Predicting Failure After Primary Arthroscopic Bankart Repair: Analysis of a Statistical Model Using Anatomic Risk Factors

Yian, Edward H.; Weathers, Michael; Knott, Jonathan R.; Sodl, Jeffrey F.; Spencer, Hillard T.

doi:10.1016/j.arthro.2019.11.109

Cited by 34 publications

(43 citation statements)

References 30 publications

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“…1 . Seven prospective [ 4 , 5 , 10 , 37 , 46 , 48 , 53 ] and 22 retrospective studies [ 1 , 3 , 6 , 7 , 14 – 17 , 22 – 25 , 30 , 31 , 33 , 35 , 36 , 43 , 45 , 49 , 57 , 58 ] were included (Table 1 ). The included studies comprised a combined sample size of 4584 shoulders (range 51–670) in 4564 patients (supplementary 2).…”

Section: Resultsmentioning

confidence: 99%

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

Verweij

Spanning

Grillo

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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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.

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Section: Resultsmentioning

confidence: 99%

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

Verweij

Spanning

Grillo

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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“…The Instability Severity Index Score has been validated extensively in the literature 19 , 27 ; however, few investigations have assessed discrimination or calibration, and its predictive power at the individual patient level remains unclear. Similarly, Yian et al 35 produced a model to predict recurrent instability after primary arthroscopic repair, with a reported AUC of 0.82 when the scoring criterion was applied to the training cohort. However, no internal or external validation of the model was reported.…”

Section: Discussionmentioning

confidence: 99%

Understanding Anterior Shoulder Instability Through Machine Learning: New Models That Predict Recurrence, Progression to Surgery, and Development of Arthritis

Pareek

Wilbur

et al. 2021

Orthopaedic Journal of Sports Medicine

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Background: Management of anterior shoulder instability (ASI) aims to reduce risk of future recurrence and prevent complications via nonoperative and surgical management. Machine learning may be able to reliably provide predictions to improve decision making for this condition. Purpose: To develop and internally validate a machine-learning model to predict the following outcomes after ASI: (1) recurrent instability, (2) progression to surgery, and (3) the development of symptomatic osteoarthritis (OA) over long-term follow-up. Study Design: Cohort study (prognosis); Level of evidence, 2. Methods: An established geographic database of >500,000 patients was used to identify 654 patients aged <40 years with an initial diagnosis of ASI between 1994 and 2016; the mean follow-up was 11.1 years. Medical records were reviewed to obtain patient information, and models were generated to predict the outcomes of interest. Five candidate algorithms were trained in the development of each of the models, as well as an additional ensemble of the algorithms. Performance of the algorithms was assessed using discrimination, calibration, and decision curve analysis. Results: Of the 654 included patients, 443 (67.7%) experienced multiple instability events, 228 (34.9%) underwent surgery, and 39 (5.9%) developed symptomatic OA. The ensemble gradient-boosted machines achieved the best performances based on discrimination (via area under the receiver operating characteristic curve [AUC]: AUCrecurrence = 0.86), AUCsurgery = 0.76, AUCOA = 0.78), calibration, decision curve analysis, and Brier score (Brierrecurrence = 0.138, Briersurgery = 0.185, BrierOA = 0.05). For demonstration purposes, models were integrated into a single web-based open-access application able to provide predictions and explanations for practitioners and researchers. Conclusion: After identification of key features, including time from initial instability, age at initial instability, sports involvement, and radiographic findings, machine-learning models were developed that effectively and reliably predicted recurrent instability, progression to surgery, and the development of OA in patients with ASI. After careful external validation, these models can be incorporated into open-access digital applications to inform patients, clinicians, and researchers regarding quantifiable risks of relevant outcomes in the clinic.

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“…Glenoid bone loss generated in a traumatic glenohumeral dislocation has become a key factor when defining therapeutic behavior. 1 Bigliani et al 15 classified these injuries according to evolution time. An acute displaced fracture-avulsion with presence of the bone fragment is considered as type I; a malunited fractureavulsion as type II; and a chronic erosion of the glenoid edge without the presence of a residual fragment as type III.…”

Section: Discussionmentioning

confidence: 99%

“…T he presence of an anterior glenoid bone defect, also called "bony Bankart lesion," is a critical prognostic factor for therapeutic decision-making in the context of glenohumeral instability. 1 A special group of patients consists of those who suffer a fracture of the anterior glenoid rim caused by the traction of the capsulolabral complex at the time of dislocation. The initial diagnosis and evaluation may be difficult in the acute episode, as conventional radiographs have low sensitivity.…”

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confidence: 99%

Arthroscopic Acute Bony Bankart Repair in Lateral Decubitus

Atala

Bongiovanni

Rossi

et al. 2020

Arthroscopy Techniques

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The optimal management of anterior shoulder instability continues to be a challenge. The presence of an anterior glenoid rim fracture in the context of a glenohumeral dislocation, also called “bony Bankart lesion,” can alter therapeutic behavior. Reduction and fixation of the bone fragment has been shown to greatly reduce the risk of recurrence once bone consolidation is achieved. However, there is no gold standard surgical technique. Stability of fixation and the healing of the bony fragment are still a concern, and there are no clinical studies comparing the different techniques to date. The aim of this report is to describe an arthroscopic double-point fragment fixation technique in lateral decubitus for the treatment of an acute traumatic shoulder dislocation with a bony Bankart lesion.

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Predicting Failure After Primary Arthroscopic Bankart Repair: Analysis of a Statistical Model Using Anatomic Risk Factors

Cited by 34 publications

References 30 publications

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

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

Understanding Anterior Shoulder Instability Through Machine Learning: New Models That Predict Recurrence, Progression to Surgery, and Development of Arthritis

Arthroscopic Acute Bony Bankart Repair in Lateral Decubitus

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