Prediction models for the risk of total knee replacement: development and validation using data from multicentre cohort studies

医学 骨关节炎 比例危险模型 物理疗法 射线照相术 队列 危险系数 预测建模 队列研究 预测值 内科学 外科 置信区间 机器学习 计算机科学 病理 替代医学
作者
Qiang Liu,Hongling Chu,Michael P. LaValley,David J. Hunter,Hua Zhang,Liyuan Tao,Siyan Zhan,Lin JianHao,Yuqing Zhang
出处
期刊:The Lancet Rheumatology [Elsevier BV]
卷期号:4 (2): e125-e134 被引量:14
标识
DOI:10.1016/s2665-9913(21)00324-6
摘要

Few prognostic prediction models for total knee replacement are available, and the role of radiographic findings in predicting its use remains unclear. We aimed to develop and validate predictive models for total knee replacement and to assess whether adding radiographic findings improves predictive performance.We identified participants with recent knee pain (in the past 3 months) in the Multicenter Osteoarthritis Study (MOST) and the Osteoarthritis Initiative (OAI). The baseline visits of MOST were initiated in 2003 and of OAI were initiated in 2004. We developed two predictive models for the risk of total knee replacement within 60 months of follow-up by fitting Cox proportional hazard models among participants in MOST. The first model included sociodemographic and anthropometric factors, medical history, and clinical measures (referred to as the clinical model). The second model added radiographic findings into the predictive model (the radiographic model). We evaluated each model's discrimination and calibration performance and assessed the incremental value of radiographic findings using both category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We tuned the models and externally validated them among participants in OAI.We included 2658 participants from MOST (mean age 62·4 years [SD 8·1], 1646 [61·9%] women) in the training dataset and 4060 participants from OAI (mean age 60·9 years [9·1], 2379 [58·6%] women) in the validation dataset. 290 (10·9%) participants in the training dataset and 174 (4·3%) in the validation dataset had total knee replacement. The retained predictive variables included in the clinical model were age, sex, race, history of knee arthroscopy, frequent knee pain, current use of analgesics, current use of glucosamine, body-mass index, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score, and the most predictive factors were age, race, and WOMAC pain score. The retained predictive variables in the radiographic model were age, sex, race, frequent knee pain, current use of analgesics, WOMAC pain score, and Kellgren-Lawrence grade, and the most predictive factors were Kellgren-Lawrence grade, race, and age. The C-statistic was 0·79 (95% CI 0·76-0·81) for the clinical model and 0·87 (0·85-0·99) for the radiographic model in the training dataset. The calibration slope was 0·95 (95% CI 0·86-1·05) and 0·96 (0·87-1·04), respectively. Adding radiograph findings significantly improved predictive performance with an NRI of 0·43 (95% CI 0·38-0·50) and IDI of 0·14 (95% CI: 0·10-0·18). Both models, with tuned coefficients, showed a good predictive performance among participants in the validation dataset.The risk of total knee replacement can be predicted based on common risk factors with good discrimination and calibration. Additionally, adding radiographic findings of knee osteoarthritis into the model substantially improves its predictive performance.National Natural Science Foundation of China, National Key Research and Development Program, and Beijing Municipal Science & Technology Commission.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
wangshibing完成签到,获得积分10
刚刚
我是老大应助专注纸鹤采纳,获得10
1秒前
zhai完成签到,获得积分10
1秒前
YHDing发布了新的文献求助10
1秒前
shain发布了新的文献求助10
2秒前
3秒前
3秒前
黑黑黑发布了新的文献求助10
3秒前
SciGPT应助Margo采纳,获得30
4秒前
4秒前
周雪艳发布了新的文献求助10
5秒前
咕噜快逃完成签到,获得积分10
5秒前
6秒前
6秒前
鄢廷芮发布了新的文献求助10
7秒前
星辰大海应助清溪浅水XZ采纳,获得10
7秒前
8秒前
8秒前
8秒前
9秒前
关关发布了新的文献求助30
9秒前
百合子发布了新的文献求助10
9秒前
9秒前
10秒前
10秒前
10秒前
祝志泽完成签到,获得积分10
10秒前
10秒前
a1313发布了新的文献求助10
10秒前
11秒前
专注纸鹤完成签到,获得积分20
11秒前
一个大花瓶完成签到 ,获得积分10
12秒前
梅雨季来信完成签到,获得积分10
13秒前
小李发布了新的文献求助10
14秒前
高兴完成签到,获得积分10
14秒前
快乐的夏岚完成签到,获得积分10
14秒前
的墨完成签到,获得积分10
14秒前
专注纸鹤发布了新的文献求助10
15秒前
15秒前
memedaaaah发布了新的文献求助10
15秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974779
求助须知:如何正确求助?哪些是违规求助? 3519193
关于积分的说明 11197417
捐赠科研通 3255311
什么是DOI,文献DOI怎么找? 1797760
邀请新用户注册赠送积分活动 877150
科研通“疑难数据库(出版商)”最低求助积分说明 806187