Monoclonal gammopathy of undetermined significance (MGUS) is a benign hematological condition with the potential to progress to malignant conditions including multiple myeloma and Waldenstrom macroglobulinemia. Medications that modify progression risk have yet to be identified. To investigate, we leveraged machine-learning and electronic health record (EHR) data to screen for drug repurposing candidates. We extracted clinical and laboratory data from a manually curated MGUS database, containing 16,752 MGUS patients diagnosed from January 1, 2000 through December 31, 2021, prospectively maintained at Mayo Clinic. We merged this with comorbidity and medication data from the EHR. Medications were mapped to 21 drug classes of interest. The XGBoost module was then used to train a primary Cox survival model; sensitivity analyses were also performed limiting the study group to those with non-IgM MGUS and those with M-spikes >0.3 g/dl. The impact of explanatory features was quantified as hazard ratios after generating distributions using bootstrapping. Medication data were available for 12,253 patients; those without medications data were excluded. Our model achieved a good fit of the data with inverse probability of censoring weights concordance index of 0.883. The presence of multivitamins, immunosuppression, non-coronary NSAIDS, proton pump inhibitors, vitamin D supplementation, opioids, statins and beta-blockers were associated with significantly lower hazard ratio for MGUS progression in our primary model; multivitamins and non-coronary NSAIDs remained significant across both sensitivity analyses. This work could inform subsequent prospective studies, or similar studies in other disease states.
未确定意义的单克隆丙种球蛋白病（MGUS）是一种具有进展为多发性骨髓瘤和瓦氏巨球蛋白血症等恶性疾病潜能的良性血液病症。目前尚未发现能够改变其进展风险的药物。为此，我们利用机器学习与电子健康记录数据进行药物重定位筛选研究。我们从梅奥诊所前瞻性维护的MGUS人工标注数据库中提取了2000年1月1日至2021年12月31日期间确诊的16,752例患者的临床及实验室数据，并与电子健康记录中的合并症及用药数据整合。药物被映射至21个目标药物类别，随后采用XGBoost模块训练主Cox生存模型；同时将研究人群限定为非IgM型MGUS患者及M蛋白>0.3g/dl亚组进行敏感性分析。通过自助法生成分布后，将解释性特征的影响量化为风险比。最终纳入12,253例具有完整用药数据的患者。模型拟合良好，经逆概率删失加权校正的一致性指数达0.883。主模型显示多种维生素、免疫抑制剂、非冠脉非甾体抗炎药、质子泵抑制剂、维生素D补充剂、阿片类药物、他汀类药物及β受体阻滞剂与显著降低的MGUS进展风险比相关；其中多种维生素与非冠脉非甾体抗炎药在两项敏感性分析中均保持显著相关性。本研究可为后续前瞻性研究及其他疾病状态的类似研究提供参考。
肿瘤(癌症)患者之家