Mortality and morbidity of extremely low birth weight infants in Japan, 2015

Survival and unique clinical practices of extremely preterm infants born at 22-23 weeks' gestation in Japan: a national survey

OBJECTIVES

To investigate prognosis and clinical practices of infants born at 22-23 weeks' gestational age (wkGA) in Japan.

DESIGN

A national institutional-level electronic questionnaire surveys performed in September 2021.

SETTING

All perinatal centres across Japan.

PATIENTS

Infants born at 22-23 wkGA in 2018-2020.

MAIN OUTCOME MEASURES

Proportion of active resuscitation and survival at neonatal intensive care unit (NICU) discharge, and various clinical practices.

RESULTS

In total, 255 of 295 NICUs (86%) responded. Among them, 145 took care of infants born at 22-23 wkGA and answered the questions regarding their outcomes and care. In most NICUs (129 of 145 (89%)), infants born at 22+0 wkGA can be actively resuscitated. In almost half of the NICUs (79 of 145 (54%)), infants born at ≥22+0 wkGA were always actively resuscitated. Among 341 and 757 infants born alive at 22 and 23 wkGA, respectively, 85% (291 of 341) and 98% (745 of 757) received active resuscitation after birth. Among infants actively resuscitated at birth, 63% (183 of 291) and 80% (594 of 745) of infants born at 22 and 23 wkGA survived, respectively. The survey revealed unique clinical management for these infants in Japan, including delivery with caul in caesarean section, cut-cord milking after clamping cord, immediate intubation at birth, hydrocortisone use for chronic lung disease, analgesia/sedation use for infants on mechanical ventilation, routine echocardiography and brain ultrasound, probiotics administration, routine glycerin enema and skin dressing to prevent pressure ulcers.

CONCLUSIONS

Many 22-23 wkGA infants were actively resuscitated in Japan and had a high survival rate. Various unique clinical practices were highlighted.

Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature

Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, and treatment. Artificial intelligence is a valuable tool that clinicians employ to reduce the screening burden on ophthalmologists and neonatologists and improve the detection of treatment-requiring ROP. Neural networks such as convolutional neural networks and deep learning (DL) systems are used to calculate a vascular severity score (VSS), an important component of various risk models. These DL systems have been validated in various studies, which are reviewed here. Most importantly, we discuss a promising study that validated a DL system that could predict the development of ROP despite a lack of clinical evidence of disease on the first retinal examination. Additionally, there is promise in utilizing these systems through telemedicine in more rural and resource-limited areas. This review highlights the value of these DL systems in early ROP diagnosis.