具有多发性骨髓瘤前体的患者根据单克隆蛋白浓度或骨髓浆细胞百分比，被划分为具有未明确意义的单克隆免疫球蛋白病或慢性多发性骨髓瘤。当前的风险分层在诊断时使用实验室测量值，但不包括时变的生物标志物。我们的目标是开发一种单克隆免疫球蛋白病和慢性多发性骨髓瘤分层算法，利用易于获取的时变生物标志物来建立从前体疾病到多发性骨髓瘤的风险模型。 在这项回顾性、多队列研究中，我们包括了年龄在18岁或以上的具有未明确意义的单克隆免疫球蛋白病或慢性多发性骨髓瘤患者。我们评估了几种建立疾病进展到多发性骨髓瘤的模型方法，利用了培训队列（包括来自美国波士顿戴纳-法伯癌症研究所的患者，注释时间为2019年11月13日至2022年4月13日）数据。我们创建了PANGEA模型，该模型利用生物标志物（单克隆蛋白浓度、游离轻链比值、年龄、肌酐浓度和骨髓浆细胞百分比）和来自病历的血红蛋白轨迹，预测从前体疾病到多发性骨髓瘤的进展。该模型在两个独立的验证队列中进行验证，分别来自雅典国立大学（希腊雅典，2020年1月26日至2022年2月7日，验证队列1）、伦敦大学学院（英国伦敦，2020年6月9日至2022年4月10日，验证队列1）和单克隆免疫球蛋白病登记处（捷克共和国，2004年1月5日至2022年3月10日，验证队列2）。我们比较了PANGEA模型（包括骨髓[BM]数据和不包括骨髓[no BM]数据）和当前标准（国际多发性骨髓瘤工作组[IMWG]未明确意义的单克隆免疫球蛋白病和20/2/20慢性多发性骨髓瘤风险标准）。 我们包括了6441名患者，其中4931人（77%）具有未明确意义的单克隆免疫球蛋白病，1510人（23%）具有慢性多发性骨髓瘤。6441名参与者中，有3430名（53%）为女性。PANGEA模型（BM）提高了预测慢性多发性骨髓瘤进展到多发性骨髓瘤的能力，与20/2/20模型相比，C统计量从第一次就诊到诊所的0.533（0.480-0.709）增加到0.756（0.629-0.785）、第二次就诊时的0.613（0.504-0.704）增加到0.720（0.592-0.775），以及第三次就诊时的0.637（0.386-0.841）增加到0.756（0.547-0.830），在验证队列1中展示出来。PANGEA模型（no BM）提高了预测慢性多发性骨髓瘤进展到多发性骨髓瘤的能力，与20/2/20模型相比，C统计量从第一次就诊时的0.534（0.501-0.672）增加到0.692（0.614-0.736）、第二次就诊时的0.573（0.518-0.647）增加到0.693（0.605-0.734），以及第三次就诊时的0.560（0.497-0.645）增加到0.692（0.570-0.708），在验证队列1中展示出来。PANGEA模型提高了未明确意义的单克隆免疫球蛋白病进展到多发性骨髓瘤的预测能力，在验证队列2的第一次就诊时与IMWG滚动模型相比，C统计量从0.640（0.518-0.718）增加到PANGEA模型（BM）的0.729（0.643-0.941），增加到PANGEA模型（no BM）的0.879（0.586-0.938）。在临床实践中使用PANGEA模型将允许具有前体疾病的患者获得更准确的多发性骨髓瘤进展风险评估，从而促进更合适的治疗策略。 SU2C Dream Team和Cancer Research UK版权所有。 ©2023该作者。由Elsevier Ltd. 发布，根据CC BY 4.0许可证进行开放访问。 Elsevier Ltd. 版权所有。

Patients with precursors to multiple myeloma are dichotomised as having monoclonal gammopathy of undetermined significance or smouldering multiple myeloma on the basis of monoclonal protein concentrations or bone marrow plasma cell percentage. Current risk stratifications use laboratory measurements at diagnosis and do not incorporate time-varying biomarkers. Our goal was to develop a monoclonal gammopathy of undetermined significance and smouldering multiple myeloma stratification algorithm that utilised accessible, time-varying biomarkers to model risk of progression to multiple myeloma.In this retrospective, multicohort study, we included patients who were 18 years or older with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma. We evaluated several modelling approaches for predicting disease progression to multiple myeloma using a training cohort (with patients at Dana-Farber Cancer Institute, Boston, MA, USA; annotated from Nov, 13, 2019, to April, 13, 2022). We created the PANGEA models, which used data on biomarkers (monoclonal protein concentration, free light chain ratio, age, creatinine concentration, and bone marrow plasma cell percentage) and haemoglobin trajectories from medical records to predict progression from precursor disease to multiple myeloma. The models were validated in two independent validation cohorts from National and Kapodistrian University of Athens (Athens, Greece; from Jan 26, 2020, to Feb 7, 2022; validation cohort 1), University College London (London, UK; from June 9, 2020, to April 10, 2022; validation cohort 1), and Registry of Monoclonal Gammopathies (Czech Republic, Czech Republic; Jan 5, 2004, to March 10, 2022; validation cohort 2). We compared the PANGEA models (with bone marrow [BM] data and without bone marrow [no BM] data) to current criteria (International Myeloma Working Group [IMWG] monoclonal gammopathy of undetermined significance and 20/2/20 smouldering multiple myeloma risk criteria).We included 6441 patients, 4931 (77%) with monoclonal gammopathy of undetermined significance and 1510 (23%) with smouldering multiple myeloma. 3430 (53%) of 6441 participants were female. The PANGEA model (BM) improved prediction of progression from smouldering multiple myeloma to multiple myeloma compared with the 20/2/20 model, with a C-statistic increase from 0·533 (0·480-0·709) to 0·756 (0·629-0·785) at patient visit 1 to the clinic, 0·613 (0·504-0·704) to 0·720 (0·592-0·775) at visit 2, and 0·637 (0·386-0·841) to 0·756 (0·547-0·830) at visit three in validation cohort 1. The PANGEA model (no BM) improved prediction of smouldering multiple myeloma progression to multiple myeloma compared with the 20/2/20 model with a C-statistic increase from 0·534 (0·501-0·672) to 0·692 (0·614-0·736) at visit 1, 0·573 (0·518-0·647) to 0·693 (0·605-0·734) at visit 2, and 0·560 (0·497-0·645) to 0·692 (0·570-0·708) at visit 3 in validation cohort 1. The PANGEA models improved prediction of monoclonal gammopathy of undetermined significance progression to multiple myeloma compared with the IMWG rolling model at visit 1 in validation cohort 2, with C-statistics increases from 0·640 (0·518-0·718) to 0·729 (0·643-0·941) for the PANGEA model (BM) and 0·670 (0·523-0·729) to 0·879 (0·586-0·938) for the PANGEA model (no BM).Use of the PANGEA models in clinical practice will allow patients with precursor disease to receive more accurate measures of their risk of progression to multiple myeloma, thus prompting for more appropriate treatment strategies.SU2C Dream Team and Cancer Research UK.Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.