Background and Purpose. The STarT Back approach comprises subgrouping of LBP patients according to risk of persistent LBP-related disability, and matches patients to appropriate treatments. In a clinical trial and implementation study, this stratified care approach was clinically and cost-effective compared to usual non-stratified care. However, the long-term cost- effectiveness is unknown, and could be established with decision modelling. A systematic review of model-based economic evaluations in LBP found shortcomings with existing models, including inadequate characterisation of the condition in health states and absence of long-term modelling. This study conceptualises the first decision model of this stratified care approach for LBP management, and assesses long-term cost-effectiveness. Methods. A cost-utility analysis from the NHS perspective compared stratified care with usual care, in patients consulting in primary care with non-specific LBP. A Markov state-transition model was constructed where long-term patient prognosis over ten years was dependent upon physical function achieved at twelve months. Consultation with experts helped define condition health states, inform the long-term modelling, and choice of sensitivity analyses. Results. Preliminary base-case results indicate this model of stratified care is cost-effective over a ten-year time horizon, delivering 0.10 additional quality-adjusted life years (QALYs) at a cost-saving of £100.27 per patient. Sensitivity analyses indicate the approach is likely to be cost-effective in all scenarios, and cost-saving in most, although sensitive to assumptions regarding long-term patient prognosis. Analysis from the societal perspective improved the associated cost-savings. Conclusion. It is likely that implementation of this stratified care model will help reduce unnecessary healthcare usage, whilst improving patient quality of life. No conflicts of interest. Funding: Research stipend for JAH by the Institute for Primary Care & Health Sciences, Keele University

Aims

To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory.