预测患者在髋关节或膝关节置换术后是否会达到最小的临床重要差异。

IF 4.7 2区 医学 Q2 CELL & TISSUE ENGINEERING
Benedikt Langenberger, Daniel Schrednitzki, Andreas M Halder, Reinhard Busse, Christoph M Pross
{"title":"预测患者在髋关节或膝关节置换术后是否会达到最小的临床重要差异。","authors":"Benedikt Langenberger,&nbsp;Daniel Schrednitzki,&nbsp;Andreas M Halder,&nbsp;Reinhard Busse,&nbsp;Christoph M Pross","doi":"10.1302/2046-3758.129.BJR-2023-0070.R2","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.</p><p><strong>Methods: </strong>MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).</p><p><strong>Results: </strong>Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases.</p><p><strong>Conclusion: </strong>MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases.</p>","PeriodicalId":9074,"journal":{"name":"Bone & Joint Research","volume":"12 9","pages":"512-521"},"PeriodicalIF":4.7000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2d/a8/BJR-12-2046-3758.129.BJR-2023-0070.R2.PMC10471446.pdf","citationCount":"1","resultStr":"{\"title\":\"Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty.\",\"authors\":\"Benedikt Langenberger,&nbsp;Daniel Schrednitzki,&nbsp;Andreas M Halder,&nbsp;Reinhard Busse,&nbsp;Christoph M Pross\",\"doi\":\"10.1302/2046-3758.129.BJR-2023-0070.R2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.</p><p><strong>Methods: </strong>MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).</p><p><strong>Results: </strong>Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases.</p><p><strong>Conclusion: </strong>MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases.</p>\",\"PeriodicalId\":9074,\"journal\":{\"name\":\"Bone & Joint Research\",\"volume\":\"12 9\",\"pages\":\"512-521\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2d/a8/BJR-12-2046-3758.129.BJR-2023-0070.R2.PMC10471446.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bone & Joint Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1302/2046-3758.129.BJR-2023-0070.R2\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CELL & TISSUE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bone & Joint Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1302/2046-3758.129.BJR-2023-0070.R2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL & TISSUE ENGINEERING","Score":null,"Total":0}
引用次数: 1

摘要

目的:相当一部分接受膝关节置换术(KA)或髋关节置换术(HA)的患者没有达到最小临床重要差异(MCID)的改善,即没有达到有意义的改善。使用三个患者报告的结果测量(PROMs),我们的目的是:1)评估机器学习(ML)、简单的术前PROM评分和逻辑回归(LR)推导的性能,以预测接受HA或KA的患者是否达到高于计算的MCID的改善;2)测试ML是否能够在预测性能上优于LR或术前PROM评分。方法:采用变化差法对1843例HA和1546例KA患者的MCIDs进行分析。应用人工神经网络、梯度增强机、最小绝对收缩和选择算子(LASSO)回归、脊回归、弹性网、随机森林、LR和术前PROM评分来预测以下PROM的MCID:EuroQol五维五水平问卷(EQ- 5d - 5l)、EQ视觉模拟量表(EQ- vas)、髋关节残疾和骨关节炎结局评分-身体功能简表(HOOS-PS)、膝关节损伤和骨关节炎结局评分-身体功能简表(KOOS-PS)。结果:每个结果的最佳模型的预测性能从HOOS-PS的0.71到EQ-VAS (HA样本)的0.84不等。6例中有2例ML的评分在统计学上显著优于LR和术前PROM评分。结论:MCIDs可预测,性能合理。ML能够胜过传统方法,尽管只是在少数情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty.

Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty.

Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty.

Predicting whether patients will achieve minimal clinically important differences following hip or knee arthroplasty.

Aims: A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.

Methods: MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).

Results: Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases.

Conclusion: MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Bone & Joint Research
Bone & Joint Research CELL & TISSUE ENGINEERING-ORTHOPEDICS
CiteScore
7.40
自引率
23.90%
发文量
156
审稿时长
12 weeks
期刊介绍: The gold open access journal for the musculoskeletal sciences. Included in PubMed and available in PubMed Central.
×
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:481959085
Book学术官方微信