Mapping the Oxford Shoulder Score onto the EQ-5D utility index.

PURPOSE: In order to enable cost-utility analysis of shoulder pain conditions and treatments, this study aimed to develop and evaluate mapping algorithms to estimate the EQ-5D health index from the Oxford Shoulder Score (OSS) when health outcomes are only assessed with the OSS. METHODS: 5437 paired OSS and EQ-5D questionnaire responses from four national multicentre randomised controlled trials investigating different shoulder pathologies and treatments were split into training and testing samples. Separate EQ-5D-3L and EQ-5D-5L analyses were undertaken. Transfer to utility (TTU) regression (univariate linear, polynomial, spline, multivariable linear, two-part logistic-linear, tobit and adjusted limited dependent variable mixture models) and response mapping (ordered logistic regression and seemingly unrelated regression (SUR)) models were developed on the training sample. These were internally validated, and their performance evaluated on the testing sample. Model performance was evaluated over 100-fold repeated training-testing sample splits. RESULTS: For the EQ-5D-3L analysis, the multivariable linear and splines models had the lowest mean square error (MSE) of 0.0415. The SUR model had the lowest mean absolute error (MAE) of 0.136. Model performance was greatest in the mid-range and best health states, and lowest in poor health states. For the EQ-5D-5L analyses, the multivariable linear and splines models had the lowest MSE (0.0241-0.0278) while the SUR models had the lowest MAE (0.105-0.113). CONCLUSION: The developed models now allow accurate estimation of the EQ-5D health index when only the OSS responses are available as a measure of patient-reported health outcome.

The arthroscopic Latarjet: a multisurgeon learning curve analysis.

BACKGROUND: The open Latarjet procedure is a standard surgical treatment option for anterior shoulder instability in patients with a high risk of failure following soft tissue stabilization. The arthroscopic technique has potential advantages of minimal invasiveness, reduced postoperative stiffness, and faster rehabilitation but is regarded as technically challenging with concern over surgical risk during the learning curve. The aim of this study was to undertake a multisurgeon, large-volume learning curve analysis of the arthroscopic Latarjet procedure using continuous learning curve analysis. METHODS: Individual patient data from 12 surgeons across 5 countries were retrospectively reviewed. A total of 573 patients undergoing the arthroscopic Latarjet procedure were included. Outcome measures of learning were collected, including operative time, computed tomography (CT) bone-block positioning, complications, and patient-reported outcome measures (PROMs). A segmented linear regression modeling technique was used for learning curve analysis. RESULTS: High-volume surgeons converged to an operative time steady state after 30-50 cases. Surgeons completing fewer procedures demonstrated a constant reduction in operative time without reaching a plateau. Low-volume surgeons completing fewer than 14 operations did not demonstrate a reduction in operative time. Accuracy of bone-block positioning on postoperative CT demonstrated constant improvement without reaching a plateau after 53 cases. There was no change in PROMs or complications with increased operative volume. CONCLUSION: Specialist shoulder surgeons require 30-50 arthroscopic Latarjet procedures to attain steady-state operative efficiency, during which there is improvement in bone-block positioning. Only surgeons expecting to undertake the arthroscopic Latarjet in high volume should consider adopting this procedure.