多发性骨髓瘤是一种血液恶性肿瘤，其典型特征是骨髓中的肿瘤性浆细胞浸润。多发性骨髓瘤的治疗包括多线化疗联合或不联合自体干细胞移植，近年来发展迅速。然而，临床试验无法为患者和临床医生提供长期预后信息，也无法为政策制定者提供对新疗法进行经济评估或做出报销决定所需的全部证据。为了解决现有证据的这些局限性，本研究旨在开发和验证 EpiMAP 骨髓瘤模型，这是一种多发性骨髓瘤疾病结果和治疗途径的离散事件模拟模型。使用澳大利亚和新西兰骨髓瘤估计风险方程

Multiple myeloma is a haematological malignancy typically characterised by neoplastic plasma cell infiltration of the bone marrow. Treatment for multiple myeloma consists of multi-line chemotherapy with or without autologous stem cell transplantation and has been rapidly evolving in recent years. However, clinical trials are unable to provide patients and clinicians with long-term prognostic information nor policymakers with the full body of evidence needed to perform economic evaluation of new therapies or make reimbursement decisions. To address these limitations of the available evidence, this study aimed to develop and validate the EpiMAP Myeloma model, a discrete-event simulation model of multiple myeloma disease outcomes and treatment pathways. Risk equations were estimated using the Australian and New Zealand Myeloma & Related Diseases Registry after multiple imputation of missing data. Risk equation coefficients were combined with multiple myeloma patients at diagnosis from the Registry to perform the simulation. The model was validated with 100 bootstraps of an out-of-sample prediction analysis using a 70/30 split of the 4,121 registry patients diagnosed between 2009 and 2023, resulting in 2,884 and 1,237 patients in the training and validation cohorts, respectively. For 90% of the 120 months in the 10-year post-diagnosis period, there was no significant difference in overall survival between the validation and simulated cohorts. These results highlight that the EpiMAP Myeloma model is robust at predicting multiple myeloma disease outcomes and treatment pathways in Australia & New Zealand. In the future, clinicians will be able to use the EpiMAP Myeloma model to provide personalised estimates of life expectancy to patients based on their specific characteristics, disease stage, and response to treatment. Policymakers will also be able to use the model to perform economic evaluation, to forecast the number of patients receiving treatment at different stages, and to determine the downstream impact of listing new, effective therapies.Copyright: © 2024 Irving et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.