BACKGROUND: Six classification systems have been proposed for describing rotator cuff tears designed to help understand their natural history and make treatment decisions. PURPOSE: To assess the interobserver variation for these classification systems and identify the method with the best interobserver agreement. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: Six rotator cuff tear classification systems were identified in a literature search. The components of these systems included partial-thickness rotator cuff tears and classification by size, shape, configuration, number of tendons involved, and by extent, topography, and nature of the biceps. Twelve fellowship-trained orthopaedic surgeons who each perform at least 30 rotator cuff repairs per year reviewed arthroscopy videos from 30 patients with a random assortment of rotator cuff tears and classified them by the 6 classification systems. Interobserver variation was determined by a kappa analysis. RESULTS: Interobserver agreement was high when distinguishing between full-thickness and partial-thickness tears (0.95, kappa = 0.85). The investigators agreed on the side (articular vs bursal) of involvement for partial-thickness tears (observed agreement 0.92, kappa = 0.85) but could not agree when classifying the depth of the partial-thickness tear (observed agreement 0.49, kappa = 0.19). The best agreement for full-thickness tears was seen when the tear was classified by topography (degree of retraction) in the frontal plane (observed agreement 0.70, kappa = 0.54). CONCLUSION: With the exception of distinguishing partial-thickness from full-thickness rotator cuff tears and identifying the side (articular vs bursal) of involvement with partial-thickness tears, currently described rotator cuff classification systems have little interobserver agreement among experienced shoulder surgeons. Researchers should consider describing full-thickness rotator cuff tears by topography (degree of retraction) in the frontal plane.
BACKGROUND: Six classification systems have been proposed for describing rotator cuff tears designed to help understand their natural history and make treatment decisions. PURPOSE: To assess the interobserver variation for these classification systems and identify the method with the best interobserver agreement. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: Six rotator cuff tear classification systems were identified in a literature search. The components of these systems included partial-thickness rotator cuff tears and classification by size, shape, configuration, number of tendons involved, and by extent, topography, and nature of the biceps. Twelve fellowship-trained orthopaedic surgeons who each perform at least 30 rotator cuff repairs per year reviewed arthroscopy videos from 30 patients with a random assortment of rotator cuff tears and classified them by the 6 classification systems. Interobserver variation was determined by a kappa analysis. RESULTS: Interobserver agreement was high when distinguishing between full-thickness and partial-thickness tears (0.95, kappa = 0.85). The investigators agreed on the side (articular vs bursal) of involvement for partial-thickness tears (observed agreement 0.92, kappa = 0.85) but could not agree when classifying the depth of the partial-thickness tear (observed agreement 0.49, kappa = 0.19). The best agreement for full-thickness tears was seen when the tear was classified by topography (degree of retraction) in the frontal plane (observed agreement 0.70, kappa = 0.54). CONCLUSION: With the exception of distinguishing partial-thickness from full-thickness rotator cuff tears and identifying the side (articular vs bursal) of involvement with partial-thickness tears, currently described rotator cuff classification systems have little interobserver agreement among experienced shoulder surgeons. Researchers should consider describing full-thickness rotator cuff tears by topography (degree of retraction) in the frontal plane.
Authors: Eduardo Baptista; Eduardo A Malavolta; Mauro E C Gracitelli; Daniel Alvarenga; Marcelo Bordalo-Rodrigues; Arnaldo A Ferreira Neto; Nestor de Barros Journal: Skeletal Radiol Date: 2019-04-02 Impact factor: 2.199
Authors: John E Kuhn; Warren R Dunn; Rosemary Sanders; Qi An; Keith M Baumgarten; Julie Y Bishop; Robert H Brophy; James L Carey; Brian G Holloway; Grant L Jones; C Benjamin Ma; Robert G Marx; Eric C McCarty; Sourav K Poddar; Matthew V Smith; Edwin E Spencer; Armando F Vidal; Brian R Wolf; Rick W Wright Journal: J Shoulder Elbow Surg Date: 2013-03-27 Impact factor: 3.019
Authors: Philipp R Heuberer; Roman Kölblinger; Stefan Buchleitner; Leo Pauzenberger; Brenda Laky; Alexander Auffarth; Philipp Moroder; Sylvia Salem; Bernhard Kriegleder; Werner Anderl Journal: Knee Surg Sports Traumatol Arthrosc Date: 2015-08-08 Impact factor: 4.342
Authors: Warren R Dunn; John E Kuhn; Rosemary Sanders; Qi An; Keith M Baumgarten; Julie Y Bishop; Robert H Brophy; James L Carey; G Brian Holloway; Grant L Jones; C Benjamin Ma; Robert G Marx; Eric C McCarty; Sourav K Poddar; Matthew V Smith; Edwin E Spencer; Armando F Vidal; Brian R Wolf; Rick W Wright Journal: J Bone Joint Surg Am Date: 2014-05-21 Impact factor: 5.284
Authors: Obiajulu Agha; Agustin Diaz; Michael Davies; Hubert T Kim; Xuhui Liu; Brian T Feeley Journal: Ann N Y Acad Sci Date: 2020-07-29 Impact factor: 5.691
Authors: Matthieu J C M Rutten; Gert-Jan Spaargaren; Ton van Loon; Maarten C de Waal Malefijt; Lambertus A L M Kiemeney; Gerrit J Jager Journal: Eur Radiol Date: 2009-09-02 Impact factor: 5.315