A Machine Learning Algorithm to Identify Patients with Tibial Shaft Fractures at Risk for Infection After Operative Treatment

Pathogens in FRI - Do bugs matter? - An analysis of FRI studies to assess your enemy

Fracture-related infection (FRI) is a devasting complication for both patients and their treating Orthopaedic surgeon that can lead to loss of limb function or even amputation. The unique and unpredictable features of FRI make its diagnosis and treatment a significant challenge. It has substantial morbidity and financial implications for patients, their families and healthcare providers. In this article, we perform an in-depth and comprehensive review of FRI through recent and seminal literature to highlight evolving definitions, diagnostic and treatment approaches, focusing on common pathogens such as Staphylococcus aureus, polymicrobial infections and multi-drug-resistant organisms (MDRO). Furthermore, multiple resistance mechanisms and adaptations for microbial survival are discussed, as well as modern evidence-based medical and surgical advancements in treatment strategies in combating FRI.

Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction

INTRODUCTION

Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making.

AIM

To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes.

METHODS

Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020.

RESULTS

The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure.

CONCLUSION

AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.

Systematic review of machine-learning models in orthopaedic trauma

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Methods

A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.

Results

A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures.

Conclusion

The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.

Developing a machine learning algorithm to predict the probability of aseptic loosening of the glenoid component after anatomical total shoulder arthroplasty: protocol for a retrospective, multicentre study

INTRODUCTION

Despite technological advancements in recent years, glenoid component loosening remains a common complication after anatomical total shoulder arthroplasty (ATSA) and is one of the main causes of revision surgery. Increasing emphasis is placed on the prevention of glenoid component failure. Previous studies have successfully predicted range of motion, patient-reported outcomes and short-term complications after ATSA using machine learning methods, but an accurate predictive model for (glenoid component) revision is currently lacking. This study aims to use a large international database to accurately predict aseptic loosening of the glenoid component after ATSA using machine learning algorithms.

METHODS AND ANALYSIS

For this multicentre, retrospective study, individual patient data will be compiled from previously published studies reporting revision of ATSA. A systematic literature search will be performed in Medline (PubMed) identifying all studies reporting outcomes of ATSA. Authors will be contacted and invited to participate in the Machine Learning Consortium by sharing their anonymised databases. All databases reporting revisions after ATSA will be included, and individual patients with a follow-up less than 2 years or a fracture as the indication for ATSA will be excluded. First, features (predictive variables) will be identified using a random forest feature selection. The resulting features from the compiled database will be used to train various machine learning algorithms (stochastic gradient boosting, random forest, support vector machine, neural network and elastic-net penalised logistic regression). The developed and validated algorithms will be evaluated across discrimination (c-statistic), calibration, the Brier score and the decision curve analysis. The best-performing algorithm will be used to create an open-access online prediction tool.

ETHICS AND DISSEMINATION

Data will be collected adhering to the WHO regulation on data sharing. An Institutional Review Board review is not applicable. The study results will be published in a peer-reviewed journal.

Septic Tibial Nonunions on Proximal and Distal Metaphysis-A Systematic Narrative Review

BACKGROUND

Infected nonunion of the tibia represents a challenging complication for orthopedic surgeons and poses a major financial burden to healthcare systems. The situation is even more compounded when the nonunion involves the metaphyseal region of long bones, a rare yet demanding complication due to the poor healing potential of infected cancellous bone; this is in addition to the increased likelihood of contamination of adjacent joints. The purpose of this study was to determine the extent and level of evidence in relation to (1) available treatment options for the management of septic tibial metaphyseal nonunions; (2) success rates and bone healing following treatment application; and (3) functional results after intervention.

METHODS

We searched the MEDLINE, Embase, and CENTRAL databases for prospective and retrospective studies through to 25 January 2021. Human-only studies exploring the efficacy of various treatment options and their results in the setting of septic, quiescent, and metaphyseal (distal or proximal) tibia nonunions in the adult population were included. For infection diagnosis, we accepted definitions provided by the authors of source studies. Of note, clinical heterogeneity rendered data pooling inappropriate.

RESULTS

In terms of the species implicated in septic tibial nonunions, staphylococcus aureus was found to be the most commonly isolated microorganism. Many authors implemented the Ilizarov external fixation device with a mean duration of treatment greater than one year. Exceptional or good bone and functional results were recorded in over 80% of patients, although the literature is scarce and possible losses of the follow-up were not recorded.

CONCLUSION

A demanding orthopedic condition that is scarcely studied is infected metaphyseal tibial nonunion. External fixation seems promising, but further research is needed.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO No. CRD42020205781.

Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making.

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.

Treatment of Infected Tibial Metaphyseal Nonunions Using the Ilizarov Method: Protocol for a Prospective Nonrandomized Study

BACKGROUND

The management of infected metaphyseal nonunion of the tibia is devastating, especially when associated with significant bone loss, poor soft tissues, draining sinuses, axial deformity, knee or ankle joint stiffness, limb discrepancy, and multiresisted pathogens. A systematic review, performed recently by the primary investigators but not yet published, yielded the lack of studies in the field and the huge heterogeneity of the presented results. We found several bias and controversies such as no clear definition of the exact part of the tibia where the nonunion was located, the pathogen causing the fracture-related infection, the number of previous interventions and time to presentation, and the exact type of treatment methods including the use of muscle flaps or bone grafting. Time to final union as a functional score is another important but missing data.

OBJECTIVE

The proposed study is designed to evaluate a sufficient number of patients with infected metaphyseal tibial nonunions using various general health, functional, and bone scores.

METHODS

This prospective clinical trial study, with a minimum follow-up period of 36 months, focuses on the effectiveness of the Ilizarov method after radical nonunion debridement and targeted antibiotic therapy in patients with infected metaphyseal tibial nonunions. The primary outcomes would be the definite healing of nonunion and infection-free results. Secondary outcomes would be limb alignment and discrepancy, alteration in the patient's quality of life, and functional results. A power analysis calculated a minimum of 11 patients to obtain statistical power, but we aim to include at least 25 patients. Limb discrepancy, clinical validation of infection eradication and fracture healing, radiographic validation, and patient-reported outcome measures will be highlighted and correlated. Statistical analysis of the results will offer data missing from the literature so far. Measurements are scheduled at specific times for each patient: preoperatively, 3 and 6 months postoperatively, 1 month after Ilizarov frame removal, and once per semester afterward until the end of the follow-up period (minimum 36 months). Laboratory evaluation will be assessed once per month. Any complication will be reported and treated when it occurs.

RESULTS

The trial has already started. It was funded in June 2020. As of May 2022, 19 participants have been recruited and no major complications have been noticed yet. Data analysis will be performed after data collection ends, and results will be published afterward.

CONCLUSIONS

An infected metaphyseal tibial nonunion is a rare condition with limited treatment options and many controversies. There is no consensus in the literature about the best treatment strategy, and this lack of evidence should be fulfilled.

TRIAL REGISTRATION

International Standard Randomized Controlled Trial Number (ISRCTN) 30905788; https://www.isrctn.com/ISRCTN30905788.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39319.

Fracture-Related infection - the role of the surgeon and surgery in prevention and treatment

Fracture-related infection (FRI) is a complication that impacts care costs, quality of life, and patient function. Great strides have been made in the last decade to obtain a common language for definition and diagnosis with the contribution of the Fracture-Related Infection Consensus. Although FRI treatment requires the participation of clinical specialists in infectious diseases for the management of antibiotics, it is necessary to understand that this complication is an eminently surgical pathology. The orthopedic surgeon must play a leadership role in the prevention and treatment of this complex disease. In this review, the most relevant aspects of prevention are updated, and a strategy for a sequential and comprehensive approach to the patient with this complication is presented.

Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-learning Algorithm

BACKGROUND

Femoral neck fractures are common and are frequently treated with internal fixation. A major disadvantage of internal fixation is the substantially high number of conversions to arthroplasty because of nonunion, malunion, avascular necrosis, or implant failure. A clinical prediction model identifying patients at high risk of conversion to arthroplasty may help clinicians in selecting patients who could have benefited from arthroplasty initially.

QUESTION/PURPOSE

What is the predictive performance of a machine-learning (ML) algorithm to predict conversion to arthroplasty within 24 months after internal fixation in patients with femoral neck fractures?

METHODS

We included 875 patients from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial. The FAITH trial consisted of patients with low-energy femoral neck fractures who were randomly assigned to receive a sliding hip screw or cancellous screws for internal fixation. Of these patients, 18% (155 of 875) underwent conversion to THA or hemiarthroplasty within the first 24 months. All patients were randomly divided into a training set (80%) and test set (20%). First, we identified 27 potential patient and fracture characteristics that may have been associated with our primary outcome, based on biomechanical rationale and previous studies. Then, random forest algorithms (an ML learning, decision tree-based algorithm that selects variables) identified 10 predictors of conversion: BMI, cardiac disease, Garden classification, use of cardiac medication, use of pulmonary medication, age, lung disease, osteoarthritis, sex, and the level of the fracture line. Based on these variables, five different ML algorithms were trained to identify patterns related to conversion. The predictive performance of these trained ML algorithms was assessed on the training and test sets based on the following performance measures: (1) discrimination (the model's ability to distinguish patients who had conversion from those who did not; expressed with the area under the receiver operating characteristic curve [AUC]), (2) calibration (the plotted estimated versus the observed probabilities; expressed with the calibration curve intercept and slope), and (3) the overall model performance (Brier score: a composite of discrimination and calibration).

RESULTS

None of the five ML algorithms performed well in predicting conversion to arthroplasty in the training set and the test set; AUCs of the algorithms in the training set ranged from 0.57 to 0.64, slopes of calibration plots ranged from 0.53 to 0.82, calibration intercepts ranged from -0.04 to 0.05, and Brier scores ranged from 0.14 to 0.15. The algorithms were further evaluated in the test set; AUCs ranged from 0.49 to 0.73, calibration slopes ranged from 0.17 to 1.29, calibration intercepts ranged from -1.28 to 0.34, and Brier scores ranged from 0.13 to 0.15.

CONCLUSION

The predictive performance of the trained algorithms was poor, despite the use of one of the best datasets available worldwide on this subject. If the current dataset consisted of different variables or more patients, the performance may have been better. Also, various reasons for conversion to arthroplasty were pooled in this study, but the separate prediction of underlying pathology (such as, avascular necrosis or nonunion) may be more precise. Finally, it may be possible that it is inherently difficult to predict conversion to arthroplasty based on preoperative variables alone. Therefore, future studies should aim to include more variables and to differentiate between the various reasons for arthroplasty.

LEVEL OF EVIDENCE

Level III, prognostic study.

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.

A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture After Wrist Trauma

PURPOSE

To identify predictors of a true scaphoid fracture among patients with radial wrist pain following acute trauma, train 5 machine learning (ML) algorithms in predicting scaphoid fracture probability, and design a decision rule to initiate advanced imaging in high-risk patients.

METHODS

Two prospective cohorts including 422 patients with radial wrist pain following wrist trauma were combined. There were 117 scaphoid fractures (28%) confirmed on computed tomography, magnetic resonance imaging, or radiographs. Eighteen fractures (15%) were occult. Predictors of a scaphoid fracture were identified among demographics, mechanism of injury and examination maneuvers. Five ML-algorithms were trained in calculating scaphoid fracture probability. ML-algorithms were assessed on ability to discriminate between patients with and without a fracture (area under the receiver operating characteristic curve), agreement between observed and predicted probabilities (calibration), and overall performance (Brier score). The best performing ML-algorithm was incorporated into a probability calculator. A decision rule was proposed to initiate advanced imaging among patients with negative radiographs.

RESULTS

Pain over the scaphoid on ulnar deviation, sex, age, and mechanism of injury were most strongly associated with a true scaphoid fracture. The best performing ML-algorithm yielded an area under the receiver operating characteristic curve, calibration slope, intercept, and Brier score of 0.77, 0.84, -0.01 and 0.159, respectively. The ML-derived decision rule proposes to initiate advanced imaging in patients with radial-sided wrist pain, negative radiographs, and a fracture probability of ≥10%. When applied to our cohort, this would yield 100% sensitivity, 38% specificity, and would have reduced the number of patients undergoing advanced imaging by 36% without missing a fracture.

CONCLUSIONS

The ML-algorithm accurately calculated scaphoid fracture probability based on scaphoid pain on ulnar deviation, sex, age, and mechanism of injury. The ML-decision rule may reduce the number of patients undergoing advanced imaging by a third with a small risk of missing a fracture. External validation is required before implementation.

TYPE OF STUDY/LEVEL OF EVIDENCE

Diagnostic II.

Feasibility of Machine Learning and Logistic Regression Algorithms to Predict Outcome in Orthopaedic Trauma Surgery

BACKGROUND

Statistical models using machine learning (ML) have the potential for more accurate estimates of the probability of binary events than logistic regression. The present study used existing data sets from large musculoskeletal trauma trials to address the following study questions: (1) Do ML models produce better probability estimates than logistic regression models? (2) Are ML models influenced by different variables than logistic regression models?

METHODS

We created ML and logistic regression models that estimated the probability of a specific fracture (posterior malleolar involvement in distal spiral tibial shaft and ankle fractures, scaphoid fracture, and distal radial fracture) or adverse event (subsequent surgery [after distal biceps repair or tibial shaft fracture], surgical site infection, and postoperative delirium) using 9 data sets from published musculoskeletal trauma studies. Each data set was split into training (80%) and test (20%) subsets. Fivefold cross-validation of the training set was used to develop the ML models. The best-performing model was then assessed in the independent testing data. Performance was assessed by (1) discrimination (c-statistic), (2) calibration (slope and intercept), and (3) overall performance (Brier score).

RESULTS

The mean c-statistic was 0.01 higher for the logistic regression models compared with the best ML models for each data set (range, -0.01 to 0.06). There were fewer variables strongly associated with variation in the ML models, and many were dissimilar from those in the logistic regression models.

CONCLUSIONS

The observation that ML models produce probability estimates comparable with logistic regression models for binary events in musculoskeletal trauma suggests that their benefit may be limited in this context.

Musculoskeletal trauma and artificial intelligence: current trends and projections

Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. However, diagnostic imaging represents a complex multifaceted system, with many aspects of its workflow prone to inefficiencies or human error. Recent technological innovations in artificial intelligence and machine learning have shown promise to revolutionize our systems for providing medical care to patients. This review will provide a general overview of the current state of artificial intelligence and machine learning applications in different aspects of trauma imaging and provide a vision for how such applications could be leveraged to enhance our diagnostic imaging systems and optimize patient outcomes.

Age, participation in competitive sports, bony lesions, ALPSA lesions, > 1 preoperative dislocations, surgical delay and ISIS score > 3 are risk factors for recurrence following arthroscopic Bankart repair: a systematic review and meta-analysis of 4584 shoulders

PURPOSE

Determining the risk of recurrent instability following an arthroscopic Bankart repair can be challenging, as numerous risk factors have been identified that might predispose recurrent instability. However, an overview with quantitative analysis of all available risk factors is lacking. Therefore, the aim of this systematic review is to identify risk factors that are associated with recurrence following an arthroscopic Bankart repair.

METHODS

Relevant studies were identified by searching PubMed, Embase/Ovid, Cochrane Database of Systematic Reviews/Wiley, Cochrane Central Register of Controlled Trials/Wiley, CINAHL/Ebsco, and Web of Science/Clarivate Analytics from inception up to November 12th 2020. Studies evaluating risk factors for recurrence following an arthroscopic Bankart repair with a minimal follow-up of 2 years were included.

RESULTS

Twenty-nine studies met the inclusion criteria and comprised a total of 4582 shoulders (4578 patients). Meta-analyses were feasible for 22 risk factors and demonstrated that age ≤ 20 years (RR = 2.02; P < 0.00001), age ≤ 30 years (RR = 2.62; P = 0.005), participation in competitive sports (RR = 2.40; P = 0.02), Hill-Sachs lesion (RR = 1.77; P = 0.0005), off-track Hill-Sachs lesion (RR = 3.24; P = 0.002), glenoid bone loss (RR = 2.38; P = 0.0001), ALPSA lesion (RR = 1.90; P = 0.03), > 1 preoperative dislocations (RR = 2.02; P = 0.03), > 6 months surgical delay (RR = 2.86; P < 0.0001), ISIS > 3 (RR = 3.28; P = 0.0007) and ISIS > 6 (RR = 4.88; P < 0.00001) were risk factors for recurrence. Male gender, an affected dominant arm, hyperlaxity, participation in contact and/or overhead sports, glenoid fracture, SLAP lesion with/without repair, rotator cuff tear, > 5 preoperative dislocations and using ≤ 2 anchors could not be confirmed as risk factors. In addition, no difference was observed between the age groups ≤ 20 and 21-30 years.

CONCLUSION

Meta-analyses demonstrated that age ≤ 20 years, age ≤ 30 years, participation in competitive sports, Hill-Sachs lesion, off-track Hill-Sachs lesion, glenoid bone loss, ALPSA lesion, > 1 preoperative dislocations, > 6 months surgical delay from first-time dislocation to surgery, ISIS > 3 and ISIS > 6 were risk factors for recurrence following an arthroscopic Bankart repair. These factors can assist clinicians in giving a proper advice regarding treatment.

LEVEL OF EVIDENCE

Level IV.