Berenguer, Abel DiazAbel DiazBerenguerBossa, Matias NicolasMatias NicolasBossaLebleu, JulienJulienLebleuPauwels, AndriesAndriesPauwelsSahli, HichemHichemSahli2025-07-112025-07-1120252398-6352WOS:001521254100002https://imec-publications.be/handle/20.500.12860/45892This 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.Journal article10.1038/s41746-025-01783-zWOS:001521254100002HEALTHSCOREMEDLINE:40593233