AIPSS-MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib

Abstract

Background: In myelofibrosis (MF), new model scores are continuously proposed to improve the ability to better identify patients with the worst outcomes. In this context, the Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS-MF), and the Response to Ruxolitinib after 6 months (RR6) during the ruxolitinib (RUX) treatment, could play a pivotal role in stratifying these patients. Aims: We aimed to validate AIPSS-MF in patients with MF who started RUX treatment, compared to the standard prognostic scores at the diagnosis and the RR6 scores after 6 months of treatment. Methods and results: At diagnosis, the AIPSS-MF performs better than the widely used IPSS for primary myelofibrosis (C-index 0.636 vs. 0.596) and MYSEC-PM for secondary (C-index 0.616 vs. 0.593). During RUX treatment, we confirmed the leading role of RR6 in predicting an inadequate response by these patients to JAKi therapy compared to AIPSS-MF (0.682 vs. 0.571). Conclusion: The new AIPSS-MF prognostic score confirms that it can adequately stratify this subgroup of patients already at diagnosis better than standard models, laying the foundations for new prognostic models developed tailored to the patient based on artificial intelligence.