Pre-transplant Predictive Factors in Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation Using Defective Cure Models

Abstract

Background: Multiple myeloma (MM) is a hematologic malignancy that leads to kidney failure, anemia, infection, and severe bone pain due to the presence of bone lesions. Combining antagonists with immunomodulating drugs has resulted in higher survival rates for patients. As a result, many patients receiving appropriate treatment can now achieve long-term survival or even be considered cured. In such cases, it is essential to use cure models to achieve accurate and reliable results with minimal bias. Objectives: The study is focused on identifying the factors that predict the response to autologous hematopoietic stem cell transplantation (ASCT) and estimating the cure fraction of MM patients from ASCT to death using cure models. Methods: This cohort study involved 77 patients diagnosed with MM, who received ASCT and were followed for 12 years. Patients’ overall survival and cure fraction were analyzed, using defective cure models. The patients’ age and clinical conditions, including Thrombocytopenia, leukopenia, anemia, and blood creatinine levels, were considered predictive factors extracted from the pre-transplantation blood tests. Results: The 5-year survival rate of patients was 67.9% and long-term survival was 59.5% in this study. The Inverse Gaussian model estimated the cure fraction at 54.4%, while the Kumaraswamy Inverse Gaussian model estimated it at 24%. The Inverse Gaussian model indicated that the age of the patients and the pre-transplant platelet count were significant factors (P < 0.05). Patients with less than average platelets had a cure fraction of 36%, indicating a lower chance of survival than patients with normal platelets, who had a cure fraction of 54%. Conclusions: The Kaplan-Meier curve has a horizontal portion that estimates the number of survived patients. After approximately 6 years and 5 months, the Kaplan-Meier curve flattened, and the estimated cure fraction was 58.5%. The Inverse Gaussian model demonstrates superior accuracy in estimating the cure fraction and identifying predictive factors that affect pre-transplantation survival rates. In this model, the cure fraction was estimated at 54.4%. So, this model warrants more attention. The study suggests low platelet count (thrombocytopenia) reduces patients’ long-term survival. Among patients with Thrombocytopenia, younger patients have a higher long-term survival rate than older patients. As a result, it is recommended to prioritize the care of patients over 60 with Thrombocytopenia to improve their survival rate and reduce mortality.