T Ong1,2, R Copeland3, C N Thiam4, G Cerda Mas5, L Marshall6, O Sahota3,7,8,9. 1. Department for Healthcare of Older People, Queens Medical Centre, Nottingham University Hospital NHS Trust, Nottingham, UK. terenceong@doctors.org.uk. 2. Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia. terenceong@doctors.org.uk. 3. Department for Healthcare of Older People, Queens Medical Centre, Nottingham University Hospital NHS Trust, Nottingham, UK. 4. Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia. 5. Consorci Sanitari de Terrassa, Terrassa, Spain. 6. Department for Trauma and Orthopaedics, Queens Medical Centre, Nottingham University Hospital NHS Trust, Nottingham, UK. 7. Division of Rehabilitation, Ageing and Wellbeing, University of Nottingham, Nottingham, UK. 8. National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre (BRC), Nottingham, UK. 9. National Institute for Health Research (NIHR) Collaboration for Applied Health Research and Care (CLAHRC) East Midlands, Nottingham, UK.
Abstract
Integration of a vertebral fracture identification service into a Fracture Liaison Service is possible. Almost one-fifth of computerised tomography scans performed identified an individual with a fracture. This increase in workload needs to be considered by any FLS that wants to utilise such a service. INTRODUCTION: This service improvement project aimed to improve detection of incidental vertebral fractures on routine imaging. It embedded a vertebral fracture identification service (Optasia Medical, OM) on routine computerised tomography (CT) scans performed in this hospital as part of its Fracture Liaison Service (FLS). METHODS: The service was integrated into the hospital's CT workstream. Scans of patients aged ≥ 50 years for 3 months were prospectively retrieved, alongside their clinical history and the CT report. Fractures were identified via OM's machine learning algorithm and cross-checked by the OM radiologist. Fractures identified were then added as an addendum to the original CT report and the hospital FLS informed. The FLS made recommendations based on an agreed algorithm. RESULTS: In total, 4461 patients with CT scans were retrieved over the 3-month period of which 850 patients had vertebra fractures identified (19.1%). Only 49% had the fractures described on hospital radiology report. On average, 61 patients were identified each week with a median of two fractures. Thirty-six percent were identified by the FLS for further action and recommendations were made to either primary care or the community osteoporosis team within 3 months of fracture detection. Of the 64% not identified for further action, almost half was because the CT was part of cancer assessment or treatment. The remaining were due to a combination of only ≤ 2 mild fractures; already known to a bone health specialist; in the terminal stages of any chronic illness; significant dependency for activities of daily living; or a life expectancy of less than 12 months CONCLUSION: It was feasible to integrate a commercial vertebral fracture identification service into the daily working of a FLS. There was a significant increase in workload which needs to be considered by any future FLS planning to incorporate such a service into their clinical practice.
Integration of a vertebral fracture identification service into a Fracture Liaison Service is possible. Almost one-fifth of computerised tomography scans performed identified an individual with a fracture. This increase in workload needs to be considered by any FLS that wants to utilise such a service. INTRODUCTION: This service improvement project aimed to improve detection of incidental vertebral fractures on routine imaging. It embedded a vertebral fracture identification service (Optasia Medical, OM) on routine computerised tomography (CT) scans performed in this hospital as part of its Fracture Liaison Service (FLS). METHODS: The service was integrated into the hospital's CT workstream. Scans of patients aged ≥ 50 years for 3 months were prospectively retrieved, alongside their clinical history and the CT report. Fractures were identified via OM's machine learning algorithm and cross-checked by the OM radiologist. Fractures identified were then added as an addendum to the original CT report and the hospital FLS informed. The FLS made recommendations based on an agreed algorithm. RESULTS: In total, 4461 patients with CT scans were retrieved over the 3-month period of which 850 patients had vertebra fractures identified (19.1%). Only 49% had the fractures described on hospital radiology report. On average, 61 patients were identified each week with a median of two fractures. Thirty-six percent were identified by the FLS for further action and recommendations were made to either primary care or the community osteoporosis team within 3 months of fracture detection. Of the 64% not identified for further action, almost half was because the CT was part of cancer assessment or treatment. The remaining were due to a combination of only ≤ 2 mild fractures; already known to a bone health specialist; in the terminal stages of any chronic illness; significant dependency for activities of daily living; or a life expectancy of less than 12 months CONCLUSION: It was feasible to integrate a commercial vertebral fracture identification service into the daily working of a FLS. There was a significant increase in workload which needs to be considered by any future FLS planning to incorporate such a service into their clinical practice.
Authors: K E Åkesson; K Ganda; C Deignan; M K Oates; A Volpert; K Brooks; D Lee; D R Dirschl; A J Singer Journal: Osteoporos Int Date: 2022-03-24 Impact factor: 5.071