In addition to randomized clinical trials, consideration of Real-World Evidence is necessary for mirroring clinical reality. However, processing such evidence for large numbers of patients often requires considerable time and effort. This is particularly true for rare tumor diseases such as multiple myeloma (MM) or for adverse effects that occur even more rarely. In such cases, artificial intelligence is able to efficiently detect patients with rare conditions. One of these rare adverse events, and the most discussed, following bone protective treatment in MM is medication-related osteonecrosis of the jaw (MRONJ). The association of bone protective treatment to MM outcome has been intensively studied. However, the impact of MRONJ resulting from such treatment on MM prognosis and outcome is poorly understood. In this retrospective study, we therefore investigated the long-term effects of MRONJ. We used natural language processing (NLP) to screen individual data of 2389 MM patients to find 50 out of 52 patients with MRONJ matching our inclusion criteria. To further improve data quality, we then performed propensity score matching. In comparison to MM patients without MRONJ, we found a significantly longer overall survival (median 126 vs. 86 months) despite slightly worse clinical features.
Keywords: Artificial intelligence; Multiple Myeloma; Natural language processing; Osteonecrosis of the jaw; Propensity score matching; Rare adverse events; Rare diseases; Real world evidence.
© 2023. The Author(s).