Elwell, Josie

ABSTRACT The goal of this radiomic analysis is to quantify the sensitivity of radiomic features on computed tomography (CT) image pre‐processing parameters and use machine learning (ML) techniques to identify the radiomic features that are highly predictive of shoulder arthroplasty outcomes. An ML framework auto‐segmented 3D masks of the deltoid muscle and scapula bone from pre‐operative CT images of 1949 primary anatomic total shoulder arthroplasty (aTSA)/reverse total shoulder arthroplasty (rTSA) patients. Radiomic features were extracted after various image pre‐processing protocols and assessed for reproducibility. The radiomic features deemed robust to image pre‐processing were used to train ML predictive outcomes models. Feature importance data were rank‐ordered to identify the radiomic features that were highly predictive of pain, motion, and function before and after aTSA/rTSA. A sensitivity analysis identified 37 deltoid muscle and 38 scapular bone radiomic features that were robust, reproducible, and unique across image pre‐processing parameters. The most predictive deltoid muscle radiomic measurements were normalized volume, elongation, flatness, fat percentage, sphericity, and max 2D diameter column. The most predictive scapular bone radiomic measurements were flatness, sphericity, elongation, max 2D diameter column, and max 2D diameter slice. Radiomic data of the deltoid and scapula were highly predictive of pain, motion, and function before and after aTSA and rTSA. Radiomic data were more predictive than patient comorbidities, diagnosis, and implant type/size data, but less predictive than pre‐operative active range of motion measurements and patient reported outcome measures, 3D measurements from planning software, or patient demographic data. Future work is required to clinically validate these radiomic features before they can be deployed in clinical decision support tools. Level of Evidence Level III, Retrospective Comparative Outcome Study.

Introduction: We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine learning (ML)-based segmentation algorithm requires rigorous validation. The aim of this study is to conduct shoulder expert validation of a novel deltoid ML auto-segmentation and quantification tool. Materials and Methods: A SwinUnetR-based ML model trained on labeled CT scans is validated by three expert shoulder surgeons for 32 unique patients. The validation evaluates the quality of the auto-segmented deltoid images. Specifically, each of the three surgeons reviewed the auto-segmented masks relative to CT images, rated masks for clinical acceptance, and performed a correction on the ML-generated deltoid mask if the ML mask did not completely contain the full deltoid muscle, or if the ML mask included any tissue other than the deltoid. Non-inferiority of the ML model was assessed by comparing ML-generated to surgeon-corrected deltoid masks versus the inter-surgeon variation in metrics, such as volume and fatty infiltration. Results: The results of our expert shoulder surgeon validation demonstrates that 97% of ML-generated deltoid masks were clinically acceptable. Only two of the ML-generated deltoid masks required major corrections and only one was deemed clinically unacceptable. These corrections had little impact on the deltoid measurements, as the median error in the volume and fatty infiltration measurements was <1% between the ML-generated deltoid masks and the surgeon-corrected deltoid masks. The non-inferiority analysis demonstrates no significant difference between the ML-generated to surgeon-corrected masks relative to inter-surgeon variations. Conclusions: Shoulder expert validation of this CT image analysis tool demonstrates clinically acceptable performance for deltoid auto-segmentation, with no significant differences observed between deltoid image-based measurements derived from the ML generated masks and those corrected by surgeons. These findings suggest that this CT image analysis tool has potential to reliably quantify deltoid muscle size, shape, and quality. Incorporating these CT image-based measurements into the pre-operative planning process may facilitate more personalized treatment decision making, and help orthopedic surgeons make more evidence-based clinical decisions.

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study’s power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10–50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches.

Background A variety of clinically important benchmarks of success (CIBS) have been defined for total shoulder arthroplasty (TSA) to quantify success. However, it is unclear how the preoperative status of the patient influences their likelihood of achieving each CIBS. Questions/purposes (1) What proportion of patients achieve commonly used CIBS after TSA? (2) Is there a relationship between a patients’ preoperative function and their probability of achieving different CIBS? (3) Does there exist preoperative ranges for each outcome measure that are associated with greater achievement of CIBS? Methods We retrospectively queried a multicenter shoulder arthroplasty database for primary anatomic TSA (aTSA) and reverse TSA (rTSA). A total of 2041 aTSAs and 3205 rTSAs were included. The mean ± SD age at time of surgery was 66 ± 9 years for aTSAs and 72 ± 8 years for rTSAs. In all, 51% (1049 of 2041) of aTSAs and 61% (1955 of 3205) of rTSAs were in patients who were women. For aTSAs, osteoarthritis with an intact rotator cuff was the most common preoperative diagnosis (92% [1869 of 2041]). For rTSAs, the most common preoperative diagnoses included rotator cuff tear arthropathy (40% [1280 of 3205]), osteoarthritis with an intact rotator cuff (24% [780 of 3205]), osteoarthritis with a torn rotator cuff (20% [632 of 3205]), and massive rotator cuff tear (10% [309 of 3205]). Outcomes were evaluated at latest follow-up (aTSA 61 ± 36 months, rTSA 47 ± 26 months) and consisted of ROM (abduction, forward elevation, external and internal rotation) as well as the most commonly used outcome scores: the Simple Shoulder Test (SST), Constant score, American Shoulder and Elbow Surgeons (ASES) score, University of California, Los Angeles (UCLA) score, Shoulder Pain and Disability Index (SPADI), and the Shoulder Arthroplasty Smart (SAS) score. The CIBS that we evaluated included the minimum clinically important difference (MCID), substantial clinical benefit (SCB), patient acceptable symptom state (PASS), minimum clinically important percentage of maximal possible improvement (MCI-%MPI), and substantial clinically important percentage of maximal possible improvement (SCI-%MPI). Prosthesis-specific anchor-based CIBS were adopted from prior publications on patients from this database. Multivariable regression was performed to identify the relationship between preoperative outcome measures and achievement of CIBS. Additionally, receiver operating characteristic (ROC) curve analyses were performed to determine whether thresholds in preoperative outcome measures were associated with achieving CIBS. Results For all ROM measures and outcome scores, poorer preoperative ROM was associated with greater odds of achieving the MCID and SCB but lower odds of achieving the PASS. For the SST and the two scores without ceiling effects (the Constant and SAS scores), poorer preoperative outcome scores were associated with greater odds of achieving the MCI-%MPI and SCI-%MPI, but no association was demonstrated for the ASES, SPADI, and UCLA scores. Graphical analysis demonstrated that patients with greater preoperative ROM and outcome scores had a lower probability of achieving the MCID and SCB but a higher probability of achieving the PASS. For outcome scores with known ceiling effects, patients with more favorable preoperative outcome scores were more likely to achieve the MCI-%MPI and SCI-%MPI than the MCID and SCB, respectively. For outcome scores without ceiling effects, patients undergoing aTSA were more likely to achieve the MCID, MCI-%MPI, and the SCI-%MPI than the SCB, but no clear trend was identified for patients undergoing rTSA. On ROC curve analysis, identified thresholds were reasonably accurate (area under the curve > 0.7) for achievement of measures of absolute improvement (the MCID and SCB), but not for absolute postoperative status (the PASS) or relative improvement (the MCI-%MPI and SCI-%MPI). Conclusion While most patients reported being “much better” (aTSA 75%, rTSA 76%) or “better” (aTSA 15%, rTSA 18%) compared with before surgery, 10% of aTSAs and 6% of rTSAs were either “unchanged” or “worse.” Patients’ likelihood of achieving CIBS depends in part on their baseline function and whether success is defined as absolute improvement, absolute postoperative status, or relative improvement. Patients with more favorable preoperative status are more likely to achieve CIBS that evaluate relative improvement (the MCI-%MPI and SCI-%MPI) than absolute improvement (the MCID and SCB). Future studies are needed to determine which individual CIBS or combinations thereof most accurately represent clinically relevant benefit. Level of Evidence Level III, therapeutic study.

Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2–3 years, and 3–5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.