The TriNetX database could improve predictive modeling in rare diseases such as myelofibrosis, according to data published in Cancers. Nikolas Von Bubnoff, MD, and colleagues used the TriNetX database—with information on more than 64,000 patients with myelofibrosis—to analyze how common parameters affect survival and complications, including acute myeloid leukemia (AML) transformation, cachexia, systemic inflammation, thrombosis, and hemorrhage. Patients aged older than 65 had higher risk for death, AML transformation, thrombosis, and hemorrhage. Additionally, anemia, leukocytosis, and thrombocytopenia were associated with reduced survival and increased risks for all assessed events. Monocytosis correlated with decreased survival, while eosinophilia and basophilia suggested improved survival. A simplified International Prognostic Scoring System (IPSS), validated through TriNetX, demonstrated predictive accuracy for clinical outcomes. The study highlighted the impact of risk factors on survival and complications, as well as how novel prognostic factors could be identified, making TriNetX a helpful tool in determining risk factors and establishing as well as validating clinical scores for rare diseases