Refining Risk Stratification for Myelofibrosis Using Real-World Data
Despite advancements in understanding the molecular basis of myelofibrosis (MF), a rare and complex bone marrow disorder, predicting disease outcomes and tailoring treatments remains challenging. A study published in Cancers used real-world data to identify risk factors and establish clinical prognostic scores for MF in order to enhance personalized treatment strategies.
Current risk stratification tools for MF, such as the International Prognostic Scoring System (IPSS) and its iterations, aid treatment decisions but are limited by their dependence on small patient cohorts and the rarity of MF. This study analyzed data from the TriNetX database, a global repository of electronic medical records (EMRs) covering over 64 000 patients with MF.
The study identified key prognostic factors impacting survival and complications. Advanced age (> 65 years), anema (hemoglobin < 10 g/dL), leukocytosis (white blood cells > 25 x 10³/µL), and thrombocytopenia (platelets < 150 x 10³/µL) were strongly associated with poor survival and heightened risks of acute myeloid leukemia transformation, thrombosis, hemorrhage, and systemic inflammation. Conversely, eosinophilia and basophilia were linked to improved survival, suggesting potential prognostic subtypes within MF. Monocytosis, associated with poor outcomes in other hematological malignancies, was also identified as a negative prognostic factor.
The study further validated a simplified IPSS based on age, hemoglobin, and leukocyte counts, demonstrating its effectiveness in stratifying patients by risk and predicting survival outcomes. This simplified model offers a practical tool for risk assessment, especially in settings with limited access to comprehensive diagnostic parameters.
Using aggregated EMR data, this research highlights the potential of large-scale databases to refine prognostic models for rare diseases like MF. These insights contribute to the development of more nuanced and dynamic risk stratification models, enabling personalized treatment strategies for patients with MF. The study had limitations, including incomplete patient histories and the inability to fully differentiate between primary MF and secondary MF. These findings highlight the need for future research to focus on integrating genomic and mutation data to enhance prognostic accuracy.
“The present study confirmed the impact of established risk factors like high age, anemia, thrombocytopenia and leukocytosis on survival and complications, and novel prognostic factors like monocytosis, eosinophilia and basophilia could be identified,” the study authors concluded. “Thus, the present study confirmed the utility of TriNetX EMRs to determine risk factors and to establish and to validate clinical scores for rare diseases like myelofibrosis.”
Reference
Kappenstein M, von Bubnoff N. Real-world electronic medical records data identify risk factors for myelofibrosis and can be used to validate established prognostic scores. Cancers. 2024;16(7):1416. doi:10.3390/cancers16071416.
