A Nomogram Integrating Clinical and Cardiac Imaging for Predicting Short-Term Left Ventricular Ejection Fraction Decline in Patients with Duchenne Muscular Dystrophy
TL;DR
This study successfully established a comprehensive prediction model incorporating clinical and imaging variables, which can accurately identify DMD patients at risk for short-term LVEF decline, and demonstrated excellent discrimination.
Abstract
Purpose This study aimed to develop and validate a nomogram for predicting short-term LVEF decline in patients with DMD. Methods This was a single-center retrospective cohort study enrolling male patients diagnosed with DMD at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, between 2015 and 2025. Data collected included patient age, cardiac troponin I (cTnI) levels, history of steroid therapy, baseline echocardiographic LVEF, and CMR data (including LGE and native T1 values). The primary outcome was the decline in LVEF (ΔLVEF ≤ −10%) during follow-up within 12 months. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was employed to identify independent risk factors and construct a nomogram-based predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve and internally validated using bootstrap resampling (1000 repetitions). Results A total of 102 patients were included, of whom 38 (37.3%) exhibited a decline in LVEF. Multivariable analysis identified older age (OR 1.16, 95% CI 1.04–1.30, p = 0.009), abnormal cTnI (cTnI ≥ 0.04 ng/mL) (OR 7.27, 95% CI 1.46–36.28, p = 0.016), longer steroid duration (OR 1.72, 95% CI 1.09–2.71, p = 0.020, likely reflecting disease severity), the presence of LGE (OR 5.45, 95% CI 1.27–23.43, p = 0.023), and higher native T1 values (OR 1.02, 95% CI 1.01–1.04, p = 0.001) as independent risk factors. A higher baseline LVEF was protective (OR 0.81, 95% CI 0.72–0.91, p<0.001). The predictive model demonstrated excellent discrimination, with an AUC of 0.943 (95% CI 0.901–0.985). Internal validation yielded an optimism-corrected C-index of 0.922. Conclusion This study successfully established a comprehensive prediction model incorporating clinical and imaging variables, which can accurately identify DMD patients at risk for short-term LVEF decline. The model demonstrated high discriminative ability (AUC 0.943) in this retrospective, single-center cohort; however, these results are preliminary and require external validation.