Anatomic Total Shoulder Arthroplasty: Component Size Prediction with 3-Dimensional Pre-Operative Digital Planning. Academic Article uri icon

Overview

abstract

  • BACKGROUND: The rate, complexity, and cost of total shoulder arthroplasty (TSA) continues to grow. Technology has advanced pre-operative templating. Reducing cost of TSA has positive impact for the patient, manufacturer, and hospital. The aim of this study was to evaluate the accuracy of implant size selection based on 3-D templating. Our hypothesis was that pre-operative templating would enable accurate implant prediction within one size. METHODS: Multicenter retrospective study of anatomic TSAs templated utilizing 3-D virtual planning technology. This program uses computed tomography (CT) scans allowing the surgeon to predict component sizes of the glenoid and humeral head and stem. Pre-operative templated implant size were compared to actual implant size at the time of surgery. Primary data analysis utilized unweighted Cohen's Kappa test. RESULTS: 111 TSAs were analyzed from five surgeons. Pre-operative templated glenoid sizes were within one size of actual implant in 99% and exactly matched in 89%. For patients requiring a posterior glenoid augment (n = 14), 100% of implants were within one size of the template and 93% matched exactly. For stemless humeral components (n = 87) implanted, 98% matched the pre-operative template within one size with 79% exactly matched. For stemmed components (n = 24), 88% of cases were within one size of the preoperative plan and exactly matching in 83%. Humeral head diameter matched within one size of the pre-operative template in 84% of cases and exactly matched in 72%. CONCLUSION: Pre-operative 3-D templating for TSAs can accurately predict glenoid and humeral component size. This study sets the groundwork for utilization of pre-operative 3-D templating as a potential method to reduce overall TSA costs by managing cost of implants, reducing inventory needs, and improving surgical efficiency.

publication date

  • May 6, 2022

Identity

PubMed Central ID

  • PMC9163733

Scopus Document Identifier

  • 85048769876

Digital Object Identifier (DOI)

  • 10.1177/24715492221098818

PubMed ID

  • 35669622

Additional Document Info

volume

  • 6