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

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.

Keywords: administrative data; critical bleeding; haemorrhage; massive transfusion; red blood cell transfusion.