Abstract Background Multiple Myeloma (MM) is a complex hematological malignancy characterized by significant heterogeneity in participant characteristics and treatment responses. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. A comprehensive descriptive analysis of MM participants within Electronic Health Records (EHR) databases can provide valuable insights into the disease's epidemiology, participant demographics, comorbidities, and treatment outcomes. Objective Using AI/ML predictive analytics, this study conducted a detailed descriptive analysis of MM participants’ EHR data in the All of Us Research Program cohort, which is enrolling 1 million diverse participants who share multiple sources of data. Methods We extracted data for participants diagnosed with MM from an extensive EHR database. The analysis included participant demographics (age, gender, race, ethnicity), diagnostic laboratory tests (SPEP, UPEP, CBC, renal function), comorbidities (renal impairment, bone disease, anemia, cardiovascular diseases), treatment details (chemotherapy, stem cell transplantation), and genetic markers (cytogenetic abnormalities). Results The analysis revealed diverse demographic patterns and a wide range of comorbidities associated with MM (Table 1). Conclusions The analysis demonstrated marked disparities in MM prevalence among African Americans and “More than one race,” revealing social and medical disparities. Implications: By understanding the diverse profiles of MM participants, stakeholders can more effectively address disparities in healthcare outcomes (1). Reference: 1. Zeng, C., Schlueter, D. J., Tran, T. C., Babbar, A., Cassini, T., Bastarache, L. A., & Denny, J. C. (2024). Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank. Journal of the American Medical Informatics Association. Retrieved from, https://doi.org/10.1093/jamia/ocad260
Read full abstract