Literature DB >> 31926270

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

Edward H Yian1, Michael Weathers2, Jonathan R Knott3, Jeffrey F Sodl3, Hillard T Spencer3.   

Abstract

PURPOSE: The purpose of this study was to establish and analyze a simplified scoring system based on anatomic imaging measurements to predict recurrent instability after primary arthroscopic shoulder capsulolabral repair.
METHODS: All patients undergoing primary arthroscopic anterior capsulolabral repair of the shoulder were reviewed. Patients were contacted and charts were reviewed for endpoint of recurrent instability and return to prior level of activity. Predictive variables for recurrent instability studied included age, sex, amount of glenoid bone loss, intact anterior articular arc (IAAA), glenohumeral tracking (off-track), contact sports and overhead sports participation.
RESULTS: 540 patients met inclusion criteria and follow-up data with magnetic resonance imaging data were available for 337 shoulders. Average follow-up was 6.2 years(range 3.4-9.3 years). Symptomatic recurrent instability occurred in 102 patients (30.3%) and 68% of contacted patients returned to pre-injury activities. In univariate analysis, age under 21 years, off-track lesions, IAAA <150°, and glenoid bone loss (GBL) of 10% or greater displayed an increased risk of recurrent instability. Multivariable analysis showed these factors remained significant: age <21 (odds ratio [ratio] 2.37), off-track glenoid (OR 2.86), IAAA <150 (OR 3.90), and GBL ≥10% (OR 7.47). A scoring system assigning 1 point each for age and off-track lesions, 2 points for IAAA <150, and 4 points for GBL >10% yielded 79% sensitivity, 75% specificity, 58% positive predictive value, and 89% negative predictive value using a probability value of 20 percent for recurrent instability.
CONCLUSION: At mid-term follow-up, recurrent shoulder instability following primary arthroscopic anterior capsulolabral repair was 30% in this series. Younger age, glenoid bone loss of 10% or more, IAAA <150° and off-track glenoid lesion conferred the greatest risk for postoperative instability. We propose a scoring system assigning 1 point for age, 1 point for off-track lesions, 2 points for IAAA <150, and 4 points for GBL >10%. This schema demonstrated moderate accuracy for predicting recurrent instability when using a cutoff threshold score above 2 points for failure. LEVEL OF EVIDENCE: Level III, Retrospective Cohort Study.
Copyright © 2020 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Entities:  

Year:  2020        PMID: 31926270     DOI: 10.1016/j.arthro.2019.11.109

Source DB:  PubMed          Journal:  Arthroscopy        ISSN: 0749-8063            Impact factor:   4.772


  4 in total

1.  The Hill-Sachs interval to glenoid track width ratio is comparable to the instability severity index score for predicting risk of recurrent instability after arthroscopic Bankart repair.

Authors:  Kun-Hui Chen; Tzu-Cheng Yang; En-Rung Chiang; Hsin-Yi Wang; Hsiao-Li Ma
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2020-04-06       Impact factor: 4.342

2.  Factors related to large bone defects of bipolar lesions and a high number of instability episodes with anterior glenohumeral instability.

Authors:  Noboru Matsumura; Kazuya Kaneda; Satoshi Oki; Hiroo Kimura; Taku Suzuki; Takuji Iwamoto; Morio Matsumoto; Masaya Nakamura; Takeo Nagura
Journal:  J Orthop Surg Res       Date:  2021-04-13       Impact factor: 2.359

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

Authors:  Yining Lu; Ayoosh Pareek; Ryan R Wilbur; Devin P Leland; Aaron J Krych; Christopher L Camp
Journal:  Orthop J Sports Med       Date:  2021-11-23

Review 4.  Sex-specific differences in outcomes after anterior shoulder surgical stabilization: a meta-analysis and systematic review of literature.

Authors:  Ezra Goodrich; Megan Wolf; Matthew Vopat; Anthony Mok; Jordan Baker; Christopher Bernard; Armin Tarakemeh; Bryan Vopat
Journal:  JSES Int       Date:  2021-11-20
  4 in total

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