High-dimensional item response theory analysis of patient-reported outcomes in total knee arthroplasty.
other · Level IV
Where this comes from
- Record sourced from PubMed, PMID 40593233.
- Also identified by DOI 10.1038/s41746-025-01783-z and PMC identifier 12215706.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
This study introduces a Bayesian multidimensional hierarchical item response theory (MHIRT) model to improve patient-reported outcome (PRO) assessments in total knee arthroplasty (TKA). Traditional unidimensional scoring fails to capture the multifaceted nature of recovery. Our model uncovers latent traits and inter-item relationships directly from PROMs such as the OKS and the EQ-5D-3L, without relying on predefined subscales. MHIRT flexibly decomposes PROMs into clinically meaningful traits like pain, mobility, self-care, and confidence. These traits captured more domain-specific variation, showed stronger sensitivity to temporal changes, and better reflected demographic factors than traditional total scores. The model was trained on a large NHS dataset and externally validated on PROMs from the moveUP digital platform. In predictive modeling of postoperative outcomes, MHIRT-derived features consistently outperformed unidimensional scores and conventional multidimensional IRT models. These findings suggest that MHIRT offers a potentially interpretable framework for tracking recovery and predicting health outcomes.