PMID- 35045838
OWN - NLM
STAT- MEDLINE
DCOM- 20220321
LR  - 20231105
IS  - 1472-6947 (Electronic)
IS  - 1472-6947 (Linking)
VI  - 22
IP  - 1
DP  - 2022 Jan 20
TI  - Can minimal clinically important differences in patient reported outcome measures 
      be predicted by machine learning in patients with total knee or hip arthroplasty? 
      A systematic review.
PG  - 18
LID - 10.1186/s12911-022-01751-7 [doi]
LID - 18
AB  - OBJECTIVES: To systematically review studies using machine learning (ML) 
      algorithms to predict whether patients undergoing total knee or total hip 
      arthroplasty achieve an improvement as high or higher than the minimal clinically 
      important differences (MCID) in patient reported outcome measures (PROMs) 
      (classification problem). METHODS: Studies were eligible to be included in the 
      review if they collected PROMs both pre- and postintervention, reported the 
      method of MCID calculation and applied ML. ML was defined as a family of models 
      which automatically learn from data when selecting features, identifying 
      nonlinear relations or interactions. Predictive performance must have been 
      assessed using common metrics. Studies were searched on MEDLINE, PubMed Central, 
      Web of Science Core Collection, Google Scholar and Cochrane Library. Study 
      selection and risk of bias assessment (ROB) was conducted by two independent 
      researchers. RESULTS: 517 studies were eligible for title and abstract screening. 
      After screening title and abstract, 18 studies qualified for full-text screening. 
      Finally, six studies were included. The most commonly applied ML algorithms were 
      random forest and gradient boosting. Overall, eleven different ML algorithms have 
      been applied in all papers. All studies reported at least fair predictive 
      performance, with two reporting excellent performance. Sample size varied widely 
      across studies, with 587 to 34,110 individuals observed. PROMs also varied widely 
      across studies, with sixteen applied to TKA and six applied to THA. There was no 
      single PROM utilized commonly in all studies. All studies calculated MCIDs for 
      PROMs based on anchor-based or distribution-based methods or referred to 
      literature which did so. Five studies reported variable importance for their 
      models. Two studies were at high risk of bias. DISCUSSION: No ML model was 
      identified to perform best at the problem stated, nor can any PROM said to be 
      best predictable. Reporting standards must be improved to reduce risk of bias and 
      improve comparability to other studies.
CI  - (c) 2022. The Author(s).
FAU - Langenberger, Benedikt
AU  - Langenberger B
AD  - Department of Health Care Management, Technische Universitat Berlin, Berlin, 
      Germany. langenberger@tu-berlin.de.
FAU - Thoma, Andreas
AU  - Thoma A
AD  - Department of Health Care Management, Technische Universitat Berlin, Berlin, 
      Germany.
FAU - Vogt, Verena
AU  - Vogt V
AD  - Department of Health Care Management, Technische Universitat Berlin, Berlin, 
      Germany.
LA  - eng
PT  - Journal Article
PT  - Systematic Review
DEP - 20220120
PL  - England
TA  - BMC Med Inform Decis Mak
JT  - BMC medical informatics and decision making
JID - 101088682
SB  - IM
MH  - *Arthroplasty, Replacement, Hip
MH  - *Arthroplasty, Replacement, Knee
MH  - Humans
MH  - Machine Learning
MH  - Minimal Clinically Important Difference
MH  - Patient Reported Outcome Measures
PMC - PMC8772225
COIS- All authors affirm that they have no competing interest.
EDAT- 2022/01/21 06:00
MHDA- 2022/03/22 06:00
PMCR- 2022/01/20
CRDT- 2022/01/20 05:36
PHST- 2021/06/15 00:00 [received]
PHST- 2022/01/06 00:00 [accepted]
PHST- 2022/01/20 05:36 [entrez]
PHST- 2022/01/21 06:00 [pubmed]
PHST- 2022/03/22 06:00 [medline]
PHST- 2022/01/20 00:00 [pmc-release]
AID - 10.1186/s12911-022-01751-7 [pii]
AID - 1751 [pii]
AID - 10.1186/s12911-022-01751-7 [doi]
PST - epublish
SO  - BMC Med Inform Decis Mak. 2022 Jan 20;22(1):18. doi: 10.1186/s12911-022-01751-7.