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2023 ISAKOS Biennial Congress Paper

Assessment of the Tendon Retraction Classification Systems for Predicting Reparability and Rotator Cuff Integrity in Arthroscopic Rotator Cuff Repair

Yoshitsugu Takeda, MD, PhD, Komatsushima, Tokushima JAPAN
Naoto Suzue, MD, PhD, Komatsushima, Tokushima JAPAN
Tokushima Red Cross Hospital, Komatsushima, Tokushima, JAPAN

FDA Status Cleared

Summary

Among 4 classification systems of SSP tendon retraction, Lhee classification was most valuable in predicting reparability and RCI after ARCR.

Abstract

Purpose

This study aimed to determine which classification system of the supraspinatus tendon retraction is most valuable for predicting the reparability and rotator cuff integrity (RCI) after arthroscopic rotator cuff repair (ARCR).

Methods

This retrospective study was performed on 463 consecutive patients who underwent ARCR for full-thickness tear. The inclusion criteria were patients who with full-thickness supraspinatus (SSP) tear and a minimum of 12 months follow-up. Patients with partial-thickness tear, osteoarthritis, instability, or a history of previous shoulder surgery were excluded. The degree of tendon retraction was assessed by the position of the retracted torn tendon in the coronal view of MRI by two shoulder surgeons who were not aware of the patient’s information, and tendon retraction was evaluated according to 4 types of classification systems (Patte, French Arthroscopy Society (SFA), Kim, Lhee). Retear after ARCR was defined as Sugaya type IV and V. Using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), the ability of each classification system to predict reparability and retear after ARCR was evaluated. The cut-off point of each classification system was determined according to Youden index.

Results

403 patients met our inclusion criteria for repairability and 370 patients for RCI with retear rate of19.2%. For the prediction of repairability, the AUC of Patte, SFA, Kim, and Lhee were 0.685, 0.784, 0.738, and 0.823, respectively. The AUC of Patte was significantly smaller than that of SFA and Lhee (p=0.015, p<0.001, respectively). For the prediction of RCI, the AUC of Patte, SFA, Kim, and Lhee were 0.581, 0.642, 0.723, and 0.751, respectively. The AUC of Patte was significantly smaller than the other three classifications (p=0.01, p<0.001, p<0.001, respectively), and Kim and Lhee was significantly greater than that of SFA. The cut-off points of Kim and Lhee were at the center and the medial one-third of the humeral head. Those of SFA and Patte were at the lower edge of the anatomical neck and the glenoid rim.

Conclusion