Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Osteoarthritis Cartilage. 2019 Jul;27(7):994-1001. doi: 10.1016/j.joca.2018.12.027. Epub 2019 Apr 16.
Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progression phenotypes that are potentially more responsive to interventions.
We used publicly available data from the Foundation for the National Institutes of Health (FNIH) osteoarthritis (OA) Biomarkers Consortium, where radiographic (medial joint space narrowing of ≥0.7 mm), and pain progression (increase of ≥9 Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] points) were defined at 48 months, as four mutually exclusive outcome groups (none, both, pain only, radiographic only), along with an extensive set of covariates. We applied distance weighted discrimination (DWD), direction-projection-permutation (DiProPerm) testing, and clustering methods to focus on the contrast (z-scores) between those progressing by both criteria ("progressors") and those progressing by neither ("non-progressors").
Using all observations (597 individuals, 59% women, mean age 62 years and BMI 31 kg/m) and all 73 baseline variables available in the dataset, there was a clear separation among progressors and non-progressors (z = 10.1). Higher z-scores were seen for the magnetic resonance imaging (MRI)-based variables than for demographic/clinical variables or biochemical markers. Baseline variables with the greatest contribution to non-progression at 48 months included WOMAC pain, lateral meniscal extrusion, and serum N-terminal pro-peptide of collagen IIA (PIIANP), while those contributing to progression included bone marrow lesions, osteophytes, medial meniscal extrusion, and urine C-terminal crosslinked telopeptide type II collagen (CTX-II).
Using methods that provide a way to assess numerous variables of different types and scalings simultaneously in relation to an outcome of interest enabled a data-driven approach that identified key variables associated with a progression phenotype.
膝骨关节炎（KOA）是一种异质性疾病，代表了多种潜在的不同表型。本研究旨在应用创新性的机器学习方法对 KOA 表型进行分析，以确定可能对干预措施更敏感的进展表型。
我们使用了美国国立卫生研究院（FNIH）骨关节炎（OA）生物标志物联盟的公开数据，其中在 48 个月时根据放射学（内侧关节间隙狭窄≥0.7mm）和疼痛进展（WOMAC 指数增加≥9 分）定义了四个相互排斥的结局组（均无、均有、仅有疼痛、仅有放射学表现），并结合了大量的协变量。我们应用距离加权判别（DWD）、方向投影置换（DiProPerm）检验和聚类方法，重点关注符合两个标准进展的个体（进展者）和不符合两个标准进展的个体（非进展者）之间的差异（z 分数）。
使用所有观察结果（597 人，59%为女性，平均年龄 62 岁，BMI 为 31kg/m）和数据集内所有 73 个基线变量，进展者和非进展者之间存在明显的分离（z=10.1）。基于 MRI 的变量的 z 分数高于人口统计学/临床变量或生化标志物。在 48 个月时，与非进展相关的基线变量包括 WOMAC 疼痛、外侧半月板外突和血清 II 型胶原氨基端前肽（PIIANP），而与进展相关的变量包括骨髓病变、骨赘、内侧半月板外突和尿 C 端交联肽型 II 胶原（CTX-II）。
使用提供了一种同时评估与感兴趣结局相关的多种不同类型和尺度变量的方法，这种数据驱动的方法能够识别与进展表型相关的关键变量。
