PREDICTING KNEE OSTEOARTHRITIS PROGRESSION USING STRUCTURAL BIOMARKERS FROM MULTIPLE JOINTS: DATA FROM THE OSTEOARTHRITIS INITIATIVE

M. Raza , T. Laffaye , R. Stein , H. Ragati-Haghi , R. Amesbury , A. Mathiessen , C.K. Kwoh , J.E. Collins , J. Duryea
{"title":"PREDICTING KNEE OSTEOARTHRITIS PROGRESSION USING STRUCTURAL BIOMARKERS FROM MULTIPLE JOINTS: DATA FROM THE OSTEOARTHRITIS INITIATIVE","authors":"M. Raza ,&nbsp;T. Laffaye ,&nbsp;R. Stein ,&nbsp;H. Ragati-Haghi ,&nbsp;R. Amesbury ,&nbsp;A. Mathiessen ,&nbsp;C.K. Kwoh ,&nbsp;J.E. Collins ,&nbsp;J. Duryea","doi":"10.1016/j.ostima.2025.100310","DOIUrl":null,"url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Clinical risk prediction models have been developed to predict knee OA progression with the goal of targeted treatment and clinical trial enrichment. It remains unclear whether, or how, OA in other joints affects knee OA progression.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate whether imaging biomarkers from non-index joints add predictive value for knee OA progression beyond those from the index knee alone.</div></div><div><h3>METHODS</h3><div>We included 648 participants from the Osteoarthritis Initiative (OAI), randomly selected with baseline KL grade of 1, 2, or 3. OAI obtained bilateral knee and hip XR and index knee MRI. Baseline imaging biomarkers included quantitative measures of index and non-index knee and hip fixed location joint space width and femorotibial angle (FTA) from XR and quantitative measures of cartilage thickness from index knee MRI. Clinical covariates were age, sex, BMI, injury history, surgery history, family history of knee replacement, and clinical hand OA (based on presence of Heberden’s nodes at the baseline clinical examination). Outcomes were knee OA progression over 48 months defined as (1) decrease in medial minimum joint space width (JSW) of ≥ 0.7mm and (2) any increase in KL grade.</div><div>We used random forests to determine the combination of predictors that maximize AUC. Random forests can model complex non-linear associations, interactions among predictors, and work well in the setting of correlated data. We examined each set of biomarkers alone and in combination: clinical covariates, index knee XR, contralateral knee XR, index hip XR, contralateral hip XR, index knee MRI. Models were tuned with 5-fold cross-validation and AUCs were computed over 1000 bootstrap samples. We used permutation-based variable importance to rank the most important variables for prediction.</div></div><div><h3>RESULTS</h3><div>The 648 OAI participants were 23% KLG 1, 48% KLG 2, and 28% KLG 3. Average age was 61 (SD 9) and average BMI 29 (SD 5). 152 (23%) had a decrease in JSW ≥0.7mm and 119 (18%) had an increase in KL grade.</div><div>In considering sets of covariates on their own, models with index knee MRI had the highest AUC for both outcomes (model 8), followed by models with index knee XR (model 3, Table). Adding contralateral hip XR to models with index knee XR improved AUC. For example, in predicting JSW≥0.7mm, the AUC increased from 0.627 (model 9) to 0.648 (model 10). Adding hip XR biomarkers did not seem to improve model discrimination (model 10 to model 11). AUCs from models from hip XR biomarkers alone were modest, though higher than for models with only clinical covariates.</div><div>Variable importance for the 10 most important biomarkers for the model with all XR biomarkers (model 12) is shown in the Figure for JSW ≥0.7mm (panel A) and KLG increase (panel B). Baseline medial minimum JSW was the most important predictor for both models. Various measures of fixed location JSW in the contralateral knee were among the top 10 most important predictors for both outcomes.</div></div><div><h3>CONCLUSION</h3><div>Multi-joint structural biomarkers improve predictive performance for knee OA progression, beyond index-knee imaging alone. These findings support broader imaging strategies to enhance RCT enrichment and guide targeted interventions in knee OA.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100310"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Osteoarthritis imaging","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772654125000509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

INTRODUCTION

Clinical risk prediction models have been developed to predict knee OA progression with the goal of targeted treatment and clinical trial enrichment. It remains unclear whether, or how, OA in other joints affects knee OA progression.

OBJECTIVE

To evaluate whether imaging biomarkers from non-index joints add predictive value for knee OA progression beyond those from the index knee alone.

METHODS

We included 648 participants from the Osteoarthritis Initiative (OAI), randomly selected with baseline KL grade of 1, 2, or 3. OAI obtained bilateral knee and hip XR and index knee MRI. Baseline imaging biomarkers included quantitative measures of index and non-index knee and hip fixed location joint space width and femorotibial angle (FTA) from XR and quantitative measures of cartilage thickness from index knee MRI. Clinical covariates were age, sex, BMI, injury history, surgery history, family history of knee replacement, and clinical hand OA (based on presence of Heberden’s nodes at the baseline clinical examination). Outcomes were knee OA progression over 48 months defined as (1) decrease in medial minimum joint space width (JSW) of ≥ 0.7mm and (2) any increase in KL grade.
We used random forests to determine the combination of predictors that maximize AUC. Random forests can model complex non-linear associations, interactions among predictors, and work well in the setting of correlated data. We examined each set of biomarkers alone and in combination: clinical covariates, index knee XR, contralateral knee XR, index hip XR, contralateral hip XR, index knee MRI. Models were tuned with 5-fold cross-validation and AUCs were computed over 1000 bootstrap samples. We used permutation-based variable importance to rank the most important variables for prediction.

RESULTS

The 648 OAI participants were 23% KLG 1, 48% KLG 2, and 28% KLG 3. Average age was 61 (SD 9) and average BMI 29 (SD 5). 152 (23%) had a decrease in JSW ≥0.7mm and 119 (18%) had an increase in KL grade.
In considering sets of covariates on their own, models with index knee MRI had the highest AUC for both outcomes (model 8), followed by models with index knee XR (model 3, Table). Adding contralateral hip XR to models with index knee XR improved AUC. For example, in predicting JSW≥0.7mm, the AUC increased from 0.627 (model 9) to 0.648 (model 10). Adding hip XR biomarkers did not seem to improve model discrimination (model 10 to model 11). AUCs from models from hip XR biomarkers alone were modest, though higher than for models with only clinical covariates.
Variable importance for the 10 most important biomarkers for the model with all XR biomarkers (model 12) is shown in the Figure for JSW ≥0.7mm (panel A) and KLG increase (panel B). Baseline medial minimum JSW was the most important predictor for both models. Various measures of fixed location JSW in the contralateral knee were among the top 10 most important predictors for both outcomes.

CONCLUSION

Multi-joint structural biomarkers improve predictive performance for knee OA progression, beyond index-knee imaging alone. These findings support broader imaging strategies to enhance RCT enrichment and guide targeted interventions in knee OA.
使用来自多个关节的结构生物标志物预测膝关节骨关节炎进展:来自骨关节炎倡议的数据
已经建立了临床风险预测模型来预测膝关节OA的进展,目的是有针对性地治疗和丰富临床试验。目前尚不清楚其他关节的OA是否或如何影响膝关节OA的进展。目的:评估非指数关节的成像生物标志物是否比单指数膝关节的生物标志物更能预测膝关节OA的进展。方法:我们纳入了来自骨关节炎倡议(OAI)的648名参与者,随机选择基线KL等级为1、2或3。OAI获得双侧膝关节和髋关节x光片和膝关节MRI。基线成像生物标志物包括定量测量指数和非指数膝关节和髋关节固定位置关节间隙宽度和股胫角(FTA),定量测量指数膝关节MRI软骨厚度。临床协变量为年龄、性别、BMI、损伤史、手术史、膝关节置换术家族史和临床手部OA(基于基线临床检查中Heberden淋巴结的存在)。结果是膝关节OA进展超过48个月,定义为(1)内侧最小关节间隙宽度(JSW)减少≥0.7mm, (2) KL等级增加。我们使用随机森林来确定最大化AUC的预测因子组合。随机森林可以模拟复杂的非线性关联,预测因子之间的相互作用,并且在相关数据的设置中工作得很好。我们单独或联合检查了每组生物标志物:临床协变量、膝关节指数XR、对侧膝关节XR、髋关节指数XR、对侧髋关节XR、膝关节指数MRI。通过5倍交叉验证调整模型,并在1000个bootstrap样本上计算auc。我们使用基于排列的变量重要性对最重要的预测变量进行排序。结果648名OAI参与者中,klg1占23%,klg2占48%,klg3占28%。平均年龄61岁(SD 9),平均BMI 29 (SD 5), JSW≥0.7mm降低152例(23%),KL分级升高119例(18%)。在单独考虑协变量集时,具有膝关节指数MRI的模型两种结果的AUC最高(模型8),其次是具有膝关节指数XR的模型(模型3,表)。对侧髋关节x光增强可改善对侧膝关节x光增强模型的AUC。例如,在预测JSW≥0.7mm时,AUC从0.627(模型9)增加到0.648(模型10)。添加髋关节XR生物标志物似乎并没有提高模型识别(模型10到模型11)。仅髋部XR生物标志物模型的auc较低,但高于仅临床协变量模型。对于所有XR生物标志物(模型12)的模型,10个最重要的生物标志物的变量重要性如图所示,JSW≥0.7mm(图A)和KLG增加(图B)。基线最小JSW是两种模型最重要的预测因子。对侧膝关节固定位置JSW的各种测量是两种结果的十大最重要预测因素之一。结论:多关节结构生物标志物提高了膝关节OA进展的预测性能,而不仅仅是单纯的膝关节指数成像。这些发现支持更广泛的成像策略,以增强RCT的丰富性,并指导膝关节OA的靶向干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Osteoarthritis imaging
Osteoarthritis imaging Radiology and Imaging
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信