Galvin, Joseph W.

Abstract Arthroscopic posterior labral repair is an effective surgical technique for managing patients with posterior labral tears and symptomatic unidirectional posterior shoulder instability. It is well documented that a curved liberator from the mid‐glenoid (anterior‐inferior) portal passed from anterior to posterior provides an effective trajectory for liberation and mobilization of the posterior and posterior‐inferior labral tear. However, biologic preparation of the posterior and inferior glenoid rim with a shaver can be challenging because the anterior mid‐glenoid portal and the posterior portal do not provide an adequate trajectory for instruments to be parallel to the posterior glenoid rim. The purpose of this technical note is to describe a simple efficient procedure for biologic preparation of the posterior and inferior glenoid rim in the setting of arthroscopic posterior labral repair. Technique Video video See video under supplementary data.

Background: Currently, biomarkers do not have a role in diagnosis or outcome prediction models for patients with recurrent anterior shoulder instability. Purpose/Hypothesis: The purpose of this study was to compare gene expression differences in the blood and tissue of patients with anterior shoulder instability with and without significant glenoid bone loss (GBL). The hypothesis was that the severity of GBL would be associated with the expression level of genes in the blood and capsular tissue of patients with anterior shoulder instability. Study Design: Descriptive laboratory study. Methods: Consecutive patients with anterior shoulder instability undergoing arthroscopic and open shoulder stabilization were prospectively enrolled. Blood and anterior capsular tissue specimens obtained during surgery were compared between patients with significant GBL (≥10%) and patients without significant GBL (<10%). RNA was extracted, and a panel of 277 inflammatory genes was utilized to quantify gene expression at the RNA level using a probe-based RNA quantification platform. Differential expression analysis was performed to identify genes expressed at different levels between patients with and without significant GBL. The expression levels of the subset of genes identified were used to generate a ridge regression model to predict the severity of GBL. Quantitative polymerase chain reaction was performed to confirm probe-based RNA findings. Results: A total of 17 patients were included, with a mean age of 26 years. Overall, 7 patients had <10% GBL (mean, 2.3%), and 10 patients had ≥10% GBL (mean, 16.4%). There were 9 genes that were identified as significantly differentially expressed in the blood, and 5 of these ( IFIT1 , IFIT3 , IFI44 , PRKCB , and OAS2 with P values of 1 × 10 −5 , 1 × 10 −4 , 1 × 10 −4 , 1 × 10 −4 , and 6 × 10 −4 , respectively) were confirmed using nonparametric tests. The authors developed a model using the 5 genes to predict the severity of GBL, and this had an accuracy of 88% (95% CI, 64%-99%). By quantitative polymerase chain reaction, IFIT3 was confirmed to be significantly differentially expressed ( P = .012). Conclusion: There were significant gene expression differences in the blood of patients with (≥10%) and without (<10%) significant GBL. The differential expression of 5 genes allowed for the development of an accurate predictive model and transcriptomic biomarker to predict the severity of GBL. Clinical Relevance: The addition of a blood biomarker to current outcome prediction models may provide increased accuracy in identifying those at risk of failure from arthroscopic Bankart repair.

Background: Patient-reported outcomes (PROs) measure progression and quality of care. While legacy PROs such as the International Knee Documentation Committee (IKDC) survey are well-validated, a lengthy PRO creates a time burden on patients, decreasing adherence. In recent years, PROs such as the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function and Pain Interference surveys were developed as computer adaptive tests, reducing time to completion. Previous studies have examined correlation between legacy PROs and PROMIS; however, no studies have developed effective prediction models utilizing PROMIS to create an IKDC index. While the IKDC is the standard knee PRO, computer adaptive PROs offer numerous practical advantages. Purpose: To develop a nonlinear predictive model utilizing PROMIS Physical Function and Pain Interference to estimate IKDC survey scores and examine algorithm sensitivity and validity. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: The MOTION (Military Orthopaedics Tracking Injuries and Outcomes Network) database is a prospectively collected repository of PROs and intraoperative variables. Patients undergoing knee surgery completed the IKDC and PROMIS surveys at varying time points. Nonlinear multivariable predictive models using Gaussian and beta distributions were created to establish an IKDC index score, which was then validated using leave-one-out techniques and minimal clinically important difference analysis. Results: A total of 1011 patients completed the IKDC and PROMIS Physical Function and Pain Interference, providing 1618 complete observations. The algorithms for the Gaussian and beta distribution were validated to predict the IKDC (Pearson = 0.84-0.86; R 2 = 0.71-0.74; root mean square error = 9.3-10.0). Conclusion: The publicly available predictive models can approximate the IKDC score. The results can be used to compare PROMIS Physical Function and Pain Interference against historical IKDC scores by creating an IKDC index score. Serial use of the IKDC index allows for a lower minimal clinically important difference than the conventional IKDC. PROMIS can be substituted to reduce patient burden, increase completion rates, and produce orthopaedic-specific survey analogs.