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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


Bone & Joint Open
Vol. 5, Issue 10 | Pages 837 - 843
7 Oct 2024
Zalikha AK Waheed MA Twal C Keeley J El-Othmani MM Hajj Hussein I

Aims

This study aims to evaluate the impact of metabolic syndrome in the setting of obesity on in-hospital outcomes and resource use after total joint replacement (TJR).

Methods

A retrospective analysis was conducted using the National Inpatient Sample from 2006 to the third quarter of 2015. Discharges representing patients aged 40 years and older with obesity (BMI > 30 kg/m2) who underwent primary TJR were included. Patients were stratified into two groups with and without metabolic syndrome. The inverse probability of treatment weighting (IPTW) method was used to balance covariates.


Bone & Joint Research
Vol. 9, Issue 11 | Pages 808 - 820
1 Nov 2020
Trela-Larsen L Kroken G Bartz-Johannessen C Sayers A Aram P McCloskey E Kadirkamanathan V Blom AW Lie SA Furnes ON Wilkinson JM

Aims

To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis.

Methods

A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression.


The Bone & Joint Journal
Vol. 102-B, Issue 7 | Pages 941 - 949
1 Jul 2020
Price AJ Kang S Cook JA Dakin H Blom A Arden N Fitzpatrick R Beard DJ

Aims

To calculate how the likelihood of obtaining measurable benefit from hip or knee arthroplasty varies with preoperative patient-reported scores.

Methods

Existing UK data from 222,933 knee and 209,760 hip arthroplasty patients were used to model an individual’s probability of gaining meaningful improvement after surgery based on their preoperative Oxford Knee or Hip Score (OKS/OHS). A clinically meaningful improvement after arthroplasty was defined as ≥ 8 point improvement in OHS, and ≥ 7 in OKS.