BRIT J HAEMATOL

Chronic myelomonocytic leukaemia (CMML) is a rare haematological disorder characterized by monocytosis and dysplastic changes in myeloid cell lineages. Accurate risk stratification is essential for guiding treatment decisions and assessing prognosis. This study aimed to validate the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS) in CMML and to assess its performance compared with traditional scores using data from a Spanish registry (n = 1343) and a Taiwanese hospital (n = 75). In the Spanish cohort, the AIPSS-MDS accurately predicted overall survival (OS) and leukaemia-free survival (LFS), outperforming the Revised-IPSS score. Similarly, in the Taiwanese cohort, the AIPSS-MDS demonstrated accurate predictions for OS and LFS, showing superiority over the IPSS score and performing better than the CPSS and molecular CPSS scores in differentiating patient outcomes. The consistent performance of the AIPSS-MDS across both cohorts highlights its generalizability. Its adoption as a valuable tool for personalized treatment decision-making in CMML enables clinicians to identify high-risk patients who may benefit from different therapeutic interventions. Future studies should explore the integration of genetic information into the AIPSS-MDS to further refine risk stratification in CMML and improve patient outcomes.This study presents an in-depth analysis of the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS), initially developed from a comprehensive registry of MDS patients under the auspices of the Spanish Group for Myelodysplastic Syndromes (GESMD). The AIPSS-MDS encompasses eight pivotal variables spanning clinical, laboratory and cytogenetic domains, offering nuanced risk predictions at the individual patient level. Our current investigation extends the application of the AIPSS-MDS model to assess its prognostic efficacy in patients diagnosed with chronic myelomonocytic leukaemia (CMML) across diverse ethnic backgrounds, specifically in cohorts from Spain and Taiwan. The empirical findings underscore the model's robust reproducibility and superior predictive accuracy in the CMML context, thereby outperforming several conventional prognostic methodologies. This underscores the potential of AI-driven models in enhancing prognostic precision and tailoring patient-centric care strategies in haematological malignancies.image