Evaluating the fairness and accuracy of machine learning-based predictions of clinical outcomes after anatomic and reverse total shoulder arthroplasty.

BACKGROUND: Machine learning (ML)-based clinical decision support tools (CDSTs) make personalized predictions for different treatments; by comparing predictions of multiple treatments, these tools can be used to optimize decision making for a particular patient. However, CDST prediction accuracy varies for different patients and also for different treatment options. If these differences are sufficiently large and consistent for a particular subcohort of patients, then that bias may result in those patients not receiving a particular treatment. Such level of bias would deem the CDST "unfair." The purpose of this study is to evaluate the "fairness" of ML CDST-based clinical outcomes predictions after anatomic (aTSA) and reverse total shoulder arthroplasty (rTSA) for patients of different demographic attributes. METHODS: Clinical data from 8280 shoulder arthroplasty patients with 19,249 postoperative visits was used to evaluate the prediction fairness and accuracy associated with the following patient demographic attributes: ethnicity, sex, and age at the time of surgery. Performance of clinical outcome and range of motion regression predictions were quantified by the mean absolute error (MAE) and performance of minimal clinically important difference (MCID) and substantial clinical benefit classification predictions were quantified by accuracy, sensitivity, and the F1 score. Fairness of classification predictions leveraged the "four-fifths" legal guideline from the US Equal Employment Opportunity Commission and fairness of regression predictions leveraged established MCID thresholds associated with each outcome measure. RESULTS: For both aTSA and rTSA clinical outcome predictions, only minor differences in MAE were observed between patients of different ethnicity, sex, and age. Evaluation of prediction fairness demonstrated that 0 of 486 MCID (0%) and only 3 of 486 substantial clinical benefit (0.6%) classification predictions were outside the 20% fairness boundary and only 14 of 972 (1.4%) regression predictions were outside of the MCID fairness boundary. Hispanic and Black patients were more likely to have ML predictions out of fairness tolerance for aTSA and rTSA. Additionally, patients <60 years old were more likely to have ML predictions out of fairness tolerance for rTSA. No disparate predictions were identified for sex and no disparate regression predictions were observed for forward elevation, internal rotation score, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form score, or global shoulder function. CONCLUSION: The ML algorithms analyzed in this study accurately predict clinical outcomes after aTSA and rTSA for patients of different ethnicity, sex, and age, where only 1.4% of regression predictions and only 0.3% of classification predictions were out of fairness tolerance using the proposed fairness evaluation method and acceptance criteria. Future work is required to externally validate these ML algorithms to ensure they are equally accurate for all legally protected patient groups.

Using machine learning to predict internal rotation after anatomic and reverse total shoulder arthroplasty.

BACKGROUND: Improvement in internal rotation (IR) after anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty is difficult to predict, with rTSA patients experiencing greater variability and more limited IR improvements than aTSA patients. The purpose of this study is to quantify and compare the IR score for aTSA and rTSA patients and create supervised machine learning that predicts IR after aTSA and rTSA at multiple postoperative time points. METHODS: Clinical data from 2270 aTSA and 4198 rTSA patients were analyzed using 3 supervised machine learning techniques to create predictive models for internal rotation as measured by the IR score at 6 postoperative time points. Predictions were performed using the full input feature set and 2 minimal input feature sets. The mean absolute error (MAE) quantified the difference between actual and predicted IR scores for each model at each time point. The predictive accuracy of the XGBoost algorithm was also quantified by its ability to distinguish which patients would achieve clinical improvement greater than the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) patient satisfaction thresholds for IR score at 2-3 years after surgery. RESULTS: rTSA patients had significantly lower mean IR scores and significantly less mean IR score improvement than aTSA patients at each postoperative time point. Both aTSA and rTSA patients experienced significant improvements in their ability to perform activities of daily living (ADLs); however, aTSA patients were significantly more likely to perform these ADLs. Using a minimal feature set of preoperative inputs, our machine learning algorithms had equivalent accuracy when predicting IR score for both aTSA (0.92-1.18 MAE) and rTSA (1.03-1.25 MAE) from 3 months to >5 years after surgery. Furthermore, these predictive algorithms identified with 90% accuracy for aTSA and 85% accuracy for rTSA which patients will achieve MCID IR score improvement and predicted with 85% accuracy for aTSA patients and 77% accuracy for rTSA which patients will achieve SCB IR score improvement at 2-3 years after surgery. DISCUSSION: Our machine learning study demonstrates that active internal rotation can be accurately predicted after aTSA and rTSA at multiple postoperative time points using a minimal feature set of preoperative inputs. These predictive algorithms accurately identified which patients will, and will not, achieve clinical improvement in IR score that exceeds the MCID and SCB patient satisfaction thresholds.

Risk factors for complications and revision surgery after anatomic and reverse total shoulder arthroplasty.

BACKGROUND: Complications and revisions following anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty have deleterious effects on patient function and satisfaction. The purpose of this study is to evaluate patient-specific, implant-specific and technique-specific risk factors for intraoperative complications, postoperative complications, and the occurrence of revisions after aTSA and rTSA. METHODS: A total of 2964 aTSA and 5616 rTSA patients were enrolled in an international database of primary shoulder arthroplasty. Intra- and postoperative complications, as well as revisions, were reported and evaluated. Multivariate analyses were performed to quantify the risk factors associated with complications and revisions. RESULTS: aTSA patients had a significantly higher complication rate (P = .0026) and a significantly higher revision rate (P < .0001) than rTSA patients, but aTSA patients also had a significantly longer average follow-up (P < .0001) than rTSA patients. No difference (P = .2712) in the intraoperative complication rate was observed between aTSA and rTSA patients. Regarding intraoperative complications, female sex (odds ratio [OR] 2.0, 95% confidence interval [CI] 1.17-3.68) and previous shoulder surgery (OR 2.9, 95% CI 1.73-4.90) were identified as significant risk factors. In regard to postoperative complications, younger age (OR 0.987, 95% CI 0.977-0.996), diagnosis of rheumatoid arthritis (OR 1.76, 95% 1.12-2.65), and previous shoulder surgery (OR 1.42, 95% CI 1.16-1.72) were noted to be risks factors. Finally, in regard to revision surgery, younger age (OR 0.964, 95% CI 0.933-0.998), more glenoid retroversion (OR 1.03, 95% CI 1.001-1.058), larger humeral stem size (OR 1.09, 95% CI 1.01-1.19), larger humeral liner thickness or offset (OR 1.50, 95% CI 1.18-1.96), larger glenosphere diameter (OR 1.16, 95% CI 1.07-1.26), and more intraoperative blood loss (OR 1.002, 95% CI 1.001-1.004) were noted to be risk factors. CONCLUSIONS: Studying the impact of numerous patient- and implant-specific risk factors and determining their impact on complications and revision shoulder arthroplasty can assist surgeons in counseling patients and guide patient expectations following aTSA or rTSA. Care should be taken in patients with a history of previous shoulder surgery, who are at increased risk of both intra- and postoperative complications.