Louis Deepak, Eshemokhai Peace, Ruth Chelsea, Cheung Kristene, Lix Lisa M, Flaten Lisa, Shah Prakesh S, Garland Allan
Section of Neonatology, Department of Pediatrics and Child Health, Rady Faculty of Medicine, University of Manitoba, Winnipeg, Canada.
Department of Pediatrics and Child Health, Rady Faculty of Medicine, University of Manitoba, Winnipeg, Canada.
Int J Popul Data Sci. 2024 Oct 8;9(1):2380. doi: 10.23889/ijpds.v9i1.2380. eCollection 2024.
The Canadian Institute of Health Information's (CIHI) Discharge Abstract Database (DAD) contains standardised administrative data on all hospitalisations in Canada, excluding Quebec.
We aimed to validate preterm birth related perinatal and neonatal data in DAD by assessing its accuracy against the reference standard of the Canadian Neonatal Network (CNN) database.
We linked birth hospitalization data between the DAD and CNN databases for all neonates born <33 weeks gestational age (GA) admitted to the Neonatal Intensive Care Units in Winnipeg, Canada, between 2010 and 2022. A comprehensive list of maternal and neonatal variables relevant to preterm birth was chosen for validation. For categorical variables, we measured correlation using Cohen's weighted kappa (k) and for continuous variables, we measured agreement using Lin's concordance correlation coefficient (LCCC).
2084 neonates were included (mean GA 29.4 ± 2.4 weeks; birth weight 1430 ± 461g). Baseline continuous maternal and neonatal variables showed excellent accuracy in DAD [Maternal age: LCCC = 0.99 (0.99, 0.99); GA: LCCC = 0.95 (0.95, 0.96); birth weight: LCCC = 0.97 (0.96, 0.97); sex: k = 0.99 (0.98-0.99)]. In contrast, the accuracy of the maternal baseline categorical variables and neonatal outcomes and interventions ranged from very good to poor [e.g., Caesarean section: k = 0.91 (0.89-0.93), pre-gestational diabetes: k = 0.04 (0.03-0.05), neonatal sepsis: k = 0.37 (0.31-0.42), bronchopulmonary dysplasia: k = 0.26 (0.19-0.33), neonatal laparotomy: k = 0.55 (0.43-067)].
Neonatal variables such as gestational age and birth weight had high accuracy in DAD, while the accuracy of maternal and neonatal morbidities and interventions were variable, with some being poor. Reasons for the inaccuracy of these variables should be identified and measures taken to improve them.
加拿大卫生信息研究所（CIHI）的出院摘要数据库（DAD）包含加拿大（不包括魁北克省）所有住院治疗的标准化管理数据。
我们旨在通过将加拿大新生儿网络（CNN）数据库的参考标准评估其准确性，来验证DAD中与早产相关的围产期和新生儿数据。
我们将2010年至2022年期间在加拿大温尼伯市新生儿重症监护病房收治的所有孕周小于33周（GA）的新生儿的出生住院数据，链接到DAD和CNN数据库之间。选择了一份与早产相关的产妇和新生儿变量的综合清单进行验证。对于分类变量，我们使用科恩加权kappa（k）测量相关性，对于连续变量，我们使用林一致性相关系数（LCCC）测量一致性。
纳入了2084例新生儿（平均GA 29.4±2.4周；出生体重1430±461g）。基线产妇和新生儿连续变量在DAD中显示出极高的准确性[产妇年龄：LCCC = 0.99（0.99，0.99）；GA：LCCC = 0.95（0.95，0.96）；出生体重：LCCC = 0.97（（0.96，0.97）；性别：k = 0.99（0.98 - 0.99）]。相比之下，产妇基线分类变量以及新生儿结局和干预措施的准确性从非常好到较差不等[例如，剖宫产：k = 0.91（0.89 - 0.93），孕前糖尿病：k = 0.04（0.03 - 0.05），新生儿败血症：k = 0.37（0.31 - 0.42），支气管肺发育不良：k = 0.26（0.19 - 0.33），新生儿剖腹手术：k = 0.55（0.43 - 0.67）]。
孕周和出生体重等新生儿变量在DAD中的准确性较高，而产妇和新生儿疾病及干预措施的准确性则各不相同，有些较差。应确定这些变量不准确的原因并采取措施加以改进。
