Developing a minimum common dataset for hip fracture audit to help countries set up national audits that can support international comparisons

Abstract

Aims The aim of this study was to explore current use of the Global Fragility Fracture Network (FFN) Minimum Common Dataset (MCD) within established national hip fracture registries, and to propose a revised MCD to enable international benchmarking for hip fracture care. Methods We compared all ten established national hip fracture registries: England, Wales, and Northern Ireland; Scotland; Australia and New Zealand; Republic of Ireland; Germany; the Netherlands; Sweden; Norway; Denmark; and Spain. We tabulated all questions included in each registry, and cross-referenced them against the 32 questions of the MCD dataset. Having identified those questions consistently used in the majority of national audits, and which additional fields were used less commonly, we then used consensus methods to establish a revised MCD. Results A total of 215 unique questions were used across the ten registries. Only 72 (34%) were used in more than one national audit, and only 32 (15%) by more than half of audits. Only one registry used all 32 questions from the 2014 MCD, and five questions were only collected by a single registry. Only 21 of the 32 questions in the MCD were used in the majority of national audits. Only three fields (anaesthetic grade, operation, and date/time of surgery) were used by all ten established audits. We presented these findings at the Asia-Pacific FFN meeting, and used an online questionnaire to capture feedback from expert clinicians from different countries. A draft revision of the MCD was then presented to all 95 nations represented at the Global FFN conference in September 2021, with online feedback again used to finalize the revised MCD. Conclusion The revised MCD will help aspirant nations establish new registry programmes, facilitate the integration of novel analytic techniques and greater multinational collaboration, and serve as an internationally-accepted standard for monitoring and improving hip fracture services. Cite this article: Bone Joint J 2022;104-B(6):721–728.

Long-term outcome collection after hip fracture in Ireland: a systematic review of traditional and grey literature

“…This study has described and appraised a broad body of literature regarding research and audit in the Republic of Ireland published since 2005. It has revealed recent practices in long-term outcome collection and recording after acute Outcomes collected as part of recent hip fracture research in Ireland are broadly in line with those suggested by the international Fragility Fracture Network minimum common dataset for hip fracture audit, namely mortality, re-operation, mobility, residence, and bone protection medication [7]. There were varying time points of outcome collection in studies included in the current review.…”

COVID‐19 during the index hospital admission confers a ‘double‐hit’ effect on hip fracture patients and is associated with a two‐fold increase in 1‐year mortality risk

“…Similar such studies are unlikely to be feasible now. Future work should utilise existing clinical audit mechanisms to monitor long‐term effects on a population‐wide level, and the compilation of data from multiple national hip fracture audit programmes (adhering to a minimum common clinical dataset) would ensure generalisability to a range of regions and healthcare contexts (Hall, Duckworth, et al., 2021 ; Johansen et al., 2022 ).…”

The IMPACT of COVID-19 on trauma & orthopaedic surgery provides lessons for future communicable disease outbreaks

“…Rapid data-sharing between regions in the form of low-cost and agile audit collaborations should be developed, with wider adoption of the revised 2022 Fragility Fracture Network Minimum Common Data Set for Hip Fracture Audit, and a pragmatic approach to information governance processes in order to facilitate cooperation. 22 These measures will ensure that future emergency audit responses can be commenced with reduced lead times, greater diversity and generalizability to global populations, and larger sample sizes through compilation of datasets using a meta-audit approach. 23 …”