In:
Plastic and Reconstructive Surgery - Global Open, Ovid Technologies (Wolters Kluwer Health), Vol. 8, No. 6 ( 2020-06-24), p. e2874-
Abstract:
There is a need for a reliable classification system to grade contour deformities and to inform reimbursement of body contouring surgery after massive weight loss. We developed the PRS Rainbow Classification, which uses select photographs to provide standardized references for evaluating patient photographs, to classify contour deformities in postbariatric patients. To assess the reliability of the PRS Rainbow Classification to classify contour deformities in massive weight loss patients. Methods: Ten independent experienced plastic surgeons, 7 experienced medical advisors of the healthcare insurance company, and 10 laypersons evaluated 50 photographs per anatomical region (arms, breast, abdomen, and medial thighs). Each participant rated the patient photographs on a scale of 1–3 in an online survey. The inter-observer and the intra-observer reliabilities were determined using intra-class correlation coefficients (ICCs). The ICC analyses were performed for each anatomical region. Results: Inter-observer reliability was moderate to good in the body regions “arms,” “abdomen,” “medial thighs,” with mean ICC values of 0.678 [95% confidence interval (CI), 0.591–0.768], 0.685 (95% CI, 0.599–0.773), and 0.658 (95% CI, 0.569–0.751), respectively. Inter-observer reliability was comparable within the 3 different professional groups. Intra-observer reliability (test–retest reliability) was moderate to good, with a mean overall ICC value of 0.723 (95% CI, 0.572–0.874) for all groups and all 4 body regions. Conclusions: The moderate to good reliability found in this study validates the use of the PRS Rainbow Classification as a reproducible and reliable classification system to assess contour deformities after massive weight loss. It holds promise as a key part of instruments to classify body contour deformities and to assess reimbursement of body contouring surgery.
Type of Medium:
Online Resource
ISSN:
2169-7574
DOI:
10.1097/GOX.0000000000002874
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2020
detail.hit.zdb_id:
2723993-7
