Jaundice is the commonest neonatal ailment requiring treatment. Untreated, it can lead to acute bilirubin encephalopathy (ABE), chronic bilirubin encephalopathy (CBE) or death. ABE and CBE have been largely eliminated in industrialised countries, but remain a problem of largely undocumented scale in low resource settings. As part of a quality-improvement intervention in the Neonatal Care Units of two paediatric referral hospitals in Myanmar, hospitals collected de-identified data on each neonate treated on new phototherapy machines over 13-20 months. The information collected included: diagnosis of ABE at hospital presentation; general characteristics such as place of birth, source of referral, and sex; and a selection of suspected causes of jaundice including prematurity, infection, G6PD status, ABO and Rh incompatibility. This information was analysed to identify risk factors for hospital presentation with ABE, using multiple logistic regression.

Risk factors for acute bilirubin encephalopathy on admission to two Myanmar national paediatric hospitals

Trevisanuto, D
Methodology
;
Thin, A A;Kumara, D;
2015

Abstract

Jaundice is the commonest neonatal ailment requiring treatment. Untreated, it can lead to acute bilirubin encephalopathy (ABE), chronic bilirubin encephalopathy (CBE) or death. ABE and CBE have been largely eliminated in industrialised countries, but remain a problem of largely undocumented scale in low resource settings. As part of a quality-improvement intervention in the Neonatal Care Units of two paediatric referral hospitals in Myanmar, hospitals collected de-identified data on each neonate treated on new phototherapy machines over 13-20 months. The information collected included: diagnosis of ABE at hospital presentation; general characteristics such as place of birth, source of referral, and sex; and a selection of suspected causes of jaundice including prematurity, infection, G6PD status, ABO and Rh incompatibility. This information was analysed to identify risk factors for hospital presentation with ABE, using multiple logistic regression.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3328236
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