A Risk Prediction Model for Distal Aortic Negative Remodeling after TEVAR in Type B Aortic Dissection was Developed by Incorporating Novel Comprehensive Inflammatory Indices

OBJECTIVE To investigate independent predictors of distal aortic negative remodeling following thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD), and to establish a risk prediction model by integrating novel composite inflammatory markers. METHODS This retrospective study included TBAD patients who underwent TEVAR at the Affiliated Hospital of Chengde Medical University between January 2015 and June 2022. Patients were categorized into negative remodeling and non-negative remodeling groups based on changes in the true and false lumen diameters of the distal aorta during follow-up, factors influencing distal aortic negative remodeling following TEVAR were analyzed. The final cohort was randomly divided into training(70%) and validation(30%) sets for internal validation.In the training set, univariate analysis was first performed to identify potential predictors,followed by receiver operating characteristic (ROC) curve analysis for significant continuous variables, variables with statistical significance were further selected using Log-Lambda transformation and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. Multivariate logistic regression was then applied to the selected variables to construct the predictive model. Model performance was assessed through discrimination(C-index), calibration curves, and clinical decision curve analysis(DCA). Model rationality was also evaluated by comparing its predictive performance with that of individual clinical indicators. RESULTS A total of 299 patients were enrolled,with 209 assigned to the training set 90 to the internal validation set; an 66 patients comprised the external validation cohort. Independent predictors for distal aortic negative remodeling after TEVAR in type B aortic dissection included a history of simple renal cysts (OR=4.80, 95% CI: 1.32-17.52), systemic inflammation response index(SIRI) (OR=1.39, 95% CI: 1.09-1.81), prognostic nutritional index(PNI) (OR=0.88, 95% CI: 0.78-0.98), number of distal tears (OR=3.67, 95% CI: 1.85-7.69), incomplete thrombosis of the false lumen (OR=2.93, 95% CI: 1.05-8.19), and patent false lumen (OR=4.91, 95% CI: 1.60-15.32), These variables were incorporated into a nomogram model. The model demonstrated strong discriminative ability, with a C-index of 0.85 (95% CI: 0.78-0.92), and the internal validation concordance index was 0.82 (95% CI: 0.72-0.92), and 0.80 (95%CI:0.63-0.97) for external validation. The nomogram demonstrated good discriminatory ability in both internal and external validation.The calibration curves of the nomogram model demonstrated good agreement between observed and predicted outcomes in both internal and external cohorts. Clinical decision curve analysis (DCA) indicated that the nomogram model provided a favorable net clinical benefit across threshold probabilities of 2%-80% (model),5%-75%(internal validation), and 5%-75% (external validation). Rationality analysis revealed superior discrimination and clinical utility compared to conventional clinical parameters. CONCLUSIONS The proposed nomogram model, combining novel composite inflammatory markers, simple renal cysts, distal tear count, incomplete thrombosis of the false lumen, and patent false lumen, enables early identification of high-risk patients for distal aortic negative remodeling following TEVAR for type B aortic dissection, supporting timely preventive or interventional strategies.