Are glenoid retroversion, humeral subluxation, and Walch classification associated with a muscle imbalance?

BACKGROUND: The etiology of humeral posterior subluxation remains unknown, and it has been hypothesized that horizontal muscle imbalance could cause this condition. The objective of this study was to compare the ratio of anterior-to-posterior rotator cuff and deltoid muscle volume as a function of humeral subluxation and glenoid morphology when analyzed as a continuous variable in arthritic shoulders. METHODS: In total, 333 computed tomography scans of shoulders (273 arthritic shoulders and 60 healthy controls) were included in this study and were segmented automatically. For each muscle, the volume of muscle fibers without intramuscular fat was measured. The ratio between the volume of the subscapularis and the volume of the infraspinatus plus teres minor (AP ratio) and the ratio between the anterior and posterior deltoids (APdeltoid) were calculated. Statistical analyses were performed to determine whether a correlation could be found between these ratios and glenoid version, humeral subluxation, and/or glenoid type per the Walch classification. RESULTS: Within the arthritic cohort, no statistically significant difference in the AP ratio was found between type A glenoids (1.09 ± 0.22) and type B glenoids (1.03 ± 0.16, P = .09), type D glenoids (1.12 ± 0.27, P = .77), or type C glenoids (1.10 ± 0.19, P > .999). No correlation was found between the AP ratio and glenoid version (ρ = -0.0360, P = .55) or humeral subluxation (ρ = 0.076, P = .21). The APdeltoid ratio of type A glenoids (0.48 ± 0.15) was significantly greater than that of type B glenoids (0.35 ± 0.16, P < .01) and type C glenoids (0.21 ± 0.10, P < .01) but was not significantly different from that of type D glenoids (0.64 ± 0.34, P > .999). When evaluating both healthy control and arthritic shoulders, moderate correlations were found between the APdeltoid ratio and both glenoid version (ρ = 0.55, P < .01) and humeral subluxation (ρ = -0.61, P < .01). CONCLUSION: This in vitro study supports the use of software for fully automated 3-dimensional reconstruction of the 4 rotator cuff muscles and the deltoid. Compared with previous 2-dimensional computed tomography scan studies, our study did not find any correlation between the anteroposterior muscle volume ratio and glenoid parameters in arthritic shoulders. However, once deformity occurred, the observed APdeltoid ratio was lower with type B and C glenoids. These findings suggest that rotator cuff muscle imbalance may not be the precipitating etiology for the posterior humeral subluxation and secondary posterior glenoid erosion characteristic of Walch type B glenoids.

A Scapular Statistical Shape Model can Reliably Predict Premorbid Glenoid Morphology in Conditions of Severe Glenoid Bone Loss.

BACKGROUND: Knowledge of premorbid glenoid parameters at the time of shoulder arthroplasty, such as inclination, version, joint line position, height, and width, can assist with implant selection, implant positioning, metal augment sizing and/or bone graft dimensions. The objective of this study was to validate a scapular statistical shape model (SSM) in predicting patient-specific glenoid morphology in scapulae with clinically relevant glenoid erosion patterns. METHODS: Computer tomography scans of 30 healthy scapulae were obtained and used as the control group. Each scapula was then virtually eroded to create seven erosion patterns (Walch A1, A2, B2, B3, D, Favard E2, and E3). This resulted in 210 uniquely eroded glenoid models, forming the eroded glenoid group. A scapular SSM, created from a different database of 85 healthy scapulae, was then applied to each eroded scapula to predict the premorbid glenoid morphology. The premorbid glenoid inclination, version, height, width, radius of best fit sphere, and glenoid joint line position were automatically calculated for each of the 210 eroded glenoids. The mean values for all outcome variables were compared across all erosion types between the healthy, eroded, and SSM predicted groups using a two-way repeated-measures analysis of variance. RESULTS: The SSM was able to predict the mean premorbid glenoid parameters of the eroded glenoids with a mean absolute difference of 3±2° for inclination, 3±2° for version, 2±1mm for glenoid height, 2±1mm for glenoid width, 5±4mm for radius of best fit sphere, and 1±1mm for glenoid joint line. The mean SSM predicted values for inclination, version, height, width, and radius were not significantly different than the control group (P>0.05). DISCUSSION: A statistical shape model has been developed that can reliably predict premorbid glenoid morphology and glenoid indices in patients with common glenoid erosion patterns. This technology can serve as a useful template to visually represent the premorbid healthy glenoid in patients with severe glenoid bony erosions. Knowledge of the premorbid glenoid preoperatively can assist with implant selection, positioning, and sizing.

Are glenoid retroversion, humeral subluxation and Walch classification associated with a muscle imbalance.

INTRODUCTION: The etiology of humeral posterior subluxation remains unknown, and it has been hypothesized that horizontal muscle imbalance could cause this condition. The objective of this study was to compare the ratio of anterior to posterior rotator cuff muscle and deltoid volumes as a function of humeral subluxation and glenoid morphology when analyzed as continuous variable in arthritic shoulders. METHODS: Three hundred and thirty-three (273 arthritic and 60 healthy controls) CT scans of shoulders were included in this study and were segmented automatically. For each muscle, the volume of muscle fibers without intra-muscular fat was then measured. The ratio between the volume of the subscapularis and the volume of the infraspinatus + teres minor (AP ratio) and the ratio between the anterior and posterior deltoid (APdeltoid) were calculated. Statistical analyses were performed to determine whether a correlation could be found between these ratios and glenoid version/ humeral subluxation/glenoid type in the Walch classification. RESULTS: Within the arthritic cohort, no statistically significant difference was found between the AP ratio between A and type B glenoids (1.09 ± 0.22 versus 1.03 ± 0.16 p=0.09), between A and D type glenoids (1.09 ± 0.22 versus 1.12 ± 0.27, p=0.77) nor between the A and C type glenoids (1.09 ± 0.22 versus 1.10 ± 0.19, p=1). No correlation was found between AP ratio and glenoid version/humeral subluxation (rho =-0.0360, p=0.55; rho = 0.076; p=0.21). The APdeltoid ratio of type A glenoids was significantly greater than that of type B glenoids (0.48 ± 0.15 versus 0.35 ± 0.16, p< 0.01), and type C glenoids (0.48 ± 0.15 versus 0.21±0.10, p < 0.01) but not significantly different from the APdeltoid ratio of type D glenoids (0.48 ± 0.15 versus 0.64 ± 0.34, p=1). When evaluating both healthy control and arthritic shoulders, moderate correlations were found between APdeltoid ratio and glenoid version/humeral subluxation (rho=0.55, p<0.01; rho=-0.61, p<0.01). CONCLUSION: As opposed to previous two-dimensional CT scan studies, we did not find any correlation between AP muscle volume ratio and glenoid parameters in arthritic shoulders. Therefore, rotator cuff muscle imbalance does not seem to be associated with posterior humeral subluxation leading to posterior glenoid erosion and subsequent retroversion characteristic of Walch B glenoids. However, our results could suggest that a larger posterior deltoid pulls the humerus posteriorly into posterior subluxation, but this requires further evaluation as the deltoid follows the humerus possibly leading to secondary asymmetry between the anterior and the posterior deltoid.

Validation of the shoulder range of motion software for measurement of shoulder ranges of motion in consultation: coupling a red/green/blue-depth video camera to artificial intelligence.

PURPOSE: Clinical evaluation of the shoulder range of motion (RoM) may vary significantly depending on the surgeon. We aim to validate an automatic shoulder RoM measurement system associating image acquisition by an RGB-D (red/green/blue-depth) video camera to an artificial intelligence (AI) algorithm. METHODS: Thirty healthy volunteers were included. A 3D RGB-D sensor that simultaneously generated a colour image and a depth map was used. Then, an open-access convolutional neural network algorithm that was programmed for shoulder recognition provided a 3D motion measure. Each volunteer adopted a randomized position successively. For each position, two observers made a visual (EyeREF) and goniometric measurement (GonioREF), blind to the automated software which was implemented by an orthopaedic surgeon. We evaluated the inter-tester intra-class correlation (ICC) between observers and the concordance correlation coefficient (CCC) between the three methods. RESULTS: For manual evaluations EyeREF and GonioREF, ICC remained constantly excellent for the widest motions in the vertical plane (i.e., abduction and flexion). It was very good for ER1 and IR2 and fairly good for adduction, extension, and ER2. Differences between the measurements' means of EyeREF and shoulder RoM was significant for all motions. Compared to GonioREF, shoulder RoM provided similar results for abduction, adduction, and flexion and EyeREF provided similar results for adduction, ER1, and ER2. The three methods showed an overall good to excellent CCC. The mean bias between the three methods remained under 10° and clinically acceptable. CONCLUSION: RGB-D/AI combination is reliable in measuring shoulder RoM in consultation, compared to classic goniometry and visual observation.

Validation of the distal filling ratio in uncemented convertible short-stem shoulder arthroplasty.

INTRODUCTION: Radiographic stress shielding is a common finding in uncemented convertible short-stem shoulder arthroplasty (UCSSSA). The distal filling ratio (DFR) has been described as a predictor for the occurrence of stress shielding. A DFR > 70% was mentioned as a risk factor for the occurrence of stress shielding for some UCSSSA. However, measurements were only performed on conventional radiographs and no validation exists for 3D automated planning tools. METHODS: DFR was manually measured on postoperative true ap radiographs of 76 shoulder arthroplasties using a standardized protocol and were compared to preoperative CT scans with an automated calculation of the DFR after virtual implantation of the stem. RESULTS: The mean DFR measured on X-rays was 75.9% (SD = 8.7; 95% CI = 74-78) vs. 78.9% (SD = 9.1; 95% CI = 76.8-83) automatically measured on CT scans. This difference was significant (p < 0.001). In 7 out of 76 cases (9%) the difference between manual measurement on radiographs and computerized measurement on CT scans was > 10%. CONCLUSION: Manual measurement of the DFR is underestimated on conventional radiographs compared to automated calculation on CT scans be a mean of 3%. Therefore, automated measurement of the DFR on CT scans seems to be beneficial, especially in cases with osteopenic cortices. Manual measurement of the DFR on conventional ap radiographs in cases without CT scans, however, is still a viable alternative. LEVEL OF EVIDENCE: Level IV, retrospective study.

Patient Posture Affects Simulated ROM in Reverse Total Shoulder Arthroplasty: A Modeling Study Using Preoperative Planning Software.

BACKGROUND: Component selection and placement in reverse total shoulder arthroplasty (RTSA) is still being debated. Recently, scapulothoracic orientation and posture have emerged as relevant factors when planning an RTSA. However, the degree to which those parameters may influence ROM and whether modifiable elements of implant configuration may be helpful in improving ROM among patients with different postures have not been thoroughly studied, and modeling them may be instructive. QUESTIONS/PURPOSES: Using a dedicated expansion of a conventional preoperative planning software, we asked: (1) How is patient posture likely to influence simulated ROM after virtual RTSA implantation? (2) Do changes in implant configuration, such as humeral component inclination and retrotorsion, or glenoid component size and centricity improve the simulated ROM after virtual RTSA implantation in patients with different posture types? METHODS: In a computer laboratory study, available whole-torso CT scans of 30 patients (20 males and 10 females with a mean age of 65 ± 17 years) were analyzed to determine the posture type (Type A, upright posture, retracted scapulae; Type B, intermediate; Type C, kyphotic posture with protracted scapulae) based on the measured scapula internal rotation as previously described. The measurement of scapular internal rotation, which defines these posture types, was found to have a high intraclass correlation coefficient (0.87) in a previous study, suggesting reliability of the employed classification. Three shoulder surgeons each independently virtually implanted a short, curved, metaphyseal impaction stem RTSA in each patient using three-dimensional (3D) preoperative surgical planning software. Modifications based on the original component positioning were automatically generated, including different humeral component retrotorsion (0°, 20°, and 40° of anatomic and scapular internal rotation) and neck-shaft angle (135°, 145°, and 155°) as well as glenoid component configuration (36-mm concentric, 36-mm eccentric, and 42-mm concentric), resulting in 3720 different RTSA configurations. For each configuration, the maximum potential ROM in different planes was determined by the software, and the effect of different posture types was analyzed by comparing subgroups. RESULTS: Irrespective of the RTSA implant configuration, the posture types had a strong effect on the calculated ROM in all planes of motion, except for flexion. In particular, simulated ROM in patients with Type C compared with Type A posture demonstrated inferior adduction (median 5° [interquartile range -7° to 20°] versus 15° [IQR 7° to 22°]; p < 0.01), abduction (63° [IQR 48° to 78°] versus 72° [IQR 63° to 82°]; p < 0.01), extension (4° [IQR -8° to 12°] versus 19° [IQR 8° to 27°]; p < 0.01), and external rotation (7° [IQR -5° to 22°] versus 28° [IQR 13° to 39°]; p < 0.01). Lower retrotorsion and a higher neck-shaft angle of the humeral component as well as a small concentric glenosphere resulted in worse overall ROM in patients with Type C posture, with severe restriction of motion in adduction, extension, and external rotation to below 0°. CONCLUSION: Different posture types affect the ROM after simulated RTSA implantation, regardless of implant configuration. An individualized choice of component configuration based on scapulothoracic orientation seems to attenuate the negative effects of posture Type B and C. Future studies on ROM after RTSA should consider patient posture and scapulothoracic orientation. CLINICAL RELEVANCE: In patients with Type C posture, higher retrotorsion, a lower neck-shaft angle, and a larger or inferior eccentric glenosphere seem to be advantageous.

Identification of threshold pathoanatomic metrics in primary glenohumeral osteoarthritis.

BACKGROUND: An assessment of the pathoanatomic parameters of the arthritic glenohumeral joint (GHJ) has the potential to identify discriminating metrics to differentiate glenoid types in shoulders with primary glenohumeral osteoarthritis (PGHOA). The aim was to identify the morphometric differences and threshold values between glenoid types including normal and arthritic glenoids with the various types in the Walch classification. We hypothesized that there would be clear morphometric discriminators between the various glenoid types and that specific numeric threshold values would allow identification of each glenoid type. METHODS: The computed tomography scans of 707 shoulders were analyzed: 585 obtained from shoulders with PGHOA and 122 from shoulders without glenohumeral pathology. Glenoid morphology was classified according to the Walch classification. All computed tomography scans were imported in a dedicated automatic 3D-software program that referenced measurements to the scapular body plane. Glenoid and humeral modeling was performed using the best-fit sphere method, and the root-mean-square error was calculated. The direction and orientation of the glenoid and humerus described glenohumeral relationships. RESULTS: Among shoulders with PGHOA, 90% of the glenoids and 85% of the humeral heads were directed posteriorly in reference to the scapular body plane. Several discriminatory pathoanatomic parameters were identified: GHJ narrowing < 3 mm was a discriminatory metric for type A glenoids. Posterior humeral subluxation > 70% discriminated type B1 from normal GHJs. The root-mean-square error was a discriminatory metric to distinguish type B2 from type A, type B3, and normal GHJs. Type B3 glenoids differed from type A2 by greater retroversion (>13°) and subluxation (>71%). The type C glenoid retroversion inferior limit was 21°, whereas normal glenoids never presented with retroversion > 16°. CONCLUSION: Pathoanatomic metrics with the identified threshold values can be used to discriminate glenoid types in shoulders with PGHOA.

Three-dimensional geometry of the normal shoulder: a software analysis.

BACKGROUND: Three-dimensional (3D) geometry of the normal glenohumeral bone anatomy and relations is poorly documented. Our aims were (1) to determine the 3D geometry of the normal glenohumeral joint (GHJ) with reference to the scapular body plane and (2) to identify spatial correlations between the orientation and direction of the humeral head and the glenoid. METHODS: Computed tomographies (CTs) of the normal, noninjured GHJ were collected from patients who had undergone CTs in the setting of (1) polytrauma, (2) traumatic head injury, (3) chronic acromioclavicular joint dislocations, and (4) unilateral trauma with a contralateral normal shoulder. We performed 3D segmentation and measurements with a fully automatic software (Glenosys; Imascap). Measurements were made in reference to the scapular body plane and its transverse axis. Geometric measurements included version, inclination, direction, orientation, best-fit sphere radius (BFSR), humeral subluxation, critical shoulder angle, reverse shoulder angle, glenoid area, and glenohumeral distance. Statistical correlations were sought between glenoid and humeral 3D measurements (Pearson correlation). RESULTS: A total of 122 normal GHJs (64 men, 58 women, age: 52 ± 17 years) were studied. The glenoid BFSR was always larger than the humerus BFSR (constant factor of 1.5, standard deviation = 0.2). The mean glenoid version and inclination were -6° ± 4° and 7° ± 5°, respectively. Men and women were found to have significantly different values for inclination (6° vs. 9°, P = .02), but not for version. Humeral subluxation was 59% ± 7%, with a linear correlation with glenoid retroversion (r = -0.70, P < .001) regardless of age. There was a significant and linear correlation between glenoid and humeral orientation and direction (r = 0.72 and r = 0.70, P < .001). CONCLUSION: The 3D geometry of the glenoid and humeral head present distinct limits in normal shoulders that can be set as references in daily practice: version and inclination are -6° and 7°, respectively, and humeral posterior subluxation is 59%; interindividual variations, regardless of the size, are relative to the scapular plane. There exists a strong correlation between the position of the humeral head and the glenoid orientation and direction.

Preoperative metaphyseal cancellous bone density is associated with intraoperative conversion to stemmed total shoulder arthroplasty.

Background: Methods to determine whether a stemless humeral component is appropriate for anatomic total shoulder arthroplasty are varied and often subjective. Objective preoperative data regarding metaphyseal bone quality may help guide surgical decision-making. This study sought to evaluate preoperative proximal humeral bone quality and determine whether it is predictive of intraoperative conversion to a stemmed humeral component. Methods: Consecutive patients who underwent primary anatomic total shoulder arthroplasty from a single-surgeon practice were enrolled. All patients received a preoperative computed tomography (CT) scan for surgical templating purposes. The exclusion criteria were lack of a preoperative CT scan, preoperative plan for a stemmed component, and intraoperative conversion to a stem for a reason other than bone quality (ie, fracture). Preoperative CT scans were analyzed with an automated templating software. Cortical index and thickness were calculated, and bone density of the proximal diaphysis, cancellous metaphysis, and cortical metaphysis was obtained by averaging Hounsfield units (HU) across anatomically defined regions using a previously validated technique. The decision to convert to a stemmed humeral component was made intraoperatively based on a lack of stability of the trial stemless component. Bone quality measurements were compared between stemless and stemmed groups. An exact logistic regression was used incorporating gender and age. Results: A total of 79 patients who underwent primary anatomic total shoulder arthroplasty were included in this study. Of these patients, 6 underwent intraoperative conversion to a stemmed humeral component (7.6%). There was no significant difference between cohorts in terms of cortical index and bone density within the proximal diaphysis and cortical metaphysis. On univariate analysis, cortical thickness, metaphyseal cancellous bone density, and gender were significantly different between groups. Patients receiving a stem had significantly lower metaphyseal cancellous bone density than those receiving stemless components (5.5 ± 11.2 HU vs. 47.6 ± 29.4 HU, P<.001). All patients converted to stems were female individuals (P = .01) and had metaphyseal cancellous bone density less than 20 HU (P<.001). Conclusions: Metaphyseal cancellous bone density can be calculated on preoperative CT scans and is associated with intraoperative conversion to a stemmed humeral component in anatomic shoulder arthroplasty. A threshold of 20 HU can be used to predict which patients are more likely to require stemmed components.

Can we predict the humerus stem component size required to achieve rotational stability in metaphyseal stability concept?

Background: Implant manufacturers typically offer several sizes of a humeral stem for shoulder arthroplasty so that time zero fixation can be achieved with the optimal size. Stem size can be templated preoperatively but is definitively determined intraoperatively. The purpose of this study was to determine if preoperatively acquired parameters, including patient demographics and imaging, could be used to reliably predict intraoperative humeral stem size. Methods: A cohort of 290 patients that underwent shoulder arthroplasty (116 anatomic and 174 reverse) was analyzed to create a regression formula to predict intraoperative stem size. The initial cohort was separated into train and test groups (randomly selected 80% and 20%, respectively). Patient demographics, anatomical measurements, and statistical shape model parameters determined from a preoperative shoulder arthroplasty planning software program were used for multilinear regression. The implant used for all cases was a short-stemmed metaphyseal-fit prosthesis. Results: Metaphyseal bone density, humeral statistical shape model parameters, and humeral intramedullary canal diameter were identified as highly predictive of intraoperative final humeral prosthesis size. On the train group, a coefficient of determination R2 of 0.63 was obtained for the multilinear regression equation combining these parameters. When analyzing the cohort for the prediction of stem size in the test group, 95% were within plus or minus one size of that used during surgery. Conclusion: Preoperative criteria such as humeral geometry and proximal humeral bone density can be combined in a single multilinear equation to predict intraoperative humeral stem size within one size variation. Embedding the surgeon's decision-making process into an automated algorithm potentially allows this process to be applied across the surgical community. Predicting intraoperative decisions such as humeral stem size also has potential implications for the management of implant stocks for both manufacturers and health-care facilities.

Anatomical plane and transverse axis of the scapula: Reliability of manual positioning of the anatomical landmarks.

Background: The aim of our study was to evaluate the accuracy of manual determination of the three key points defining the anatomical plane of the scapula, which conditions the reliability of planning software programs based on manual method. Method: We included 82 scapula computed tomography scans (56 pathologic and 26 normal glenoid), excluding truncation and major three-dimensional artifact. Four observers independently picked the three key points for each case. Inter- and intra-observer agreement was calculated for each point, using the intraclass correlation method. The mean error (mm) between the observers was calculated as the diameter of the smallest sphere including the four chosen positions. Results: Lower inter-observer agreement was found for the trigonum superoinferior position and for the glenoid center anteroposterior position. The mean positioning error between the four observers was 6.9 mm for the trigonum point, and error greater than 10 mm was recorded in 25% of the cases. The mean positioning error was 3.5 mm for the glenoid center in altered glenoid, compared to 1.8 mm for normal glenoid. Discussion: Manual determination of an anatomical plane of the scapula suffers from inaccuracy especially due to the variability in trigonum picking, and in a lesser extent, to the variability of glenoid center picking in altered glenoid.

Development and assessment of 3-dimensional computed tomography measures of proximal humeral bone density: a comparison to established 2-dimensional measures and intraoperative findings in patients undergoing shoulder arthroplasty.

BACKGROUND: The purpose of this study was to develop novel three-dimensional (3D) measures of bone density from computed tomography (CT) scans and to compare them with validated two-dimensional (2D) radiographic assessments of bone density. Patient demographic data were also analyzed to see if there were any predictors of bone density (age, sex, etiology). METHODS: The study group consisted of 290 consecutive patients undergoing primary shoulder arthroplasty surgery (total anatomic, reverse, and hemiarthroplasty). All underwent preoperative CT imaging. Three 3D CT measurements (metaphysis cancellous, metaphysis cortical, and proximal diaphysis) were developed and automated into software. The developed 3D measurements were compared with validated 2D measures (Tingart and Gianotti Index). Patient demographic data were correlated with these measurements. The difference between the size of the final sounder and of the final stem was calculated as Delta. RESULTS: There was moderately strong correlation between Tingart and Gianotti measures (0.674, P < .001), as well as between 3D metaphysis cancellous measurements and Tingart (0.645, P < .001). Decreased bone density was highly correlated with female sex. Tingart (area under the curve [AUC]: 0.87, 95% confidence interval [CI]: 0.82-0.91) and 3D metaphysis cancellous (AUC: 0.78, 95% CI: 0.72-0.84) had the highest correlation. These were significantly more than other measures of bone density (P < .01). Decreased bone density measured with Tingart also had moderate correlation with advanced age (AUC: 0.67, 95% CI: 0.6-0.73), but less so for etiology (AUC: 0.62, 95% CI: 0.55-0.69). The 3D metaphysis cancellous measure had lower correlation with age (AUC: 0.59, 95% CI: 0.52-0.66) and etiology (AUC: 0.59, 95% CI: 0.52-0.65). The highest correlation with Delta (the difference between the final sounder and the stem size) was with the 3D metaphysis cancellous measure (AUC: 0.67, 95% CI: 0.59-0.73), followed by Tingart (AUC: 0.647, 95% CI: 0.57-0.671). A multiple regression model to predict Delta demonstrated the stronger prediction using 3D metaphysis cancellous (analysis of variance F-ratio of 42.6, P < .001) than Tingart (35.9, P < .001). CONCLUSION: This study demonstrates that automated measures of bone density can be obtained from 3D CT scans. Of the three novel 3D measurements of bone density, the humeral metaphysis cancellous measurement was most correlated to the known 2D measures and most correlated to the intraoperative assessment of bone density (delta).