Methods for predicting impingement of the conjoined tendon following reverse total shoulder arthroplasty

Abstract

A method for predicting a risk of impingement of the conjoined tendon following reverse total shoulder arthroplasty includes receiving one or more measured anatomical input parameters, and generating a prediction of a level of baseline risk of conjoined tendon impingement based on the one or more measured anatomical input parameters.

Description

METHODS FOR PREDICTING IMPINGEMENT OF THE CONJOINED TENDON FOLLOWING REVERSE TOTAL SHOULDER ARTHROPLASTYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 778,068, filed on March 26, 2025, and entitled METHODS FOR PREDICTING IMPINGEMENT OF THE CONJOINED TENDON FOLLOWING REVERSE TOTAL SHOULDER ARTHROPLASTY, the contents of which are hereby incorporated by reference herein in its entirety.TECHNICAL FIELD

[0002] This disclosure relates generally to a method for predicting the risk of impingement of the conjoined tendon following a reverse total shoulder arthroplasty, and more particularly, predicting separate contributions to the risk of impingement based on an anatomically derived baseline risk, and an implant component derived surgical risk.BACKGROUND

[0003] Reverse total shoulder arthroplasty (RTSA) is increasingly utilized for managing severe shoulder pathology, including rotator cuff arthropathy and end-stage glenohumeral arthritis. While RTSA generally provides significant functional improvement and pain relief, up to 21% of patients report dissatisfaction following the procedure. Common postoperative complications include instability, infection, component loosening, periprosthetic fracture, and persistent pain. However, postoperative pain, including anterior shoulder pain, remains a challenge to manage due to its diverse underlying causes.

[0004] Anterior shoulder pain following RTSA has been associated with pathologies of the coracoid process, lesser tuberosity (LT) of the humerus, and anterior glenohumeral musculature. One possible cause of anterior shoulder pain is impingement between the LT of the humeral head and the conjoined tendon (CJT). It has been proposed that increased tension in the CJT due to arm lengthening may contribute to coracoid fractures and subsequent anterior shoulder pain, particularly in patients with a smaller body habitus. Consistent with this hypothesis, multiple studies have shown that CJT lengthening can alleviate anterior shoulder discomfort. Impingement between the CJT and proximal humerus during internal rotation has also been suggested as a potential pain source, identifying RTSA component design parameters 14906-6931-3179294564-003016WOPTand positions - such as larger glenospheres and increased humeral component retroversion -that may heighten the risk. However, the influence of individual variations in native anatomy on this impingement risk has not been previously explored.

[0005] A need therefore exists for a model to 1) predict how a patient’s preoperative anatomical parameters and planned surgical parameters may separately contribute to the likelihood of CJT impingement, and also 2) to predict the probability of impingement based on each of the separate contributions.SUMMARY

[0006] According to some implementations of the present disclosure, a method for predicting a risk of impingement of the conjoined tendon (CJT) following reverse total shoulder arthroplasty (RTSA) includes receiving one or more measured anatomical input parameters, and generating a prediction of a level of baseline risk of CJT impingement based on the one or more measured anatomical input parameters.

[0007] According to some implementations of the present disclosure, the one or more measured anatomical input parameters are part of a first category of anatomical input parameters. The method further includes receiving one or more of a second category of surgical input parameters, and generating a prediction of a level of surgical risk of CJT impingement based on the one or more of the second category of surgical input parameters.

[0008] According to some implementations of the present disclosure, the first category of anatomical input parameters are parameters that are inherent to a patient, and the second category of surgical input parameters are parameters that are selectable by a surgeon.

[0009] According to some implementations of the present disclosure, the first category of anatomical parameters includes at least a measure of a normalized coracoid elevation relative to the glenoid of the patient.

[0010] According to some implementations of the present disclosure, the second category of surgical parameters include at least one of a difference between a planned retroversion of a humeral component and a native humeral version of an implant, or a planned retroversion of a glenoid component of the implant.

[0011] According to some implementations of the present disclosure, the method further includes generating an interactive interface that presents the predicted levels of baseline risk and surgical risk as separate indications.24906-6931-3179294564-003016WOPT

[0012] According to some implementations of the present disclosure, the method further includes generating an interactive menu of input choices on the interactive interface, the input choices being selections of the second category of surgical input parameters and values for each of the selections.

[0013] According to some implementations of the present disclosure, the interactive menu of input choices includes one of a spinning wheel type menu, a slider, a direct numerical input received from the surgeon, or a combination thereof.

[0014] According to some implementations of the present disclosure, the interactive menu of input choices allows the surgeon to interactively change a values of the second category of surgical parameters and interactively see an updated prediction for the predicted level of surgical risk.

[0015] According to some implementations of the present disclosure, the one or more of the first category of anatomical input parameters includes at least three anatomical input parameters, and the one or more of the second category of surgical input parameters includes at least three surgical input parameters.

[0016] According to some implementations of the present disclosure, a system for predicting a risk of impingement of the CJT following RTSA includes a memory storing machine-readable instructions, and one or more processors coupled to the memory. At least one of the one or more processors is configured to execute the machine-readable instructions to receive one or more measured anatomical input parameters, and generate a prediction of a level of baseline risk of CJT impingement based on the one or more measured anatomical input parameters.

[0017] According to some implementations of the present disclosure, the one or more measured anatomical input parameters are part of a first category of anatomical input parameters. The at least one of the one or more processors is further configured to execute the machine-readable instructions to receive one or more of a second category of surgical input parameters, and generate a prediction of a level of surgical risk of CJT impingement based on the one or more of the second category of surgical input parameters.

[0018] According to some implementations of the present disclosure, the first category of anatomical input parameters are parameters that are inherent to a patient, and the second category of surgical input parameters are parameters that are selectable by a surgeon.34906-6931-3179294564-003016WOPT

[0019] According to some implementations of the present disclosure, the first category of anatomical parameters includes at least a measure of a normalized coracoid elevation relative to the glenoid of the patient.

[0020] According to some implementations of the present disclosure, the second category of surgical parameters include at least one of a difference between a planned retroversion of a humeral component and a native humeral version of an implant, or a planned retroversion of a glenoid component of the implant.

[0021] According to some implementations of the present disclosure, the at least one of the one or more processors is further configured to execute the machine-readable instructions to generate an interactive interface that presents the predicted levels of baseline risk and surgical risk as separate indications.

[0022] According to some implementations of the present disclosure, the at least one of the one or more processors is further configured to execute the machine-readable instructions to generate an interactive menu of input choices on the interactive interface, the input choices being selections of the second category of surgical input parameters and values for each of the selections.

[0023] According to some implementations of the present disclosure, the interactive menu of input choices includes one of a spinning wheel type menu, a slider, a direct numerical input received from the surgeon, or a combination thereof.

[0024] According to some implementations of the present disclosure, the interactive menu of input choices allows the surgeon to interactively change a values of the second category of surgical parameters and interactively see an updated prediction for the predicted level of surgical risk.

[0025] According to some implementations of the present disclosure, the one or more of the first category of anatomical input parameters includes at least three anatomical input parameters, and the one or more of the second category of surgical input parameters includes at least three surgical input parameters.

[0026] The above summary is not intended to represent each embodiment or every aspect of the present disclosure. Rather, the foregoing summary merely provides an example of some of the novel aspects and features set forth herein. The above features and advantages, and other features and advantages of the present disclosure, will be readily apparent from the following detailed description of representative embodiments and modes for carrying out the present44906-6931-3179294564-003016WOPTinvention, when taken in connection with the accompanying drawings and the appended claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The foregoing and other advantages of the present disclosure will become apparent upon reading the following detailed description and upon reference to the drawings.

[0028] FIG. 1 is an exemplary plot of predicted risk for CJT impingement for symptomatic and asymptomatic cohorts of patients, according to aspects of the present disclosure.

[0029] FIG. 2 is an exemplary plot of predicted risk for a patient separated into baseline risk based on parameter inputs of the patient’s anatomy, and surgical risk based on surgical parameter inputs, according to aspects of the present disclosure.

[0030] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations have been shown by way of example in the drawings and will be described in further detail herein. It should be understood, however, that the present disclosure is not intended to be limited to the particular forms disclosed. Rather, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.DETAILED DESCRIPTION

[0031] Methods for predicting impingement of the CJT following RTSA are described herein. The ability to assess the risk of pain-inducing CJT lengthening or impingement during preoperative planning enables surgeons to take proactive measures, reducing avoidable complications and optimizing patient outcomes. To assess this risk, anatomical and surgical predictors of CJT impingement following RTSA were identified. It was hypothesized that the size of the humeral LT, distance between the coracoid and the glenoid center, and version and lateralization of the implanted components would affect the incidence of impingement.

[0032] A study was conducted of eighteen patients who had undergone RTSA. The patients were separated into two cohorts: symptomatic (distortion observed of the CJT by the LT, presumed to be symptomatic) and asymptomatic. Each patient received a computed tomography (CT) scan which was used to generate patient anatomy and preoperative planning. A variety of anatomical parameters, such as the superior displacement of the coracoid process in relation to the glenoid center, and surgical parameters, such as the glenoid component retroversion and humeral stem retroversion change from native, were measured. Statistical 54906-6931-3179294564-003016WOPTanalysis was performed to determine any significant correlations between each parameter and impingement state. Logistic regression was used for multi-factor analysis, from which a prediction model was generated, the prediction model evaluated by leave-one-out cross-validation (LOOCV). An accurate prediction was considered where the model predicted over 50% likelihood of impingement for symptomatic patients, and under 50% for asymptomatic patients.

[0033] Nine patients were identified intraoperatively based on dynamic evaluation for CJT impingement. Following component implantation, the operating surgeon performed range of motion (ROM) assessments, positioning the shoulder through forward elevation, adduction, and internal and external rotation to identify any mechanical conflict between the LT of the humerus and the CJT. CJT impingement was defined as a distinct displacement or snap caused when the LT passed under the lateral edge of the CJT before the tendon settled into the bicipital groove. In cases where impingement was observed, the tendon was managed via a surgical cascade. The procedure began with debulking of the LT. If necessary, this was followed by CJT recession, in which approximately half of the leading edge of the CJT was released and folded back on itself. Finally, the folded edge was sutured to its medial border, a technique referred to as CJT imbrication.

[0034] Following surgical correction of the CJT impingement, the shoulder was reevaluated through the same ROM to confirm resolution of impingement prior to wound closure. These patients were subsequently classified as exhibiting intraoperative findings consistent with symptomatic CJT impingement following RTSA. At the start of the study period, intraoperative assessment was performed selectively in cases where impingement was suspected; however, as clinical awareness increased, routine intraoperative and postoperative screening were implemented for all RTSA cases.

[0035] A cohort of equal size (n = 9 per group) matched by sex, operative side, and age was identified from asymptomatic patients who had previously undergone RTSA with no intraoperatively observed CJT impingement and no postoperative anterior shoulder pain at a minimum of 12 months’ follow-up. Inclusion criteria for both cohorts were patients undergoing primary RTSA surgery with the AltiVate Reverse® prosthesis (Enovis Corporation, Wilmington, DE, USA) with preoperative planning. Patients undergoing revision surgery were excluded. A total of 18 patients (9 symptomatic and 9 asymptomatic / control) were included, with 12 (66.7%) female and a mean age of 66.9 ± 7.8 (56-82). All procedures were performed by 1 of 3 participating surgeons from the same center.64906-6931-3179294564-003016WOPT

[0036] All patients had undergone a routine preoperative shoulder computed tomography (CT) scan as part of the standard clinical protocol. Each of the CT scans was segmented and landmarked with a validated protocol. The reverse prosthesis was virtually implanted in the ASTRA™ surgical planning platform (Enovis Corporation, Wilmington, DE, USA) by a surgeon prior to surgery, and patient-specific instrumentation (PSI) were designed and printed to execute the planned alignments for both the humeral and glenoid components.

[0037] As part of the process applied, 40 preoperative anatomical and planned surgical parameters that were hypothesized to contribute to CJT impingement were measured or calculated. The full list of parameters and their definitions are tabulated in Table 1.Table 1: Preoperative Anatomical and Planned Surgical Parameters Parameter Description UnitPreoperative HumerusHumeral head diameter mm Humeral head % subluxationHumeral neck-shaft ° anglePreoperative GlenoidGlenoid version Anteversion (+) / Retroversion (-) ° Glenoid inclination Superior (+) / Inferior (-) inclination ° Reverse shoulder angle ° Preoperative Coracoid ProcessCoracoid process angle Angle between the Friedman line and a line ° (coronal) through the axis of the coracoid process in thecoronal scapular plane.Coracoid process angle As above, in the axial scapular plane. ° (axial)Coracoid hooking Angle between a line from the coracoid base to ° the body, and a line from the body to the apex, inthe axial scapular plane.Coracoid length Distance between the apex of the coracoid process mm and the articulation with the scapular body.74906-6931-3179294564-003016WOPTCoracoid length As above, divided by the length of the Friedman (normalized) line.Coracoid width Distance between the anterior and posterior mm cortices of the coracoid process at the point of inflection.Coracoid width As above, divided by the length of the Friedman (normalized) line.Coracoid anterior Anteroposterior distance between the conjoined mm position tendon origin on the coracoid process andextension of the Friedman line.Coracoid anterior As above, divided by the length of the Friedman position (normalized) line.Coracoid lateral position Mediolateral distance between the conjoined mm tendon origin on the coracoid process andextension of the Friedman line.Coracoid lateral position As above, divided by the length of the Friedman (normalized) line.Coracoid superior Superoinferior distance between the conjoined mm position tendon origin on the coracoid process andextension of the Friedman line.Coracoid superior As above, divided by the length of the Friedman position (normalized) line.Preoperative Lesser TuberosityLesser tuberosity width Largest mediolateral distance of the lesser mm tuberosity in the CT axial plane.Lesser tuberosity width As above, divided by humeral head diameter.(normalized)Lesser tuberosity depth Perpendicular distance to the lesser tuberosity mm width.Lesser tuberosity depth As above, divided by humeral head diameter.(normalized)84906-6931-3179294564-003016WOPTLesser tuberosity angle Angle between humeral head axis and most ° anterior point of the lesser tuberosity in the CTaxial plane.Planned Humeral ComponentStem sizeHumeral resection depth Perpendicular distance from osteotomy plane to the mm most distant point of the native humeral head.Humeral resection Mediolateral component of resection depths. mm (lateral)Stem version change Anteversion (+) / Retroversion (-) ° (from native)Stem anterior translation Translation of the humeral stem component from mm the native humeral canal. Anterior (+) / Posterior(-)Stem lateral translation Lateral (+) / Medial (-) mm Stem flexion angle Flexion (+) / Extension (-) ° Stem valgus angle Valgus (+) / Varus (-) ° Planned Glenoid ComponentGlenosphere diameter mm Glenosphere thickness Distance between glenosphere base and apex. mm Baseplate version Anteversion (+) / Retroversion (-) ° Glenoid version change Preoperative glenoid version minus planned ° (from native) baseplate version.Baseplate inclination Superior (+) / Inferior (-) inclination ° Glenoid inclination Preoperative glenoid inclination minus planned ° change baseplate inclination.Glenoid ream depth Medial (+) / Lateral (-) mm Baseplate superior Translation of the glenoid baseplate component mm translation from the native glenoid centre. Superior (+) / Inferior (-)3D, 3-dimensional; mm, millimetres; SD, standard deviation94906-6931-3179294564-003016WOPT

[0038] Preoperative parameters were taken against the preoperative CT scan or 3-dimensional (3D) segmented bones. Notably, the size of the humeral LT was assessed in 3D by measuring its greatest mediolateral distance (width) and anteroposterior distance (depth). To account for body habitus, both measurements were normalized by dividing them by the humeral head diameter. The position of the coracoid was measured as the distance from the origin of the biceps brachii on the coracoid process to the glenoid center. The lateral and superior measurements were taken based on the projection of these two points onto the scapular coronal plane (defined by the glenoid center, trigonum spinae, and inferior angle of the scapula) along and perpendicular to the Friedman line, respectively. The anterior measurement was taken perpendicular to the Friedman line in the scapular axial plane. The measurements for coracoid position were likewise normalized by expressing them as a ratio relative to the length of the Friedman line.

[0039] Surgical parameters were taken from the planned component orientations or calculated from these values. Planned humeral version was defined as the angle between the implant’s neck axis and the anatomic neck of the humerus in the axial plane. Planned glenoid version was defined as the angle between the central screw of the baseplate and the Friedman line in the axial plane.

[0040] Descriptive statistics were used to determine means and standard deviations. Balance between the symptomatic and asymptomatic cohorts was evaluated by calculating standardized mean differences for each matching variable (sex, operative side, and age). Normality of variables were assessed using the Shapiro-Wilk test. Two-sample T-tests and Wilcoxon Rank-Sum tests were performed to compare means between symptomatic and asymptomatic cohorts for normally distributed and non-normally distributed parameters, respectively. Statistical significance was set at p < 0.05.

[0041] Generalized linear models using binomial logistic regression were used to predict symptomatic status. An exhaustive model selection process was conducted, evaluating all possible combinations of preoperative and planned parameters that showed a trend towards significance in univariate analysis (p < 0.1). For each combination of parameters, model generation and refinement with backward stepwise selection was repeated 10 times. The bias-corrected error was averaged across iterations, and the parameter combination with the lowest error was selected. The performance of the final model was evaluated using LOOCV to maximize training data usage and provide an unbiased performance estimate. In each iteration, one symptomatic and one asymptomatic patient were excluded, a model was trained on the 104906-6931-3179294564-003016WOPTremaining data, and predicted probabilities of being symptomatic were generated for the excluded patients. Classifications were considered correct if symptomatic patients had a predicted likelihood greater than or equal to 50%, or if asymptomatic patients had a likelihood less than 50%. Model performance was also evaluated using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). For each metric, 95% confidence intervals (CI) were calculated using the exact binomial method.

[0042] The unadjusted standardized mean difference for age between the symptomatic and asymptomatic groups was -0.028, indicating minimal imbalance. Gender and operative side were perfectly balanced between the two groups.

[0043] The generalized linear model with the lowest average bias-corrected error (0.17) trended towards significance (p = 0.10) and included three parameters: planned stem version change from native, planned baseplate version, and the preoperative normalized coracoid superior position. The symptomatic cohort were likely to have a more retroverted planned stem version, a less retroverted planned baseplate version, and a less superiorly positioned coracoid process relative to the glenoid center. None of the individual parameters were significantly different between the two cohorts (p > 0.07).

[0044] LOOC V produced 81 trained models, corresponding to the 81 unique symptomatic-asymptomatic patient pairs. Each model generated two predictions per iteration, one for each excluded patient, resulting in 162 total predictions across all iterations. Out of these, 128 predictions (79.0%) were correct. The model achieved a sensitivity of 0.75 (95% CI 0.63-0.84) and specificity of 0.82 (95% CI 0.71-0.90) with PPV of 0.81 (95% CI 0.69-0.89) and NPV of 0.77 (95% CI 0.66-0.86), respectively. Referring to FIG. 1, the average predicted likelihood of impingement was 76.1% (± 30.9) for symptomatic patients 100, and 22.2% (± 16.2) for asymptomatic patients 110, which was statistically significantly different (p = 0.0012).

[0045] Some significant parametric relationships have been discovered in regard to patients experiencing CJT impingement. Patients with a more retroverted planned humeral component are at a greater risk of being symptomatic due to reduced anterior bone removal with a more retroverted osteotomy, resulting in earlier possible onset of impingement. A less retroverted planned glenoid component is also correlated with an increased risk of CJT impingement due to a more anteriorized position of the postoperative humerus. Finally, symptomatic patients are likely to have a less superiorly positioned coracoid process, as the reduced clearance between the tendon origin and point of impingement may result in the bowstring phenomenon (where114906-6931-3179294564-003016WOPTgreater force is required to displace a tendon closer to its attachment) and exacerbate severity of impingement.

[0046] Impingement of the CJT is a multifactorial issue; however, a predictive model based on anatomical and surgical parameters has shown promising accuracy and offers a valuable preliminary tool. The model suggests that patients with an increased offset in their arc of rotation have a higher risk of impingement. Moreover, the bowstring phenomenon (where greater force is required to displace a tendon closer to its attachment) may exacerbate the severity of impingement and increase pain likelihood. Future work involves developing a 3D simulation, which, when combined with the predictors identified in our study, may facilitate more accurate predictions of CJT impingement in RTSA based on patient-specific anatomy and implant alignment. This advancement will enhance preoperative decision-making, helping to prevent CJT impingement and ultimately leading to improved patient outcomes.

[0047] Recent studies have highlighted this gap, with current literature being unclear in how tendons within the shoulder respond to the new biomechanics and length tensioning following the placement of a reverse prosthesis. Although the lateralized philosophy aims to better mimic native anatomy compared to the Grammont-style prostheses, which prioritizes medialization and distalization of the center of rotation (CoR) to achieve arm elevation, both designs alter soft tissue tension and joint mechanics.

[0048] A biomechanical study highlighted the significant influence of humeral component version on joint stability in RTSA. Greater humeral component retroversion was also associated with a significant reduction in external rotation in another study. Meanwhile, several studies have correlated anterior humeral component offset to poor outcomes following RTSA. Accuracy of humeral osteotomy is therefore a critical preoperative and intraoperative consideration for both anatomic and reverse shoulder arthroplasty.

[0049] The methodology does not account for any intraoperative adjustment made by the surgeon, which could significantly impact the final component positioning. While the use of PSI likely reduced variability (particularly in glenoid version and inclination, and humeral version, neck-shaft angle and osteotomy height) postoperative imaging was not consistently available, preventing verification of the implanted component orientation and limiting the direct clinical applicability of the findings. Future prospective studies could address this limitation by evaluating the final implanted orientation using standardized postoperative imaging, such as multi-planar x-rays or CT scans.124906-6931-3179294564-003016WOPT

[0050] An example analysis of the identified patient cohorts who underwent RTSA as disclosed above is further described here. Any number of the parameters in Table 1, and / or other parameters not included in Table 1, could be relevant to a prediction of CJT. In this example, the eighteen patients were separated into two cohorts: symptomatic (postoperative anterior shoulder pain or intraoperative adjustments for tendon impingement) and asymptomatic. All patients had undergone preoperative computed tomography (CT) scans and surgical planning using the ASTRA™ planning software.

[0051] All embodiments of a predictive model described herein are implemented on a system having one or more processors. The system also includes a memory having stored thereon machine readable instructions. The one or more processors are coupled to the memory, and the predictive model is implemented when the machine executable instructions in the memory are executed by at least one of the one or more processors of the system. The components of system may communicate via an internal bus or an external network, either wired or wireless, and the memory can include cloud storage as known in the art.

[0052] In an exemplary embodiment of the predictive model, three anatomical and surgical parameters were extracted from preoperative CT scans and ASTRA™ planning software and analyzed. Thus, in the exemplary embodiment, three anatomical and surgical parameters are included; however, in other embodiments of the predictive model, one, two, or more than three of the forty parameters listed in Table 1 and / or other parameters not listed in Table 1, that may overlap with or be entirely different from the three parameters in the exemplary embodiment of the predictive model can be used for analysis.

[0053] The three anatomical and surgical parameters included in the exemplary embodiment of the predictive model are:Coracoid superior position normalized (CSGC), which is a measure of coracoid elevation relative to the glenoid (anatomical);Stem version change from native (SV), which is the difference between the planned retroversion of the humeral component and native humeral version (surgical); and Baseplate version (BV), which is the planned retroversion of the glenoid component (surgical).

[0054] Individual parameters were statistically analyzed to determine correlations with the symptomatic cohort. A processor Logistic regression modeling was employed to assess multifactor relationships between anatomical and surgical parameters and postoperative anterior134906-6931-3179294564-003016WOPTpain. Model performance was evaluated using LOOCV. Statistical significance was set at p<0.05.

[0055] All of the three parameters were identified as potential contributors to CJT impingement. Key findings included the following.CSGC: Lower CSGC values were associated with higher rates of tendon impingement (p=0.154).SV: Greater retroversion of the humeral component trended toward increased impingement risk (p=0.184).BV: Less retroversion of the glenoid component was associated with a higher impingement risk (p=0.114).

[0056] The logistic regression model integrating CSGC, SV, and BV achieved an accuracy of 79% when validated using LOOCV. As shown in FIG. 1, predicted likelihoods of impingement were significantly different between symptomatic 100 and asymptomatic 110 patients: Symptomatic patients 100 had a predicted likelihood of 76.1±30.9%, and Asymptomatic patients 110 had a predicted likelihood of 22.2±16.2% (p=0.001).

[0057] This study underscores the critical role of preoperative anatomical and surgical planning in mitigating anterior shoulder pain following RTSA. Key parameters — CSGC, SV, and B V — demonstrated trending correlations with CJT impingement. These findings align with previous literature highlighting the biomechanical impact of implant positioning and soft tissue tension.

[0058] In another embodiment of the predictive model, the parameters listed in Table 1, and / or other parameters can be split into a first category for preoperative anatomical parameters of a patient, and a second category of planned surgical parameters for an implant surgery. For example, referring to Table 1, all of the parameters listed in the sub-categories of Preoperative Humerus, Preoperative Glenoid, Preoperative Coracoid Process, and Preoperative Lesser Tuberosity, can be grouped into the first category of preoperative anatomical parameters. These are parameters that are inherent to a patient’ s anatomy and over which a surgeon has no control. All of the other parameters listed in Table 1 can be grouped into the second category of planned surgical parameters, over which a surgeon does have control.

[0059] In an embodiment, the predictive model receives as inputs any or all of the first category of inputs, and / or other anatomical parameters not listed in Table 1, and outputs a baseline risk of CJT impingement based on the first category of anatomical input parameters alone. Separately, the predictive model receives as inputs any or all of the second category of 144906-6931-3179294564-003016WOPTinputs, and / or other planned surgical parameters not listed in Table 1, and outputs a surgical plan risk of CJT impingement based on the second category of input parameters alone.

[0060] Referring to FIG. 2, in an embodiment, the predictive model includes an interactive interface 200 that allows a surgeon to quickly and accurately assess the level of risk associated with the one or more input parameters. The plot in FIG. 2 includes a baseline contribution 210 to the level of risk of CJT infringement resulting from anatomical input parameters, and a separate surgical contribution 220 to the level of risk of CJT impingement resulting from surgical input parameters. In an embodiment, only anatomical parameters from Table 1 and / or not listed in Table 1 are included in the predictive model. In such an embodiment, the surgical contribution 220 to the level of risk of CJT impingement indicated in the plot of FIG. 2 is zero. In other embodiments, both anatomical and surgical parameters from Table 1 and / or not listed in Table 1 are included in the predictive model.

[0061] Still referring to FIG. 2, if surgical contribution 220 to the level of risk based on the second category of inputs (surgical) is zero, then there is no model output to inform the surgeon. In this circumstance, all of the risk of CJT impingement is the baseline contribution 210 to the level of risk based the patient’s anatomy. The surgeon can explain the risks to the patient who may then choose to go forward with or to forego the surgery. However, if the surgical contribution 220 to the level of risk is non-zero as illustrated in the example of FIG. 2, the surgeon could re-examine the input surgical parameters to consider alternatives of sizes and / or placement parameters that may not cause impingement. As provided by the included “Surgical Parameter Menu” that is built into the interactive interface 200 of FIG. 2, this process is interactive and occurs effectively in real-time, which allows for a significant saving of time and effort for the surgeon. For example, the surgeon could change the values of input surgical parameters interactively presented on dropdown menus 230 of the interactive interface 200 coincident with the plot of FIG. 2. The dropdown menus include two sections - a first section 240 allows the surgical parameter to be selected, for example, as represented by the list of parameters on the left side of the “Surgical Parameter Menu” in FIG. 2. A second section 250 allows a value for the surgical parameter to be selected, given either in mm or degrees, or in some other unit, and selectable via a spinning wheel type menu, a slider, by direct numerical input from the surgeon, or another form of input selection.

[0062] By selecting a particular surgical parameter and adjusting its value, the effect of the change in surgical parameter on the level of surgical risk is interactively shown in FIG. 2. This process provides for the input of a vast amount of three dimensional technical, geometrical,154906-6931-3179294564-003016WOPTand measurement data that would not be possible for a human mind to absorb, and for the output of detailed computations derived from the vast amount of data that again would be impossible for a human mind to execute. The model then outputs the surgical contribution 220 to the level of risk of CJT impingement to the interactive interface 200.

[0063] The integration of soft tissue parameters into preoperative planning platforms could revolutionize surgical decision-making for RTSA. The study demonstrates the utility of defining anatomical and surgical parameters capable of identifying at-risk patients and optimizing implant positioning to minimize postoperative complications.

[0064] This study was limited by its small cohort size and reliance on retrospective data. Prospective studies incorporating larger samples, detailed postoperative follow-ups, and patient-reported outcome measures (PROMs) are essential to validate these findings. Moreover, integrating offset measurements and exploring additional soft tissue structures may provide a more comprehensive understanding of RTSA biomechanics.

[0065] This study demonstrates the relationship between anatomical and surgical parameters and CJT impingement following RTSA. By assessing parameters such as CSGC, SV, and BV during surgical planning, it may be possible to improve patient outcomes and reduce postoperative anterior shoulder pain. Further work is needed to validate and refine these models for widespread clinical application.

[0066] It will be appreciated by persons skilled in the art that numerous variations and / or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.4906-6931-3179294564-003016WOPT

Claims

CLAIMSWhat is claimed is:

1. A method for predicting a risk of impingement of the conjoined tendon following reverse total shoulder arthroplasty, the method comprising:receiving one or more measured anatomical input parameters;generating a prediction of a level of baseline risk of conjoined tendon impingement based on the one or more measured anatomical input parameters.

2. The method of claim 1, wherein the one or more measured anatomical input parameters are part of a first category of anatomical input parameters, the method further comprising:receiving one or more of a second category of surgical input parameters;andgenerating a prediction of a level of surgical risk of conjoined tendon impingement based on the one or more of the second category of surgical input parameters.

3. The method of claim 2, wherein the first category of anatomical input parameters are parameters that are inherent to a patient, and the second category of surgical input parameters are parameters that are selectable by a surgeon.

4. The method of claim 2, wherein the first category of anatomical parameters includes at least a measure of a normalized coracoid elevation relative to the glenoid of the patient.

5. The method of claim 2, wherein the second category of surgical parameters include at least one of a difference between a planned retroversion of a humeral component and a native humeral version of an implant, or a planned retroversion of a glenoid component of the implant.

6. The method of claim 2, further comprising:generating an interactive interface that presents the predicted levels of baseline risk and surgical risk as separate indications.174906-6931-3179294564-003016WOPT7. The method of claim 6, further comprising:generating an interactive menu of input choices on the interactive interface, the input choices being selections of the second category of surgical input parameters and values for each of the selections.

8. The method of claim 7, wherein the interactive menu of input choices includes one of a spinning wheel type menu, a slider, a direct numerical input received from the surgeon, or a combination thereof.

9. The method of claim 7, wherein the interactive menu of input choices allows the surgeon to interactively change a values of the second category of surgical parameters and interactively see an updated prediction for the predicted level of surgical risk.

10. The method of claim 2, wherein the one or more of the first category of anatomical input parameters includes at least three anatomical input parameters, and the one or more of the second category of surgical input parameters includes at least three surgical input parameters.

11. A system for predicting a risk of impingement of the conjoined tendon following reverse total shoulder arthroplasty, the system comprising:a memory storing machine-readable instructions; andone or more processors coupled to the memory, at least one of the one or more processors configured to execute the machine-readable instructions to: receive one or more measured anatomical input parameters;generate a prediction of a level of baseline risk of conjoined tendon impingement based on the one or more measured anatomical input parameters.

12. The system of claim 11, wherein the one or more measured anatomical input parameters are part of a first category of anatomical input parameters, and the at least one of the one or more processors is further configured to execute the machine-readable instructions to:receive one or more of a second category of surgical input parameters; and184906-6931-3179294564-003016WOPTgenerate a prediction of a level of surgical risk of conjoined tendon impingement based on the one or more of the second category of surgical input parameters.

13. The system of claim 12, wherein the first category of anatomical input parameters are parameters that are inherent to a patient, and the second category of surgical input parameters are parameters that are selectable by a surgeon.

14. The system of claim 12, wherein the first category of anatomical parameters includes at least a measure of a normalized coracoid elevation relative to the glenoid of the patient.

15. The system of claim 12, wherein the second category of surgical parameters include at least one of a difference between a planned retroversion of a humeral component and a native humeral version of an implant, or a planned retroversion of a glenoid component of the implant.

16. The system of claim 12, wherein the at least one of the one or more processors is further configured to execute the machine-readable instructions to:generate an interactive interface that presents the predicted levels of baseline risk and surgical risk as separate indications.

17. The system of claim 16, wherein the at least one of the one or more processors is further configured to execute the machine-readable instructions to:generate an interactive menu of input choices on the interactive interface, the input choices being selections of the second category of surgical input parameters and values for each of the selections.

18. The system of claim 17, wherein the interactive menu of input choices includes one of a spinning wheel type menu, a slider, a direct numerical input received from the surgeon, or a combination thereof.

19. The system of claim 17, wherein the interactive menu of input choices allows the surgeon to interactively change a values of the second category of surgical parameters and interactively see an updated prediction for the predicted level of surgical risk.194906-6931-3179294564-003016WOPT20. The system of claim 12, wherein the one or more of the first category of anatomical input parameters includes at least three anatomical input parameters, and the one or more of the second category of surgical input parameters includes at least three surgical input parameters.204906-6931-3179294564-003016WOPT

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