PMID- 32309761
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220414
IS  - 2472-7245 (Electronic)
IS  - 2472-7245 (Linking)
VI  - 5
IP  - 1
DP  - 2020 Jan-Mar
TI  - Use of Computerized Adaptive Testing to Develop More Concise Patient-Reported 
      Outcome Measures.
PG  - e0052
LID - 10.2106/JBJS.OA.19.00052 [doi]
LID - e0052
AB  - BACKGROUND: Patient-reported outcome measures (PROMs) are essential tools that 
      are used to assess health status and treatment outcomes in orthopaedic care. Use 
      of PROMs can burden patients with lengthy and cumbersome questionnaires. 
      Predictive models using machine learning known as computerized adaptive testing 
      (CAT) offer a potential solution. The purpose of this study was to evaluate the 
      ability of CAT to improve efficiency of the Veterans RAND 12 Item Health Survey 
      (VR-12) by decreasing the question burden while maintaining the accuracy of the 
      outcome score. METHODS: A previously developed CAT model was applied to the 
      responses of 19,523 patients who had completed a full VR-12 survey while 
      presenting to 1 of 5 subspecialty orthopaedic clinics. This resulted in the 
      calculation of both a full-survey and CAT-model physical component summary score 
      (PCS) and mental component summary score (MCS). Several analyses compared the 
      accuracy of the CAT model scores with that of the full scores by comparing the 
      means and standard deviations, calculating a Pearson correlation coefficient and 
      intraclass correlation coefficient, plotting the frequency distributions of the 2 
      score sets and the score differences, and performing a Bland-Altman assessment of 
      scoring patterns. RESULTS: The CAT model required 4 fewer questions to be 
      answered by each subject (33% decrease in question burden). The mean PCS was 1.3 
      points lower in the CAT model than with the full VR-12 (41.5 +/- 11.0 versus 42.8 +/- 
      10.4), and the mean MCS was 0.3 point higher (57.3 +/- 9.4 versus 57.0 +/- 9.6). The 
      Pearson correlation coefficients were 0.97 for PCS and 0.98 for MCS, and the 
      intraclass correlation coefficients were 0.96 and 0.97, respectively. The 
      frequency distribution of the CAT and full scores showed significant overlap for 
      both the PCS and the MCS. The difference between the CAT and full scores was less 
      than the minimum clinically important difference (MCID) in >95% of cases for the 
      PCS and MCS. CONCLUSIONS: The application of CAT to the VR-12 survey demonstrated 
      an ability to lessen the response burden for patients with a negligible effect on 
      score integrity.
CI  - Copyright (c) 2020 The Authors. Published by The Journal of Bone and Joint Surgery, 
      Incorporated. All rights reserved.
FAU - Kane, Liam T
AU  - Kane LT
AD  - Rothman Orthopaedic Institute, Philadelphia, Pennsylvania.
FAU - Namdari, Surena
AU  - Namdari S
AD  - Rothman Orthopaedic Institute, Philadelphia, Pennsylvania.
FAU - Plummer, Otho R
AU  - Plummer OR
AD  - Universal Research Solutions, Columbia, Missouri.
FAU - Beredjiklian, Pedro
AU  - Beredjiklian P
AD  - Rothman Orthopaedic Institute, Philadelphia, Pennsylvania.
FAU - Vaccaro, Alexander
AU  - Vaccaro A
AD  - Rothman Orthopaedic Institute, Philadelphia, Pennsylvania.
FAU - Abboud, Joseph A
AU  - Abboud JA
AD  - Rothman Orthopaedic Institute, Philadelphia, Pennsylvania.
LA  - eng
PT  - Journal Article
DEP - 20200312
PL  - United States
TA  - JB JS Open Access
JT  - JB & JS open access
JID - 101726219
PMC - PMC7147635
EDAT- 2020/04/21 06:00
MHDA- 2020/04/21 06:01
PMCR- 2020/03/12
CRDT- 2020/04/21 06:00
PHST- 2020/04/21 06:00 [entrez]
PHST- 2020/04/21 06:00 [pubmed]
PHST- 2020/04/21 06:01 [medline]
PHST- 2020/03/12 00:00 [pmc-release]
AID - JBJSOA-D-19-00052 [pii]
AID - 10.2106/JBJS.OA.19.00052 [doi]
PST - epublish
SO  - JB JS Open Access. 2020 Mar 12;5(1):e0052. doi: 10.2106/JBJS.OA.19.00052. 
      eCollection 2020 Jan-Mar.