Understanding current UK practice for the incidental identification of vertebral fragility fractures from CT scans: an expert elicitation study

Garima Dalal, Paul Bromiley, Elena Kariki, Shawn Luetchens, Timothy Cootes, Katherine Payne

Research output: Contribution to journalArticlepeer-review

Abstract

Background: There is an emerging interest in using automated approaches to enable the incidental identification of vertebral fragility fractures (VFFs) on existing medical images visualising the spine. Aim: To quantify values, and the degree of uncertainty associated with them, for the incidental identification of VFFs from computed tomography (CT) scans in current practice. Methods: An expert elicitation exercise was conducted to generate point estimates and measures of uncertainty for four values representing the probability of: VFF being correctly reported by the radiologist; the absence of VFF being correctly assessed by the radiologist; being referred for management when a VFF is identified; having a dual-energy X-ray absorptiometry (DXA) scan after general practitioner (GP) referral. Data from a sample of seven experts in the diagnosis and management of people with VFFs were pooled using mathematical aggregation. Results: The estimated mean values for each probability parameter were: VFF being correctly reported by the radiologist = 0.25 (standard deviation (SD): 0.21); absence of VFF being correctly assessed by the radiologist = 0.89 (0.10); being referred for management when a VFF is identified by the radiologist = 0.15 (0.12); having a DXA scan after GP referral = 0.66 (0.28). Discussion: These estimates could be used to facilitate the subsequent early economic evaluation of potential new approaches to improve the health outcomes of people with VFFs. Conclusion: In the absence of epidemiological studies, this study produced point estimates and measures of uncertainty for key parameters needed to describe current pathways for the incidental diagnosis of VFFs.

Original languageEnglish
JournalAging Clinical and Experimental Research
DOIs
Publication statusPublished - 18 Apr 2022

Keywords

  • Artificial intelligence
  • CT
  • Diagnosis
  • Expert elicitation
  • Incidental identification
  • Osteoporosis
  • Prevention
  • Radiological
  • Vertebral fragility fractures

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