AUTHOR=Xu Wennan , Zheng Zitian , Xue Qingyun 

TITLE=Development and Validation of a Web-Based Dynamic Nomogram to Improve the Diagnostic Performance of Subscapularis Tendon Tear

JOURNAL=Frontiers in Surgery

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.874800

DOI=10.3389/fsurg.2022.874800

ISSN=2296-875X

ABSTRACT=Background: There are still some challenges in diagnosing subscapularis (SSC) tendon tears as accurately as posterosuperior rotator cuff tears on MRI. The omission of SSC tendon tear can lead to muscle atrophy, fatty infiltration, and increased tear accompanied by aggravated shoulder pain and loss of function. An effective non-invasive evaluation tool will be beneficial to early identification and intervention. The study aims to identify sensitive predictors associated with SSC tendon tear and develop a dynamic nomogram to improve diagnostic performance.
Methods: From July 2016 to October 2021, 528 consecutive cases of patients who underwent shoulder arthroscopic surgery with preoperative shoulder magnetic resonance imaging (MRI) were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) method was used to identify the sensitive factors associated with SSC tendon tear which were then incorporated into the nomogram. The prediction performance of the nomogram was evaluated by concordance index (C index), and calibration with 1000 bootstrap samples combined with external validation of another cohort.
Results: Lasso method shows that six items including coracohumeral distance (oblique sagittal plane), effusion (Y-face), effusion (subcoracoid), malposition of the long head tendon of the biceps, multiple posterosuperior rotator cuff tears, and considering SSC tendon tear on MRI were determined as sensitive predictors. The nomogram achieved a good C index of 0.878 (95% CI, 0.839-0.918) with a good agreement on the risk estimation of calibration plots. The areas under the receiver operator characteristic curves (ROC) of the two methods showed that dynamic nomogram had better prediction performance than MRI (training set 0.878 vs. 0.707, validation set 0.890 vs. 0.704). 
Conclusion: The study identified sensitive predictors associated with SSC tendon tear and firstly developed a web-based dynamic nomogram as a good supplementary evaluation tool for imaging diagnosis which could provide an individualized risk estimate with superior prediction performance, even in patients with small or partial tears.