Anterior-posterior (AP) x-rays are routinely taken following total hip replacement to assess placement and orientation of implanted components. Pelvic orientation at the time of an AP x-ray can influence projected implant orientation.¹However, the extent of pelvic orientation varies between patients.²Without compensation for patient specific pelvic orientation, misleading measurements for implant orientation may be obtained. These measurements are used as indicators for post-operative dislocation stability and range of motion. Errors in which could result in differences between expectations and the true outcome achieved. The aim of this research was to develop a tool that could be utilised to determine pelvic orientation from an AP x-ray.

An algorithm based on comparing projections of a statistical shape model of the pelvis (n=20) with the target X-ray was developed in MATLAB. For each iteration, the average shape was adjusted, rotated (to account for patient-specific pelvic orientation), projected onto a 2D plane, and the simulated outline determined. With respect to rotation, the pelvis was allowed to rotate about its transverse axis (pelvic flexion/extension) and anterior-posterior axis (pelvic adduction/abduction). Minimum root mean square error between the outline of the pelvis from the X-ray and the projected shape model outline was used to select final values for flexion and adduction. To test the algorithm, virtual X-rays (n=6) of different pelvis in known orientations were created using the algorithm described by Freud et al.³The true pelvic orientation for each case was randomly generated. Angular error was defined as the difference between the true pelvic orientation and that selected by the algorithm.

Initial testing has exhibited similar accuracy in determining true pelvic flexion (x̄_error = 2.74°, σ_error=±2.21°) and true pelvic adduction (x̄_error = 2.38°, σ_error=±1.76°). For both pelvic flexion and adduction the maximum angular error observed was 5.62°. The minimum angular error for pelvic flexion was 0.37°, whilst for pelvic adduction it was 1.08°.

Although the algorithm is still under development, the low mean, maximum, and standard deviations of error from initial testing indicate the approach is promising. Ongoing work will involve the use of additional landmarks for registration and training shapes to improve the shape model. This tool will allow surgeons to more accurately determine true acetabular orientation relative to the pelvis without the use of additional x-ray views or CT scans. In turn, this will help improve diagnoses of post-operative range of motion and dislocation stability.

Objective

The STarT Back Screening Tool (STarT) is a 9-item patient self-report questionnaire that classifies low back pain patients into low, medium or high risk of poor prognosis. When assessed by GPs, these subgroups can be used to triage patients into different evidence-based treatment pathways. The objective of this study was to translate the English version of STarT into Danish (STarT-dk) and test its discriminative validity.

Background

One untested back pain treatment model is to stratify management depending on prognosis (low, medium or high-risk). This 2-arm RCT investigated: (i) overall clinical and cost-effectiveness of stratified primary care (intervention), versus non-stratified current best practice (control); and (ii) whether low-risk patients had non-inferior outcomes, and medium/high-risk groups had superior outcomes.

Introduction

The STarT Back trial demonstrated that targeting back pain treatment according to patient prognosis (low, medium or high-risk subgroups) is effective. However, the mechanisms leading to these improved treatment outcomes remain unknown. This study aimed to identify which psychological variables included in the study were mediating treatment outcome for all patients and within the low, medium and high-risk subgroups.