Development and Internal Validation of a Nomogram for Predicting Early Muscular Calf Venous Thrombosis Following Hip Fracture

Summary

This retrospective study developed and internally validated a six-variable nomogram to estimate the risk of early muscular calf vein thrombosis within 48 h of hip fracture. The model accounts for nonlinear triglyceride effects and may support risk awareness pending external validation.

Abstract

Early muscular calf vein thrombosis (MCVT) detected within 48 h after injury may affect perioperative management in patients with hip fractures. This study aimed to develop and internally validate a nomogram for estimating early MCVT risk in this population. This study retrospectively screened hip fracture patients admitted between 2021 and 2024. Patients were categorized into MCVT and non-MCVT groups based on an admission ultrasound performed within 48 h after injury. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection, and restricted cubic splines (RCS) were used to evaluate nonlinear associations. A multivariable logistic regression model was constructed and visualized as a nomogram. Model performance was assessed by discrimination (area under the receiver operating characteristic curve, AUC), calibration (Brier score), and clinical utility (decision curve analysis, DCA), with internal validation using 1,000 bootstrap resamples, following Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Plus Artificial Intelligence (TRIPOD + AI) guidance. Among 665 patients included, 44 (6.6%) had early MCVT. Six variables were retained in the final prediction model: time from injury to admission (TFIA), triglycerides (TG), total cholesterol (TC), activated partial thromboplastin time (APTT), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). RCS analysis revealed a significant nonlinear association between TG and MCVT risk (p-value for non-linearity <0.001). The model showed good discrimination (AUC: 0.894; 95% CI: 0.829–0.941) and acceptable calibration (Brier score: 0.041). The optimism-corrected C-index was 0.884. DCA suggested potential clinical net benefit across relevant threshold probabilities. In conclusion, the developed nomogram may assist early risk stratification, but external validation in independent cohorts is required before clinical application.

Introduction

Hip fractures in older adults represent a significant global public health challenge and are frequently associated with high morbidity, mortality, and substantial healthcare expenditure¹. Current clinical guidelines strongly advocate accelerated surgical intervention, ideally within 48 h of admission, to alleviate pain, facilitate early mobilization, and mitigate life-threatening complications^2,3. However, preoperative deep vein thrombosis (DVT) remains a formidable barrier to timely surgery. Muscular calf vein thrombosis (MCVT), a specific subtype of distal DVT, involves thrombus formation within the venous plexuses of the calf muscles, primarily the soleus and gastrocnemius muscles⁴. Although often asymptomatic and historically perceived as less clinically significant than proximal DVT, emerging evidence suggests that MCVT can propagate cephalad into proximal veins or trigger pulmonary embolism (PE), thereby compromising patient safety^5,6. Consequently, MCVT detected early after injury may necessitate surgical postponement, prolong hospital stays, and complicate perioperative management by requiring anticoagulant bridging or inferior vena cava filter placement. Therefore, a robust tool to identify hip fracture patients at high risk for early MCVT, as detected on admission ultrasound, is clinically important for optimizing surgical pathways and improving patient outcomes.

Despite routine ultrasound screening, early risk stratification for acute MCVT in hip fracture patients remains clinically challenging. Previous investigations have attempted to identify risk factors for perioperative thrombosis; however, most have focused on isolated demographic characteristics or traditional coagulation parameters^4,7. These studies are often constrained by the inherent limitations of linear regression models, which may fail to capture the intricate, non-linear dynamics and the complex interplay of the trauma-induced thrombo-inflammatory response. From a pathophysiological standpoint, acute fractures trigger a massive release of inflammatory cytokines, which subsequently activate the coagulation cascade^8,9. While novel composite inflammatory indices—such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR)—have demonstrated significant prognostic value in cardiovascular and surgical domains, their utility in predicting early-phase MCVT remains largely underexplored^10,11.

To bridge this research gap, a multidimensional and individualized predictive tool is needed. This study aimed to investigate predictors of early MCVT in a well-defined cohort of hip fracture patients admitted within 48 h of injury. By employing Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection and restricted cubic splines (RCS) to characterize complex nonlinear associations, this study sought to develop and internally validate a clinically interpretable nomogram. This study hypothesized that integrating systemic inflammatory markers and metabolic profiles would improve early risk stratification and support surveillance planning during the acute phase of hip fracture care.

Protocol

The study protocol was approved by the hospital's Institutional Review Board (IRB) ([2025B], IIT Ethics Approval No. 0407).

Study design and participants

This single-center retrospective cohort study was conducted at the First Affiliated Hospital, Zhejiang University School of Medicine. This study consecutively screened the clinical records of patients admitted with hip fractures between January 2021 and December 2024. As this was a retrospective study, a waiver of informed consent from study participants was requested and granted. Based on findings from routine color Doppler ultrasound performed at emergency admission, patients were categorized into the MCVT or non-MCVT groups.

Hip fractures were defined as fractures of the proximal femur, including the femoral neck, intertrochanteric, and subtrochanteric regions, while excluding pelvic fractures. To ensure the purity of the study population, the inclusion criteria were strictly defined as follows: (1) primary closed hip fracture confirmed by X-ray or computed tomography; (2) time from injury to admission (TFIA) within 48 h; (3) completion of bilateral lower extremity deep venous color Doppler ultrasound upon admission (generally within 2 h after radiographic confirmation of hip fracture and before initiation of anticoagulant therapy); and (4) planned surgical intervention. Patients were excluded based on the following criteria: (1) multiple traumas (e.g., concurrent craniocerebral, thoracic, or abdominal injuries); (2) pathological fractures (e.g., bone metastasis); (3) a history of DVT or PE, or anticoagulant/antiplatelet therapy ≥ 30 days prior to admission; (4) severe hepatic or renal dysfunction significantly impairing coagulation factor synthesis or metabolism; (5) concurrent malignant tumors; (6) substantial missing data regarding primary clinical or laboratory variables; and (7) proximal DVT detected on admission ultrasound (e.g., thrombosis involving the popliteal, femoral, common femoral, or iliac veins).

Data collection and candidate predictors

All candidate predictors were retrospectively extracted from the institutional electronic medical record (EMR) system. To ensure data accuracy and integrity, the extraction process was independently performed by two researchers, with any discrepancies resolved through consensus or consultation with a senior physician. The collected data were categorized into the following domains: (1) Demographic Characteristics: Including age, gender, and body mass index (BMI). (2) Clinical and Injury-related Variables: The TFIA (measured in hours) was precisely documented. Additionally, baseline comorbidities were quantified using the Charlson Comorbidity Index (CCI). (3) Laboratory Biomarkers: Laboratory parameters were obtained from the initial venous blood samples collected within 24 h of admission. These encompassed the complete blood count (CBC), liver and renal function tests, and coagulation profiles. Additionally, composite systemic inflammatory indices, specifically the NLR and PLR, were calculated from the admission CBC results.

Outcome definition and diagnostic criteria

The primary endpoint of this study was the occurrence of early MCVT, defined as thrombosis detected during the initial assessment upon admission. All included patients underwent bilateral lower-extremity Doppler ultrasound on admission, prior to initiation of any anticoagulant therapy. The examination was conducted using EPIQ-5 with eL18-4. Patients were examined in the supine and/or lateral position as tolerated. Diagnosis was established via routine bilateral lower extremity color Doppler ultrasound. According to established sonographic criteria for the acute phase, a positive diagnosis of MCVT was confirmed based on the following manifestations¹²: (1) significant dilation and tortuosity of the calf muscular venous lumens; (2) the presence of intraluminal flocculent or uniform hypoechoic echoes, appearing as multiple circular or oval masses on transverse sections; (3) complete non-compressibility of the vein under transducer pressure; (4) absence of spontaneous or phasic blood flow signals, with no detectable flow even during distal limb augmentation (manual compression); and (5) a distinct demarcation between the thrombosed segment and the adjacent muscular tissue. Bilateral lower-extremity Doppler ultrasound was performed upon admission by certified ultrasound physicians using the standardized institutional protocol. Ambiguous cases were reviewed by a senior ultrasound physician.

Statistical analysis

All statistical analyses were performed using R software (version 4.5.2). The normality of continuous variables was assessed using the Shapiro-Wilk test. Continuous data were presented as means ± standard deviations (SD) or medians with interquartile ranges (IQR), as appropriate. Categorical variables were expressed as frequencies and percentages.

Variables with a p-value < 0.01 in univariable analysis and variables considered clinically important were entered into LASSO regression. Candidate predictors were selected based on clinical relevance and availability at admission. Continuous variables were standardized before LASSO regression. Eleven candidate features were evaluated in the LASSO procedure, comprising nine continuous variables and two dummy variables for fracture type. LASSO logistic regression with 10-fold cross-validation was used to reduce dimensionality and screen variables for the final multivariable model. The lambda.min and lambda.1se values were examined, and final variable retention was determined using penalized selection together with clinical relevance and subsequent RCS assessment. Associations between predictors and MCVT were quantified using odds ratios (ORs) and corresponding 95% confidence intervals (CIs).

Based on the predictors identified in the multivariable logistic regression, a clinical nomogram was constructed to facilitate the individualized estimation of early MCVT risk. The predictive performance of the nomogram was comprehensively evaluated across three dimensions: discrimination, calibration, and clinical utility. Discrimination was quantified using the area under the receiver operating characteristic curve (AUC). Calibration was assessed using calibration plots with 1,000 bootstrap samples to evaluate agreement between predicted probabilities and observed outcomes; the Brier score was also calculated to measure overall predictive accuracy. Finally, Decision Curve Analysis (DCA) was performed to determine the model's clinical net benefit across a wide range of threshold probabilities. All statistical analyses were two-sided, and p-values < 0.05 were considered statistically significant.

Consistent with the retrospective design, the sample size was determined by the availability of consecutive patient data over the four-year study period, rather than an a priori power calculation. Given the relatively low incidence of positive MCVT events (n = 44), the entire dataset was utilized for model development without partitioning into separate training and validation sets to maintain statistical power. To mitigate the risk of overfitting associated with this event rate, LASSO regression was employed for feature selection and shrinkage regularization. The final model included six predictors but seven regression parameters, excluding the intercept, because TG was represented by two spline terms; therefore, the parameter-based EPV was approximately 6.3. The model did not meet the traditional events per candidate (EPV) ≥10 heuristic, which is now regarded as a rough guideline rather than an absolute criterion. Therefore, this study additionally evaluated overfitting using bootstrap-corrected performance estimates and calibration slope. Internal validation was performed using 1,000 bootstrap resamples to estimate and adjust for optimistic bias. This study was conducted in accordance with the TRIPOD+AI reporting guidance¹³.

To facilitate independent validation and implementation, the complete model equation, including the intercept, regression coefficients, triglycerides (TG) spline coefficients, and RCS knot locations, is provided in Table 1. The predicted probability of early MCVT was calculated as P = 1 / [1 + exp(-LP)], where LP is the linear predictor.

Results

Demographic data

A total of 665 patients with hip fractures met the inclusion criteria and were enrolled in this study (Figure 1). Among them, 44 patients (6.6%) were diagnosed with early MCVT detected on admission ultrasound within 48 h after injury. A detailed comparison of the demographic and clinical characteristics between the MCVT and non-MCVT groups is summarized in Table 2.

LASSO regress...

Discussion

Following a hip fracture, restricted lower limb mobility can reduce gastrocnemius pump function and venous blood flow velocity. Due to their relatively small diameters and paucity of valves, the muscular calf veins are susceptible to blood stasis and may become an early site of thrombus detection. A previous study by Zhao et al. reported a preoperative MCVT incidence of 23.5% in hip fracture patients¹⁴. In contrast, the cohort incidence was lower at 6.6%. This discrepancy may be attributable to st...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Color Doppler ultrasound system	Philips	EPIQ-5 with eL18-4	Detection and diagnosis of MCVT
Coagulation analyzer	Stago	STAR MAX (https://www.stago-cn.com)	APTT measurement
Hematology analyzer	Cobas	8000 (https://www.cobase.com/)	Blood cell counts, NLR, PLR
Laboratory biochemical analyzer	Sysmex	BC7500 (https://www.sysmex.com.cn/)	TG and TC measurement
Microsoft Excel	Microsoft	2023 (https://www.microsoft.com)	Data organization
R software	R Foundation	https://www.r-project.org/	Statistical analysis
R package glmnet	CRAN	https://cran.r-project.org/web/packages/glmnet/index.html	LASSO regression
R package rms	CRAN	https://cran.r-project.org/web/packages/rms/index.html	RCS, nomogram, calibration
R package pROC	CRAN	https://cran.r-project.org/web/packages/pROC/index.html	ROC analysis
R package rmda / dcurves	CRAN	https://cran.r-project.org/web/packages/dcurves/index.html	Decision curve analysis