Construction of a Prediction Model for Post-thrombotic Syndrome after Deep Vein Thrombosis Incorporating Novel Inflammatory Response Parameter Scoring.

OBJECTIVE

To investigate the independent predictive factors for post-thrombotic syndrome (PTS) and to construct a risk prediction model for PTS by incorporating a novel inflammatory response parameter (NPM score) scoring.

METHODS

A retrospective study analyzed patients diagnosed with lower extremity deep vein thrombosis (LEDVTs at the Affiliated Hospital of Chengde Medical College from January 2018 to January 2022. The Villalta scale was used to assess the occurrence of PTS 6-24 months after discharge. Patients were randomly divided into a training set and a validation set at a ratio of 7:3. In the training set, univariate analysis was performed on meaningful continuous variables, and those with differences were converted into dichotomous variables based on optimal cutoff values. Variable selection was performed using Log Lambda and Least Absolute Shrinkage and Selection Operator 10-fold cross-validation, followed by multivariable logistic regression analysis on selected variables for model construction. The model underwent internal validation in the validation set and external validation in an independent external cohort, including discriminative analysis, calibration analysis, and clinical decision curve analysis (DCA), with the model's rationale being evaluated lastly.

RESULTS

A total of 356 patients with lower extremity DVT were included, with 249 in the training set for model construction and 107 in the validation set for internal validation, along with 37 external patients for external validation. A composite score of inflammatory response parameters, including the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to high-density lipoprotein cholesterol ratio (MHR) (NLR-PLR-MHR score, NPM score), was developed, showing a significantly higher NPM score in the PTS group compared to the non-PTS group (P < 0.05). Predictive factors related to the risk of PTS occurrence included staging (OR = 6.83, 95% CI: 2.74-18.04), varicose veins (OR = 7.30, 95% CI: 2.29-25.75), homocysteine (Hcy) (OR = 1.12, 95% CI: 1.04-1.22), NPM score (OR = 3.13, 95% CI: 1.94-5.36), standardized anticoagulant therapy (OR = 5.77, 95% CI: 1.25-27.62), and one-stop treatment (OR = 0.04, 95% CI: 0.00-0.35) were incorporated into the Nomogram model. The model showed good discrimination with a concordance index of 0.918 (95% CI: 0.876-0.959) for model construction, 0.843 (95% CI: 0.741-0.945) for internal validation, and 0.823 (95% CI: 0.667-0.903) for external validation. In the Nomogram model, internal and external validation calibration curves showed good agreement between observed and predicted values. DCA indicated that the Nomogram model predicted PTS risk probability thresholds ranging from 3% to 98% for model construction, 5%-97% for internal validation, and 10%-80% for external validation, demonstrating better net benefit for predicting PTS risk in the model, internal, and external validation. Rationality analysis showed the model and internal validation had higher discrimination and clinical net benefit than other clinical indices.

CONCLUSIONS

The NPM score combined with stage, varicose veins, Hcy, standardized anticoagulant therapy, and one-stop treatment in the Nomogram model provides a practical tool for health care professionals to assess the risk of PTS in DVT patients, enabling early identification of high-risk patients for effective PTS prevention.