© 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2007–2014, 2018.

Quantitative evaluation of retrieved reverse total shoulder arthroplasty liner surface deviation and volumetric wear

Michael D. Kurdziel Michael D. Newton Samantha Hartner Kevin C. Baker Jerome Michael Wiater

Polyethylene wear is a known complication in total joint arthroplasty, however, in vivo wear rates in reverse total shoulder arthroplasty (RTSA) remain largely unknown. This study aimed to quantify volumetric and surface deviation changes in retrieved RTSA humeral liners using a novel micro‐computed tomography (μCT)‐based technique. After IRB‐approval, 32 humeral liners (single manufacturer and model) with term‐of‐service greater than 90 days were analyzed. Clinical demographics and surgical data were collected via chart review. Unworn liners were used as geometric controls. Retrieved and unworn liners underwent μCT scanning. Retrieved liner volumes were isolated, co‐registered to controls of matching geometry, and surface deviations of the articulation surface and rim were computed. Differences in total volume loss (TVL), volumetric wear rate (VWR), and surface deviation were reported. Semi‐quantitative grading evaluated rim damage presence and severity. Mean term‐of‐service for all liners was 2.07 ± 1.33 years (range: 0.30–4.73). Mean TVL and VWR were 181.3 ± 208.2 mm3 and 114.5 ± 160.3 mm3/year, respectively. Mean articulation and rim surface deviations were 0.084 ± 0.065 and 0.177 ± 0.159 mm, respectively. Articulation surface deviation was positively correlated to term‐of‐service. Rim damage was present on 63% of liners and correlated significantly to rim surface deviation. This study reports in vivo wear rates of retrieved RTSA implants. Our results demonstrate volumetric and articulation surface wear in select RTSA liners that is correlated to term‐of‐service. Calculation of in vivo wear rates can help bridge the gap between clinical outcomes and experimental models such as wear simulations and computational models.

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