Harron Katie, Goldstein Harvey, Wade Angie, Muller-Pebody Berit, Parslow Roger, Gilbert Ruth
Institute of Child Health, University College London, London, United Kingdom.
Institute of Child Health, University College London, London, United Kingdom ; Graduate School of Education, University of Bristol, Bristol, United Kingdom.
PLoS One. 2013 Dec 20;8(12):e85278. doi: 10.1371/journal.pone.0085278. eCollection 2013.
Linkage of risk-factor data for blood-stream infection (BSI) in paediatric intensive care (PICU) with bacteraemia surveillance data to monitor risk-adjusted infection rates in PICU is complicated by a lack of unique identifiers and under-ascertainment in the national surveillance system. We linked, evaluated and performed preliminary analyses on these data to provide a practical guide on the steps required to handle linkage of such complex data sources.
Data on PICU admissions in England and Wales for 2003-2010 were extracted from the Paediatric Intensive Care Audit Network. Records of all positive isolates from blood cultures taken for children <16 years and captured by the national voluntary laboratory surveillance system for 2003-2010 were extracted from the Public Health England database, LabBase2. "Gold-standard" datasets with unique identifiers were obtained directly from three laboratories, containing microbiology reports that were eligible for submission to LabBase2 (defined as "clinically significant" by laboratory microbiologists). Reports in the gold-standard datasets were compared to those in LabBase2 to estimate ascertainment in LabBase2. Linkage evaluated by comparing results from two classification methods (highest-weight classification of match weights and prior-informed imputation using match probabilities) with linked records in the gold-standard data. BSI rate was estimated as the proportion of admissions associated with at least one BSI.
Reporting gaps were identified in 548/2596 lab-months of LabBase2. Ascertainment of clinically significant BSI in the remaining months was approximately 80-95%. Prior-informed imputation provided the least biased estimate of BSI rate (5.8% of admissions). Adjusting for ascertainment, the estimated BSI rate was 6.1-7.3%.
Linkage of PICU admission data with national BSI surveillance provides the opportunity for enhanced surveillance but analyses based on these data need to take account of biases due to ascertainment and linkage error. This study provides a generalisable guide for linkage, evaluation and analysis of complex electronic healthcare data.
由于缺乏唯一标识符以及国家监测系统中的报告不完整，将儿科重症监护病房（PICU）中血流感染（BSI）的危险因素数据与菌血症监测数据相链接，以监测PICU中风险调整后的感染率变得复杂。我们对这些数据进行了链接、评估和初步分析，以提供一份关于处理此类复杂数据源链接所需步骤的实用指南。
从儿科重症监护审计网络中提取了2003 - 2010年英格兰和威尔士PICU入院的数据。从英国公共卫生数据库LabBase2中提取了2003 - 2010年全国自愿实验室监测系统捕获的所有<16岁儿童血培养阳性分离株的记录。具有唯一标识符的“金标准”数据集直接从三个实验室获得，其中包含有资格提交到LabBase2的微生物学报告（被实验室微生物学家定义为“具有临床意义”）。将金标准数据集中的报告与LabBase2中的报告进行比较，以估计LabBase2中的报告完整性。通过将两种分类方法（匹配权重的最高权重分类和使用匹配概率的先验信息插补）的结果与金标准数据中的链接记录进行比较来评估链接情况。BSI率估计为与至少一次BSI相关的入院比例。
在LabBase2的548/2596个实验室月中发现了报告缺口。其余月份中具有临床意义的BSI报告完整性约为80 - 95%。先验信息插补提供的BSI率估计偏差最小（入院的5.8%）。调整报告完整性后，估计的BSI率为6.1 - 7.3%。
PICU入院数据与国家BSI监测的链接为加强监测提供了机会，但基于这些数据的分析需要考虑报告不完整和链接错误导致的偏差。本研究为复杂电子医疗数据的链接、评估和分析提供了可推广的指南。
