3D-printed Handheld Models Do Not Improve Recognition of Specific Characteristics and Patterns of Three-part and Four-part Proximal Humerus Fractures

Background Reliably recognizing the overall pattern and specific characteristics of proximal humerus fractures may aid in surgical decision-making. With conventional onscreen imaging modalities, there is considerable and undesired interobserver variability, even when observers receive training in the application of the classification systems used. It is unclear whether three-dimensional (3D) models, which now can be fabricated with desktop printers at relatively little cost, can decrease interobserver variability in fracture classification. Questions/purposes Do 3D-printed handheld models of proximal humerus fractures improve agreement among residents and attending surgeons regarding (1) specific fracture characteristics and (2) patterns according to the Neer and Hertel classification systems? Methods Plain radiographs, as well as two-dimensional (2D) and 3D CT images, were collected from 20 patients (aged 18 years or older) who sustained a three-part or four-part proximal humerus fracture treated at a Level I trauma center between 2015 and 2019. The included images were chosen to comprise images from patients whose fractures were considered as difficult-to-classify, displaced fractures. Consequently, the images were assessed for eight fracture characteristics and categorized according to the Neer and Hertel classifications by four orthopaedic residents and four attending orthopaedic surgeons during two separate sessions. In the first session, the assessment was performed with conventional onscreen imaging (radiographs and 2D and 3D CT images). In the second session, 3D-printed handheld models were used for assessment, while onscreen imaging was also available. Although proximal humerus classifications such as the Neer classification have, in the past, been shown to have low interobserver reliability, we theorized that by receiving direct tactile and visual feedback from 3D-printed handheld fracture models, clinicians would be able to recognize the complex 3D aspects of classification systems reliably. Interobserver agreement was determined with the multirater Fleiss kappa and scored according to the categorical rating by Landis and Koch. To determine whether there was a difference between the two sessions, we calculated the delta (difference in the) kappa value with 95% confidence intervals and a two-tailed p value. Post hoc power analysis revealed that with the current sample size, a delta kappa value of 0.40 could be detected with 80% power at alpha = 0.05. Results Using 3D-printed models in addition to conventional imaging did not improve interobserver agreement of the following fracture characteristics: more than 2 mm medial hinge displacement, more than 8 mm metaphyseal extension, surgical neck fracture, anatomic neck fracture, displacement of the humeral head, more than 10 mm lesser tuberosity displacement, and more than 10 mm greater tuberosity displacement. Agreement regarding the presence of a humeral head-splitting fracture was improved but only to a level that was insufficient for clinical or scientific use (fair to substantial, delta kappa = 0.33 [95% CI 0.02 to 0.64]). Assessing 3D-printed handheld models adjunct to onscreen conventional imaging did not improve the interobserver agreement for pattern recognition according to Neer (delta kappa = 0.02 [95% CI -0.11 to 0.07]) and Hertel (delta kappa = 0.01 [95% CI -0.11 to 0.08]). There were no differences between residents and attending surgeons in terms of whether 3D models helped them classify the fractures, but there were few differences to identify fracture characteristics. However, none of the identified differences improved to almost perfect agreement (kappa value above 0.80), so even those few differences are unlikely to be clinically useful. Conclusion Using 3D-printed handheld fracture models in addition to conventional onscreen imaging of three-part and four-part proximal humerus fractures does not improve agreement among residents and attending surgeons on specific fracture characteristics and patterns. Therefore, we do not recommend that clinicians expend the time and costs needed to create these models if the goal is to classify or describe patients' fracture characteristics or pattern, since doing so is unlikely to improve clinicians' abilities to select treatment or estimate prognosis.