PMID- 34275163
OWN - NLM
STAT- MEDLINE
DCOM- 20220418
LR  - 20220418
IS  - 1554-527X (Electronic)
IS  - 0736-0266 (Linking)
VI  - 40
IP  - 4
DP  - 2022 Apr
TI  - Development of machine learning algorithms to predict achievement of minimal 
      clinically important difference for the KOOS-PS following total knee 
      arthroplasty.
PG  - 808-815
LID - 10.1002/jor.25125 [doi]
AB  - As cost-effective measures become increasingly implemented in the US healthcare 
      system, changes in patient-reported outcome measure (PROM) scores can be utilized 
      to indicate patient satisfaction following procedures including total knee 
      arthroplasty (TKA). The primary aim of this study was to develop and evaluate 
      machine learning algorithms to predict achievement of the minimal clinically 
      important difference (MCID) for the Knee Injury and Osteoarthritis Outcome 
      Score-Physical Function Short Form (KOOS-PS) at 1-year following TKA. A 
      retrospective review of primary TKA patients between 2016 and 2018 was performed. 
      Variables considered for prediction included demographics and preoperative PROMs. 
      The KOOS-PS MCID was calculated via a distribution-based method. Five machine 
      learning algorithms were developed and tested by discrimination, calibration, 
      Brier score, and decision curve analysis. Among the 744 patients who met the 
      inclusion criteria, 385 (72.8%) patients achieved the MCID. The elastic-net 
      penalized logistic regression model was selected as the best performing model 
      (c-statistic 0.77, calibration intercept -0.02, calibration slope 1.15, and Brier 
      score 0.14). The most important variables for MCID achievement were preoperative 
      KOOS-PS score, preoperative VAS Pain, preoperative opioid use, preoperative 
      PROMIS global mental health score, age, and sex. Algorithms were incorporated 
      into an open-access digital application available at 
      https://sorg-apps.shinyapps.io/tka_koos_mcid/. This study is the first to predict 
      the probability of achieving the KOOS-PS MCID following TKA using a machine 
      learning-based approach. The results were used to develop a clinical decision aid 
      based on commonly collected predictive variables to preoperatively predict an 
      individual patient's likelihood of attaining an acceptable outcome following TKA.
CI  - (c) 2021 Orthopaedic Research Society. Published by Wiley Periodicals LLC.
FAU - Katakam, Akhil
AU  - Katakam A
AUID- ORCID: 0000-0003-0774-5702
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
AD  - Department of Orthopaedic Surgery, Newton-Wellesley Hospital, Newton, 
      Massachusetts, USA.
FAU - Karhade, Aditya V
AU  - Karhade AV
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
FAU - Collins, Austin
AU  - Collins A
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
FAU - Shin, David
AU  - Shin D
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
FAU - Bragdon, Charles
AU  - Bragdon C
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
FAU - Chen, Antonia F
AU  - Chen AF
AUID- ORCID: 0000-0003-2040-8188
AD  - Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical 
      School, Boston, Massachusetts, USA.
FAU - Melnic, Christopher M
AU  - Melnic CM
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
AD  - Department of Orthopaedic Surgery, Newton-Wellesley Hospital, Newton, 
      Massachusetts, USA.
FAU - Schwab, Joseph H
AU  - Schwab JH
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
FAU - Bedair, Hany S
AU  - Bedair HS
AD  - Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard 
      Medical School, Boston, Massachusetts, USA.
AD  - Department of Orthopaedic Surgery, Newton-Wellesley Hospital, Newton, 
      Massachusetts, USA.
LA  - eng
PT  - Journal Article
DEP - 20210718
PL  - United States
TA  - J Orthop Res
JT  - Journal of orthopaedic research : official publication of the Orthopaedic 
      Research Society
JID - 8404726
SB  - IM
MH  - Algorithms
MH  - *Arthroplasty, Replacement, Knee
MH  - Humans
MH  - Machine Learning
MH  - Minimal Clinically Important Difference
MH  - Patient Reported Outcome Measures
MH  - Retrospective Studies
MH  - Treatment Outcome
OTO - NOTNLM
OT  - KOOS
OT  - MCID
OT  - TKA
OT  - machine learning
EDAT- 2021/07/19 06:00
MHDA- 2022/04/19 06:00
CRDT- 2021/07/18 21:07
PHST- 2021/02/08 00:00 [revised]
PHST- 2020/06/01 00:00 [received]
PHST- 2021/06/09 00:00 [accepted]
PHST- 2021/07/19 06:00 [pubmed]
PHST- 2022/04/19 06:00 [medline]
PHST- 2021/07/18 21:07 [entrez]
AID - 10.1002/jor.25125 [doi]
PST - ppublish
SO  - J Orthop Res. 2022 Apr;40(4):808-815. doi: 10.1002/jor.25125. Epub 2021 Jul 18.