ArticleBMJ open2026
Using administrative data to predict the outcomes of prematurity (EB-POC): a protocol for a linkage study from an Australian Neonatal Network.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
introductionPerinatal healthcare providers need access to accurate and current outcome data to counsel parents facing the birth of extremely premature infants. Parents want to know their infant's risk of mortality, as well as the risk of hospital morbidities and neurodevelopmental outcomes, if their infant survives. Such data must be personalised to the precise infant-specific circumstances, including antenatal and perinatal risk factors unique to that infant.The evidence-based preterm outcome calculator (EB-POC) cohort study uses linked population data to design, model, construct and validate an EB-POC to predict outcomes of premature birth (mortality, hospital complications and neurodisability). METHODS AND ANALYSIS: Information from eight routinely collected administrative databases will be linked for all births registered in the Australian Capital Territory (ACT) and New South Wales, Australia, between 1 January 2007 and 31 December 2022 (or the latest available at the time of linkage). Key outcome measures will include an EB-POC to predict mortality, hospital morbidities and neurodisability. Data analysis will be conducted using Minitab and R software. ETHICS AND DISSEMINATION: Approval was obtained from the ACT Human Research Ethics Committee (2022.LRE.00164 and 2024.LRE.00188), ACT Aboriginal and Torres Strait Islander Consumer Reference Group and the eight data custodians. The results are expected to be released in December 2025. The results will be presented at medical conferences and published in peer-reviewed academic journals. The calculator will be available free of charge through a user-friendly website and a mobile app, enabling prospective parents of premature babies and clinicians to make evidence-based, personalised, precision-based decisions.
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