At the 2018 American Society of Hematology (ASH) Annual Meeting in San Diego, Aziz Nazha, MD of Cleveland Clinic sat down with CheckRare to provide an overview of myelodysplastic syndromes (MDS) — a group of blood disorders characterized by abnormal development of blood cells within the bone marrow — as well as talk about the results of a personalized prediction model for Myelodysplastic Syndromes (MDS). Dr. Nazha and colleagues reported that the model outperformed current prognostic scoring systems in predicting an MDS patient’s risk of mortality and transformation to acute myeloid leukemia (AML), a more aggressive type of bone marrow cancer.

People with MDS have abnormally low blood cell levels (low blood counts). Signs and symptoms may include dizziness, fatigue, weakness, shortness of breath, bruising and bleeding, frequent infections, and headaches. In some people with MDS, the condition progresses to bone marrow failure or develops into acute leukemia.

MDS develops when a cell with a mutation replicates, and the resulting copies begin to predominate in the bone marrow and suppress healthy stem cells. The mutation may result from a genetic predisposition, or from injury to the DNA caused by an exposure such as chemotherapy or radiation. In many people with MDS there is no obvious exposure or cause.

Patients with MDS have survival outcomes that can range from months to decades. Although several prognostic scoring systems have been developed to risk stratify MDS patients, survival varies even within distinct categories, which may lead to over- or under-treatment. Researchers hypothesized that discrepancies may be due to analytic approaches or lack of incorporation of molecular data. The machine learning model outperformed International Prognostic Scoring System (IPSS) and Revised IPSS (IPSS-R) in predicting survival outcomes and risk for acute myeloid leukemia (AML) transformation among a training cohort of 1471 patients.