Evaluation of clinical coding data to determine causes of critical bleeding in patients receiving massive transfusion: a bi‐national, multicentre, cross‐sectional study

McQuilten, Zoe; Zatta, Amanda J.; Andrianopoulos, Nick; Aoki, Naomi; Stevenson, Lisa; Badami, Krishna G; Bird, Robert; Cole‐Sinclair, Merrole; Hurn, Catherine; Cameron, Peter; Phillips, L.; Wood, Erica M.

doi:10.1111/tme.12377

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…From 95 publications initially identified, 52 were excluded either because they did not include a gold standard data source, used administrative claims data as the gold standard, or otherwise did not meet our criteria for inclusion. A total of 43 studies from six countries were identified (Table ). Of these, 72.1% ( n = 31) were published within the last 5 years (ie, since 2012).…”

Section: Resultsmentioning

confidence: 99%

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Validation studies of claims data in the Asia‐Pacific region: A comprehensive review

Koram

Delgado

Stark

et al. 2018

Pharmacoepidemiology and Drug

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Section: Resultsmentioning

confidence: 99%

“…Overall, the quality of published studies varied. Seven studies used both medical records as a gold standard and a full set of validity measures. These were multiple institution studies published mainly from Australia ( n = 3) and Japan ( n = 2).…”

Section: Type Of Outcomes Validatedmentioning

confidence: 99%

Validation studies of claims data in the Asia‐Pacific region: A comprehensive review

Koram

Delgado

Stark

et al. 2018

Pharmacoepidemiology and Drug

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show abstract

“…Bleeding context was categorized using the diagnostic and procedure codes into 10 categories: (1) trauma; (2) cardiothoracic surgery, (3) vascular surgery, (4) cardiothoracic surgery plus vascular surgery, (5) liver transplant, (6) gastrointestinal hemorrhage, (7) gastrointestinal hemorrhage plus surgical bleed, (8) other surgery, (9) medical/other, and (10) obstetric hemorrhage. Details on the categorization of bleeding contexts have been previously published 30 …”

Section: Methodsmentioning

confidence: 99%

Age of red blood cells is not associated with in-hospital mortality in massively transfused patients

Saadah¹,

Wood²,

Bailey³

et al. 2021

J Trauma Acute Care Surg

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BACKGROUND:Studies comparing mortality following massive transfusion (MT) with fresher versus longer-stored red blood cells (RBCs) have focused on trauma patients. The Australian and New Zealand Massive Transfusion Registry collects data on all adult MT cases (≥5 RBCs within 4 hours, any bleeding context, ≥18 years) at participating hospitals. METHODS:Years 2007 to 2018 data from 29 hospitals were analyzed to quantify the association between mortality and RBC storage time in adult MT cases. We ran three logistic regression models separately on each of seven bleeding contexts, with in-hospital mortality as the outcome and, in turn, (1) mean storage time (ST mean ) quartiles, (2) proportion of RBCs ≥30 days old (prop OLD ), and (3) scalar age of blood index as predictors. RESULTS:A total of 8,685 adult MT cases involving transfusion of 126,622 RBCs were analyzed with Australian and New Zealand data analyzed separately. Mean storage times for these cases were (by quartile in ascending order) as follows:

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“…The ANZ‐MTR contains data for patients ≥18 years of age who received a MT, defined as ≥5 red blood cell units within a 4‐h period for any bleeding context . MT cases in the registry have been algorithmically categorized into broad groups of critical bleeding events as previously described . In summary, the critical bleeding categories include trauma, obstetrics, surgical bleeding (cardiothoracic, vascular, liver, other surgery), GIB, cases with both GIB and surgical bleeding, and other medical conditions.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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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 TransfusionRegistry 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. ResultsOf 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 (1Á3%) cases had both upper and lower GIB. Allocation by the algorithm into a 'probable' GIB category had a 95Á7% (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.

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Evaluation of clinical coding data to determine causes of critical bleeding in patients receiving massive transfusion: a bi‐national, multicentre, cross‐sectional study

Abstract: Algorithms using ICD codes can determine the cause of critical bleeding in patients requiring MT with good to excellent agreement with clinical history. DRG are less suitable to determine critical bleeding causes.

Cited by 7 publications

References 15 publications

Validation studies of claims data in the Asia‐Pacific region: A comprehensive review

Validation studies of claims data in the Asia‐Pacific region: A comprehensive review

Age of red blood cells is not associated with in-hospital mortality in massively transfused patients

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

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