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

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.

Elucidating the clinical characteristics of patients captured using different definitions of massive transfusion.

BACKGROUND AND OBJECTIVES: The type and clinical characteristics of patients identified with commonly used definitions of massive transfusion (MT) are largely unknown. The objective of this study was to define the clinical characteristics of patients meeting different definitions of MT for the purpose of patient recruitment in observational studies. MATERIALS AND METHODS: Data were extracted on all patients who received red blood cell (RBC) transfusions in 2010 at three tertiary Australian hospitals. MT patients were identified according to three definitions: ≥10 units RBC in 24 h (10/24 h), ≥6 units RBC in 6 h (6/6 h) and ≥5 units RBC in 4 h (5/4 h). Clinical coding data were used to assign bleeding context. Data on in-hospital mortality were also extracted. RESULTS: Five hundred and forty-two patients met at least one MT definition, with 236 (44%) included by all definitions. The most inclusive definition was 5/4 h (508 patients, 94%) followed by 6/6 h (455 patients, 84%) and 10/24 h (251 patients, 46%). Importantly, 40-55% of most types of critical bleeding events and 82% of all obstetric haemorrhage cases were excluded by the 10/24 h definition. Patients who met both the 5/4 h and 10/24 h definitions were transfused more RBCs (19 vs. 8 median total RBC units; P < 0·001), had longer ventilation time (120 vs. 55 h; P < 0·001), median ICU (149 vs. 99 h; P < 0·001) and hospital length of stay (23 vs. 18 h; P = 0·006) and had a higher in-hospital mortality rate (23·3% vs. 16·4%; P = 0·050). CONCLUSION: The 5/4 h MT definition was the most inclusive, but combination with the 10/24 h definition appeared to identify a clinically important patient cohort.