Abstract

Revision anterior cruciate ligament reconstruction (ACLR) is a challenging surgery occurring in 3 to 24% of primary reconstructions. A meticulous planning to study the precise size and location of both femoral and tibial bone tunnels is mandatory. The aim of the study was to evaluate the intra- and interoperator differences in the decision-making process between experienced surgeons after they were asked to make preoperative planning for ACL revision reconstruction with the use of both the computed tomography (CT) scan and a three-dimensional (3D)-printed model of the knee. Data collected from 23 consecutive patients undergoing revision of ACLR for graft failure at a single institute between September 2018 and February 2020 were prospectively reviewed. The double-blinded collected data were presented to three board-certificate attending surgeons. Surgeons were asked to decide whether to perform one-stage or two-stage revision ACLR based on the evaluation of the CT scan images and the 3D-printed custom-made models at two different rounds, T0 and T1, respectively, 7 days apart one from the other. Interoperator consensus following technical mistake was 52% at T0 and 56% at T1 using the CT scans, meanwhile concordance was 95% at T0 and 94% at T1 using the 3D models. Concordance between surgeons following new knee injury was 66% at T0 and 70% at T1 using CT scans, while concordance was 96% both at T0 and T1 using 3D models. Intraoperative variability using 3D models was extremely low: concordance at T0 and T1 was 98%. McNemar test showed a statistical significance in the use of 3D model for preoperative planning (p < 0.005). 3D-printed model reliability resulted to be higher compared with CT as intraoperator surgery technique selection was not modified throughout time from T0 to T1 (p < 0.005). The use of 3D-printed models had the most impact when evaluating femoral and tibial tunnels, resulting to be a useful instrument during preoperative planning of revision ACLR between attending surgeons with medium-high workflow.

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