The short-term mortality and morbidity of very low birth weight infants with trisomy 18 or trisomy 13 in Japan.
Hidenori KawasakiTakahiro YamadaYoshimitsu TakahashiTakeo NakayamaTakahito WadaShinji Kosuginull nullPublished in: Journal of human genetics (2020)
Trisomy 18 (T18) and trisomy 13 (T13) are major concerns in prenatal genetic testing due to their poor prognosis; very low birth weight (VLBW) is also a concern in neonatology. The aim of this study was to investigate the mortality and morbidity of VLBW infants diagnosed with T18/T13 in Japan, compared with those with no birth defects (BD-). Maternal and neonatal data were collected prospectively from infants weighing <1501 g and were admitted to centers of the Neonatal Research Network of Japan during 2003 to 2016. Among 60,136 infants, 563 and 60 was diagnosed with T18 and T13, respectively. Although the age of mothers of infants with T18/T13 was higher, the frequency of maternal complications was lower than those with BD-. With maternal and neonatal characteristic adjustments, T18/T13 had a higher incidence of each morbidity when compared with BD-. Mortality rates in the NICU were 70, 77, and 5.8% for T18, T13, and BD-, respectively, while the survival discharge rates of T18 and T13 were 29.5 and 23.3%, respectively, which was significantly higher than previous reports. This was the first nationwide survey for VLBW infants with T18/T13 in Japan; this novel data will be relevant and useful for prenatal genetic counseling and perinatal management. Although T18/T13 were considered to be fatal in the past, with proper epidemiological information, discussions with affected families, and compassionate patient care, the mortality rate of T18/T13 can be improved.
Keyphrases
- low birth weight
- preterm infants
- poor prognosis
- risk factors
- cardiovascular events
- human milk
- preterm birth
- pregnant women
- pregnancy outcomes
- long non coding rna
- birth weight
- electronic health record
- cardiovascular disease
- healthcare
- big data
- machine learning
- human immunodeficiency virus
- physical activity
- dna methylation
- deep learning
- hiv infected
- social media
- weight gain
- network analysis