A prognostic nomogram for recurrence survival in post-surgical patients with varicose veins of the lower extremities

Varicose veins of the lower extremities (VVLEs) are prevalent globally. This study aims to identify prognostic factors and develop a prediction model for recurrence survival (RS) in VVLEs patients after surgery. A retrospective analysis of VVLEs patients from the Third Hospital of Nanchang was conducted between April 2017 and March 2022. A LASSO (Least Absolute Shrinkage and Selection Operator) regression model pinpointed significant recurrence predictors, culminating in a prognostic nomogram. The model’s performance was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The LASSO regression identified seven predictors for the nomogram predicting 1-, 2-, and 5-year RS. These predictors were age, body mass index (BMI), hypertension, diabetes, the Clinical Etiological Anatomical Pathophysiological (CEAP) grade, iliac vein compression syndrome (IVCS), and postoperative compression stocking duration (PCSD). The nomogram’s C-index was 0.716, with AUCs (Area Under the Curve scores) of 0.705, 0.725, and 0.758 for 1-, 2-, and 5-year RS, respectively. Calibration and decision curve analyses validated the model’s predictive accuracy and clinical utility. Kaplan–Meier analysis distinguished between low and high-risk groups with significant prognostic differences (P < 0.05). This study has successfully developed and validated a nomogram for predicting RS in patients with VVLEs after surgery, enhancing personalized care and informing clinical decision-making.

recent studies highlighting their efficacy and patient satisfaction scores 9,10 .Moreover, the role of genetic predisposition and molecular biology in the development and progression of VVLEs is gaining attention 11 , suggesting potential areas for therapeutic intervention and risk assessment.
Previous studies have focused on factors influencing postoperative recurrence, previously identified as age, genetics, gender, body mass index (BMI), occupational habits (prolonged standing or sitting), and disease severity (CEAP grade) [12][13][14] .Limitations of earlier studies include small, single-center samples and basic statistical analyses, restricting the accuracy and applicability of their findings.
In response, we introduce the Nomogram model, a graphical predictive tool integrating multiple prognostic factors to yield a precise predictive score.Already successful in forecasting outcomes in cancers, cardiovascular, and chronic kidney diseases, this study explores the Nomogram model's efficacy in predicting postoperative recurrence risk in VVLEs patients.

Study population
This study retrospectively analyzed patients with primary VVLEs admitted to the Third Hospital of Nanchang City between April 2017 and March 2022.Eligible participants were adults aged 18 to 75 years with Doppler ultrasound and/or venography-confirmed VVLEs, without concurrent lower limb injuries or fractures, and had at least one postoperative follow-up.Exclusions included incomplete demographic data, prior varicose vein surgery, or pregnancy/lactation.Recurrence post-surgery was defined as persistence or return of varicose veins 15,16 .Study flow is illustrated in Fig. 1.

Statistical analyses
The statistical analyses for this study were conducted using R software (version 4.1.2).The baseline characteristics between the recurrence and non-recurrence groups were compared using the Chi-square test and Fisher's exact test.LASSO regression analysis was performed using the 'glmnet' package in R. By applying the 'cox' family, the Cox proportional hazards model was implemented in the LASSO analysis to select the most predictive variables for survival.Survival analysis was conducted and a nomogram was created using the 'rms' and 'survival' packages in R. packages in R. The 'survminer' package was used to visualize the survival analysis.The performance of the nomogram was assessed by generating a receiver operating characteristic (ROC) curve with the 'timeROC' and 'pROC' packages.The predictive accuracy and discriminative ability of the nomogram were further evaluated by Harrell's concordance index (C-index) and the area under the curve (AUC) 20 .Calibration of the nomogram was performed by generating a calibration curve with the 'calibrationCurves' package to assess the agreement between the predicted and observed outcomes.We also conducted a decision curve analysis (DCA) using the 'DCA.R' or 'rmda' packages to evaluate the clinical usefulness and net benefit of the nomogram.X-tile software is used to find the best cutoff value for the total point, patients in both groups were then classified into high, medium and low risk groups.Survival was compared using Kaplan-Meier curves with log-rank tests.P-value < 0.05 (two-sided) was considered as statistically significant.

Ethics statement
Ethical approval was obtained from the hospital's ethics committee, adhering to the Helsinki Declaration.Written informed consent was obtained from all participants, in accordance with the Helsinki Declaration.

Nomogram to stratify patient's recurrence risk
Using a nomogram, we calculated the total score for each patient, which served as a basis for risk stratification.The optimal cutoff value for categorizing patients into different risk groups was established at 225, determined by the X-tile software analysis.Consequently, patients were divided into two main categories based on their total scores: those with scores of 225 or less were placed into the low risk subgroup, whereas patients with scores  www.nature.com/scientificreports/exceeding 225 were classified into the high risk subgroup.This stratification was further refined as patients were grouped into low-score and high-score categories according to the nomogram's criteria.The impact of these risk categories on patient outcomes was evaluated using Kaplan-Meier analysis, coupled with a log-rank test, which revealed significant differences in the survival outcomes between the low and high-risk groups (P < 0.05), as illustrated in Fig. 5. Notably, patients within the high-risk group exhibited a significantly poorer prognosis when compared to their counterparts in the low-risk group.This clear demarcation between the groups underscores the utility of the nomogram.

Discussion
This study enhances the existing body of knowledge by developing and validating a nomogram designed to predict the risk of recurrence following surgical procedures for VVLEs.Given the significant impact of VVLE recurrence on patient quality of life and healthcare costs, a reliable predictive tool is crucial for improving postoperative management and facilitating tailored treatment strategies.
In our analysis, we employed LASSO regression to identify key predictors of recurrence from a wide range of variables.We determined that age, BMI, hypertension, diabetes, CEAP grade, and IVCS are significant factors influencing the likelihood of recurrence, highlighting the multifaceted nature of VVLEs' recurrence.Age, an unmodifiable factor, is associated with reduced venous wall elasticity and valvular dysfunction, aligning with increased postoperative recurrence risk reported in literature 21 .An increased BMI is linked to heightened venous system pressure, possibly leading to the formation and recurrence of varicosities 22 .Hypertension and diabetes, prevalent chronic conditions worldwide, may increase the risk of VVLEs recurrence by affecting the vascular wall and microcirculation 23,24 .Evidence suggests that these systemic diseases are linked to venous thrombosis through endothelial dysfunction and heightened inflammatory states, potentially leading to valvular incompetence [25][26][27] .The CEAP grade, which gauges the severity of VVLEs, demonstrates the impact of disease  extent on recurrence risk 28 .IVCS, a common cause of lower extremity venous reflux, significantly increases the risk of postoperative recurrence by raising venous pressure and impeding venous return, potentially leading to new venous abnormalities and exacerbating symptoms of venous insufficiency 29 .Our findings also highlight the duration of PCSD as an important predictor of recurrence, in agreement with previous studies [30][31][32] .Long-term use of compression stockings can reduce the risk of varicose vein recurrence by alleviating postoperative swelling, supporting the venous system, and minimizing blood stasis, thereby maintaining valvular function 33 .However, there is no consensus on the optimal duration of PCSD, suggesting that it should be tailored based on individual patient circumstances and risk factors, a customization our nomogram model supports.
By integrating these variables into the nomogram model, we provide an intuitive tool to calculate the probability of recurrence at various time points post-surgery for VVLEs patients.The model's C-index of 0.716 indicates good predictive ability.The area under the ROC curve (AUC) demonstrates the model's predictive capacity at 1, 2, and 5 years as 0.705, 0.725, and 0.758, respectively, signifying its accuracy in distinguishing between high and low-risk patients.The calibration curve's high concordance further emphasizes the reliability of our predictions.Additionally, decision curve analysis (DCA) indicates that our nomogram has practical value in clinical decisionmaking, offering personalized treatment recommendations and preventing unnecessary or excessive treatment.
However, our study has limitations.Firstly, being retrospective, it may be subject to selection and information bias.Secondly, despite its good predictive performance, the model's small sample size of recurrence cases, only 84, necessitates validation in larger-scale, multicenter studies.Furthermore, the diversity of surgical techniques www.nature.com/scientificreports/prevented their consideration as a unified predictive factor, another limitation.Lastly, we acknowledge the need for future studies to delve into potential influencing factors, such as genetics, lifestyle, and socioeconomic aspects, to gain a more comprehensive understanding of the complex causes behind the recurrence of VVLEs post-surgery.It is also worth noting that integrating computational biology into VVLEs research could significantly enhance the understanding and management of the disease 11,34 .By delving into the roles of miRNAs and lncRNAs in gene regulation relevant to VVLEs, researchers have the potential to uncover genetic markers and new targets for treatment, paving the way for personalized medical approaches and improved treatment effectiveness [35][36][37] .Additionally, concentrating on the interactions between miRNAs and lncRNAs emerges as a promising avenue for advancing VVLEs management and patient care [38][39][40] .The employment of theoretical models based on ordinary differential equations (ODE) is particularly crucial in shedding light on the regulatory mechanisms within gene/protein signaling networks associated with VVLEs recurrence [41][42][43] .Such models offer profound insights into the complex biological systems governing variceal recurrence, highlighting the importance of these mechanisms for a deeper understanding of the disease's causes 35,44,45 .By investigating these regulatory mechanisms, researchers are poised to identify novel therapeutic targets and intervention strategies, ultimately contributing to a more robust scientific foundation for VVLEs management.This approach is not only pivotal for enhancing our comprehension of the disease but also provides a crucial direction for future research aimed at predicting and managing VVLEs, thereby underscoring the significance of incorporating theoretical models into VVLEs research.Despite these limitations, our study provides new insights into the postoperative management of VVLEs.Using our nomogram model, physicians can offer more accurate risk assessments and treatment recommendations to patients.Future research should focus on refining and validating the model and its applicability across different populations for broader clinical use.
In conclusion, the developed nomogram is a valuable tool for predicting postoperative recurrence risk in patients with VVLEs.By analyzing key predictive factors comprehensively, this model aids in guiding personalized patient management and treatment decisions.Reducing postoperative recurrence can enhance patients' quality of life and decrease the burden on healthcare systems.We hope this model will be widely applied and enhanced in diverse clinical settings.

Conclusion
This study successfully developed and validated a nomogram for predicting RS in patients with VVLEs after surgery.Integrating multiple important predictive factors, including age, BMI, hypertension, diabetes, CEAP classification, IVCS, and PCSD, this model holds significant clinical value.It enables physicians to provide more precise risk assessments and treatment recommendations for their patients.

Figure 2 .
Figure 2. Variable selection by the LASSO cox logistic regression model.(A) 1000 bootstrap replicates by lasso Cox regression analysis for variable selection.(B) LASSO coefficients of clinical characteristics.Each curve represents a clinical characteristic.

Figure 4 .
Figure 4. Model validation of ROC curve (A, B, and C), calibration plot (D, E, and F), and DCA (G, H, and I) for the 1-year, 2-year, and 5-year RS.ROC receiver operating characteristic curve, DCA decision curve analysis, RS, recurrence survival.

Figure 5 .
Figure 5.The Overall RS curves of the low-risk and high-risk groups in the entire Cohort.RS Recurrence survival.

Table 2 .
Selected Variables and Their Lasso Coefficients.Selected variables and their lasso coefficients from cox proportional hazards model.