ArticleArchives of disease in childhood. Fetal and neonatal edition2023
Variation in hospital morbidities in an Australian neonatal intensive care unit network.
Article in Archives of disease in childhood. Fetal and neonatal edition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThere is an expectation among the public and within the profession that the performance and outcome of neonatal intensive care units (NICUs) should be comparable between centres with a similar setting. This study aims to benchmark and audit performance variation in a regional Australian network of eight NICUs.
designCohort study using prospectively collected data.
settingAll eight perinatal centres in New South Wales and the Australian Capital Territory, Australia. PATIENTS: All live-born infants born between 23
main outcome measuresEarly and late confirmed sepsis, intraventricular haemorrhage, medically and surgically treated patent ductus arteriosus, chronic lung disease (CLD), postnatal steroid for CLD, necrotising enterocolitis, retinopathy of prematurity (ROP), surgery for ROP, hospital mortality and home oxygen.
resultsNICUs showed variations in maternal and neonatal characteristics and resources. The unadjusted funnel plots for neonatal outcomes showed apparent variation with multiple centres outside the 99.8% control limits of the network values. The hierarchical model-based risk-adjustment accounting for differences in patient characteristics showed that discharged home with oxygen is the only outcome above the 99.8% control limits.
conclusionsHierarchical model-based risk-adjusted estimates of morbidity rates plotted on funnel plots provide a robust and straightforward visual graphical tool for presenting variations in outcome performance to detect aberrations in healthcare delivery and guide timely intervention. We propose using hierarchical model-based risk adjustment and funnel plots in real or near real-time to detect aberrations and start timely intervention.
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