A poster titled, “Early identification of patients likely to benefit from paroxysmal nocturnal hemoglobinuria workup using machine learning on large-scale real-world data,” was presented at the American Society of Hematology (ASH) 67th Annual Meeting by authors Jananee Muralidharan, MD, C. William Pike, MD, Saurabh Gombar, MD, PhD, Sandeep Jain, MD, and Jason Jones, PhD.
Short Summary:
Paroxysmal Nocturnal Haemoglobinuria (PNH) is a rare (3.81 per 100K), treatable, clonal, hematopoietic stem cell (HSC) disorder characterized by intravascular hemolysis, thrombosis, and smooth muscle dystonias, with bone marrow failure occurring in some cases. Patients undergo lengthy diagnostic journeys, frequently exceeding a year. Diagnosis is often made following a high morbidity/mortality event, such as a stroke. Earlier identification and treatment may improve disease burden. In this research, the authors sought to replicate and extend predictive modeling for earlier PNH identification, using a large, real-world dataset to pinpoint patients likely to benefit from PNH workup 3–12 months before diagnosis.