OBJECTIVES: Understand if, and how, using an artificial intelligence-based algorithm (AI) to identify vertebral fragility fractures (VFFs) from routine computed tomography scans (CT) should be used to maximise added value to patients.

METHODS: A de-novo decision-analytic model (lifetime horizon; NHS-England perspective) linking a bespoke decision-tree with a published discrete-event simulation (DES) was conceptualised and developed for a cohort of 400,000 individuals aged 70 years. The intervention (ASPIRE^TM) used AI to identify VFFs from existing CT with referral (to Fracture Liaison Service (FLS) or general practitioner) to start a bisphosphonate. The comparator was the current practice of radiologists identifying VFFs from CT and referral to start a bisphosphonate. Technical validation was completed using TECH-VER criteria. Model input parameters were identified from published literature and structured expert elicitation. For ASPIRE^TM and current practice the base-case analysis reported: number of VFFs identified; costs (£; 2014); quality-adjusted-life-years (QALYs). Uncertainty was quantified using one-way, two-way, scenario, threshold and probabilistic sensitivity analyses.

RESULTS: ASPIRE^TM identified 47,029 additional VFFs, costing an additional £8,681,804 (95% confidence interval (CI): £8,606,882 to £8,756,726) generating 139 (CI: 137 to 140) QALYs. The incremental cost-effectiveness ratio was £185 per additional VFF identified. All QALY gains (0.00035 per person) were derived from starting a bisphosphonate (the DES component). Threshold analysis showed increasing QALY gains to 0.00108 resulted in £20,000 per QALY gained. Key drivers of cost-effectiveness were: specificity; ASPIRE^TM unit cost; radiologists’ time averted by ASPIRE^TM and FLS cost. There was substantial uncertainty in the limited evidence available.

CONCLUSIONS: Indicative cost-effectiveness analysis shows the importance of embedding ASPIRE^TM into a complex intervention directing people to effective bone management strategies (fall prevention, exercise and nutrition programmes) in addition to bisphosphonates and have data to show radiologists’ time averted. Importantly, decision-makers must be sufficiently certain in the model-estimated QALY gains from bisphosphonates to understand the potential value of ASPIRE^TM.