PMID- 33359160
OWN - NLM
STAT- MEDLINE
DCOM- 20210611
LR  - 20210611
IS  - 1526-3231 (Electronic)
IS  - 0749-8063 (Linking)
VI  - 37
IP  - 4
DP  - 2021 Apr
TI  - Machine Learning Algorithms Predict Clinically Significant Improvements in 
      Satisfaction After Hip Arthroscopy.
PG  - 1143-1151
LID - S0749-8063(20)30976-2 [pii]
LID - 10.1016/j.arthro.2020.11.027 [doi]
AB  - PURPOSE: To develop machine learning algorithms to predict failure to achieve 
      clinically significant satisfaction after hip arthroscopy. METHODS: We queried a 
      clinical repository for consecutive primary hip arthroscopy patients treated 
      between January 2012 and January 2017. Five supervised machine learning 
      algorithms were developed in a training set of patients and internally validated 
      in an independent testing set of patients by discrimination, Brier score, 
      calibration, and decision-curve analysis. The minimal clinically important 
      difference (MCID) for the visual analog scale (VAS) score for satisfaction was 
      derived by an anchor-based method and used as the primary outcome. RESULTS: A 
      total of 935 patients were included, of whom 148 (15.8%) did not achieve the MCID 
      for the VAS satisfaction score at a minimum of 2 years postoperatively. The 
      best-performing algorithm was the neural network model (C statistic, 0.94; 
      calibration intercept, -0.43; calibration slope, 0.94; and Brier score, 0.050). 
      The 5 most important features to predict failure to achieve the MCID for the VAS 
      satisfaction score were history of anxiety or depression, lateral center-edge 
      angle, preoperative symptom duration exceeding 2 years, presence of 1 or more 
      drug allergies, and Workers' Compensation. CONCLUSIONS: Supervised machine 
      learning algorithms conferred excellent discrimination and performance for 
      predicting clinically significant satisfaction after hip arthroscopy, although 
      this analysis was performed in a single population of patients. External 
      validation is required to confirm the performance of these algorithms. LEVEL OF 
      EVIDENCE: Level III, therapeutic case-control study.
CI  - Copyright (c) 2020 Arthroscopy Association of North America. Published by Elsevier 
      Inc. All rights reserved.
FAU - Kunze, Kyle N
AU  - Kunze KN
AD  - Department of Orthopedic Surgery, Division of Sports Medicine, Section of Young 
      Adult Hip Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.. 
      Electronic address: Kylekunze7@gmail.com.
FAU - Polce, Evan M
AU  - Polce EM
AD  - Department of Orthopedic Surgery, Division of Sports Medicine, Section of Young 
      Adult Hip Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.
FAU - Rasio, Jonathan
AU  - Rasio J
AD  - Department of Orthopedic Surgery, Division of Sports Medicine, Section of Young 
      Adult Hip Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.
FAU - Nho, Shane J
AU  - Nho SJ
AD  - Department of Orthopedic Surgery, Division of Sports Medicine, Section of Young 
      Adult Hip Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.
LA  - eng
PT  - Journal Article
DEP - 20201224
PL  - United States
TA  - Arthroscopy
JT  - Arthroscopy : the journal of arthroscopic & related surgery : official 
      publication of the Arthroscopy Association of North America and the International 
      Arthroscopy Association
JID - 8506498
SB  - IM
CIN - Arthroscopy. 2021 Apr;37(4):1152-1154. PMID: 33812519
MH  - Adult
MH  - *Algorithms
MH  - *Arthroscopy
MH  - Calibration
MH  - Case-Control Studies
MH  - Female
MH  - Hip/*surgery
MH  - Humans
MH  - *Machine Learning
MH  - Male
MH  - Neural Networks, Computer
MH  - *Patient Satisfaction
MH  - Postoperative Period
MH  - ROC Curve
MH  - Risk
MH  - Treatment Outcome
MH  - Young Adult
EDAT- 2020/12/29 06:00
MHDA- 2021/06/12 06:00
CRDT- 2020/12/28 10:44
PHST- 2020/03/27 00:00 [received]
PHST- 2020/11/05 00:00 [revised]
PHST- 2020/11/06 00:00 [accepted]
PHST- 2020/12/29 06:00 [pubmed]
PHST- 2021/06/12 06:00 [medline]
PHST- 2020/12/28 10:44 [entrez]
AID - S0749-8063(20)30976-2 [pii]
AID - 10.1016/j.arthro.2020.11.027 [doi]
PST - ppublish
SO  - Arthroscopy. 2021 Apr;37(4):1143-1151. doi: 10.1016/j.arthro.2020.11.027. Epub 
      2020 Dec 24.