Providence Bracing: Predicting the Progression to Surgery in Patients With Braced Idiopathic Scoliosis.

BACKGROUND: Idiopathic scoliosis (IS) is a common spinal abnormality, in which orthotic management can reduce progression to surgery. However, predictors of bracing success are still not fully understood. We studied a large patient population treated with the nighttime Providence orthosis, utilizing multivariable logistic regression to assess results and predict future spine surgery. METHODS: We retrospectively reviewed patients with IS meeting Scoliosis Research Society inclusion and assessment criteria presenting from April 1994 to June 2020 at a single institution and treated with a Providence orthosis. A predictive logistic regression model was developed utilizing the following candidate features: age, sex, body mass index, Risser classification, Lenke classification, curve magnitude at brace initiation, percentage correction in a brace, and total months of brace use. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. The importance of individual features was assessed using the variable importance score. RESULTS: There were 329 consecutive patients with IS with a mean age of 12.8 ± 1.4 years that met inclusion and assessment criteria. Of these, 113 patients (34%) ultimately required surgery. The model's area under the curve (AUC) was 0.72 on the testing set, demonstrating good discrimination. The initial curve magnitude (Importance score: 100.0) and duration of bracing (Importance score: 82.4) were the 2 most predictive features for curve progression leading to surgery. With respect to skeletal maturity, Risser 1 (Importance score: 53.9) had the most predictive importance for future surgery. For the curve pattern, Lenke 6 (Importance score: 52.0) had the most predictive importance for future surgery. CONCLUSION: Out of 329 patients with IS treated with a Providence nighttime orthosis, 34% required surgery. This is similar to the findings of the BrAist study of the Boston orthosis, in which 28% of monitored braced patients required surgery. In addition, we found that predictive logistic regression can evaluate the likelihood of future spine surgery in patients treated with the Providence orthosis. The severity of the initial curve magnitude and total months of bracing were the 2 most important variables when assessing the probability of future surgery. Surgeons can use this model to counsel families on the potential benefits of bracing and risk factors for curve progression.

Surgical Level Selection in Adolescent Idiopathic Scoliosis: An Evidence-Based Approach.

The selection of fusion levels in the treatment of adolescent idiopathic scoliosis remains complex. The goals of surgery are to minimize the risk of future progression and optimize spinal balance while fusing the least number of levels necessary. Several classifications, rules, and algorithms exist to guide decision making, although these have previously not been easily referenced in a study. This review aims to provide an evidence-based approach of selecting fusion levels that balances the expert opinion of the authors with the current literature.

Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes.

Machine learning (ML) has not yet been used to identify factors predictive for post-operative functional outcomes following arthroscopic rotator cuff repair (ARCR). We propose a novel algorithm to predict ARCR outcomes using machine learning. This is a retrospective cohort study from a prospectively collected database. Data were collected from the Surgical Outcome System Global Registry (Arthrex, Naples, FL, USA). Pre-operative and 3-month, 6-month, and 12-month post-operative American Shoulder and Elbow Surgeons (ASES) scores were collected and used to develop a ML model. Pre-operative factors including demography, comorbidities, cuff tear, tissue quality, and fixation implants were fed to the ML model. The algorithm then produced an expected post-operative ASES score for each patient. The ML-produced scores were compared to actual scores using standard test-train machine learning principles. Overall, 631 patients who underwent shoulder arthroscopy from January 2011 to March 2020 met inclusion criteria for final analysis. A substantial number of the test dataset predictions using the XGBoost algorithm were within the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) thresholds: 67% of the 12-month post-operative predictions were within MCID, while 84% were within SCB. Pre-operative ASES score, pre-operative pain score, body mass index (BMI), age, and tendon quality were the most important features in predicting patient recovery as identified using Shapley additive explanations (SHAP). In conclusion, the proposed novel machine learning algorithm can use pre-operative factors to predict post-operative ASES scores accurately. This can further supplement pre-operative counselling, planning, and resource allocation. Level of Evidence: III.