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Skeletal Radiology
Erschienen in:

24.01.2024 | Review Article

Clinical prediction models for knee pain in patients with knee osteoarthritis: a systematic review

verfasst von: Beibei Tong, Hongbo Chen, Cui Wang, Wen Zeng, Dan Li, Peiyuan Liu, Ming Liu, Xiaoyan Jin, Shaomei Shang

Erschienen in: Skeletal Radiology | Ausgabe 6/2024

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Abstract

Objective

To identify and describe existing models for predicting knee pain in patients with knee osteoarthritis.

Methods

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.

Results

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.

Conclusion

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.
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Metadaten
Titel
Clinical prediction models for knee pain in patients with knee osteoarthritis: a systematic review
verfasst von
Beibei Tong
Hongbo Chen
Cui Wang
Wen Zeng
Dan Li
Peiyuan Liu
Ming Liu
Xiaoyan Jin
Shaomei Shang
Publikationsdatum
24.01.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Skeletal Radiology / Ausgabe 6/2024
Print ISSN: 0364-2348
Elektronische ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-024-04590-x

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