Tong Beibei, Chen Hongbo, Wang Cui, Zeng Wen, Li Dan, Liu Peiyuan, Liu Ming, Jin Xiaoyan, Shang Shaomei
School of Nursing, Peking University, Beijing, China.
Nursing Department of Peking University Third Hospital, Beijing, China.
Skeletal Radiol. 2024 Jun;53(6):1045-1059. doi: 10.1007/s00256-024-04590-x. Epub 2024 Jan 24.
To identify and describe existing models for predicting knee pain in patients with knee osteoarthritis.
The electronic databases PubMed, EMBASE, CINAHL, Web of Science, and Cochrane Library were searched from their inception to May 2023 for any studies to develop and validate a prediction model for predicting knee pain in patients with knee osteoarthritis. Two reviewers independently screened titles, abstracts, and full-text qualifications, and extracted data. Risk of bias was assessed using the PROBAST. Data extraction of eligible articles was extracted by a data extraction form based on CHARMS. The quality of evidence was graded according to GRADE. The results were summarized with descriptive statistics.
The search identified 2693 records. Sixteen articles reporting on 26 prediction models were included targeting occurrence (n = 9), others (n = 7), progression (n = 5), persistent (n = 2), incident (n = 1), frequent (n = 1), and flares (n = 1) of knee pain. Most of the studies (94%) were at high risk of bias. Model discrimination was assessed by the AUROC ranging from 0.62 to 0.81. The most common predictors were age, BMI, gender, baseline pain, and joint space width. Only frequent knee pain had a moderate quality of evidence; all other types of knee pain had a low quality of evidence.
There are many prediction models for knee pain in patients with knee osteoarthritis that do show promise. However, the clinical extensibility, applicability, and interpretability of predictive tools should be considered during model development.
识别并描述现有的预测膝关节骨关节炎患者膝关节疼痛的模型。
检索电子数据库PubMed、EMBASE、CINAHL、Web of Science和Cochrane图书馆，从建库至2023年5月，查找任何关于开发和验证预测膝关节骨关节炎患者膝关节疼痛的预测模型的研究。两名评审员独立筛选标题、摘要和全文资格，并提取数据。使用PROBAST评估偏倚风险。根据CHARMS通过数据提取表提取符合条件文章的数据。根据GRADE对证据质量进行分级。结果用描述性统计进行总结。
检索到2693条记录。纳入了16篇文章，报告了26个预测模型，目标是膝关节疼痛的发生（n = 9）、其他情况（n = 7）、进展（n = 5）、持续存在（n = 2）、新发（n = 1）、频繁发作（n = 1）和发作（n = 1）。大多数研究（94%）存在高偏倚风险。通过AUROC评估模型辨别力，范围为0.62至0.81。最常见的预测因素是年龄、体重指数、性别、基线疼痛和关节间隙宽度。只有频繁膝关节疼痛有中等质量的证据；所有其他类型的膝关节疼痛证据质量低。
有许多针对膝关节骨关节炎患者膝关节疼痛的预测模型确实显示出前景。然而，在模型开发过程中应考虑预测工具的临床可扩展性、适用性和可解释性。
