Abstract
The acute-remitting course (the time interval from remission to the next relapse) is a distinct signature of neuromyelitis optica spectrum disorder (NMOSD), which harms patients and perplexes physicians. However, clinically actionable biomarkers to anticipate NMOSD relapse timing are unavailable. Recently, it is found that Cuproptosis-related genes would have an impact on the acute-remitting course in patients with aquaporin-4 (AQP4)-immunoglobulin G antibody positive NMOSD. A machine learning-based algorithm is developed to construct a consensus Cuproptosis-related prognosis risk (CRPR) model, followed by training a proportional hazards model to divide patients with NMOSD into high/low relapse risk groups. The CRPR is related to the clinical symptoms of patients with NMOSD. Finally, a nomogram is created using CRPR to predict the possibility of relapse in new cases of NMOSD. This prediction model can assist physicians and help develop appropriate interventions to prevent disease relapse.