Key Points• GATA1 mutations are common in neonates with Down syndrome but are often unsuspected and detectable only with sensitive methods.• Multilineage blood abnormalities in all Down syndrome neonates in the absence of GATA1 mutations suggests that trisomy 21 itself perturbs hemopoiesis.Transient abnormal myelopoiesis (TAM), a preleukemic disorder unique to neonates with Down syndrome (DS), may transform to childhood acute myeloid leukemia (ML-DS). Acquired GATA1 mutations are present in both TAM and ML-DS. Current definitions of TAM specify neither the percentage of blasts nor the role of GATA1 mutation analysis.To define TAM, we prospectively analyzed clinical findings, blood counts and smears, and GATA1 mutation status in 200 DS neonates. All DS neonates had multiple blood count and smear abnormalities. Surprisingly, 195 of 200 (97.5%) had circulating blasts. GATA1 mutations were detected by Sanger sequencing/denaturing high performance liquid chromatography (Ss/DHPLC) in 17 of 200 (8.5%), all with blasts >10%. Furthermore lowabundance GATA1 mutant clones were detected by targeted next-generation resequencing (NGS) in 18 of 88 (20.4%; sensitivity ∼0.3%) DS neonates without Ss/DHPLC-detectable GATA1 mutations. No clinical or hematologic features distinguished these 18 neonates. We suggest the term "silent TAM" for neonates with DS with GATA1 mutations detectable only by NGS. To identify all babies at risk of ML-DS, we suggest GATA1 mutation and blood count and smear analyses should be performed in DS neonates. Ss/DPHLC can be used for initial screening, but where GATA1 mutations are undetectable by Ss/DHPLC, NGS-based methods can identify neonates with small GATA1 mutant clones. (Blood. 2013;122(24):3908-3917)

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The neonatal early-onset sepsis (EOS) calculator is a clinical risk stratification tool increasingly used to guide the use of empirical antibiotics for newborns. Evidence on the effectiveness and safety of the EOS calculator is essential to inform clinicians considering implementation.OBJECTIVE To assess the association between management of neonatal EOS guided by the neonatal EOS calculator (compared with conventional management strategies) and reduction in antibiotic therapy for newborns.DATA SOURCES Electronic searches in MEDLINE, Embase, Web of Science, and Google Scholar were conducted from 2011 (introduction of the EOS calculator model) through January 31, 2019.STUDY SELECTION All studies with original data that compared management guided by the EOS calculator with conventional management strategies for allocating antibiotic therapy to newborns suspected to have EOS were included.DATA EXTRACTION AND SYNTHESIS Following PRISMA-P guidelines, relevant data were extracted from full-text articles and supplements. CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and GRADE (Grades of Recommendation, Assessment, Development and Evaluation) tools were used to assess the risk of bias and quality of evidence. Meta-analysis using a random-effects model was conducted for studies with separate cohorts for EOS calculator and conventional management strategies. MAIN OUTCOMES AND MEASURESThe difference in percentage of newborns treated with empirical antibiotics for suspected or proven EOS between management guided by the EOS calculator and conventional management strategies. Safety-related outcomes involved missed cases of EOS, readmissions, treatment delay, morbidity, and mortality. RESULTSThirteen relevant studies analyzing a total of 175 752 newborns were included. All studies found a substantially lower relative risk (range, 3%-60%) for empirical antibiotic therapy, favoring the EOS calculator. Meta-analysis revealed a relative risk of antibiotic use of 56% (95% CI, 53%-59%) in before-after studies including newborns regardless of exposure to chorioamnionitis. Evidence on safety was limited, but proportions of missed cases of EOS were comparable between management guided by the EOS calculator (5 of 18 [28%]) and conventional management strategies (8 of 28 [29%]) (pooled odds ratio, 0.96; 95% CI, 0.26-3.52; P = .95).CONCLUSIONS AND RELEVANCE Use of the neonatal EOS calculator is associated with a substantial reduction in the use of empirical antibiotics for suspected EOS. Available evidence regarding safety of the use of the EOS calculator is limited, but shows no indication of inferiority compared with conventional management strategies.

ObjectiveIn 2015, the Department of Health in England announced an ambition to reduce ‘brain injuries occurring during or soon after birth’. We describe the development of a pragmatic case definition and present annual incidence rates.DesignRetrospective cohort study using data held in the National Neonatal Research Database (NNRD) extracted from neonatal electronic patient records from all National Health Service (NHS) neonatal units in England, Wales and Scotland. In 2010–2011, population coverage in the NNRD was incomplete, hence rate estimates are presented as a range; from 2012, population coverage is complete, and rates (95% CIs) are presented. Rates are per 1000 live births.SettingNHS neonatal units in England.PatientsInfants admitted for neonatal care; denominator: live births in England.Main outcome measure‘Brain injuries occurring at or soon after birth’ defined as infants with seizures, hypoxic-ischaemic encephalopathy, stroke, intracranial haemorrhage, central nervous system infection and kernicterus and preterm infants with cystic periventricular leucomalacia.ResultsIn 2010, the lower estimate of the rate of ‘Brain injuries occurring at or soon after birth’ in England was 4.53 and the upper estimate was 5.19; in 2015, the rate was 5.14 (4.97, 5.32). For preterm infants, the population incidence in 2015 was 25.88 (24.51, 27.33) and 3.47 (3.33, 3.62) for term infants. Hypoxic-ischaemic encephalopathy was the largest contributor to term brain injury, and intraventricular/periventricular haemorrhage was the largest contributor to preterm brain injury.ConclusionsAnnual incidence rates for brain injuries can be estimated from data held in the NNRD; rates for individual conditions are consistent with published rates. Routinely recorded clinical data can be used for national surveillance, offering efficiencies over traditional approaches.