Using novel data linkage of congenital heart disease biobank data with administrative health data to identify cardiovascular outcomes to inform genomic analysis.

ABSTRACT

Introduction: Contemporary care of congenital heart disease (CHD) is largely standardised, however there is heterogeneity in post-surgical outcomes that may be explained by genetic variation. Data linkage between a CHD biobank and routinely collected administrative datasets is a novel method to identify outcomes to explore the impact of genetic variation. Objective: Use data linkage to identify and validate patient outcomes following surgical treatment for CHD. Methods: Data linkage between clinical and biobank data of children born from 2001-2014 that had a procedure for CHD in New South Wales, Australia, with hospital discharge data, education and death data. The children were grouped according to CHD lesion type and age at first cardiac surgery. Children in each 'lesion/age at surgery group' were classified into 'favourable' and 'unfavourable' cardiovascular outcome groups based on variables identified in linked administrative data including; total time in intensive care, total length of stay in hospital, and mechanical ventilation time up to 5 years following the date of the first cardiac surgery. A blind medical record audit of 200 randomly chosen children from 'favourable' and 'unfavourable' outcome groups was performed to validate the outcome groups. Results: Of the 1872 children in the dataset that linked to hospital or death data, 483 were identified with a 'favourable' cardiovascular outcome and 484 were identified as having a 'unfavourable' cardiovascular outcome. The medical record audit found concordant outcome groups for 182/192 records (95%) compared to the outcome groups categorized using the linked data. Conclusions: The linkage of a curated biobank dataset with routinely collected administrative data is a reliable method to identify outcomes to facilitate a large-scale study to examine genetic variance. These genetic hallmarks could be used to identify patients who are at risk of unfavourable cardiovascular outcomes, to inform strategies for prevention and changes in clinical care.