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
Transfusion Research Unit, Monash University, Melbourne, Australia.
Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), Monash University, Melbourne, Australia.
Transfus Med. 2017 Apr;27(2):114-121. doi: 10.1111/tme.12377. Epub 2016 Dec 13.
To evaluate the use of routinely collected data to determine the cause(s) of critical bleeding in patients who receive massive transfusion (MT).
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.
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).
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.
评估使用常规收集的数据来确定接受大量输血（MT）患者严重出血的原因。
常规收集的数据越来越多地用于描述和评估输血实践。
材料/方法：对澳大利亚和新西兰48家医院随机抽取的10例MT患者进行病历审查，以确定严重出血的原因。分别提取诊断相关分组（DRG）和国际疾病分类（ICD）编码，并用于为每位患者确定严重出血的原因。使用百分比一致性和kappa统计量将这些结果与病历审查结果进行比较。
共纳入427例MT患者，分别有427例（100%）和396例（93%）患者有完整的ICD和DRG数据。病历审查与ICD编码之间总体一致性良好（78.3%；κ=0.74，95%CI 0.70-0.79），与DRG的总体一致性一般（51%；κ=0.45，95%CI 0.40-0.50）。ICD和DRG在分类产科出血患者方面均敏感且准确（敏感性98%，κ>0.94）。然而，与ICD算法相比，DRG在将胃肠道出血、创伤、心脏和血管手术导致的出血进行分类时，敏感性和准确性较低（胃肠道出血：74%对8%；κ=0.75对0.1；创伤：92%对62%；κ=0.78对0.67；心脏：80%对57%；κ=0.79对0.60；血管手术：64%对56%；κ=0.69对0.65）。
使用ICD编码的算法能够确定需要MT患者严重出血的原因，与临床病史的一致性良好至极佳。DRG不太适合确定严重出血的原因。
