Clinical coding data algorithm to categorize type of gastrointestinal bleeding as a primary reason for massive transfusion: results from the Australian and New Zealand Massive Transfusion Registry

• Published in: Vox sanguinis 2019-11, Vol.114 (8), p.853-860
• Main Authors: Ket, Shara N., Sparrow, Rosemary L., McQuilten, Zoe K., Tacey, Mark, Gibson, Peter R., Brown, Gregor J., Wood, Erica M.
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Australia; Blood Transfusion - standards; Clinical Coding - methods; Clinical Coding - standards; clinical outcomes; Cohort Studies; Female; gastrointestinal; Gastrointestinal Hemorrhage - classification; Gastrointestinal Hemorrhage - therapy; haemorrhage; Humans; Male; Middle Aged; New Zealand; protocol; Registries; registry; Retrospective Studies; transfusion; validation
• ISSN: 0042-9007; 1423-0410
• DOI: 10.1111/vox.12840

Background Management of major gastrointestinal bleeding (GIB) may require massive transfusion (MT), but limited data are available. Upper and lower GIB have different aetiologies, prognosis, bleeding patterns and outcomes. Better understanding of current transfusion management and outcomes in these patients is important. We sought to define and validate an algorithm based on clinical coding data to distinguish critical upper and lower GIB using data from the Australian and New Zealand Massive Transfusion Registry (ANZ‐MTR). Study Design and Methods Australian and New Zealand Massive Transfusion Registry hospital‐source data on adult patients receiving a MT (defined as ≥5 red cell units within 4 h) for any bleeding context were used. An algorithm allocating ICD‐10‐AM codes into ‘probable’ or ‘possible’ causes of GIB was developed and applied to the ANZ‐MTR. Source medical records of 69 randomly selected cases were independently reviewed to validate the algorithm. Results Of 5482 MT cases available from 25 hospitals, 716 (13%) were identified as GIB with 538/716 (75%) categorized ‘probable’ and 178/716 ‘possible’ GIB. Upper and lower GIB causes of MT were identified for 455/538 (85%) and 76/538 (14%) ‘probable’ cases, respectively; 7/538 (1·3%) cases had both upper and lower GIB. Allocation by the algorithm into a ‘probable’ GIB category had a 95·7% (CI: 90–100%) positive predictive value when validated against source medical records. Conclusion An algorithm based on ICD‐10‐AM codes can be used to accurately categorize patients with luminal GIB as the primary reason for MT, enabling further study of this critically unwell and resource‐intensive cohort of patients.