Comparison of Range of Motion between Two-Year Clinical Outcomes and Predictions of a Static Scapula Preoperative Planning Software for Reverse Shoulder Arthroplasty


Ashish Gupta, MBBS, MSc, FRACS, FAORTHOA
AUSTRALIA

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Comparison of Range of Motion between Two-Year Clinical Outcomes and Predictions of a Static Scapula Preoperative Planning Software for Reverse Shoulder Arthroplasty

Marco Branni, PhD, AUSTRALIA Asma Salhi, PhD, AUSTRALIA Kristine R. Italia, MD, FPOA, PHILIPPINES Luke Gilliland, BEng, AUSTRALIA Marine Launay, MEng, AUSTRALIA Roberto Pareyon, MEXICO Jashint Maharaj, MBBS, FRSPH, AUSTRALIA Angus Lane, BEng, AUSTRALIA Helen Ingoe, MBBS, FRCS (Tr+Orth), MD, MSc, PGCert, NEW ZEALAND Peter Pivonka, PhD, AUSTRALIA Kenneth Cutbush, MBBS, FRACS, FAOrthA, AUSTRALIA Ashish Gupta, MBBS, MSc, FRACS, FAORTHOA, AUSTRALIA

Queensland Unit for Advanced Shoulder Research (QUASR), Brisbane, QLD, AUSTRALIA

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Anatomic Location

Shoulder Glenohumeral

Treatment / Technique

Arthroplasty Total Joint Replacement

Anatomic Structure

Bones

Sports Medicine

Outcome Studies

Summary: This study aims to compare the range of motion based on preoperative planning software by using the implant position from postoperative CT images (P-ROM), with the clinical range of motion (C-ROM) assessed at minimum of 2 years follow-up.

Introduction

Preoperative planning using commercially available software has gained popularity for reverse shoulder arthroplasty (RSA). Three-dimensional automatic segmentation of scapula and humerus offers a visual insight of the pathology, as well providing arc of motion for the implanted articulation and identifying potential areas of bony impingement. However, these software algorithms utilize a fixed scapular model, disregarding the preoperative clinical range of motion of the patient, be it glenohumeral or scapulothoracic, as well as any soft tissue parameters. This study aims to compare the range of motion based on preoperative planning software by using the implant position from postoperative CT images (P-ROM), with the clinical range of motion (C-ROM) assessed at minimum of 2 years follow-up.

Methods

Pre- and post-operative CT-scans of 50 patients who underwent a primary RSA between 2017 and 2021 were analyzed. At the postoperative two-year review, each patient was assessed for active range of motion. Implant size and position based on operative notes and postoperative CT scans were used to replicate the performed surgery into the planning platform. Abduction, flexion, and external rotation motions were simulated and recorded. The relationship between C-ROM and P-ROM was investigated using linear regression analysis, Pearson correlation coefficient, and paired t-test.

Results

P-ROM was significantly lower than C-ROM at two years postoperatively (P<0.001), with an average discrepancy of 77 degrees in abduction, 50 degrees in flexion, and 39 degrees in external rotation (C-ROM: abduction 154±21 (80-180 degrees); flexion 160±17 (90-180 degrees); external rotation 52±14 (10-80 degrees) versus P-ROM: abduction 77±14 (53-107 degrees); flexion 110±24 (67-180 degrees); external rotation 13±19 (0-79 degrees). The linear regression analysis indicated weak agreement between C-ROM and P-ROM (abduction R2=0.01; flexion R2=0.01; external rotation R2=0.07). Pearson’s correlation coefficients revealed weak correlations of -0.09, 0.11 and 0.27 for abduction, flexion, and external rotation.

Conclusion

P-ROM-based preoperative software, in its current form, does not allow the prediction of the C-ROM at two-year follow-up for patients who underwent an RSA. Further research and development are needed to make the ROM simulation feature a more clinically reliable tool that can guide the surgical decision-making.

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