Saxer F, Demanse D, Brett A, Laurent D, Mindeholm L, Conaghan P G, Schieker M
Novartis Biomedical Research, Novartis Campus, 4002, Basel, Switzerland.
Medical Faculty, University of Basel, 4002, Basel, Switzerland.
Osteoarthr Cartil Open. 2024 Mar 4;6(2):100458. doi: 10.1016/j.ocarto.2024.100458. eCollection 2024 Jun.
Developing new therapies for knee osteoarthritis (KOA) requires improved prediction of disease progression. This study evaluated the prognostic value of clinical clusters and machine-learning derived quantitative 3D bone shape B-score for predicting total and partial knee replacement (KR).
This retrospective study used longitudinal data from the Osteoarthritis Initiative. A previous study used patients' clinical profiles to delineate phenotypic clusters. For these clusters, the distribution of B-scores was assessed (employing Tukey's method). The value of both cluster allocation and B-score for KR-prediction was then evaluated using multivariable Cox regression models and Kaplan-Meier curves for time-to-event analyses. The impact of using B-score vs. cluster was evaluated using a likelihood ratio test for the multivariable Cox model; global performances were assessed by concordance statistics (Harrell's C-index) and time dependent receiver operating characteristic (ROC) curves.
B-score differed significantly for the individual clinical clusters (p < 0.001). Overall, 9.4% of participants had a KR over 9 years, with a shorter time to event in clusters with high B-score at baseline. Those clusters were characterized clinically by a high rate of comorbidities and potential signs of inflammation. Both phenotype and B-score independently predicted KR, with better prediction if combined (P < 0.001). B-score added predictive value in groups with less pain and radiographic severity but limited physical activity.
B-scores correlated with phenotypes based on clinical patient profiles. B-score and phenotype independently predicted KR surgery, with higher predictive value if combined. This can be used for patient stratification in drug development and potentially risk prediction in clinical practice.
开发治疗膝关节骨关节炎（KOA）的新疗法需要改进对疾病进展的预测。本研究评估了临床聚类和机器学习得出的定量三维骨形状B评分对预测全膝关节置换和部分膝关节置换（KR）的预后价值。
这项回顾性研究使用了骨关节炎倡议组织的纵向数据。先前的一项研究利用患者的临床资料来划分表型聚类。对于这些聚类，评估了B评分的分布（采用Tukey方法）。然后使用多变量Cox回归模型和Kaplan-Meier曲线进行事件发生时间分析，评估聚类分配和B评分对KR预测的价值。使用多变量Cox模型的似然比检验评估使用B评分与聚类的影响；通过一致性统计量（Harrell氏C指数）和时间依赖性受试者工作特征（ROC）曲线评估整体性能。
各个临床聚类的B评分存在显著差异（p < 0.001）。总体而言，9.4%的参与者在9年期间进行了KR置换，基线时B评分高的聚类中事件发生时间较短。这些聚类在临床上的特征是合并症发生率高和存在潜在炎症迹象。表型和B评分均可独立预测KR置换，两者结合时预测效果更佳（P < 0.001）。B评分在疼痛较轻、影像学严重程度较低但身体活动受限的组中增加了预测价值。
B评分与基于患者临床资料的表型相关。B评分和表型均可独立预测KR手术，两者结合时预测价值更高。这可用于药物研发中的患者分层以及临床实践中的潜在风险预测。
