PMID- 37652447
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230906
IS  - 2046-3758 (Print)
IS  - 2046-3758 (Electronic)
IS  - 2046-3758 (Linking)
VI  - 12
IP  - 9
DP  - 2023 Sep 1
TI  - Predicting whether patients will achieve minimal clinically important differences 
      following hip or knee arthroplasty.
PG  - 512-521
LID - 10.1302/2046-3758.129.BJR-2023-0070.R2 [doi]
AB  - 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.
CI  - (c) 2023 Author(s) et al.
FAU - Langenberger, Benedikt
AU  - Langenberger B
AUID- ORCID: 0000-0002-0825-0464
AD  - Health Care Management, Technische Universitat Berlin, Berlin, Germany.
FAU - Schrednitzki, Daniel
AU  - Schrednitzki D
AD  - Orthopedics, Sana Kliniken Sommerfeld, Kremmen, Germany.
FAU - Halder, Andreas M
AU  - Halder AM
AD  - Orthopedics, Sana Kliniken Sommerfeld, Kremmen, Germany.
FAU - Busse, Reinhard
AU  - Busse R
AD  - Health Care Management, Technische Universitat Berlin, Berlin, Germany.
FAU - Pross, Christoph M
AU  - Pross CM
AD  - Health Care Management, Technische Universitat Berlin, Berlin, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230901
PL  - England
TA  - Bone Joint Res
JT  - Bone & joint research
JID - 101586057
PMC - PMC10471446
COIS- D. Schrednitzki reports payments for lectures and courses on knee arthroplasty 
      and robotics from Zimmer Biomet, unrelated to this study. R. Busse reports 
      institutional grants from Roche and Stryker, and speaker payments from AbbVie, 
      all of which are unrelated to this study. R. Busse is also involved with the 
      Government Commission on Hospital Reform. A. Halder reports royalties or 
      licenses, speaker payments, and support for attending meetings and/or travel from 
      Zimmer Biomet and DePuy, unrelated to this study. A. Halder is also President of 
      the German Orthopaedic Society (DGOOC) 2022 Board Member European Knee Society. 
      C. Pross is employed by Stryker, and reports stock in Stryker, unrelated to this 
      study.
EDAT- 2023/09/01 00:41
MHDA- 2023/09/01 00:42
PMCR- 2023/09/01
CRDT- 2023/08/31 20:03
PHST- 2023/09/01 00:42 [medline]
PHST- 2023/09/01 00:41 [pubmed]
PHST- 2023/08/31 20:03 [entrez]
PHST- 2023/09/01 00:00 [pmc-release]
AID - BJR-2023-0070.R2 [pii]
AID - 10.1302/2046-3758.129.BJR-2023-0070.R2 [doi]
PST - epublish
SO  - Bone Joint Res. 2023 Sep 1;12(9):512-521. doi: 
      10.1302/2046-3758.129.BJR-2023-0070.R2.