Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair.

PURPOSE: The aim of this study was to develop and train a machine learning (ML) algorithm to create a clinical decision support tool (i.e., ML-driven probability calculator) to be used in clinical practice to estimate recurrence rates following an arthroscopic Bankart repair (ABR). METHODS: Data from 14 previously published studies were collected. Inclusion criteria were (1) patients treated with ABR without remplissage for traumatic anterior shoulder instability and (2) a minimum of 2 years follow-up. Risk factors associated with recurrence were identified using bivariate logistic regression analysis. Subsequently, four ML algorithms were developed and internally validated. The predictive performance was assessed using discrimination, calibration and the Brier score. RESULTS: In total, 5591 patients underwent ABR with a recurrence rate of 15.4% (n = 862). Age <35 years, participation in contact and collision sports, bony Bankart lesions and full-thickness rotator cuff tears increased the risk of recurrence (all p < 0.05). A single shoulder dislocation (compared to multiple dislocations) lowered the risk of recurrence (p < 0.05). Due to the unavailability of certain variables in some patients, a portion of the patient data had to be excluded before pooling the data set to create the algorithm. A total of 797 patients were included providing information on risk factors associated with recurrence. The discrimination (area under the receiver operating curve) ranged between 0.54 and 0.57 for prediction of recurrence. CONCLUSION: ML was not able to predict the recurrence following ABR with the current available predictors. Despite a global coordinated effort, the heterogeneity of clinical data limited the predictive capabilities of the algorithm, emphasizing the need for standardized data collection methods in future studies. LEVEL OF EVIDENCE: Level IV, retrospective cohort study.

A Knotless Single-Row Suture Bridge Technique to Repair a Partial Articular Suproaspinatus Tendon Avulsion-Type Rotator Cuff Tear: Surgical Technique.

With improving surgical and technological solutions for repairing rotator cuff tears, there has been increased interest in treatment of partial rotator cuff tears. The most prevalent type of partial tear is the PASTA (partial articular supraspinatus tendon avulsion) lesion. There is an ongoing debate on the best surgical technique to repair a PASTA lesion, which has led to the development of many different arthroscopic techniques. This Technical Note provides a cost-effective and reproducible technique of a transtendinous single-row bridge repair of PASTA lesions, using two 1.8 FiberTak Knotless Soft Anchors.

Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery: protocol for a retrospective, multicentre study.

INTRODUCTION: The effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient selection is thought to be essential in reaching satisfactory results. However, no clear consensus has been reached on which factors are most predictive of successful surgery. A clinical decision tool that encompassed all aspects is still to be made. Artificial intelligence (AI) and machine learning algorithms use complex self-learning models that can be used to make patient-specific decision-making tools. The aim of this study is to develop and train an algorithm that can be used as an online available clinical prediction tool, to predict the risk of retear in patients undergoing rotator cuff repair. METHODS AND ANALYSIS: This is a retrospective, multicentre, cohort study using pooled individual patient data from multiple studies of patients who have undergone rotator cuff repair and were evaluated by advanced imaging for healing at a minimum of 6 months after surgery. This study consists of two parts. Part one: collecting all potential factors that might influence retear risks from retrospective multicentre data, aiming to include more than 1000 patients worldwide. Part two: combining all influencing factors into a model that can clinically be used as a prediction tool using machine learning. ETHICS AND DISSEMINATION: For safe multicentre data exchange and analysis, our Machine Learning Consortium adheres to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. Institutional Review Board approval does not apply to the current study protocol.

Development and training of a machine learning algorithm to identify patients at risk for recurrence following an arthroscopic Bankart repair (CLEARER): protocol for a retrospective, multicentre, cohort study.

INTRODUCTION: Shoulder instability is a common injury, with a reported incidence of 23.9 per 100 000 person-years. There is still an ongoing debate on the most effective treatment strategy. Non-operative treatment has recurrence rates of up to 60%, whereas operative treatments such as the Bankart repair and bone block procedures show lower recurrence rates (16% and 2%, respectively) but higher complication rates (<2% and up to 30%, respectively). Methods to determine risk of recurrence have been developed; however, patient-specific decision-making tools are still lacking. Artificial intelligence and machine learning algorithms use self-learning complex models that can be used to make patient-specific decision-making tools. The aim of the current study is to develop and train a machine learning algorithm to create a prediction model to be used in clinical practice-as an online prediction tool-to estimate recurrence rates following a Bankart repair. METHODS AND ANALYSIS: This is a multicentre retrospective cohort study. Patients with traumatic anterior shoulder dislocations that were treated with an arthroscopic Bankart repair without remplissage will be included. This study includes two parts. Part 1, collecting all potential factors influencing the recurrence rate following an arthroscopic Bankart repair in patients using multicentre data, aiming to include data from >1000 patients worldwide. Part 2, the multicentre data will be re-evaluated (and where applicable complemented) using machine learning algorithms to predict outcomes. Recurrence will be the primary outcome measure. ETHICS AND DISSEMINATION: For safe multicentre data exchange and analysis, our Machine Learning Consortium adhered to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. No Institutional Review Board is required for this study.

The Benefit of Prophylactic Implantable Cardioverter Defibrillator Implantation in Asymptomatic Heart Failure Patients With a Reduced Ejection Fraction.

Recommendations for prophylactic implantable cardioverter defibrillator (ICD) implantation in asymptomatic heart failure patients with a reduced left ventricular ejection fraction (LVEF) differ between guidelines. Evidence on the risk of appropriate device therapy (ADT) and death in New York Heart Association (NYHA) class I patients is scarce. Aim of this study is to evaluate ADT and mortality in NYHA-I primary prevention ICD patients with a LVEF ≤35%. A retrospective cohort was studied, including 572 patients with LVEF ≤35% who received a prophylactic ICD with or without resynchronization therapy (CRT-D). To evaluate the incidence of ADT and mortality, NYHA-I was compared with NYHA-II-III using Cox regression analysis. During a follow-up of 4.1 ± 2.4 years, 33% of the NYHA-I patients received ADT compared with 20% of the NYHA-II-III patients (hazard ratio 1.5, 95% confidence interval 1.04 to 2.31, p = 0.03). No differences in mortality were observed (hazard ratio 0.70, 95% confidence interval 0.49 to 1.07, p = 0.10). Additional analyses showed no difference in time to ADT excluding CRT patients (ICD-NYHA-I patients vs ICD-NYHA-II-III patients, p = 0.17) and comparing ischemic and nonischemic cardiomyopathy NYHA-I patients (p = 0.13). Multivariable Cox regression analyses showed that NYHA class was the strongest independent predictor of ADT. In conclusion, primary prevention NYHA-I ICD patients showed a higher incidence of ADT compared with NYHA-II-III ICD patients. These results strongly suggest that primary prevention NYHA-I patients with a LVEF ≤35% are likely to benefit from ICD therapy and should not be excluded from a potentially life-saving therapy.