Development and validation of a predictive model for outcomes in shoulder arthroplasty: a multicenter analysis of nearly 2000 patients

Thresholds for diminishing returns in postoperative range of motion after total shoulder arthroplasty

BACKGROUND

Satisfaction following total shoulder arthroplasty (TSA), which is commonly reported using patient-reported outcome measures (PROMs), is partially dependent upon restoring shoulder range of motion (ROM). We hypothesized there exists a minimum amount of ROM necessary to perform functional tasks queried in PROM questionnaires, beyond which further ROM may provide no further improvement in PROMs.

METHODS

A retrospective review of a multicenter international shoulder arthroplasty database was performed between 2004 and 2020 for patients undergoing anatomic or reverse TSA (aTSA, rTSA), with minimum 2-year follow-up. Our primary outcome was to determine the threshold in postoperative active ROM (abduction, forward elevation [FE], external rotation [ER], and internal rotation [IR] score), whereby additional improvement was not associated with additional improvement in PROMs (Simple Shoulder Test, American Shoulder and Elbow Surgeons score, and the Shoulder Pain and Disability Index). For comparison, we also evaluated the Shoulder Arthroplasty Smart (SAS) score, which is not subject to the ceiling effect.

RESULTS

We included 4459 TSAs (1802 aTSAs, 2657 rTSAs) with minimum 2-year follow-up (mean, 56 ± 32 months). The threshold in postoperative ROM that were associated with no further improvement were active abduction, 107-113° for PROMs vs. 163° for the SAS score; active FE, 149-162° for PROMs vs. 176° for the SAS score; active ER, 50-52° for PROMs vs. 72° for the SAS score; IR score, 4-5 points for all PROMs vs. 6 points for the SAS score. Out of 3508 TSAs with complete postoperative ROM data, 8.5% achieved or exceeded all ROM thresholds (14.5% aTSAs, 4.8% rTSAs).

CONCLUSIONS

Our findings demonstrate that postoperative ROM exceeding 113° of abduction, 162° of FE, 52° of ER, and IR to L1 is associated with minimal additional improvement in PROMs. While individual patient needs vary, the thresholds may provide helpful targets for patients undergoing postoperative rehabilitation.

Artificial intelligence in shoulder and elbow surgery: overview of current and future applications

Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.

A machine learning analysis of patient and imaging factors associated with achieving clinically substantial outcome improvements following total shoulder arthroplasty: Implications for selecting anatomic or reverse prostheses

Background

Indications for reverse total shoulder arthroplasty(rTSA) continue to expand making it challenging to predict whether patients will benefit more from anatomic TSA(aTSA) or rTSA. The purpose of this study was to determine which factors differ between aTSA and rTSA patients that achieve meaningful outcomes and may influence surgical indication.

Methods

Random Forest dimensionality reduction was applied to reduce 23 features into a model optimizing substantial clinical benefit (SCB) prediction of the American Shoulder and Elbow Surgeon score using 1117 consecutive patients with 2-year follow up. Features were compared between aTSA patients stratified by SCB achievement and subsequently with rTSA SCB achievers.

Results

Eight combined features optimized prediction (accuracy = 87.1%, kappa = 0.73): (1) age, (2) body mass index (BMI), (3) sex, (4) history of rheumatic disease, (5) humeral head subluxation (HH) on computed tomography (CT), (6) HH-acromion distance on X-ray, (7) glenoid retroversion on CT, and (8) Walch classification on CT. A higher proportion of males (65.6% vs. 54.9%, p = 0.022), Walch B-C glenoid morphologies (49.5% vs. 37.9%, p < 0.001), and greater BMI (30.1 vs. 26.5 kg/m2, p = 0.038) were observed in aTSA nonachievers compared with aTSA achievers, while aTSA nonachievers were statistically similar to rTSA achievers.

Discussion

Patients with glenohumeral osteoarthritis and intact rotator cuffs that have a BMI > 30 kg/m2 and exhibit Walch B-C glenoids may be less likely to achieve the SCB following aTSA and should be considered for rTSA.

Does achieving clinically important thresholds after first shoulder arthroplasty predict similar outcomes of the contralateral shoulder?

BACKGROUND

Patients are increasingly undergoing bilateral total shoulder arthroplasty (TSA). At present, it is unknown whether success after the first TSA is predictive of success after contralateral TSA. We aimed to determine whether exceeding clinically important thresholds of success after primary TSA predicts similar outcomes for subsequent contralateral TSA.

METHODS

We performed a retrospective review of a prospectively collected shoulder arthroplasty database for patients undergoing bilateral primary anatomic (aTSA) or reverse (rTSA) total shoulder arthroplasty since January 2000 with preoperative and 2- or 3-year clinical follow-up. Our primary outcome was whether exceeding clinically important thresholds in the American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) score for the first TSA was predictive of similar success of the contralateral TSA; thresholds for the ASES score were adopted from prior literature and included the minimal clinically important difference (MCID), the substantial clinical benefit (SCB), 30% of maximal possible improvement (MPI), and the patient acceptable symptomatic state (PASS). The PASS is defined as the highest level of symptom beyond which patients consider themselves well, which may be a better indicator of a patient's quality of life. To determine whether exceeding clinically important thresholds was independently predictive of similar success after second contralateral TSA, we performed multivariable logistic regression adjusted for age at second surgery, sex, BMI, and type of first and second TSA.

RESULTS

Of the 134 patients identified that underwent bilateral shoulder arthroplasty, 65 (49%) had bilateral rTSAs, 45 (34%) had bilateral aTSAs, 21 (16%) underwent aTSA/rTSA, and 3 (2%) underwent rTSA/aTSA. On multivariable logistic regression, exceeding clinically important thresholds after first TSA was not associated with greater odds of achieving thresholds after second TSA when success was evaluated by the MCID, SCB, and 30% MPI. In contrast, exceeding the PASS after first TSA was associated with 5.9 times greater odds (95% confidence interval 2.5-14.4, P < .001) of exceeding the PASS after second TSA. Overall, patients who exceeded the PASS after first TSA exceeded the PASS after second TSA at a higher rate (71% vs. 29%, P < .001); this difference persisted when stratified by type of prosthesis for first and second TSA.

CONCLUSIONS

Patients who achieve the ASES score PASS after first TSA have greater odds of achieving the PASS for the contralateral shoulder regardless of prostheses type.

Initial clinical experience with a predictive clinical decision support tool for anatomic and reverse total shoulder arthroplasty

PURPOSE

Clinical decision support tools (CDSTs) are software that generate patient-specific assessments that can be used to better inform healthcare provider decision making. Machine learning (ML)-based CDSTs have recently been developed for anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty to facilitate more data-driven, evidence-based decision making. Using this shoulder CDST as an example, this external validation study provides an overview of how ML-based algorithms are developed and discusses the limitations of these tools.

METHODS

An external validation for a novel CDST was conducted on 243 patients (120F/123M) who received a personalized prediction prior to surgery and had short-term clinical follow-up from 3 months to 2 years after primary aTSA (n = 43) or rTSA (n = 200). The outcome score and active range of motion predictions were compared to each patient's actual result at each timepoint, with the accuracy quantified by the mean absolute error (MAE).

RESULTS

The results of this external validation demonstrate the CDST accuracy to be similar (within 10%) or better than the MAEs from the published internal validation. A few predictive models were observed to have substantially lower MAEs than the internal validation, specifically, Constant (31.6% better), active abduction (22.5% better), global shoulder function (20.0% better), active external rotation (19.0% better), and active forward elevation (16.2% better), which is encouraging; however, the sample size was small.

CONCLUSION

A greater understanding of the limitations of ML-based CDSTs will facilitate more responsible use and build trust and confidence, potentially leading to greater adoption. As CDSTs evolve, we anticipate greater shared decision making between the patient and surgeon with the aim of achieving even better outcomes and greater levels of patient satisfaction.

Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Surgeon confidence in planning total shoulder arthroplasty improves after consulting a clinical decision support tool

PURPOSE

Software algorithms are increasingly available as clinical decision support tools (CDSTs) to support shared decision-making. We sought to understand if patient-specific predictions from a CDST would impact orthopedic surgeons' preoperative planning decisions and corresponding confidence.

METHODS

We performed a survey study of orthopedic surgeons with at minimum of 2 years of independent shoulder arthroplasty experience. We generated patient profiles for 18 faux cases presenting with glenohumeral osteoarthritis and emailed 93 surgeons requesting their recommendation for anatomic or reverse total shoulder arthroplasty for each case and their certainty in their recommendation on a 4-point Likert scale. The thirty respondents were later sent a second survey with the same cases that now included predicted patient-specific outcomes and complication rates generated by a CDST.

RESULTS

Initial recommendations and changes in recommendation varied widely by surgeon and by case. After viewing the results of the CDST, surgeons switched from anatomic to reverse recommendations in 46 instances (12% of initial anatomic) and from reverse to anatomic in 22 instances (6% of initial reverse). Overall, surgeon change in confidence increased significantly across all responses (p = 0.0001), with certain cases and certain surgeons having significant changes. Change in confidence did not correlate with surgeon-specific factors, including years in practice.

CONCLUSION

The addition of CDST reports to preoperative planning for anatomic and reverse total shoulder arthroplasty informed decision-making but did not direct recommendations uniformly. However, the CDST information provided did increase surgeon confidence regardless of implant selection and irrespective of surgeon experience.

Can the Single Assessment Numeric Evaluation (SANE) be used as a stand-alone outcome instrument in patients undergoing total shoulder arthroplasty?

BACKGROUND AND HYPOTHESIS

There is no consensus as to which patient-determined shoulder outcome scores should be considered when analyzing patient outcomes for either clinical or research purposes. Use of multiple patient-determined outcomes may be redundant and cause increased responder burden. To date, the Single Assessment Numeric Evaluation (SANE) has not been widely accepted as a stand-alone shoulder-specific outcome measure. The hypothesis of this study was that the SANE would correlate with and be equal or superior in responsiveness to other outcome measures that have been used in a stand-alone fashion in patients undergoing total shoulder arthroplasty (American Shoulder and Elbow Surgeons [ASES], Western Ontario Osteoarthritis of the Shoulder [WOOS], and Simple Shoulder Test [SST] scores). In addition, it was hypothesized that the SANE would be more relevant to each patient than the ASES assessment, further supporting the use of the SANE as a stand-alone shoulder-specific outcome measure.

METHODS

A retrospective review of a database of patients undergoing total shoulder arthroplasty was performed, in which the SANE score was recorded simultaneously with the ASES, WOOS, and/or SST score. Correlations were determined using the Pearson coefficient. Subgroup analysis was performed to determine whether correlations differed in (1) preoperative outcome and (2) postoperative outcome determinations. Responsiveness was determined by calculating the standardized response mean and the effect size of all scores. The relevance of the SANE and ASES assessments was examined using the scores of 150 consecutive patients to determine the number of questions on each assessment that were not answered.

RESULTS

Correlation was excellent for the SANE score and the ASES score (n = 1447, r = 0.82, P < .0001), WOOS score (n = 1514, r = 0.83, P < .0001), and SST score (n = 1095, r = 0.81, P < .0001). The correlation of preoperative scores was moderate and that of postoperative scores was strong-moderate when the SANE score was compared with all 3 other scores. All scores were highly responsive, with standardized response mean values of 2.2 for the SANE score, 2.3 for the ASES score, 1.4 for the WOOS score, and 1.6 for the SST score. The effect size of the SANE score was 2.9; ASES score, 2.9; WOOS score, 2.9; and SST score, 2.3. One hundred percent of the SANE questions were answered completely compared with 61% of the ASES questions (P < .0001).

CONCLUSION

In patients undergoing total shoulder arthroplasty, the SANE score highly correlated with the WOOS, ASES, and SST scores, which have been used as stand-alone shoulder-specific outcome measures. The SANE score may provide the same information as the WOOS, ASES, and SST score regarding outcomes with a significant reduction in responder burden. It is logical that the SANE can be used as a stand-alone instrument for patients undergoing total shoulder arthroplasty.