Because of faster clinical decline and limited treatment response (Sorensen et al., 2020), many studies have aimed to predict the transition to secondary progressive multiple sclerosis MS (SPMS) (Lavorgna et al., 2014). Early identification of high-risk patients is key for personalized treatment and neuroprotective efforts. With this background, we previously applied systematic literature review and advanced techniques for feature selection, weighting, and ordinalization to develop the DAAE Score (Fuchs et al., 2024), a tool for estimating the risk of clinical disease progression over five years based on Disease duration, Age, Age of disease onset, and Expanded Disability Status Scale (EDSS) (Kurtzke, 1983). Using a point-based risk stratification system, this tool is similar to other tools developed for neurologic (Lip et al., 2010) and non-neurologic populations (Durand and Valla, 2005). Using clinically available data, the DAAE Score (0–12 points) generates consistent risk estimates in different clinical cohorts in Buffalo, New York, United States and Amsterdam, The Netherlands (Fuchs et al., 2024). It performed well, with a 38 % risk of conversion in high-risk patients and area-under-receiver-operating-curve (AUROC)=0.820. We aimed to perform an external validation of the DAAE Score with data from the MS Center of Verona.