Anatomic characteristics of shoulder based on MRI accurately predict incomplete rotator cuff injuries in patients: relevance for predictive, preventive, and personalized healthcare strategies

Background and PPPM-related working hypothesis In the diagnosis of incomplete rotator cuff injuries (IRCI), magnetic resonance imaging (MRI) and ultrasound examination often have false-positive and false-negative results, while arthroscopy is expensive, invasive, and complex. From the strategy of predictive, preventive, and personalized medicine (PPPM), shoulder anatomical characteristics based on MRI have been demonstrated to accurately predict IRCI and their clinical applicability for personalized prediction of IRCI. Aims This study aimed to develop and validate a nomogram based on anatomical features of the shoulder on MRI to identify IRCI for PPPM healthcare strategies. Methods The medical information of 257 patients undergoing preoperative MRI examination was retrospectively reviewed and served as the primary cohort. Partial-thickness rotator cuff tears (RCTs) and tendinopathy observed under arthroscopy were considered IRCI. Using logistic regression analyses and least absolute shrinkage and selection operator (LASSO), IRCI was identified among various preoperative factors containing shoulder MRI and clinical features. A nomogram was constructed and subjected to internal and external validations (80 patients). Results The following eight independent risk factors for IRCI were identified: Age The left injured sides The Goutallier classification of supraspinatus in oblique coronal position The Goutallier classification of supraspinatus in the axial position Acromial thickness Acromiohumeral distance Coracohumeral distance Abnormal acromioclavicular joint signals The nomogram accurately predicted IRCI in the development (C-index, 0.932 (95% CI, 0.891, 0.973)) and validation (C-index, 0.955 (95% CI, 0.918, 0.992)) cohorts. The calibration curve was consistent between the predicted IRCI probability and the actual IRCI ratio of the nomogram. The decision curve analysis and clinical impact curves demonstrated that the model had high clinical applicability. Conclusions Eight independent factors that accurately predicted IRCI were determined using MRI anatomical findings. These personalized factors can prevent unnecessary diagnostic interventions (e.g., arthroscopy) and can assist surgeons in implementing individualized clinical decisions in medical practice, thus addressing the goals of PPPM.