Abstract
PURPOSE: To derive an effective nomogram for predicting Marfan syndrome (MFS) in children with congenital ectopia lentis (CEL) using regularly collected data. METHODS: Diagnostic standards (Ghent nosology) and genetic test were applied in all patients with CEL to determine the presence or absence of MFS. Three potential MFS predictors were tested and chosen to build a prediction model using logistic regression. The predictive performance of the nomogram was validated internally through time-dependent receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS: Eyes from 103 patients under 20 years old and with CEL were enrolled in this study. Z score of body mass index (odds ratio [OR] = 0.659; 95% confidence interval [CI], 0.453-0.958), corneal curvature radius (OR = 3.397; 95% CI, 1.829-6.307), and aortic root diameter (OR = 2.342; 95% CI, 1.403-3.911) were identified as predictors of MFS. The combination of the above predictors shows good predictive ability, as indicated by area under the curve of 0.889 (95% CI, 0.826-0.953). The calibration curves showed good agreement between the prediction of the nomogram and the actual observations. In addition, decision curve analysis showed that the nomogram was clinically useful and had better discriminatory power in identifying patients with MFS. For better individual prediction, an online MFS calculator was created. CONCLUSIONS: The nomogram provides accurate and individualized prediction of MFS in children with CEL who cannot be identified with the Ghent criteria, enabling clinicians to personalize treatment plans and improve MFS outcomes. TRANSLATIONAL RELEVANCE: The prediction model may help clinicians identify MFS in its early stages, which could reduce the likelihood of developing severe symptoms and improve MFS outcomes.