The effect of pathological shoulder rhythm on muscle and joint forces after reverse shoulder arthroplasty, a numerical analysis

Reversed total shoulder arthroplasty for rotator cuff arthropathy is associated with increased scapulothoracic motion: A longitudinal two-year kinematic study

BACKGROUND

Reversed total shoulder arthroplasty (RTSA) is a standard surgical procedure for the treatment of rotator cuff tear arthropathy (CTA), aimed at restoring active arm elevation. Shoulder elevation relies on both scapulothroacic (ST) and glenohumeral (GH) motion, but RTSA computer planning primarily focuses on the GH joint due to challenges in visualizing scapulothroacic (ST) motion.

RESEARCH QUESTION

Does the scapulohumeral rhythm, by means of the relative contributions of ST rotation and GH elevation per degree of arm elevation, in a longitudinal setting for up to two years postoperatively after RTSA for CTA change?

METHODS

In a prospective longitudinal study, shoulder kinematics were studied in 20 patients (22 shoulders) before and at three, six, 12, and 24 months after RTSA implantation for CTA. Skin markers were tracked using 3D motion analysis. The relative ST and GH contributions per degree of arm elevation were assessed and were compared using statistical non-parametric mapping with Bayesian inference.

RESULTS

Mean arm elevation was 89 ± 33° preoperatively, 135 ± 28° at 3 months, 161 ± 20° at 6 months, 169 ± 18° at 12 months, and 165 ± 19° at 24 months. Between 48-66°, 62-93°, 53-94°, 60-97° and 72-104° of arm elevation at the measurement time points pre, 3-months, 6-months, 12-months and 24-months postoperatively, respectively, the ST rotation had a significantly greater contribution to arm elevation compared to GH elevation; a pattern that was not found in controls.

SIGNIFICANCE

While RTSA successfully restored active arm elevation through improved GH and ST motion, the scapulohumeral rhythm exhibited a consistent pattern up to two years postoperatively, resembling the preoperative state. In the midrange of motion, ST rotation dominated over GH elevation, potentially contributing to muscular fatigue and explaining the documented decline in functional outcomes over time after RTSA. The findings highlight the importance of incorporating ST kinematics into modern computer planning for RTSA.

Glenohumeral joint force prediction with deep learning

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM could predict MSM results and reduce computational costs. Within the total shoulder arthroplasty (TSA) domain, the glenohumeral joint force represents a critical MSM outcome as it can influence joint function, joint stability, and implant durability. Here, we aimed to employ deep learning techniques to predict both the magnitude and direction of the glenohumeral joint force. To achieve this, 959 virtual subjects were generated using the Markov-Chain Monte-Carlo method, providing patient-specific parameters from an existing clinical registry. A DLM was constructed to predict the glenohumeral joint force components within the scapula coordinate system for the generated subjects with a coefficient of determination of 0.97, 0.98, and 0.98 for the three components of the glenohumeral joint force. The corresponding mean absolute errors were 11.1, 12.2, and 15.0 N, which were about 2% of the maximum glenohumeral joint force. In conclusion, DLM maintains a comparable level of reliability in glenohumeral joint force estimation with MSM, while drastically reducing the computational costs.