Constructing and Validating a Nomogram Model for Short-Term Prognosis of Patients with AChR-Ab+ GMG.

Abstract

This study aimed to establish and validate a nomogram prognostic model for predicting short-term efficacy of acetylcholine receptor antibody-positive (AChR-Ab+) generalized myasthenia gravis (GMG). A retrospective observational study was conducted at the First Hospital of Shanxi Medical University, enrolling patients diagnosed with AChR-Ab+ GMG from May 2020 to September 2022. The primary outcome was the change in the Myasthenia Gravis Foundation of America (MGFA) post-intervention status after 6months of standard treatment. Predictive factors were identified through univariate and multivariate logistic regression analyses, with significant factors incorporated into the nomogram. The bootstrap test was used for internal validation of the nomogram model. Model performance was assessed using calibration curves, receiver-operating characteristic curve analysis, and decision curve analysis (DCA). A total of 90 patients were enrolled, of whom 30 achieved unchanged or worse status after 6months of standard therapy. Univariate logistic regression analysis showed that quantitative myasthenia gravis score, gender, body mass index, course of disease, hemoglobin levels, and white blood cell counts were six potential predictors. These factors were used for multivariate logistic regression analysis, and a nomogram was constructed. The calibration curve showed that the predicted value was in good agreement with the actual value (p = 0.707), and the area under the curve value (0.792, 95% CI 0.686-0.899) indicated good discrimination ability. DCA suggests that this model has potential clinical application value. The constructed nomogram, based on key patient indicators, shows promise as a clinically useful tool for predicting the short-term efficacy of treatment of AChR-Ab+ GMG. Validation in larger, multicenter cohorts is needed to further substantiate its applicability.