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Bone & Joint Research
Vol. 12, Issue 9 | Pages 512 - 521
1 Sep 2023
Langenberger B Schrednitzki D Halder AM Busse R Pross CM

Aims. A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. Methods. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS). Results. Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases. Conclusion. MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases. Cite this article: Bone Joint Res 2023;12(9):512–521


The Bone & Joint Journal
Vol. 105-B, Issue 9 | Pages 977 - 984
1 Sep 2023
Kamp T Gademan MGJ van Zon SKR Nelissen RGHH Vliet Vlieland TPM Stevens M Brouwer S

Aims

For the increasing number of working-age patients undergoing total hip or total knee arthroplasty (THA/TKA), return to work (RTW) after surgery is crucial. We investigated the association between occupational class and time to RTW after THA or TKA.

Methods

Data from the prospective multicentre Longitudinal Leiden Orthopaedics Outcomes of Osteoarthritis Study were used. Questionnaires were completed preoperatively and six and 12 months postoperatively. Time to RTW was defined as days from surgery until RTW (full or partial). Occupational class was preoperatively assessed and categorized into four categories according to the International Standard Classification of Occupations 2008 (blue-/white-collar, high-/low-skilled). Cox regression analyses were conducted separately for THA and TKA patients. Low-skilled blue-collar work was used as the reference category.


The Bone & Joint Journal
Vol. 99-B, Issue 9 | Pages 1167 - 1175
1 Sep 2017
Luna IE Kehlet H Peterson B Wede HR Hoevsgaard SJ Aasvang EK

Aims

The purpose of this study was to assess early physical function after total hip or knee arthroplasty (THA/TKA), and the correlation between patient-reported outcome measures, physical performance and actual physical activity (measured by actigraphy).

Patients and Methods

A total of 80 patients aged 55 to 80 years undergoing THA or TKA for osteoarthritis were included in this prospective cohort study. The main outcome measure was change in patient reported hip or knee injury and osteoarthritis outcome score (HOOS/KOOS) from pre-operatively until post-operative day 13 (THA) or 20 (TKA). Secondary measures were correlations to objectively assessed change in physical performance (paced-walk, chair-stand, stair-climb tests) at day 14 (THA) or 21 (TKA) and actual physical activity (actigraphy) measured at day 12 and 13 (THA) or 19 and 20 (TKA).