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.
Diaz Berenguer, A, Bossa Bossa, MN, Sahli, H, Lebleu, J & Pauwels, A 2025, 'High-dimensional item response theory analysis of patient-reported outcomes in total knee arthroplasty', npj Digital Medicine, vol. 8, no. 1, 391, pp. 1-16. https://doi.org/10.1038/s41746-025-01783-z
Diaz Berenguer, A., Bossa Bossa, M. N., Sahli, H., Lebleu, J., & Pauwels, A. (2025). High-dimensional item response theory analysis of patient-reported outcomes in total knee arthroplasty. npj Digital Medicine, 8(1), 1-16. Article 391. https://doi.org/10.1038/s41746-025-01783-z
@article{b8e5a76eec474372ae41e19e5be99a89,
title = "High-dimensional item response theory analysis of patient-reported outcomes in total knee arthroplasty",
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.",
author = "{Diaz Berenguer}, Abel and {Bossa Bossa}, {Mat{\'i}as Nicol{\'a}s} and Hichem Sahli and Julien Lebleu and Andries Pauwels",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",
year = "2025",
month = jul,
day = "1",
doi = "10.1038/s41746-025-01783-z",
language = "English",
volume = "8",
pages = "1--16",
journal = " npj Digital Medicine",
issn = "2398-6352",
publisher = "Springer Nature",
number = "1",
}