A Nomogram Integrating Inflammatory Biomarkers and Echocardiographic Parameters for Predicting Left Ventricular Outflow Tract Obstruction Risk in Hypertrophic Cardiomyopathy

Objective: Although echocardiography is the gold standard for diagnosing left ventricularoutflow tract obstruction (LVOTO) in hypertrophic cardiomyopathy (HCM), relying solely on imaging is insufficient for precise risk stratification, particularly in borderline or atypical patients, and fails to capture systemic pathophysiological alterations. In recent years, novel inflammatory biomarkers derived from routine blood tests have shown sensitivity in capturing micro-inflammatory states; however, their specific roles in the obstructive phenotype of HCM remain unclear. This study aims to screen hematological and cardiac parameters associated with HCM obstruction and to construct an individualized predictive model. Therefore, this study aims to screen hematological and cardiac parameters associated with HCM obstruction and to develop and validate a nomogram for individualized prediction of current LVOTO risk in HCM patients. Methods: A total of 230 HCM patients hospitalized at The Central Hospital of Wuhan from January 2019 to December 2025 were retrospectively enrolled. Based on the left ventricular outflow tract pressure gradient (LVOTPG), they were divided into a non-obstruction group (n = 177) and an obstruction group (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was used to screen feature variables, and multivariate logistic regression analysis was employed to identify independent predictors and construct a nomogram prediction model. The discrimination, calibration, and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Multivariate logistic regression analysis revealed that a history of alcohol consumption (OR = 3.68, 95% CI: 1.49–9.09), peak LVOTPG (OR = 3.96, 95% CI: 1.42–11.05), maximum ventricular wall thickness (OR = 1.02, 95% CI: 1.00–1.04), peak mitral valve E-wave velocity (OR = 1.02, 95% CI: 1.00–1.04), and neutrophil count (OR = 2.42, 95% CI: 1.13–5.21) were independent predictors of LVOTO in HCM patients. The nomogram model constructed based on these factors achieved area under the curve (AUC) values of 0.810 and 0.850 in the training and validation sets, respectively. The calibration curve demonstrated good agreement between predicted and actual probabilities, and DCA indicated that the model provided clinical net benefit within a threshold probability range of 10% to 30%.Conclusions: A nomogram integrating alcohol consumption history, peak LVOTPG, maximum ventricular wall thickness, peak mitral E-wave velocity, and neutrophil count accurately predicts LVOTO risk in HCM patients (AUC: 0.810–0.850), providing a low-cost tool for early risk stratification. External prospective validation is warranted.