury to hospital admission, N(%) 0.590 <24hours 933(61.1%) 451(62.1%) 394(61.0%) 133(58.3%) ≥ 24hours 593(38.9%) 275(37.9%) 223(39.0%) 95(41.7%) Comorbidity N (%) Coronary heart disease < 0.001 No 1121(73.5%) 490(67.5%) 486(85.0%) 145(63.6%) Yes 405(26.5%) 236(32.5%) 86(15.0%) 83(36.4%) Atrial fibrillation < 0.001 No 1257(82.4%) 623(85.8%) 482(84.3%) 152(66.7%) Yes 269(17.6%) 103(14.2%) 90(15.7%) 76(33.3%) Heart valve disease < 0.001 No 1331(87.2%) 666(91.7%) 485(84.8%) 180(79.0%) Yes 195(13.6%) 60(8.3%) 87(15.2%) 48(21.0%) Hypertension < 0.001 No 797(52.2%) 419(57.7%) 277(48.4%) 101(44.3%) Yes 729(47.8%) 307(42.3%) 295(51.6%) 127(55.7%) Old cerebral infarction < 0.001 No 1110(72.7%) 485(66.8%) 485(84.8%) 140(61.4%) Yes 416(27.2%) 241(33.2%) 87(15.2%) 88(38.6%) Chronic renal failure 0.240 No 1346(88.2%) 636(87.6%) 514(89.9%) 196(86.0%) Yes 180(11.8%) 90(12.4%) 58(10.1%) 32(14.0%) Diabetes 0.002 No 1149(75.3%) 562(77.4%) 436(76.2%) 151(66.2%) Yes 377(24.7%) 164(22.6%) 136(23.8%) 77(33.8%) COPD 0.017 No 1376(90.2%) 664(91.5%) 518(90.6%) 194(85.0%) Yes 150(9.8%) 62(8.5%) 54(9.4%) 34(15.0%) Liver dysfunction < 0.001 No 1474(96.6%) 708(97.5%) 556(97.2%) 210(92.1%) Yes 52(3.4%) 18(2.5%) 16(2.8%) 18(7.9%) Complications N (%) Acute myocardial infarction < 0.001 No 1343(88.0%) 658(90.6%) 517(90.4%) 168(73.7%) Yes 183(12.0%) 68(9.4%) 55(9.6%) 60(26.3%) Pulmonary infection 0.002 No 1275(83.5%) 630(86.8%) 455(79.5%) 190(83.3%) Yes 251(16.4%) 96(13.2%) 117(20.5%) 38(16.7%) Ventricular arrhythmia 0.086 No 1339(87.7%) 644(88.7%) 505(88.3%) 190(83.3%) Yes 187(12.3%) 82(11.3%) 67(11.7%) 38(16.7%) Acute kidney injury < 0.001 No 1405(92.1%) 680(93.7%) 531(92.8%) 194(85.1%) Yes 121(7.9%) 46(6.3%) 41(7.2%) 34(14.9%) Stress ulcer 0.662 No 1458(95.5%) 690(95.0%) 549(96.0%) 219(96.1%) Yes 68(4.5%) 36(5.0%) 23(4.0%) 9(3.9%) Anemia 0.087 No 1360(89.1%) 658(90.6%) 507(88.6%) 195(85.5%) Yes 166(10.9%) 68(9.4%) 65(11.4%) 33(14.5%) Hypokalemia < 0.001 No 1300(85.2%) 635(87.5%) 491(85.8%) 174(76.3%) Yes 226(14.8%) 91(12.5%) 81(14.2%) 54(23.7%) Hyponatremia < 0.001 No 1277(83.7%) 628(86.5%) 489(85.5%) 160(70.2%) Yes 249(16.3%) 98(13.5%) 83(14.5%) 68(29.8%) Hypoalbuminemia < 0.001 No 1270(83.7%) 632(87.1%) 491(85.8%) 154(67.5%) Yes 226(16.3%) 94(12.9%) 81(14.2%) 74(32.5%) Lower extremity deep vein thrombosis < 0.001 No 1306(85.6%) 654(90.1%) 455(79.5%) 197(86.4%) Yes 220(14.4%) 72(9.9%) 117(20.5%) 31(13.6%) Laboratory data Total cholesterol 4.68 ± 1.43 4.70 ± 1.41 4.77 ± 1.49 4.40 ± 1.32 0.003 Triglycerides 1.73 ± 0.59 1.50 ± 0.47 2.05 ± 0.53 1.68 ± 0.73 <0.001 Glycosylated hemoglobin 6.48 ± 1.30 6.31 ± 1.30 6.43 ± 1.07 7.12 ± 1.59 <0.001 Fasting plasma glucose(FPG) 7.66 ± 2.57 7.36 ± 2.17 7.51 ± 2.75 8.98 ± 2.88 <0.001 Random Blood Sugar(RBS) 8.13 ± 2.79 7.90 ± 2.45 8.19 ± 2.79 8.72 ± 3.57 <0.001 PLT 179.4 ± 65.8 179.8 ± 64.2 180.2 ± 66.3 176.1 ± 63.1 0.704 C-reactive protein 45.8 ± 22.6 44.2 ± 21.3 47.2 ± 23.2 47.5 ± 24.9 0.030 D-dimer 7.57 ± 4.58 7.34 ± 4.54 7.31 ± 4.22 8.90 ± 5.29 <0.001 Echocardiogram results LEVD 54.5 ± 4.7 50.8 ± 2.2 56.5 ± 2.4 61.8 ± 4.0 <0.001 EF 49.9 ± 8.6 57.3 ± 4.7 46.1 ± 2.8 36.2 ± 2.8 <0.001 CO 5.1 ± 1.7 6.2 ± 1.6 4.4 ± 1.3 3.7 ± 0.9 <0.001 SV 77.4 ± 19.0 87.6 ± 16.6 72.5 ± 15.4 57.1 ± 12.1 <0.001 E/A 1.20 ± 0.37 1.55 ± 0.11 0.95 ± 0.15 0.73 ± 0.19 <0.001 E/e' 8.81 ± 3.08 6.23 ± 1.52 10.60 ± 1.87 12.54 ± 2.06 <0.001 Values are presented as mean ± standard deviation, median (interquartile range), or number (percentage) as appropriate, SD Standard deviation, BMI Body Mass Index, COPD Chronic Obstructive Pulmonary Disease, EF Ejection Fraction, LEVD Left Ventricular End-Diastolic Volume, CO Cardiac Output, SV Stroke Volume, E/A The ratio of early (E) to late (A) diastolic mitral inflow velocity, E/e' The ratio of early diastolic mitral inflow velocity to mitral annular early diastolic velocity, PLT Platelet Count, FPG Fasting Plasma Glucose, RBS Random Blood Sugar, CRP C-reactive Protein, D-dimer A fibrin degradation product indicating fibrinolytic activity Clinical Outcomes The Kaplan-Meier survival curves demonstrated significant differences in survival probabilities during the 60-month follow-up period across the three EF groups (normal, mid-range, and reduced; p < 0.0001, Fig. 2 ). Patients in the reduced EF group (EF ≤ 40%) had the lowest survival probability, followed by the mid-range EF group (EF 41%-49%), while the normal EF group (EF ≥ 50%) had the highest survival probability. During the follow-up period, the survival curves progressively diverged, indicating that a lower ejection fraction was strongly associated with poorer long-term survival outcomes. Nonlinear Analysis Restricted cubic spline analysis revealed a significant nonlinear relationship between EF and all-cause mortality (Fig. 3 , p for overall < 0.001, p for nonlinear < 0.001). As shown in the figure, mortality risk increased exponentially as EF declined, particularly when EF dropped below 44%. This finding suggests that 44% may represent a critical threshold in the relationship between EF and mortality risk. When EF exceeded 44%, mortality risk stabilized and remained relatively low. This result reinforces the importance of EF as an independent protective factor for all-cause mortality. Patients with lower EF had significantly poorer long-term survival outcomes compared to those with higher EF. Furthermore, the RCS curve closely aligns with the results of the Cox proportional hazards regression analysis, consistently highlighting EF as a critical predictor of survival outcomes in this patient population. Feature Selection and Cox Regression Analysis Results Using the Boruta algorithm (Fig. 4 ), several variables were identified as significant predictors of 5-year all-cause mortality. These variables included triglycerides, ejection fraction, age, left ventricular end-diastolic, fasting plasma glucose, coronary artery disease, acute myocardial infarction, total cholesterol, hypoalbuminemia, random blood sugar, diabetes, the E/A ratio, the E/e’ ratio, stroke volume, and atrial fibrillation. Cox proportional hazards regression analysis revealed that the following variables were significantly associated with 5-year all-cause mortality (p < 0.05): triglycerides, EF, age, LVED, total cholesterol, random blood sugar, diabetes, the E/A ratio, and stroke volume. Among these, EF emerged as a significant protective factor (HR = 0.957, 95% CI: 0.93–0.985, p = 0.003), indicating that for every 1% increase in EF, the mortality risk decreased by approximately 4.3%. This finding highlights that lower EF is strongly associated with higher mortality risk, emphasizing EF's clinical value as a critical predictor of long-term prognosis in elderly hip fracture patients. Notably, certain variables, such as fasting plasma glucose, AMI, and hypoalbuminemia, did not demonstrate significant associations in multivariate analysis (p > 0.05). These results suggest that baseline characteristics and chronic comorbidities (e.g., age, CAD, and diabetes), metabolic markers (e.g., triglycerides and total cholesterol), and cardiac functional parameters (e.g., EF, LVED, E/A ratio, and stroke volume) are key determinants of long-term prognosis in elderly hip fracture patients (Table 2 and Fig. 5 ). Table 2 Cox proportional hazards regression analysis for mortality risk factors in elderly patients with hip fracture Univariate analysis Multivariate analysis p-value HR (95% CI) p-value HR (95% CI) Triglycerides <0.001 1.638 (1.353–1.982) 0.015 1.255 (1.046–1.507) EF <0.001 0.933 (0.92–0.946) 0.003 0.957 (0.93–0.985) Age <0.001 1.079 (1.061–1.097) <0.001 1.065 (1.047–1.085) LEVD <0.001 1.162 (1.139–1.185) <0.001 1.219 (1.175–1.264) Fasting Plasma Glucose 0.001 1.076 (1.029–1.124) 0.466 1.017 (0.973–1.062) Coronary heart disease <0.001 1.952 (1.543–2.47) 0.001 1.53 (1.188–1.971) Acute myocardial infarction <0.001 1.837 (1.37–2.464) 0.339 1.167 (0.851–1.599) Total cholesterol <0.001 1.159 (1.07–1.255) <0.001 1.172 (1.079–1.273) Hypoalbuminemia <0.001 1.904 (1.466–2.474) 0.733 1.053 (0.784–1.413) Random blood sugar <0.001 1.083 (1.042–1.126) 0.05 1.041 (0.997–1.083) Diabetes <0.001 1.705 (1.34–2.171) 0.028 1.324 (1.031-1.7) E/A <0.001 0.52 (0.381–0.71) <0.001 7.737 (4.219–14.186) E/e' <0.001 1.079 (1.04–1.12) 0.05 0.943 (0.89-1) SV 0.013 0.992 (0.986–0.998) 0.006 1.011 (1.003–1.019) Atrial fibrillation <0.001 1.693 (1.302–2.202) 0.856 0.974 (0.728–1.301) HR hazard ratio, LEVD Left Ventricular End-Diastolic Volume, E/A The ratio of early (E) to late (A) diastolic mitral inflow velocity, E/e' The ratio of early diastolic mitral inflow velocity to mitral annular early diastolic velocity Development of a Cox Regression Prediction Model and Nomogram Based on the results of the Cox proportional hazards regression analysis, a nomogram was developed to predict the 5-year all-cause mortality risk in elderly hip fracture patients (Fig. 6 A). The nomogram incorporates key variables such as age, EF, triglycerides, coronary artery disease, total cholesterol, diabetes, the E/A ratio, and stroke volume. By assigning risk scores to each variable, the nomogram provides an individualized risk assessment and predicts overall survival probabilities at 1, 3, and 5 years. A web-based nomogram ( https://yingtaosnow.shinyapps.io/new_dynnomapp/ ) was also developed to improve the model’s accessibility and usability, allowing clinicians to quickly calculate individualized risk estimates for their patients (Fig. 6 B). This tool provides a practical, efficient way for clinicians to assess mortality risk and guide personalized treatment planning in elderly hip fracture patients. The model's discriminative performance was evaluated using C-index and AUC. The results showed that the nomogram had a C-index of 0.827 ± 0.011, with AUC values of 0.840 for 1-year survival, 0.820 for 3-year survival, and 0.817 for 5-year survival (Fig. 7 ), indicating excellent predictive capability. Additionally, calibration curves (Fig. 8 ) demonstrated strong agreement between the predicted and observed survival probabilities for 1, 3, and 5 years, confirming the model’s reliability and precision. Decision curve analysis further evaluated the clinical utility of the model across different time points (1 year, 3 years, and 5 years). The analysis showed that the model provided substantial net benefit over a wide range of threshold probabilities, indicating its effectiveness in guiding clinical decision-making (Fig. 9 ). This finding highlights the model's ability to reduce unnecessary interventions while minimizing the risk of missed diagnoses. Discussion The novelty of this study lies in being the first to establish a link between ejection fraction and mortality risk in elderly hip fracture patients, while also validating EF's role as an independent prognostic factor in this unique population. Previous studies have primarily focused on perioperative management in hip fracture patients, with limited attention to long-term survival outcomes. Research on the relationship between cardiac function parameters and prognosis in this population has been almost nonexistent. This study not only fills this gap but also integrates EF with other key variables through the development of a nomogram, enabling individualized risk quantification. This provides a scientific foundation for clinical stratification, management, and intervention. The findings demonstrate that EF, as a critical marker of cardiac function, is significantly associated with postoperative survival outcomes in elderly hip fracture patients. Specifically, a lower EF (< 44%) substantially increases the risk of mortality, and EF was identified as an independent prognostic factor in this patient population. Kaplan-Meier survival analysis revealed significant differences in 5-year all-cause mortality among patients in different EF categories (normal group: EF ≥ 50%; mid-range group: EF 41%-49%; reduced group: EF ≤ 40%) (p < 0.0001). Patients in the reduced EF group exhibited significantly lower survival rates compared to those in the mid-range and normal EF groups, indicating that reduced EF is a strong adverse prognostic factor for long-term survival. Furthermore, Cox proportional hazards regression analysis confirmed the critical role of EF as an independent protective factor. The results showed that for every 1% increase in EF, the risk of 5-year all-cause mortality decreased by approximately 4.3% (HR = 0.957, 95% CI: 0.93–0.985, p = 0.003). This finding underscores the importance of EF in predicting long-term survival outcomes after hip fracture surgery. Restricted cubic spline (RCS) analysis further clarified the nonlinear relationship between EF and mortality risk. When EF was greater than 44%, mortality risk remained relatively stable and low. However, when EF fell below 44%, mortality risk increased significantly, suggesting that 44% may represent a critical threshold for EF’s impact on the prognosis of hip fracture patients. Reduced EF likely reflects impaired cardiac pumping capacity, which can lead to inadequate postoperative organ perfusion, delayed fracture healing, and an increased incidence of postoperative complications[ 18 ]. These factors may collectively contribute to the elevated mortality risk observed in patients with low EF. By providing evidence for EF stratification, this study establishes a scientific basis for its use in clinical grading and management. These findings highlight the clinical importance of monitoring EF in elderly hip fracture patients and incorporating it into risk assessment models to guide tailored interventions and improve long-term outcomes. In this study, the Boruta algorithm was employed to screen feature variables and identify those significantly associated with the 5-year all-cause mortality risk in elderly patients with hip fractures. The Boruta algorithm, a feature selection method based on random forests, introduces randomness (e.g., by adding “shadow features”) to simulate the predictive contribution of variables to the target outcome[ 17 ]. This approach enables a more accurate assessment of the importance of each variable. The results showed that EF consistently occupied the green zone, indicating a high importance score during feature selection. This suggests that EF plays a crucial role in this study and is significantly associated with the research objective. In the univariate Cox regression analysis, we evaluated the impact of each variable (including comorbidities, perioperative complications, EF, etc.) on mortality and identified the variables significantly associated with mortality. In the subsequent multivariate Cox regression analysis, we included these potential confounding factors (such as comorbidities and perioperative complications) as covariates in the model to control for their effect on the outcome. This ensures that the results accurately reflect the independent impact of each variable, particularly the ejection fraction (EF), by adjusting for the confounding influence of other factors. Additionally, a multivariable Cox regression analysis was performed to construct a nomogram model. This nomogram was developed to predict 1-year, 3-year, and 5-year all-cause mortality risks in elderly patients with hip fractures. By assigning a score to each variable and integrating diverse clinical characteristics, the nomogram provides a personalized risk assessment tool for mortality prediction. The accuracy of the model was evaluated using both the C-index and AUC, demonstrating excellent discriminative ability. Furthermore, calibration curves showed a strong agreement between the predicted and observed survival probabilities for 1-year, 3-year, and 5-year outcomes, validating the reliability and accuracy of the model. These findings underscore the model's effectiveness in providing precise survival predictions over varying time horizons. The construction of this nomogram highlights the importance of EF as an independent prognostic indicator while also confirming the added predictive value of combining it with other metabolic and cardiac function variables. While the decision curve analysis demonstrates the model’s net benefit, the clinical utility of this model is enhanced by linking the stratified risk groups to specific interventions. For high-risk patients (with EF < 40%), we recommend intensified cardiovascular monitoring, including frequent ECG assessments and hemodynamic evaluations. These patients may also benefit from personalized rehabilitation plans to optimize functional recovery. For moderate-risk patients (with EF between 41%-49%), routine monitoring and standard rehabilitation protocols are recommended. In contrast, low-risk patients (with EF ≥ 50%) may receive standard care with regular follow-ups. These risk-based interventions enable clinicians to tailor care, improving patient outcomes and optimizing resource utilization. Comparison of EF with Other Cardiovascular Indicators Ejection fraction, as a core measure of cardiac pumping function, was demonstrated in this study to be an independent predictor of all-cause mortality (HR = 0.957, 95% CI: 0.93–0.985, p = 0.003). In contrast, other cardiac function parameters—such as left ventricular end-diastolic volume, the E/A ratio, and stroke volume—while essential for evaluating cardiac function, may have their predictive ability influenced by the complexity of diastolic dysfunction. LEVD is more closely related to ventricular volume load, while the E/A ratio primarily reflects the dynamic changes in left ventricular filling pressures, with abnormalities often indicating increased ventricular stiffness[ 19 , 20 ]. Stroke volume, on the other hand, directly measures the amount of blood ejected with each ventricular contraction, offering a dynamic reflection of cardiac pumping efficiency under stress[ 21 , 22 ]. Although these parameters each have their unique roles in assessing cardiac function, EF, as a comprehensive indicator of overall cardiac performance, exhibits stronger independence and broader clinical applicability. EF more holistically reflects the overall ventricular pumping capacity. In clinical practice, the distinct characteristics of EF, LEVD, E/A ratio, and SV should be considered collectively to develop a more comprehensive cardiac function assessment framework, which can better guide postoperative interventions and personalized management strategies. Furthermore, EF outperformed metabolic indicators (e.g., triglycerides and total cholesterol) in its independent predictive ability for mortality risk in this study. While metabolic markers are important predictors of cardiovascular events, they primarily reflect the long-term effects of chronic metabolic disorders on systemic organs rather than the immediate prognostic response under acute stress conditions[ 23 – 27 ]. Consequently, EF is better at capturing the dynamic changes in short- and mid-term mortality risk after surgery. It is worth noting that triglycerides and total cholesterol, as metabolism-related variables, also exhibited significant predictive ability in the multivariable Cox regression analysis. This may be attributed to the role of lipid metabolism disorders in exacerbating systemic inflammatory responses and atherosclerosis, which further impair cardiovascular and skeletal system functions[ 28 , 29 ]. This finding underscores the importance of addressing metabolic dysregulation in patient management. However, compared to EF, the predictive value of these metabolic indicators is more likely to be influenced by external factors such as pharmacologic treatments and lifestyle modifications. As a direct measure of ventricular systolic performance, EF holds greater potential for widespread clinical application. Comparison with Existing Risk Stratification Tools Several risk stratification tools have been developed for patients with hip fracture, notably the Nottingham Hip Fracture Score (NHFS) and the Charlson Comorbidity Index (CCI). These models have proven useful in predicting short-term mortality or estimating comorbidity burden, particularly in emergency or perioperative settings[ 30 , 31 ]. NHFS incorporates demographic and clinical factors such as age, hemoglobin level, cognitive status, and comorbidities, while the CCI provides a cumulative score based on the presence and severity of chronic illnesses. While both tools are widely accepted and have demonstrated prognostic value, they largely emphasize baseline health status and static clinical characteristics. In contrast, our model integrates cardiac-specific parameters (e.g., ejection fraction, E/A ratio, LVED) alongside metabolic markers (e.g., triglycerides, total cholesterol), offering a more nuanced assessment of patients’ physiological reserve and cardiovascular function. To our knowledge, few existing models have incorporated echocardiographic indicators such as EF into mortality risk prediction after hip fracture surgery. This may result in under-recognition of high-risk patients with subclinical cardiac dysfunction. Our model addresses this gap by offering individualized predictions based on comprehensive cardiovascular evaluation, potentially enabling earlier intervention and targeted perioperative management. While our current study does not conduct a direct performance comparison with existing scores, we recognize the importance of benchmarking. Future research may consider formal performance comparisons with existing tools, incorporating metrics such as Net Reclassification Improvement (NRI) to evaluate incremental predictive value. Mechanisms Linking Reduced EF to Mortality in Elderly Hip Fracture Patients The biological mechanisms underlying the association between reduced EF and increased mortality risk in elderly hip fracture patients are multifaceted. Cardiovascular dysfunction plays a central role, as reduced EF indicates impaired cardiac pumping capacity, leading to inadequate tissue perfusion, particularly during the postoperative period. This insufficient perfusion can delay wound healing and recovery, increasing the risk of postoperative complications such as heart failure, arrhythmias, and prolonged rehabilitation[ 32 ]. Moreover, systemic inflammation is often observed in patients with low EF, particularly those with chronic heart failure, and is associated with elevated levels of inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6). Chronic inflammation compromises immune function and impairs tissue repair, thus contributing to an increased incidence of postoperative infections, such as pneumonia, which can elevate mortality risk[ 33 ]. Additionally, patients with low EF tend to have reduced physiological reserve, meaning their bodies are less able to handle the physiological stress imposed by surgery. Aging, combined with the impact of comorbidities such as diabetes and hypertension, reduces the body’s ability to recover postoperatively, making these patients more susceptible to complications and ultimately, death[ 34 ]. These mechanisms help explain the biological plausibility of our findings and emphasize the need to assess EF as part of a comprehensive risk evaluation for elderly hip fracture patients. Limitations of the Study This study has several limitations that should be acknowledged. First, as a single-center retrospective cohort study, it is subject to potential selection bias despite the relatively large sample size. This limitation may restrict the external validity and generalizability of the findings to broader populations, particularly across different geographic regions and healthcare settings. Future multicenter prospective studies are needed to validate the conclusions drawn from this research and assess the model’s performance across diverse populations. Second, because the study data were derived from a single medical center, they may not fully capture the clinical characteristics of patients from multiple centers or different geographic regions. Future multicenter prospective studies are needed to validate the conclusions drawn from this research. Third, the study did not include certain variables, such as psychological status and levels of social support, which have been shown in numerous studies to significantly influence long-term outcomes in elderly patients. In addition, other geriatric factors such as frailty and musculoskeletal health were not considered. Standardized frailty assessment tools (e.g., Fried Frailty Phenotype, Clinical Frailty Scale) and indicators of musculoskeletal function may further improve the model’s predictive power. The omission of these factors represents a potential avenue for further optimization of the predictive model's performance. Future research should integrate these psychosocial factors to further optimize the predictive model and enhance its relevance in real-world clinical settings. Additionally, this study did not conduct a cause-specific analysis of mortality. The lack of classification by cause of death may have influenced the precision of the findings. Future research should consider incorporating more granular categorization of mortality causes and additional potential confounders to enhance the predictive accuracy and clinical utility of the model. Future studies should consider incorporating cause-specific mortality data to provide a more comprehensive analysis of the factors influencing mortality in elderly patients. Conclusion In this study, we systematically analyzed the relationship between EF and the 5-year all-cause mortality in elderly patients with hip fractures. Our findings revealed that EF, as a core indicator of cardiac function, is an independent and significant predictor of mortality risk. We observed that reduced EF, particularly below 44%, was associated with a substantial increase in mortality, highlighting its critical role in risk assessment for this population. Based on these results, we constructed a nomogram incorporating EF and other clinical variables. This model demonstrated strong predictive accuracy, with consistent calibration and discrimination, offering a reliable tool for individualized mortality risk prediction. To enhance the practical application and accessibility of this model, future efforts will focus on integrating it into electronic health record systems (EMR) for automatic risk assessment during patient admission based on preoperative data such as EF, age, and comorbidities. Additionally, we plan to develop a mobile application or a simplified scoring sheet, enabling clinicians, especially those without statistical training, to easily calculate individual mortality risk at the bedside. These integrations aim to support timely decision-making and personalized management, ultimately improving outcomes for elderly hip fracture patients. Declarations Data availability The datasets utilized in the present study are contained within the internal network of the First Hospital of Qinhuangdao. Due to existing data privacy policies, these datasets are not publicly accessible. However, they can be made available from the corresponding author upon reasonable request. Acknowledgments We are grateful to all those who took part in or assisted with this study project. Ethics approval and consent to participate The ethical review board of the First Hospital of Qinhuangdao evaluated and sanctioned this research protocol, ensuring adherence to the Helsinki Declaration. The approval was granted under the reference number 202401A111. Due to the retrospective nature of data gathering in this study, the board also provided a waiver for informed consent. Prior to analysis, all patient data were anonymized to protect privacy. Consent for publication Not applicable. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding None Clinical trial number Not applicable. Clinical trial registration Not applicable Study design Retrospective analysis Author contributions XG conceived of the study and drafted the manuscript. LXM provided critical guidance on the statistical design and interpretation. LY gathered and processed the data. YL statistical analysis of the data. FZ and YW supervision and revised the manuscript. All authors contributed to the article and approved the submitted version. References Melton LJR. Hip fractures: a worldwide problem today and tomorrow. Bone. 1993; 14 Suppl 1:S1-S8. Kannus P, Parkkari J, Sievanen H, Heinonen A, Vuori I, Jarvinen M. Epidemiology of hip fractures. Bone. 1996; 18(1 Suppl):57S-63S. Lofman O, Berglund K, Larsson L, Toss G. 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Gallo A, Charriere S, Vimont A, Chapman MJ, Angoulvant D, Boccara F, Cariou B, Carreau V, Carri? A, Bruckert E, et al. SAFEHEART risk-equation and cholesterol-year-score are powerful predictors of cardiovascular events in French patients with familial hypercholesterolemia. Atherosclerosis. 2020; 306:41-49. Poznyak A, Grechko AV, Poggio P, Myasoedova VA, Alfieri V, Orekhov AN. The Diabetes Mellitus-Atherosclerosis Connection: The Role of Lipid and Glucose Metabolism and Chronic Inflammation. Int J Mol Sci. 2020; 21(5). Hurtubise J, Mclellan K, Durr K, Onasanya O, Nwabuko D, Ndisang JF. The Different Facets of Dyslipidemia and Hypertension in Atherosclerosis. Curr Atheroscler Rep. 2016; 18(12):82. Marufu TC, White SM, Griffiths R, Moonesinghe SR, Moppett IK. Prediction of 30-day mortality after hip fracture surgery by the Nottingham Hip Fracture Score and the Surgical Outcome Risk Tool. Anaesthesia. 2016; 71(5):515-521. 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Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted Editorial decision: Revision requested 28 Nov, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers invited by journal 05 Sep, 2025 Editor assigned by journal 28 Aug, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 28 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7476617","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512760241,"identity":"c79c3364-bd15-4c4f-86aa-f172f759f673","order_by":0,"name":"Xue Ge","email":"","orcid":"","institution":"The First Hospital of Qinhuangdao","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Ge","suffix":""},{"id":512760242,"identity":"164b0824-4ea2-476c-b57b-8f4520dfdffb","order_by":1,"name":"Lixiang Ma","email":"","orcid":"","institution":"The First Hospital of Qinhuangdao","correspondingAuthor":false,"prefix":"","firstName":"Lixiang","middleName":"","lastName":"Ma","suffix":""},{"id":512760243,"identity":"d121e1ac-c055-4648-944d-9d2e7079aaea","order_by":2,"name":"Lan Yao","email":"","orcid":"","institution":"The First Hospital of Qinhuangdao","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Yao","suffix":""},{"id":512760244,"identity":"e29960ec-8383-4107-9d8b-8608fea4436e","order_by":3,"name":"Yan Liu","email":"","orcid":"","institution":"Beijing Tiantan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""},{"id":512760245,"identity":"93f9021d-1ea3-434d-a5f4-918713ccff6a","order_by":4,"name":"Yi Wang","email":"","orcid":"","institution":"The First Hospital of Qinhuangdao","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wang","suffix":""},{"id":512760247,"identity":"311dc34e-ee64-41d8-8251-f519cea0e60d","order_by":5,"name":"Fang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIie3QsQrCMBCA4ZTATadd49L6CCcFp76JS0Ihm3uHDgGhLoIPU3BuCTgVfIU8gqObVp0cJHFzyD/fx3HHWCz2j/HB9JJKTDm3LoxANThX62yxB02BRFcrN9qCLrgUQSJvcS1Uy1VnkRFryo2XJG8C6mRnvWNnvTU+wvPDk+BE5pISY/0E4LVFqG6HJIIIAlQkRyqIhxIBfHCylpmw05NlyC15m5jhRndMj9a6a1P6yWfyt/FYLBaLfesBi2E9P/KU9gwAAAAASUVORK5CYII=","orcid":"","institution":"The First Hospital of Qinhuangdao","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-08-28 05:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7476617/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7476617/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12891-026-09695-z","type":"published","date":"2026-03-05T15:58:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91086404,"identity":"3c9f9106-5168-41b9-b63d-3561cabb9f6e","added_by":"auto","created_at":"2025-09-11 12:25:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe patient flow chart in our study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/53b9eb141cc4248290e3ac25.png"},{"id":91086422,"identity":"caed1874-60af-41c8-829b-395ba1cf7404","added_by":"auto","created_at":"2025-09-11 12:25:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":443793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves showing the survival probabilities over 60 months for each ejection fraction (EF) group. EF: Group 1 (EF ≥ 50%), Group 2 (EF 41%–49%), and Group 3 (EF ≤40%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/da9320ab5b771503dd4955c2.png"},{"id":91086450,"identity":"d0433671-e860-4dad-9f9d-c252dbc86d3d","added_by":"auto","created_at":"2025-09-11 12:25:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":395033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRCS analysis of all-cause mortality over EF levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe curves represent the estimated adjusted hazard ratios for all-cause mortality across different ejection fraction levels, with the shaded ribbons indicating 95% confidence intervals. The vertical dotted line at EF = 44% represents the critical cutoff value, below which mortality risk increases exponentially. The horizontal dashed line at HR = 1.0 indicates the reference point for hazard ratios.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/03cefc884bd132faa2e483f7.png"},{"id":91086324,"identity":"da5929fa-d8f4-4185-8209-48332176c2a1","added_by":"auto","created_at":"2025-09-11 12:25:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":607775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature selection based on the Boruta algorithm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe horizontal axis represents the variables analyzed, and the vertical axis indicates the importance score for each variable in predicting 5-year all-cause mortality. The box plots display the distribution of importance scores for each variable during the Boruta algorithm calculation. Variables are classified into three categories: Green boxes: Represent variables confirmed as significant predictors. Yellow boxes: Represent tentative predictors, whose importance is uncertain. Red boxes: Represent rejected variables deemed not predictive of mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/129493397e197ac2f383afd6.png"},{"id":91086320,"identity":"997e2f4a-80d6-496b-8a75-e1f08d2f4000","added_by":"auto","created_at":"2025-09-11 12:25:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot for 5-year all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A: Univariate Cox regression analysis forest plot showing the hazard ratios of each variable selected through the Boruta algorithm in predicting 5-year all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B: Multivariate Cox regression analysis forest plot adjusted for significant variables identified in the univariate analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/964daa6ea02a8efbde31a220.png"},{"id":91086357,"identity":"57285a98-5bd7-42c9-8003-fa002cfcf232","added_by":"auto","created_at":"2025-09-11 12:25:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe nomogram established by identified risk factors for predicting the overall survival rate in elderly hip fracture patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A: The nomogram assigns risk scores to key variables identified through statistical analysis. Based on the total score calculated, the nomogram predicts the 1-year, 3-year, and 5-year overall survival probabilities for individual patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B: A web-based interactive version of the nomogram is available at \u003c/strong\u003e\u003ca href=\"https://yingtaosnow.shinyapps.io/new_dynnomapp/\" target=\"_new\"\u003e\u003cstrong\u003ehttps://yingtaosnow.shinyapps.io/new_dynnomapp/\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e. Clinicians can input patient-specific data into the web application to generate real-time survival probability predictions along with confidence intervals. This tool provides dynamic and practical support for personalized risk assessment and treatment planning\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/f6d65a96c63c8a4f047c0453.png"},{"id":91087223,"identity":"68fb25ad-1963-46f5-b20d-2dd2085fb6fa","added_by":"auto","created_at":"2025-09-11 12:33:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":227308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe AUC of ROC curve over time for the nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe figure illustrates the area under the curve (AUC) of the receiver operating characteristic (ROC) curve over time for the nomogram. The mean AUC values demonstrate the model's discriminative performance in predicting overall survival at different time points.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/bc5a5babf975a6c4768260bf.png"},{"id":91086424,"identity":"adbface2-ae7f-4059-a195-55a48554ca5c","added_by":"auto","created_at":"2025-09-11 12:25:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":73473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the nomogram for predicting the overall survival rate in elderly hip fracture patients at different time points\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A: The calibration curve for 1-year overall survival (OS) demonstrates strong agreement between the nomogram-predicted probabilities and the actual observed survival probabilities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B: The calibration curve for 3-year OS similarly shows excellent alignment between the predicted and observed survival rates, indicating the model's reliability over mid-term prediction horizons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel C: The calibration curve for 5-year OS confirms the model's precision in long-term survival prediction, with minimal deviation from the ideal diagonal line\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/9bed51c8845868940590c928.png"},{"id":91086315,"identity":"4c7f77cf-e801-4952-affd-90af7a91cebd","added_by":"auto","created_at":"2025-09-11 12:25:26","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":371122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis (DCA) for the model's clinical utility at different time points\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe decision curve analysis evaluates the net benefit of the predictive model at 1-year, 3-year, and 5-year time points. The blue, red, and green lines represent the net benefits for 1-year, 3-year, and 5-year predictions, respectively, while the black and pink lines represent the “None” and “All” strategies\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/b3eddec90a0c33a9bfb0ebfa.png"},{"id":104251643,"identity":"a71b1fcd-8dce-4978-9ca5-1f8e20d50da9","added_by":"auto","created_at":"2026-03-09 16:14:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7474991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7476617/v1/c46634a9-787c-4303-a4c4-59f177d6a0cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between different ejection fractions and all-cause mortality in elderly patients with hip fractures: a retrospective cohort study and the development of a predictive model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHip fractures are a common medical condition worldwide, particularly among the elderly population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the increasing trend of global population aging, the incidence of hip fractures in older adults has been steadily rising, making it one of the most serious health challenges for this demographic[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Osteoporosis is commonly prevalent among the elderly, making their bones more fragile and prone to fractures. In addition, as people age, they often develop multiple chronic diseases, such as cardiovascular diseases, diabetes, and hypertension. These conditions increase the risk of falls and complicate postoperative recovery. Hip fractures in the elderly not only affect their independence and quality of life, but also remain one of the leading causes of death among elderly patients. Studies have shown that the all-cause mortality rate is 5%-10% within 30 days and 20%-30% within the first year post-surgery[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mortality risk escalates dramatically with advancing age[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEjection Fraction (EF) is a critical measure of the heart's pumping ability, assessed primarily through imaging techniques such as echocardiography. EF quantifies the percentage of blood ejected from the ventricle with each contraction relative to the total volume of blood in the ventricle. As a direct indicator of myocardial contractility, a reduced EF often signals impaired cardiac contractile function, adversely affecting overall cardiac output and systemic blood circulation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Under normal conditions, EF typically ranges between 50% and 70%. However, an EF below 40% is generally considered indicative of significant cardiac dysfunction, often associated with clinical heart failure and other severe cardiovascular diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In recent years, EF has gained increasing recognition for its prognostic value across a range of medical conditions. Research highlights its critical role in predicting outcomes in chronic heart failure, coronary artery disease, and myocardial infarction. A declining EF is not only associated with the severity of these conditions but also correlates closely with long-term survival outcomes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Even in patients without overt cardiac disease, particularly older adults, EF serves as a valuable marker of overall health status and is increasingly utilized in prognostic evaluations[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe high mortality rate observed in elderly hip fracture patients is closely linked to multiple factors, with underlying comorbidities being among the most significant contributors. Older adults often have chronic conditions such as cardiovascular disease, diabetes, and hypertension, which increase surgical risks and prolong recovery. Yombi, Omer, and colleagues found that patients with an EF below 40% face significantly higher postoperative mortality risks, largely due to the elevated incidence of cardiac complications, such as heart failure and arrhythmias[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Reduced EF is often accompanied by dysfunction in the heart and other organ systems, further increasing the likelihood of complications. Common postoperative complications, such as pneumonia, deep vein thrombosis (DVT), and pressure ulcers, are frequent contributors to mortality[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, EF plays a crucial role in postoperative recovery. Research by Lerman and colleagues has shown that patients with normal EF typically experience faster functional recovery after surgery, whereas those with reduced EF are more likely to suffer from complications, experience slower recovery, and face an increased risk of mortality[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As a key marker of cardiac function, EF has critical prognostic value in elderly hip fracture patients. Monitoring EF levels can help assess postoperative mortality risks and guide clinical decision-making, particularly in tailoring personalized treatment plans and implementing early interventions.\u003c/p\u003e\u003cp\u003eExisting research primarily focuses on the relationship between EF and cardiovascular diseases. However, the application of EF in hip fracture patients, particularly the relationship between EF stratification (normal, intermediate, and reduced groups) and mortality risk in elderly hip fracture patients, has not been fully explored. Therefore, this study aims to fill this gap by assessing the impact of different EF levels on the mortality risk of elderly hip fracture patients through EF stratification. Additionally, by incorporating other clinical data, such as underlying conditions and preoperative status, we aim to develop a mortality risk prediction model. This model will provide clinicians with a more precise risk assessment tool, ultimately improving the long-term survival rate and quality of life for elderly patients with hip fractures.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients and Study Design\u003c/h2\u003e\u003cp\u003eThis study was a retrospective cohort study that collected medical data of inpatients undergoing hip fracture surgery in the Department of Orthopedics at Qinhuangdao First Hospital from January 2015 to December 2019. Inclusion criteria for patients were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;65 years; (2) a confirmed diagnosis of femoral neck fracture or intertrochanteric femur fracture; (3) having undergone hip fracture-related surgical treatment, including internal fixation with plates/screws, hemiarthroplasty, or total hip arthroplasty; (4) fracture resulting from low-energy trauma, primarily due to falls; and (5) availability of complete medical records, including echocardiographic data on EF and relevant laboratory test results. Exclusion criteria included the following: (1) hip fractures caused by pathological fractures; (2) patients treated conservatively; (3) incomplete medical records, laboratory test results, or echocardiographic data; (4) fractures caused by high-energy trauma, including but not limited to motor vehicle accidents or falls from significant heights; and (5) patients lost to follow-up.\u003c/p\u003e\u003cp\u003eDuring the initial screening, a total of 2,247 cases diagnosed with hip fractures were identified in the hospital database. After excluding 324 cases for not meeting the age requirement, 227 cases for receiving conservative treatment, 58 duplicate cases, and 112 cases for loss to follow-up, 1,526 patients met the inclusion criteria and were ultimately included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study was based on a retrospective analysis of pre-existing medical records. All patient data were strictly anonymized to ensure privacy protection. The study was approved by the Ethics Committee of Qinhuangdao First Hospital (Approval No. 202401A111) and complied with relevant medical ethical standards.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasurement of Ejection Fraction\u003c/h3\u003e\n\u003cp\u003eIn this study, the assessment of Ejection Fraction (EF) was performed using high-precision two-dimensional echocardiography. All examinations were conducted with a Philips EPIC 7C echocardiographic system equipped with a 2.5 to 3.5 MHz phased-array transducer, operated by highly experienced sonographers. The EF measurement strictly adhered to the guidelines established by the American Society of Echocardiography (ASE). During the measurement process, images of the left ventricle at end-diastole and end-systole were obtained from the apical four-chamber long-axis view, with the modified biplane Simpson\u0026rsquo;s method used to automatically calculate the left ventricular ejection fraction. To ensure the accuracy of the measurements, particular attention was given to obtaining clear images with distinct chamber boundaries and minimal artifacts, ensuring that the procedure was performed while the patient remained in a stable breathing state. For each patient, left ventricular end-diastolic volume (LVEDV) and end-systolic volume (LVESV) were measured over two consecutive cardiac cycles, and the EF was automatically calculated using the formula: EF = [(LVEDV - LVESV)/LVEDV] \u0026times; 100%. The EF value for each patient was independently assessed by two echocardiography specialists to evaluate measurement consistency, ensuring the reproducibility of the results.\u003c/p\u003e\n\u003ch3\u003eData Extraction\u003c/h3\u003e\n\u003cp\u003ePatient perioperative demographic and clinical data were extracted from the hospital\u0026rsquo;s electronic medical record database and were standardized to ensure data completeness and consistency. EF stratification was performed according to the 2021 European Society of Cardiology (ESC) classification: normal EF (\u0026ge;\u0026thinsp;50%), mid-range EF (41%-49%), and reduced EF (\u0026le;\u0026thinsp;40%). Demographic data included sex, age, body mass index (BMI), smoking history (current or former smoker), and alcohol consumption history (current or former drinker). Fracture-related variables included fracture type (femoral neck fracture or intertrochanteric femur fracture) and the time from injury to hospital admission (recorded in hours). Comorbidity data encompassed medical histories of coronary artery disease, atrial fibrillation, valvular heart disease, hypertension, prior stroke, chronic kidney disease, diabetes mellitus, chronic obstructive pulmonary disease (COPD), and liver dysfunction. Perioperative complications were documented, including acute myocardial infarction, pneumonia, ventricular arrhythmias, acute kidney injury, and stress ulcers. Cardiac function and echocardiographic parameters included preoperative ejection fraction, left ventricular end-diastolic (LVED), cardiac output (CO), stroke volume (SV), the ratio of early to late diastolic mitral inflow velocity (E/A ratio), and the ratio of left ventricular end-diastolic pressure to mitral inflow velocity (E/e\u0026rsquo; ratio). Preoperative laboratory tests included platelet count, C-reactive protein (CRP), total cholesterol, triglycerides, glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and random blood glucose levels. Additionally, D-dimer levels and anemia status were recorded, with anemia defined as hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;120 g/L in men and \u0026lt;\u0026thinsp;110 g/L in women. Electrolyte and biochemical abnormalities were also documented: hyponatremia was defined as serum sodium\u0026thinsp;\u0026lt;\u0026thinsp;135 mmol/L, hypokalemia as serum potassium\u0026thinsp;\u0026lt;\u0026thinsp;3.5 mmol/L, and hypoalbuminemia as serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35 g/L.\u003c/p\u003e\n\u003ch3\u003eEndpoints and Follow-Up\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint of this study was 5-year all-cause mortality, defined as death from any cause within 5 years following surgery. Confirmation of all-cause mortality was achieved through review of hospital medical records, data from the national death registration system, or death certificates provided by the patient\u0026rsquo;s family. All patients underwent systematic follow-up after discharge. The follow-up schedule is as follows: the first follow-up will occur at 1 month, 3 months, 6 months, and 1 year after discharge, with subsequent follow-ups every six months. Data will be collected through telephone interviews or outpatient visits. After that, annual follow-ups will be conducted, during which survival status, hospitalization details, and relevant medical events will be recorded via telephone interviews or outpatient visits. All death events will be cross-verified using hospital records, family interviews, and official death registration databases to ensure the accuracy and reliability of the data.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables with a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared between groups using one-way analysis of variance (ANOVA). Continuous variables with a non-normal distribution were expressed as median and interquartile range (IQR) and compared between groups using the Kruskal-Wallis test. Categorical variables were presented as frequencies and percentages, and group comparisons were performed using the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test. The 5-year survival curves for patients with normal EF (\u0026ge;\u0026thinsp;50%), mid-range EF (41%-49%), and reduced EF (\u0026le;\u0026thinsp;40%) were generated using the Kaplan-Meier method. The log-rank test was used to compare survival differences between groups.\u003c/p\u003e\u003cp\u003eTo optimize variable selection for the Cox proportional hazards regression model, the study incorporated the Boruta algorithm, a feature selection method based on random forests that effectively identifies statistically significant variables[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Using the selected variables, a Cox proportional hazards regression model was constructed to comprehensively evaluate the impact of each variable on mortality risk. Additionally, to explore the potential nonlinear relationship between ejection fraction and all-cause mortality, a restricted cubic spline (RCS) analysis was performed, providing an intuitive depiction of the nonlinear effects of the variable on the outcome. RCS is a flexible method that allows for the modeling of nonlinear relationships without assuming a specific functional form between the variable and the outcome. Based on the results of the multivariable Cox regression model, a nomogram was developed to predict individualized 1-year, 3-year, and 5-year survival probabilities. To assess the performance of the prediction model, the area under the receiver operating characteristic curve (AUC-ROC) was utilized to quantitatively measure the model\u0026rsquo;s discriminative ability. Calibration curves were also employed to evaluate the consistency between predicted and observed risks. After internal validation by bootstrapping, a calibration curve was used to assess the agreement between the actual and predicted survival probabilities, thereby enhancing the model\u0026rsquo;s internal reliability. Furthermore, decision curve analysis (DCA) was applied to evaluate the clinical utility and net benefit of the model, thereby validating its reliability and practical value from multiple perspectives. All statistical analyses were conducted using R software (version 4.3.1) and Python, ensuring the flexibility and rigor of the analytical tools. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant, and all results were based on two-sided tests to ensure the scientific robustness and credibility of the conclusions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eA total of 1,526 elderly patients with hip fractures were included in this study. Patients were categorized into three groups based on their ejection fraction : the normal EF group (EF\u0026thinsp;\u0026ge;\u0026thinsp;50%, N\u0026thinsp;=\u0026thinsp;726), the mid-range EF group (EF 41%-49%, N\u0026thinsp;=\u0026thinsp;572), and the reduced EF group (EF\u0026thinsp;\u0026le;\u0026thinsp;40%, N\u0026thinsp;=\u0026thinsp;228). Baseline characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no statistically significant differences among the three groups in terms of sex, BMI, smoking history, alcohol consumption history, fracture type, fracture site, or time from injury to admission (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Regarding comorbidities, patients in the reduced EF group had significantly higher rates of coronary artery disease (36.4%), atrial fibrillation (33.3%), valvular heart disease (21.0%), hypertension (55.7%), and prior stroke (38.6%) compared to the normal EF and mid-range EF groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The prevalence of diabetes mellitus (33.8%, p\u0026thinsp;=\u0026thinsp;0.002) and COPD (15.0%, p\u0026thinsp;=\u0026thinsp;0.017) was also significantly higher in the reduced EF group. For perioperative complications, the reduced EF group had significantly higher incidences of acute myocardial infarction (26.3%), acute kidney injury (14.9%), hypokalemia (23.7%), and hypoalbuminemia (32.5%) compared to the other groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Laboratory results revealed that patients in the reduced EF group had significantly higher levels of total cholesterol, triglycerides, HbA1c, and D-dimer compared to the other groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of cardiac function parameters, the reduced EF group exhibited significantly lower LVED and EF, while the E/A and E/e' ratios were significantly elevated (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline clinical characteristics in older patients with hip fracture\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal(N\u0026thinsp;=\u0026thinsp;1526)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNormal EF group (N\u0026thinsp;=\u0026thinsp;726)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMid-range EF group (N\u0026thinsp;=\u0026thinsp;572)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReduced EF group (N\u0026thinsp;=\u0026thinsp;228)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, N (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e644(42.2%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e304(41.9%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e245(42.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95(41.7%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.927\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e882(57.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e422(58.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e327(57.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e133(58.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e76.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e77.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.503\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking history, N(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.294\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e993(65.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e464(63.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e386(67.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e143(62.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e533(34.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e262(36.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e186(32.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e85(37.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking history, N(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.571\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1186(77.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e564(77.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e452(79.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e170(74.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e340(22.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e162(22.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e120(21.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e58(25.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eType of fracture, N(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.957\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemoral neck\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e683(44.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e400(55.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e257(44.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e128(56.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntertrochanteric\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e883(55.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e326(44.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e315(55.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e100(43.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFracture site, N(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.979\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLeft\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e775(50.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e369(50.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e289(50.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e117(51.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e751(49.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e357(49.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e283(49.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e111(48.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThe time from injury to hospital admission, N(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.590\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;24hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e933(61.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e451(62.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e394(61.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e133(58.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;24hours\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e593(38.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e275(37.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e223(39.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e95(41.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidity N (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCoronary heart disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1121(73.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e490(67.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e486(85.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e145(63.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e405(26.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e236(32.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e86(15.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e83(36.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAtrial fibrillation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1257(82.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e623(85.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e482(84.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e152(66.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e269(17.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e103(14.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e90(15.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e76(33.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeart valve disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1331(87.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e666(91.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e485(84.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e180(79.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e195(13.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e60(8.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e87(15.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e48(21.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e797(52.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e419(57.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e277(48.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e101(44.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e729(47.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e307(42.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e295(51.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e127(55.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOld cerebral infarction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1110(72.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e485(66.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e485(84.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e140(61.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e416(27.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e241(33.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e87(15.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e88(38.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic renal failure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.240\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1346(88.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e636(87.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e514(89.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e196(86.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e180(11.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e90(12.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e58(10.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e32(14.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1149(75.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e562(77.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e436(76.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e151(66.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e377(24.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e164(22.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e136(23.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e77(33.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1376(90.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e664(91.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e518(90.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e194(85.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e150(9.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e62(8.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e54(9.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e34(15.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiver dysfunction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1474(96.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e708(97.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e556(97.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e210(92.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e52(3.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e18(2.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e16(2.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e18(7.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComplications N (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcute myocardial infarction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1343(88.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e658(90.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e517(90.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e168(73.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e183(12.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e68(9.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e55(9.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e60(26.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePulmonary infection\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1275(83.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e630(86.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e455(79.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e190(83.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e251(16.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e96(13.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e117(20.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e38(16.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVentricular arrhythmia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.086\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1339(87.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e644(88.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e505(88.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e190(83.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e187(12.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e82(11.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e67(11.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e38(16.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcute kidney injury\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1405(92.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e680(93.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e531(92.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e194(85.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e121(7.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e46(6.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e41(7.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e34(14.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStress ulcer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.662\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1458(95.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e690(95.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e549(96.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e219(96.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e68(4.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e36(5.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e23(4.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e9(3.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.087\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1360(89.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e658(90.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e507(88.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e195(85.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e166(10.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e68(9.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e65(11.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e33(14.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypokalemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1300(85.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e635(87.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e491(85.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e174(76.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e226(14.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e91(12.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e81(14.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e54(23.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHyponatremia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1277(83.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e628(86.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e489(85.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e160(70.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e249(16.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e98(13.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e83(14.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e68(29.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypoalbuminemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1270(83.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e632(87.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e491(85.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e154(67.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e226(16.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e94(12.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e81(14.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e74(32.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLower extremity deep vein thrombosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1306(85.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e654(90.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e455(79.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e197(86.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e220(14.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e72(9.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e117(20.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e31(13.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal cholesterol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglycerides\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlycosylated hemoglobin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e7.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFasting plasma glucose(FPG)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e7.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e7.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e8.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Blood Sugar(RBS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e8.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e8.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e8.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e179.4\u0026thinsp;\u0026plusmn;\u0026thinsp;65.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e179.8\u0026thinsp;\u0026plusmn;\u0026thinsp;64.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e180.2\u0026thinsp;\u0026plusmn;\u0026thinsp;66.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e176.1\u0026thinsp;\u0026plusmn;\u0026thinsp;63.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.704\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC-reactive protein\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e44.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;24.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eD-dimer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e8.90\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEchocardiogram results\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLEVD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e54.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e50.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e46.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e77.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e87.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e72.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e57.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE/A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE/e'\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e8.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e10.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e12.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (interquartile range), or number (percentage) as appropriate, SD Standard deviation, BMI Body Mass Index, COPD Chronic Obstructive Pulmonary Disease, EF Ejection Fraction, LEVD Left Ventricular End-Diastolic Volume, CO Cardiac Output, SV Stroke Volume, E/A The ratio of early (E) to late (A) diastolic mitral inflow velocity, E/e' The ratio of early diastolic mitral inflow velocity to mitral annular early diastolic velocity, PLT Platelet Count, FPG Fasting Plasma Glucose, RBS Random Blood Sugar, CRP C-reactive Protein, D-dimer A fibrin degradation product indicating fibrinolytic activity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical Outcomes\u003c/h3\u003e\n\u003cp\u003eThe Kaplan-Meier survival curves demonstrated significant differences in survival probabilities during the 60-month follow-up period across the three EF groups (normal, mid-range, and reduced; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients in the reduced EF group (EF\u0026thinsp;\u0026le;\u0026thinsp;40%) had the lowest survival probability, followed by the mid-range EF group (EF 41%-49%), while the normal EF group (EF\u0026thinsp;\u0026ge;\u0026thinsp;50%) had the highest survival probability. During the follow-up period, the survival curves progressively diverged, indicating that a lower ejection fraction was strongly associated with poorer long-term survival outcomes.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eNonlinear Analysis\u003c/h2\u003e\u003cp\u003eRestricted cubic spline analysis revealed a significant nonlinear relationship between EF and all-cause mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, p for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As shown in the figure, mortality risk increased exponentially as EF declined, particularly when EF dropped below 44%. This finding suggests that 44% may represent a critical threshold in the relationship between EF and mortality risk. When EF exceeded 44%, mortality risk stabilized and remained relatively low. This result reinforces the importance of EF as an independent protective factor for all-cause mortality. Patients with lower EF had significantly poorer long-term survival outcomes compared to those with higher EF. Furthermore, the RCS curve closely aligns with the results of the Cox proportional hazards regression analysis, consistently highlighting EF as a critical predictor of survival outcomes in this patient population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eFeature Selection and Cox Regression Analysis Results\u003c/h2\u003e\u003cp\u003eUsing the Boruta algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), several variables were identified as significant predictors of 5-year all-cause mortality. These variables included triglycerides, ejection fraction, age, left ventricular end-diastolic, fasting plasma glucose, coronary artery disease, acute myocardial infarction, total cholesterol, hypoalbuminemia, random blood sugar, diabetes, the E/A ratio, the E/e\u0026rsquo; ratio, stroke volume, and atrial fibrillation. Cox proportional hazards regression analysis revealed that the following variables were significantly associated with 5-year all-cause mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05): triglycerides, EF, age, LVED, total cholesterol, random blood sugar, diabetes, the E/A ratio, and stroke volume. Among these, EF emerged as a significant protective factor (HR\u0026thinsp;=\u0026thinsp;0.957, 95% CI: 0.93\u0026ndash;0.985, p\u0026thinsp;=\u0026thinsp;0.003), indicating that for every 1% increase in EF, the mortality risk decreased by approximately 4.3%. This finding highlights that lower EF is strongly associated with higher mortality risk, emphasizing EF's clinical value as a critical predictor of long-term prognosis in elderly hip fracture patients. Notably, certain variables, such as fasting plasma glucose, AMI, and hypoalbuminemia, did not demonstrate significant associations in multivariate analysis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These results suggest that baseline characteristics and chronic comorbidities (e.g., age, CAD, and diabetes), metabolic markers (e.g., triglycerides and total cholesterol), and cardiac functional parameters (e.g., EF, LVED, E/A ratio, and stroke volume) are key determinants of long-term prognosis in elderly hip fracture patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCox proportional hazards regression analysis for mortality risk factors in elderly patients with hip fracture\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.638 (1.353\u0026ndash;1.982)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.255 (1.046\u0026ndash;1.507)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.933 (0.92\u0026ndash;0.946)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.957 (0.93\u0026ndash;0.985)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.079 (1.061\u0026ndash;1.097)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.065 (1.047\u0026ndash;1.085)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLEVD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.162 (1.139\u0026ndash;1.185)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.219 (1.175\u0026ndash;1.264)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFasting Plasma Glucose\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.076 (1.029\u0026ndash;1.124)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.466\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.017 (0.973\u0026ndash;1.062)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCoronary heart disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.952 (1.543\u0026ndash;2.47)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.53 (1.188\u0026ndash;1.971)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcute myocardial infarction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.837 (1.37\u0026ndash;2.464)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.339\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.167 (0.851\u0026ndash;1.599)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal cholesterol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.159 (1.07\u0026ndash;1.255)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.172 (1.079\u0026ndash;1.273)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypoalbuminemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.904 (1.466\u0026ndash;2.474)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.733\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.053 (0.784\u0026ndash;1.413)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom blood sugar\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.083 (1.042\u0026ndash;1.126)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.041 (0.997\u0026ndash;1.083)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.705 (1.34\u0026ndash;2.171)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.324 (1.031-1.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE/A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.52 (0.381\u0026ndash;0.71)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e7.737 (4.219\u0026ndash;14.186)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE/e'\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.079 (1.04\u0026ndash;1.12)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.943 (0.89-1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.992 (0.986\u0026ndash;0.998)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.011 (1.003\u0026ndash;1.019)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAtrial fibrillation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.693 (1.302\u0026ndash;2.202)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.856\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.974 (0.728\u0026ndash;1.301)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHR hazard ratio, LEVD Left Ventricular End-Diastolic Volume, E/A The ratio of early (E) to late (A) diastolic mitral inflow velocity, E/e' The ratio of early diastolic mitral inflow velocity to mitral annular early diastolic velocity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDevelopment of a Cox Regression Prediction Model and Nomogram\u003c/h2\u003e\u003cp\u003eBased on the results of the Cox proportional hazards regression analysis, a nomogram was developed to predict the 5-year all-cause mortality risk in elderly hip fracture patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The nomogram incorporates key variables such as age, EF, triglycerides, coronary artery disease, total cholesterol, diabetes, the E/A ratio, and stroke volume. By assigning risk scores to each variable, the nomogram provides an individualized risk assessment and predicts overall survival probabilities at 1, 3, and 5 years. A web-based nomogram ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yingtaosnow.shinyapps.io/new_dynnomapp/\u003c/span\u003e\u003cspan address=\"https://yingtaosnow.shinyapps.io/new_dynnomapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) was also developed to improve the model\u0026rsquo;s accessibility and usability, allowing clinicians to quickly calculate individualized risk estimates for their patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). This tool provides a practical, efficient way for clinicians to assess mortality risk and guide personalized treatment planning in elderly hip fracture patients. The model's discriminative performance was evaluated using C-index and AUC. The results showed that the nomogram had a C-index of 0.827\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011, with AUC values of 0.840 for 1-year survival, 0.820 for 3-year survival, and 0.817 for 5-year survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), indicating excellent predictive capability. Additionally, calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) demonstrated strong agreement between the predicted and observed survival probabilities for 1, 3, and 5 years, confirming the model\u0026rsquo;s reliability and precision. Decision curve analysis further evaluated the clinical utility of the model across different time points (1 year, 3 years, and 5 years). The analysis showed that the model provided substantial net benefit over a wide range of threshold probabilities, indicating its effectiveness in guiding clinical decision-making (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This finding highlights the model's ability to reduce unnecessary interventions while minimizing the risk of missed diagnoses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe novelty of this study lies in being the first to establish a link between ejection fraction and mortality risk in elderly hip fracture patients, while also validating EF's role as an independent prognostic factor in this unique population. Previous studies have primarily focused on perioperative management in hip fracture patients, with limited attention to long-term survival outcomes. Research on the relationship between cardiac function parameters and prognosis in this population has been almost nonexistent. This study not only fills this gap but also integrates EF with other key variables through the development of a nomogram, enabling individualized risk quantification. This provides a scientific foundation for clinical stratification, management, and intervention. The findings demonstrate that EF, as a critical marker of cardiac function, is significantly associated with postoperative survival outcomes in elderly hip fracture patients. Specifically, a lower EF (\u0026lt;\u0026thinsp;44%) substantially increases the risk of mortality, and EF was identified as an independent prognostic factor in this patient population.\u003c/p\u003e\u003cp\u003eKaplan-Meier survival analysis revealed significant differences in 5-year all-cause mortality among patients in different EF categories (normal group: EF\u0026thinsp;\u0026ge;\u0026thinsp;50%; mid-range group: EF 41%-49%; reduced group: EF\u0026thinsp;\u0026le;\u0026thinsp;40%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Patients in the reduced EF group exhibited significantly lower survival rates compared to those in the mid-range and normal EF groups, indicating that reduced EF is a strong adverse prognostic factor for long-term survival. Furthermore, Cox proportional hazards regression analysis confirmed the critical role of EF as an independent protective factor. The results showed that for every 1% increase in EF, the risk of 5-year all-cause mortality decreased by approximately 4.3% (HR\u0026thinsp;=\u0026thinsp;0.957, 95% CI: 0.93\u0026ndash;0.985, p\u0026thinsp;=\u0026thinsp;0.003). This finding underscores the importance of EF in predicting long-term survival outcomes after hip fracture surgery.\u003c/p\u003e\u003cp\u003eRestricted cubic spline (RCS) analysis further clarified the nonlinear relationship between EF and mortality risk. When EF was greater than 44%, mortality risk remained relatively stable and low. However, when EF fell below 44%, mortality risk increased significantly, suggesting that 44% may represent a critical threshold for EF\u0026rsquo;s impact on the prognosis of hip fracture patients. Reduced EF likely reflects impaired cardiac pumping capacity, which can lead to inadequate postoperative organ perfusion, delayed fracture healing, and an increased incidence of postoperative complications[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These factors may collectively contribute to the elevated mortality risk observed in patients with low EF. By providing evidence for EF stratification, this study establishes a scientific basis for its use in clinical grading and management. These findings highlight the clinical importance of monitoring EF in elderly hip fracture patients and incorporating it into risk assessment models to guide tailored interventions and improve long-term outcomes.\u003c/p\u003e\u003cp\u003eIn this study, the Boruta algorithm was employed to screen feature variables and identify those significantly associated with the 5-year all-cause mortality risk in elderly patients with hip fractures. The Boruta algorithm, a feature selection method based on random forests, introduces randomness (e.g., by adding \u0026ldquo;shadow features\u0026rdquo;) to simulate the predictive contribution of variables to the target outcome[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This approach enables a more accurate assessment of the importance of each variable. The results showed that EF consistently occupied the green zone, indicating a high importance score during feature selection. This suggests that EF plays a crucial role in this study and is significantly associated with the research objective.\u003c/p\u003e\u003cp\u003eIn the univariate Cox regression analysis, we evaluated the impact of each variable (including comorbidities, perioperative complications, EF, etc.) on mortality and identified the variables significantly associated with mortality. In the subsequent multivariate Cox regression analysis, we included these potential confounding factors (such as comorbidities and perioperative complications) as covariates in the model to control for their effect on the outcome. This ensures that the results accurately reflect the independent impact of each variable, particularly the ejection fraction (EF), by adjusting for the confounding influence of other factors. Additionally, a multivariable Cox regression analysis was performed to construct a nomogram model. This nomogram was developed to predict 1-year, 3-year, and 5-year all-cause mortality risks in elderly patients with hip fractures. By assigning a score to each variable and integrating diverse clinical characteristics, the nomogram provides a personalized risk assessment tool for mortality prediction. The accuracy of the model was evaluated using both the C-index and AUC, demonstrating excellent discriminative ability. Furthermore, calibration curves showed a strong agreement between the predicted and observed survival probabilities for 1-year, 3-year, and 5-year outcomes, validating the reliability and accuracy of the model. These findings underscore the model's effectiveness in providing precise survival predictions over varying time horizons. The construction of this nomogram highlights the importance of EF as an independent prognostic indicator while also confirming the added predictive value of combining it with other metabolic and cardiac function variables.\u003c/p\u003e\u003cp\u003eWhile the decision curve analysis demonstrates the model\u0026rsquo;s net benefit, the clinical utility of this model is enhanced by linking the stratified risk groups to specific interventions. For high-risk patients (with EF\u0026thinsp;\u0026lt;\u0026thinsp;40%), we recommend intensified cardiovascular monitoring, including frequent ECG assessments and hemodynamic evaluations. These patients may also benefit from personalized rehabilitation plans to optimize functional recovery. For moderate-risk patients (with EF between 41%-49%), routine monitoring and standard rehabilitation protocols are recommended. In contrast, low-risk patients (with EF\u0026thinsp;\u0026ge;\u0026thinsp;50%) may receive standard care with regular follow-ups. These risk-based interventions enable clinicians to tailor care, improving patient outcomes and optimizing resource utilization.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eComparison of EF with Other Cardiovascular Indicators\u003c/h2\u003e\u003cp\u003eEjection fraction, as a core measure of cardiac pumping function, was demonstrated in this study to be an independent predictor of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.957, 95% CI: 0.93\u0026ndash;0.985, p\u0026thinsp;=\u0026thinsp;0.003). In contrast, other cardiac function parameters\u0026mdash;such as left ventricular end-diastolic volume, the E/A ratio, and stroke volume\u0026mdash;while essential for evaluating cardiac function, may have their predictive ability influenced by the complexity of diastolic dysfunction. LEVD is more closely related to ventricular volume load, while the E/A ratio primarily reflects the dynamic changes in left ventricular filling pressures, with abnormalities often indicating increased ventricular stiffness[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Stroke volume, on the other hand, directly measures the amount of blood ejected with each ventricular contraction, offering a dynamic reflection of cardiac pumping efficiency under stress[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although these parameters each have their unique roles in assessing cardiac function, EF, as a comprehensive indicator of overall cardiac performance, exhibits stronger independence and broader clinical applicability. EF more holistically reflects the overall ventricular pumping capacity. In clinical practice, the distinct characteristics of EF, LEVD, E/A ratio, and SV should be considered collectively to develop a more comprehensive cardiac function assessment framework, which can better guide postoperative interventions and personalized management strategies.\u003c/p\u003e\u003cp\u003eFurthermore, EF outperformed metabolic indicators (e.g., triglycerides and total cholesterol) in its independent predictive ability for mortality risk in this study. While metabolic markers are important predictors of cardiovascular events, they primarily reflect the long-term effects of chronic metabolic disorders on systemic organs rather than the immediate prognostic response under acute stress conditions[\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, EF is better at capturing the dynamic changes in short- and mid-term mortality risk after surgery. It is worth noting that triglycerides and total cholesterol, as metabolism-related variables, also exhibited significant predictive ability in the multivariable Cox regression analysis. This may be attributed to the role of lipid metabolism disorders in exacerbating systemic inflammatory responses and atherosclerosis, which further impair cardiovascular and skeletal system functions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This finding underscores the importance of addressing metabolic dysregulation in patient management. However, compared to EF, the predictive value of these metabolic indicators is more likely to be influenced by external factors such as pharmacologic treatments and lifestyle modifications. As a direct measure of ventricular systolic performance, EF holds greater potential for widespread clinical application.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eComparison with Existing Risk Stratification Tools\u003c/h2\u003e\u003cp\u003eSeveral risk stratification tools have been developed for patients with hip fracture, notably the Nottingham Hip Fracture Score (NHFS) and the Charlson Comorbidity Index (CCI). These models have proven useful in predicting short-term mortality or estimating comorbidity burden, particularly in emergency or perioperative settings[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. NHFS incorporates demographic and clinical factors such as age, hemoglobin level, cognitive status, and comorbidities, while the CCI provides a cumulative score based on the presence and severity of chronic illnesses. While both tools are widely accepted and have demonstrated prognostic value, they largely emphasize baseline health status and static clinical characteristics. In contrast, our model integrates cardiac-specific parameters (e.g., ejection fraction, E/A ratio, LVED) alongside metabolic markers (e.g., triglycerides, total cholesterol), offering a more nuanced assessment of patients\u0026rsquo; physiological reserve and cardiovascular function. To our knowledge, few existing models have incorporated echocardiographic indicators such as EF into mortality risk prediction after hip fracture surgery. This may result in under-recognition of high-risk patients with subclinical cardiac dysfunction. Our model addresses this gap by offering individualized predictions based on comprehensive cardiovascular evaluation, potentially enabling earlier intervention and targeted perioperative management. While our current study does not conduct a direct performance comparison with existing scores, we recognize the importance of benchmarking. Future research may consider formal performance comparisons with existing tools, incorporating metrics such as Net Reclassification Improvement (NRI) to evaluate incremental predictive value.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMechanisms Linking Reduced EF to Mortality in Elderly Hip Fracture Patients\u003c/h2\u003e\u003cp\u003eThe biological mechanisms underlying the association between reduced EF and increased mortality risk in elderly hip fracture patients are multifaceted. Cardiovascular dysfunction plays a central role, as reduced EF indicates impaired cardiac pumping capacity, leading to inadequate tissue perfusion, particularly during the postoperative period. This insufficient perfusion can delay wound healing and recovery, increasing the risk of postoperative complications such as heart failure, arrhythmias, and prolonged rehabilitation[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, systemic inflammation is often observed in patients with low EF, particularly those with chronic heart failure, and is associated with elevated levels of inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6). Chronic inflammation compromises immune function and impairs tissue repair, thus contributing to an increased incidence of postoperative infections, such as pneumonia, which can elevate mortality risk[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, patients with low EF tend to have reduced physiological reserve, meaning their bodies are less able to handle the physiological stress imposed by surgery. Aging, combined with the impact of comorbidities such as diabetes and hypertension, reduces the body\u0026rsquo;s ability to recover postoperatively, making these patients more susceptible to complications and ultimately, death[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These mechanisms help explain the biological plausibility of our findings and emphasize the need to assess EF as part of a comprehensive risk evaluation for elderly hip fracture patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations of the Study\u003c/h2\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. First, as a single-center retrospective cohort study, it is subject to potential selection bias despite the relatively large sample size. This limitation may restrict the external validity and generalizability of the findings to broader populations, particularly across different geographic regions and healthcare settings. Future multicenter prospective studies are needed to validate the conclusions drawn from this research and assess the model\u0026rsquo;s performance across diverse populations. Second, because the study data were derived from a single medical center, they may not fully capture the clinical characteristics of patients from multiple centers or different geographic regions. Future multicenter prospective studies are needed to validate the conclusions drawn from this research. Third, the study did not include certain variables, such as psychological status and levels of social support, which have been shown in numerous studies to significantly influence long-term outcomes in elderly patients. In addition, other geriatric factors such as frailty and musculoskeletal health were not considered. Standardized frailty assessment tools (e.g., Fried Frailty Phenotype, Clinical Frailty Scale) and indicators of musculoskeletal function may further improve the model\u0026rsquo;s predictive power. The omission of these factors represents a potential avenue for further optimization of the predictive model's performance. Future research should integrate these psychosocial factors to further optimize the predictive model and enhance its relevance in real-world clinical settings. Additionally, this study did not conduct a cause-specific analysis of mortality. The lack of classification by cause of death may have influenced the precision of the findings. Future research should consider incorporating more granular categorization of mortality causes and additional potential confounders to enhance the predictive accuracy and clinical utility of the model. Future studies should consider incorporating cause-specific mortality data to provide a more comprehensive analysis of the factors influencing mortality in elderly patients.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we systematically analyzed the relationship between EF and the 5-year all-cause mortality in elderly patients with hip fractures. Our findings revealed that EF, as a core indicator of cardiac function, is an independent and significant predictor of mortality risk. We observed that reduced EF, particularly below 44%, was associated with a substantial increase in mortality, highlighting its critical role in risk assessment for this population. Based on these results, we constructed a nomogram incorporating EF and other clinical variables. This model demonstrated strong predictive accuracy, with consistent calibration and discrimination, offering a reliable tool for individualized mortality risk prediction. To enhance the practical application and accessibility of this model, future efforts will focus on integrating it into electronic health record systems (EMR) for automatic risk assessment during patient admission based on preoperative data such as EF, age, and comorbidities. Additionally, we plan to develop a mobile application or a simplified scoring sheet, enabling clinicians, especially those without statistical training, to easily calculate individual mortality risk at the bedside. These integrations aim to support timely decision-making and personalized management, ultimately improving outcomes for elderly hip fracture patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in the present study are contained within the internal network of the First Hospital of Qinhuangdao. Due to existing data privacy policies, these datasets are not publicly accessible. However, they can be made available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all those who took part in or assisted with this study project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical review board of the First Hospital of Qinhuangdao evaluated and sanctioned this research protocol, ensuring adherence to the Helsinki Declaration. The approval was granted under the reference number\u0026nbsp;202401A111. Due to the retrospective nature of data gathering in this study, the board also provided a waiver for informed consent. Prior to analysis, all patient data were anonymized to protect privacy.\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\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRetrospective analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXG conceived of the study and drafted the manuscript. LXM provided critical guidance on the statistical design and interpretation. LY gathered and processed the data. YL statistical analysis of the data. FZ and YW supervision and revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMelton LJR. Hip fractures: a worldwide problem today and tomorrow. Bone. 1993; 14 Suppl 1:S1-S8.\u003c/li\u003e\n\u003cli\u003eKannus P, Parkkari J, Sievanen H, Heinonen A, Vuori I, Jarvinen M. Epidemiology of hip fractures. Bone. 1996; 18(1 Suppl):57S-63S.\u003c/li\u003e\n\u003cli\u003eLofman O, Berglund K, Larsson L, Toss G. Changes in hip fracture epidemiology: redistribution between ages, genders and fracture types. Osteoporosis Int. 2002; 13(1):18-25.\u003c/li\u003e\n\u003cli\u003eSing C, Lin T, Bartholomew S, Bell JS, Bennett C, Beyene K, Bosco-Levy P, Bradbury BD, Chan AHY, Chandran M, et al. 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Prognostic implications of ejection fraction from linear echocardiographic dimensions: the Strong Heart Study. Am Heart J. 2003; 146(3):527-534.\u003c/li\u003e\n\u003cli\u003eYu Q, Fu M, Wang Z, Hou Z. Predictive characteristics and model development for acute heart failure preceding hip fracture surgery in elderly hypertensive patients: a retrospective machine learning approach. Bmc Geriatr. 2024; 24(1):296.\u003c/li\u003e\n\u003cli\u003eYombi JC, Putineanu DC, Cornu O, Lavand\u0026apos;Homme P, Cornette P, Castanares-Zapatero D. Low haemoglobin at admission is associated with mortality after hip fractures in elderly patients. Bone Joint J. 2019; 101-B(9):1122-1128.\u003c/li\u003e\n\u003cli\u003eOmer S, Adeseye A, Jimenez E, Cornwell LD, Massarweh NN. Low left ventricular ejection fraction, complication rescue, and long-term survival after coronary artery bypass grafting. J Thorac Cardiov Sur. 2022; 163(1):111-119.\u003c/li\u003e\n\u003cli\u003eLerman BJ, Popat RA, Assimes TL, Heidenreich PA, Wren SM. Association of Left Ventricular Ejection Fraction and Symptoms With Mortality After Elective Noncardiac Surgery Among Patients With Heart Failure. Jama-J Am Med Assoc. 2019; 321(6):572-579.\u003c/li\u003e\n\u003cli\u003eYan F, Chen X, Quan X, Wang L, Wei X, Zhu J. Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning. Cardiovasc Diabetol. 2024; 23(1):163.\u003c/li\u003e\n\u003cli\u003eChen X, Ma Y, Deng Z, Li Q, Liao J, Zheng Q. Prediction of Early Postoperative Major Cardiac Events and In-Hospital Mortality in Elderly Hip Fracture Patients: The Role of Different Types of Preoperative Cardiac Abnormalities on Echocardiography Report. Clin Interv Aging. 2020; 15:755-762.\u003c/li\u003e\n\u003cli\u003eGanau A, Devereux RB, Pickering TG, Roman MJ, Schnall PL, Santucci S, Spitzer MC, Laragh JH. Relation of left ventricular hemodynamic load and contractile performance to left ventricular mass in hypertension. Circulation. 1990; 81(1):25-36.\u003c/li\u003e\n\u003cli\u003eHoffmann R, Lambertz H, Thoennissen G, Flachskampf FA, Hanrath P. Altered left ventricular diastolic function post-atrial pacing in coronary artery disease and left ventricular hypertrophy: further insights by pulmonary venous flow analysis. Eur Heart J. 1994; 15(8):1096-1105.\u003c/li\u003e\n\u003cli\u003eSt?hr EJ, Gonz?lez-Alonso J, Shave R. Left ventricular mechanical limitations to stroke volume in healthy humans during incremental exercise. Am J Physiol-Heart C. 2011; 301(2):H478-H487.\u003c/li\u003e\n\u003cli\u003eBorlaug BA, Melenovsky V, Russell SD, Kessler K, Pacak K, Becker LC, Kass DA. Impaired chronotropic and vasodilator reserves limit exercise capacity in patients with heart failure and a preserved ejection fraction. Circulation. 2006; 114(20):2138-2147.\u003c/li\u003e\n\u003cli\u003eBittner V, Johnson BD, Zineh I, Rogers WJ, Vido D, Marroquin OC, Bairey-Merz CN, Sopko G. The triglyceride/high-density lipoprotein cholesterol ratio predicts all-cause mortality in women with suspected myocardial ischemia: a report from the Women\u0026apos;s Ischemia Syndrome Evaluation (WISE). Am Heart J. 2009; 157(3):548-555.\u003c/li\u003e\n\u003cli\u003eLeiherer A, Ulmer H, Muendlein A, Saely CH, Vonbank A, Fraunberger P, Foeger B, Brandtner EM, Brozek W, Nagel G, et al. Value of total cholesterol readings earlier versus later in life to predict cardiovascular risk. Ebiomedicine. 2021; 67:103371.\u003c/li\u003e\n\u003cli\u003eZhou L, Mai J, Li Y, Guo M, Wu Y, Gao X, Wu Y, Liu X, Zhao L. Triglyceride to high-density lipoprotein cholesterol ratio and risk of atherosclerotic cardiovascular disease in a Chinese population. Nutr Metab Cardiovas. 2020; 30(10):1706-1713.\u003c/li\u003e\n\u003cli\u003ePark G, Cho Y, Won K, Yang YJ, Park S, Ann SH, Kim Y, Park EJ, Kim S, Lee S, et al. Triglyceride glucose index is a useful marker for predicting subclinical coronary artery disease in the absence of traditional risk factors. Lipids Health Dis. 2020; 19(1):7.\u003c/li\u003e\n\u003cli\u003eGallo A, Charriere S, Vimont A, Chapman MJ, Angoulvant D, Boccara F, Cariou B, Carreau V, Carri? A, Bruckert E, et al. SAFEHEART risk-equation and cholesterol-year-score are powerful predictors of cardiovascular events in French patients with familial hypercholesterolemia. Atherosclerosis. 2020; 306:41-49.\u003c/li\u003e\n\u003cli\u003ePoznyak A, Grechko AV, Poggio P, Myasoedova VA, Alfieri V, Orekhov AN. The Diabetes Mellitus-Atherosclerosis Connection: The Role of Lipid and Glucose Metabolism and Chronic Inflammation. Int J Mol Sci. 2020; 21(5).\u003c/li\u003e\n\u003cli\u003eHurtubise J, Mclellan K, Durr K, Onasanya O, Nwabuko D, Ndisang JF. The Different Facets of Dyslipidemia and Hypertension in Atherosclerosis. Curr Atheroscler Rep. 2016; 18(12):82.\u003c/li\u003e\n\u003cli\u003eMarufu TC, White SM, Griffiths R, Moonesinghe SR, Moppett IK. Prediction of 30-day mortality after hip fracture surgery by the Nottingham Hip Fracture Score and the Surgical Outcome Risk Tool. Anaesthesia. 2016; 71(5):515-521.\u003c/li\u003e\n\u003cli\u003eLiow MHL, Ganesan G, Chen JDY, Koh JSB, Howe TS, Yong E, Kramer MS, Tan KB. Excess mortality after hip fracture: fracture or pre-fall comorbidity? Osteoporosis Int. 2021; 32(12):2485-2492.\u003c/li\u003e\n\u003cli\u003eMichaelsson E, Lund LH, Hage C, Shah SJ, Voors AA, Saraste A, Redfors B, Grove EL, Barasa A, Richards AM, et al. Myeloperoxidase Inhibition Reverses Biomarker Profiles Associated With Clinical Outcomes in HFpEF. Jacc-Heart Fail. 2023; 11(7):775-787.\u003c/li\u003e\n\u003cli\u003eCastanheira FVS, Kubes P. Neutrophils and NETs in modulating acute and chronic inflammation. Blood. 2019; 133(20):2178-2185.\u003c/li\u003e\n\u003cli\u003eTan AX, Shah SJ, Sanders JL, Psaty BM, Wu C, Gardin JM, Peralta CA, Newman AB, Odden MC. Association Between Myocardial Strain and Frailty in CHS. Circ-Cardiovasc Imag. 2021; 14(5):e012116.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hip fracture, Ejection fraction, Elderly, All-cause mortality, Predictive model","lastPublishedDoi":"10.21203/rs.3.rs-7476617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7476617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHip fractures in the elderly are a major public health concern due to high mortality and poor outcomes. While low ejection fraction (EF) is linked to increased mortality in many populations, its impact on elderly hip fracture patients is unclear. This study investigates the relationship between EF and all-cause mortality and develops a predictive model based on EF and other clinical factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was conducted, including 1,500 elderly patients who suffered hip fractures and had EF data recorded. The patients were stratified into three groups based on their EF: normal (EF\u0026thinsp;\u0026ge;\u0026thinsp;50%), mildly reduced (EF 40%-49%), and severely reduced (EF\u0026thinsp;\u0026lt;\u0026thinsp;40%). The primary outcome was all-cause mortality, and the secondary outcome was 1-year mortality. Statistical analysis was performed using Kaplan-Meier curves, Cox proportional hazards regression, and multivariate analysis to examine the relationship between EF and mortality. A predictive model for all-cause mortality was developed using multiple clinical factors, and its accuracy was evaluated with the C-index.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 1,526 elderly patients with hip fractures were included in the study, with a mean follow-up of 60 months. Multivariate Cox regression analysis identified nine key predictors for 5-year all-cause mortality: age, EF, triglycerides, coronary artery disease, total cholesterol, diabetes, the E/A ratio, stroke volume, and LVED. A nomogram incorporating these variables was developed, enabling individualized risk assessment for predicting 1-year, 3-year, and 5-year mortality. Additionally, a web-based dynamic nomogram was created to enhance accessibility, allowing clinicians to input patient-specific data and obtain real-time survival predictions. The nomogram demonstrated excellent predictive performance, with a C-index of 0.827 and AUCs of 0.840, 0.820, and 0.817 for 1-year, 3-year, and 5-year survival, respectively. Calibration curves showed strong agreement between predicted and observed survival probabilities, while decision curve analysis confirmed the model's clinical utility in guiding personalized risk management.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLow EF is a strong predictor of increased all-cause mortality in elderly patients with hip fractures. The predictive model based on EF and clinical characteristics provides valuable information for clinicians to identify high-risk patients and improve patient management.\u003c/p\u003e","manuscriptTitle":"The association between different ejection fractions and all-cause mortality in elderly patients with hip fractures: a retrospective cohort study and the development of a predictive model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 12:24:28","doi":"10.21203/rs.3.rs-7476617/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T12:32:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T15:52:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178565073975893916616443111795277923465","date":"2025-11-19T09:47:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T00:10:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215762634819390692031136805229180918545","date":"2025-09-05T21:57:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-05T07:08:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-29T03:12:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-29T03:11:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2025-08-28T05:49:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e42d2d85-8425-42d0-bdc9-6b99ac302a0b","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:11:13+00:00","versionOfRecord":{"articleIdentity":"rs-7476617","link":"https://doi.org/10.1186/s12891-026-09695-z","journal":{"identity":"bmc-musculoskeletal-disorders","isVorOnly":false,"title":"BMC Musculoskeletal Disorders"},"publishedOn":"2026-03-05 15:58:40","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2025-09-11 12:24:28","video":"","vorDoi":"10.1186/s12891-026-09695-z","vorDoiUrl":"https://doi.org/10.1186/s12891-026-09695-z","workflowStages":[]},"version":"v1","identity":"rs-7476617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7476617","identity":"rs-7476617","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}