Moby Parsons, MD
There is ample support in the literature that superior inclination of the glenoid baseplate in reverse total shoulder arthroplasty (rTSA) can lead to a higher risk of instability and premature implant failure. This is because a superiorly inclined baseplate experiences a greater shear vector imparted by the action of the deltoid muscle. Because cuff deficient arthritic shoulders often develop superior glenoid erosion, shoulder surgeons must carefully assess preoperative glenoid inclination when planning rTSA to avoid implant malposition. Preoperative planning platforms now allow surgeons virtually correct glenoid deformity while also optimizing implant fixation, backside contact with host bone, and avoidance of bone impingement such as scapular notching.
Boileau et al recently described a new measurement of glenoid inclination called the Reverse Shoulder Angle (RSA). Their contention is that referencing the more traditional ß-angle (the angle formed by a line connecting the superior and inferior glenoid face with a perpendicular to a line along the floor of the supraspinatus fossa) may inadvertently cause surgeons to superiorly incline small, flat-backed reverse baseplates when placed on the inferior half of the glenoid. Therefore, the RSA angle measures only the inferior half of the glenoid rather than the full glenoid face. This results in an average measured inclination of 25° + 8°, which is a 10° + 5° compared to the ß-angle. The authors note that Favard E1 glenoids with central erosion are at risk for baseplate malposition.
If the goal of rTSA is correction of glenoid inclination to neutral, then according to this paradigm a vast majority of baseplates would require a bonegraft to correct over 20° of superior inclination when using the RSA angle and a small, inferiorly-positioned implant. While this may be true for this specific implant design, it does not necessarily translate to all other baseplates, such as those with a curved back. Curved-backed implants fit the natural curvature of the glenoid bone and require less bone removal as they do not convert a curved surface to a flat surface.
Furthermore, an oval-shaped implant with an offset central peg can allow inferior shift of the glenosphere without having to exaggerate the baseplate position on the inferior half of the glenoid. The combination of these design features with 10° superior augmentation allows even severely eroded glenoids to achieve neutral inclination correction with less bone loss, better host bone contact, and no need for additional bone grafting such as in the BIO-RSA.
While the RSA angle does shed important light on the necessity of carefully considering the impact of erosion on the assessment of glenoid inclination, the clinical applicability of this angle may not be generalizable across implant systems. Prior to adopting the RSA over other methods of inclination measurement such as the ß-angle, Friedman axis or glenoid centerline, surgeons need to understand the relationship of implant design and implant options to correction of glenoid deformity. The RSA angle would suggest that all glenoids have considerable superior inclination and would require the establishment of a new Gaussian distribution of normal values. According to this paradigm, measurement of normal glenoids without wear could result in an RSA angle > 10°. To correct such glenoids to neutral (assuming this is defined as perpendicular to the floor of the supraspinatus fossa), may result in over-correction. This, in turn, may have unintended consequences such as further distalization and lateralization of the implant.
The varied methods of measuring inclination, each with its range of normal values, invites further efforts to define consensus on establishing optimal implant position with respect to fixation stability, joint biomechanics, and mitigation of complications such as instability and bone impingement. Our efforts to correlate implant position with clinical outcomes using the ExactechGPS® shoulder navigation platform will ultimately help us to establish guidelines through machine-learning applied to large clinical data sets.
Moby Parsons, MD, is in private practice in New Hampshire. He completed his internship and residency at University of Pittsburgh and fellowships at the University of Washington, Southern California Orthopaedic Institute and the University of Sydney. His work has produced multiple published articles and international presentations.