利用行政住院数据集和数据链接评估澳大利亚和新西兰透析和移植登记的数据质量。

Dharmenaan Palamuthusingam, Elaine M Pascoe, Carmel M Hawley, David W Johnson, Gishan Ratnayake, Stephen McDonald, Neil Boudville, Matthew Jose, Magid Fahim
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引用次数: 1

摘要

背景:临床质量注册表为临床质量监测和研究目的提供了丰富和有用的数据,但容易受到数据质量问题的影响,从而影响其使用。目的:本研究评估了澳大利亚和新西兰透析和移植(ANZDATA)登记处记录的合并症与国家住院数据集记录的合并症之间的一致性。方法:在新南威尔士州、南澳大利亚州、塔斯马尼亚州、维多利亚州和西澳大利亚州ANZDATA中记录的2000年7月1日至2015年12月31日期间需要慢性肾脏替代治疗(KRT)的所有患者与基于州的医院入院数据集相关联。根据国际疾病分类(ICD-10-AM)的定义,采用kappa统计和logistic回归分析,对每年人口普查日期ANZDATA中记录的冠状动脉疾病、糖尿病、脑血管疾病、慢性肺部疾病和周围血管疾病进行总体、时间和不同KRT方式与住院数据集中记录的合共病进行比较。结果:共发现207,369例住院患者29,334例。在人口普查日期对每个患者进行比较。总体而言,糖尿病患者的一致性为“非常好”(92%,k = 0.84),其他患者的一致性为“差”至“一般”(21-61%,k = 0.02-0.22)。糖尿病记录准确率最高(灵敏度93%(±SE 0.2),特异度93%(±SE 0.2)),脑血管疾病记录准确率最低(灵敏度54%(±SE 0.2),特异度21%(±SE 0.3))。脑血管疾病、外周血管疾病和慢性气道疾病的假阳性率在18% ~ 33%之间。在所有合并症中,肾移植患者的假阳性概率最低,而血液透析患者的假阳性概率最高。结论和意义:临床质量登记和住院数据集之间的一致性是可变的,ANZDATA中合并症的患病率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating data quality in the Australian and New Zealand dialysis and transplant registry using administrative hospital admission datasets and data-linkage.

Background: Clinical quality registries provide rich and useful data for clinical quality monitoring and research purposes but are susceptible to data quality issues that can impact their usage. Objective: This study assessed the concordance between comorbidities recorded in the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry and those in state-based hospital admission datasets. Method: All patients in New South Wales, South Australia, Tasmania, Victoria and Western Australia recorded in ANZDATA as requiring chronic kidney replacement therapy (KRT) between 01/07/2000 and 31/12/2015 were linked with state-based hospital admission datasets. Coronary artery disease, diabetes mellitus, cerebrovascular disease, chronic lung disease and peripheral vascular disease recorded in ANZDATA at each annual census date were compared overall, over time and between different KRT modalities to comorbidities recorded in hospital admission datasets, as defined by the International Classification of Diseases (ICD-10-AM), using both the kappa statistic and logistic regression analysis. Results: 29, 334 patients with 207,369 hospital admissions were identified. Comparison was made at census date for every patient comparison. Overall agreement was "very good" for diabetes mellitus (92%, k = 0.84) and "poor" to "fair" (21-61%, k = 0.02-0.22) for others. Diabetes mellitus recording had the highest accuracy (sensitivity 93% (±SE 0.2) and specificity 93% (±SE 0.2)), and cerebrovascular disease had the lowest (sensitivity 54% (±SE 0.2) and specificity 21% (±SE 0.3)). The false positive rates for cerebrovascular disease, peripheral vascular disease and chronic airway disease ranged between 18 and 33%. The probability of a false positive was lowest for kidney transplant patients for all comorbidities and highest for patients on haemodialysis. Conclusions and Implications: Agreement between the clinical quality registry and hospital admission datasets was variable, with the prevalence of comorbidities being higher in ANZDATA.

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