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

McQuilten, Z. K., Zatta, A. J., Andrianopoulos, N., Aoki, N., Stevenson, L., Badami, K. G., Bird, R., Cole-Sinclair, M. F., Hurn, C., Cameron, P. A., Isbister, J. P., Phillips, L. E. and Wood, E. M. (2017) Evaluation of clinical coding data to determine causes of critical bleeding in patients receiving massive transfusion: a bi-national, multicentre, cross-sectional study. Transfusion Medicine, 27 2: 114-121. doi:10.1111/tme.12377


Author McQuilten, Z. K.
Zatta, A. J.
Andrianopoulos, N.
Aoki, N.
Stevenson, L.
Badami, K. G.
Bird, R.
Cole-Sinclair, M. F.
Hurn, C.
Cameron, P. A.
Isbister, J. P.
Phillips, L. E.
Wood, E. M.
Title Evaluation of clinical coding data to determine causes of critical bleeding in patients receiving massive transfusion: a bi-national, multicentre, cross-sectional study
Journal name Transfusion Medicine   Check publisher's open access policy
ISSN 1365-3148
0958-7578
Publication date 2017-04-01
Year available 2016
Sub-type Article (original research)
DOI 10.1111/tme.12377
Open Access Status Not yet assessed
Volume 27
Issue 2
Start page 114
End page 121
Total pages 8
Place of publication Chichester, West Sussex, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Subject 2720 Hematology
Abstract Objectives: To evaluate the use of routinely collected data to determine the cause(s) of critical bleeding in patients who receive massive transfusion (MT). Background: Routinely collected data are increasingly being used to describe and evaluate transfusion practice. Materials/methods: Chart reviews were undertaken on 10 randomly selected MT patients at 48 hospitals across Australia and New Zealand to determine the cause(s) of critical bleeding. Diagnosis-related group (DRG) and International Classification of Diseases (ICD) codes were extracted separately and used to assign each patient a cause of critical bleeding. These were compared against chart review using percentage agreement and kappa statistics. Results: A total of 427 MT patients were included with complete ICD and DRG data for 427 (100%) and 396 (93%), respectively. Good overall agreement was found between chart review and ICD codes (78·3%; κ = 0·74, 95% CI 0·70–0·79) and only fair overall agreement with DRG (51%; κ = 0·45, 95% CI 0·40–0·50). Both ICD and DRG were sensitive and accurate for classifying obstetric haemorrhage patients (98% sensitivity and κ > 0·94). However, compared with the ICD algorithm, DRGs were less sensitive and accurate in classifying bleeding as a result of gastrointestinal haemorrhage (74% vs 8%; κ = 0·75 vs 0·1), trauma (92% vs 62%; κ = 0·78 vs 0·67), cardiac (80% vs 57%; κ = 0·79 vs 0·60) and vascular surgery (64% vs 56%; κ = 0·69 vs 0·65). Conclusion: 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.
Formatted abstract
Objectives: To evaluate the use of routinely collected data to determine the cause(s) of critical bleeding in patients who receive massive transfusion (MT).

Background: Routinely collected data are increasingly being used to describe and evaluate transfusion practice.

Materials/methods: Chart reviews were undertaken on 10 randomly selected MT patients at 48 hospitals across Australia and New Zealand to determine the cause(s) of critical bleeding. Diagnosis-related group (DRG) and International Classification of Diseases (ICD) codes were extracted separately and used to assign each patient a cause of critical bleeding. These were compared against chart review using percentage agreement and kappa statistics.

Results: A total of 427 MT patients were included with complete ICD and DRG data for 427 (100%) and 396 (93%), respectively. Good overall agreement was found between chart review and ICD codes (78·3%; κ = 0·74, 95% CI 0·70–0·79) and only fair overall agreement with DRG (51%; κ = 0·45, 95% CI 0·40–0·50). Both ICD and DRG were sensitive and accurate for classifying obstetric haemorrhage patients (98% sensitivity and κ > 0·94). However, compared with the ICD algorithm, DRGs were less sensitive and accurate in classifying bleeding as a result of gastrointestinal haemorrhage (74% vs 8%; κ = 0·75 vs 0·1), trauma (92% vs 62%; κ = 0·78 vs 0·67), cardiac (80% vs 57%; κ = 0·79 vs 0·60) and vascular surgery (64% vs 56%; κ = 0·69 vs 0·65).

Conclusion: 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.
Keyword Administrative data
Critical bleeding
Haemorrhage
Massive transfusion
Red blood cell transfusion
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID APP1111485
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Medicine Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Tue, 25 Apr 2017, 00:26:36 EST by Web Cron on behalf of Learning and Research Services (UQ Library)