Diagnostic accuracy of the glenoid track concept in predicting lesion engagement in recurrent anterior shoulder instability

Background: Recurrent anterior shoulder dislocations are often associated with bipolar bone loss, involving both the glenoid and humeral head articular surfaces. The glenoid track concept, particularly the on-track/off-track method, integrates these lesions to predict engagement on arthroscopy, assess the risk of redislocation, and assist in surgical planning. This study aims to determine the clinical applicability of the glenoid track concept in the preoperative evaluation and surgical planning of patients with recurrent anterior shoulder dislocation. Methods: A retrospective, observational, diagnostic-accuracy study was conducted on 50 patients (52% female; mean age 48.5 ± 16.1 years) at a multispecialty tertiary care hospital in Pune after receiving ethics approval. Patients were predominantly aged 41-60 years (44%), followed by 61-80 years (30%) and 21-40 years (24%); one patient (2%) was older than 80 years. Magnetic resonance imaging (MRI) and computed tomography (CT) were used to assess glenoid and humeral bone loss. The glenoid track and Hill-Sachs interval were measured, and lesions were classified as on-track or off-track using the glenoid track concept (83% of glenoid width). Arthroscopic findings were used to confirm lesion engagement. SPSS version 21 was used for diagnostic metrics analysis. Results: The study population included 50 patients; 48.0% demonstrated engaging (off-track) lesions on imaging, of which 22 were confirmed arthroscopically. The glenoid track concept demonstrated a sensitivity of 91.7% (95% confidence interval [CI]: 74.2%-97.7%), a specificity of 92.3% (95% CI: 75.9%-97.9%), and an overall diagnostic accuracy of 92.0% (95% CI: 81.2%-96.8%) in predicting lesion engagement compared with arthroscopic findings, with a highly significant association on Fisher’s exact test ( P < 0.0001). Glenoid bone loss exceeding 25% was significantly associated with engagement. Most patients with less than 25% glenoid and less than 40% humeral bone loss were successfully managed with arthroscopic Bankart repair. Conclusion: The on-track/off-track method using the glenoid track concept accurately predicts engagement of Hill-Sachs lesions and facilitates individualized surgical decision-making in anterior shoulder instability.

Authors

Institutions

Publication Details

Journal
Digital Medicine
Published
2026-04-16
DOI
https://doi.org/10.1097/dm-2025-00008
Primary Topic
Shoulder Injury and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diagnostic accuracy of the glenoid track concept in predicting lesion engagement in recurrent anterior shoulder instability

Najeebuddin Patel, Anil Pardeshi
Digital Medicine
Shoulder Injury and Treatment
article

Diagnostic accuracy of the glenoid track concept in predicting lesion engagement in recurrent anterior shoulder instability

Najeebuddin Patel, Anil Pardeshi
article en

Abstract

Background: Recurrent anterior shoulder dislocations are often associated with bipolar bone loss, involving both the glenoid and humeral head articular surfaces. The glenoid track concept, particularly the on-track/off-track method, integrates these lesions to predict engagement on arthroscopy, assess the risk of redislocation, and assist in surgical planning. This study aims to determine the clinical applicability of the glenoid track concept in the preoperative evaluation and surgical planning of patients with recurrent anterior shoulder dislocation. Methods: A retrospective, observational, diagnostic-accuracy study was conducted on 50 patients (52% female; mean age 48.5 ± 16.1 years) at a multispecialty tertiary care hospital in Pune after receiving ethics approval. Patients were predominantly aged 41-60 years (44%), followed by 61-80 years (30%) and 21-40 years (24%); one patient (2%) was older than 80 years. Magnetic resonance imaging (MRI) and computed tomography (CT) were used to assess glenoid and humeral bone loss. The glenoid track and Hill-Sachs interval were measured, and lesions were classified as on-track or off-track using the glenoid track concept (83% of glenoid width). Arthroscopic findings were used to confirm lesion engagement. SPSS version 21 was used for diagnostic metrics analysis. Results: The study population included 50 patients; 48.0% demonstrated engaging (off-track) lesions on imaging, of which 22 were confirmed arthroscopically. The glenoid track concept demonstrated a sensitivity of 91.7% (95% confidence interval [CI]: 74.2%-97.7%), a specificity of 92.3% (95% CI: 75.9%-97.9%), and an overall diagnostic accuracy of 92.0% (95% CI: 81.2%-96.8%) in predicting lesion engagement compared with arthroscopic findings, with a highly significant association on Fisher’s exact test ( P < 0.0001). Glenoid bone loss exceeding 25% was significantly associated with engagement. Most patients with less than 25% glenoid and less than 40% humeral bone loss were successfully managed with arthroscopic Bankart repair. Conclusion: The on-track/off-track method using the glenoid track concept accurately predicts engagement of Hill-Sachs lesions and facilitates individualized surgical decision-making in anterior shoulder instability.

Digital MedicineVol. 12(2)
King Edward Memorial Hospital and Seth G.S. Medical College (IN), Ruby Hall Clinic (IN), Dr. D. Y. Patil Medical College, Hospital and Research Centre (IN), Dr. D.Y. Patil Vidyapeeth, Pune (IN), College of Medicine & JNM Hospital (IN)
Gender equality
Openalex Percentile: Top 6%
Shoulder Injury and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.