Year : 2022, Volume : 11, Issue : 1

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Factors affecting the length of stay in the intensive care unit of surviving infants with very low birth weight: A cross-sectional study

Myeryekye Amantai, Metin Dincer

DOI: 10.5455/medscience.2021.11.374 · Page: 354-60 · 116 Views · 8 Downloads · 0 Citations

Abstract

Low birth weight causing mortality, morbidity, and lifelong disability is an important public health problem. Infants with very low birth have long hospital lengths of stay. The study aims to determine the factors affecting the length of stay of very low birth weight (<1500 g) infants receiving treatment in the tertiary level neonatal intensive care unit (NICU). This descriptive, retrospective and cross-sectional study were conducted with 774 infants who were discharged alive from the NICU between January 01, 2013, and May 30, 2019. Perinatal characteristics, morbidities, and length of stay of the infants were obtained from the electronic records of the hospital. A regression model was built to determine the factors affecting the length of stay of infants in the NICU.The median birth weight of the babies was 1150 (490; 1495) grams and the median gestational age was 205 (161; 261) days. The median length of stay of the infants was found to be 53 days (14; 357). In the regression model, increased birth weight, increased gestational age, application of phototherapy caused the length of stay to decrease, while respiratory distress syndrome, Bronchopulmonary dysplasia, retinopathy of prematurity, necrotizing enterocolitis, non-invasive ventilation, invasive ventilation, and multiple pregnancies had an increasing effect on the length of stay. The length of stay of infants in the NICU can be predicted with the regression model. Thus, it will contribute to more effective NICU use and cost control.

Keywords: Very low birth weight infant, neonatal intensive care unit, length of stay, prematurity, linear regression

Keywords : Very low birth weight infant; neonatal intensive care unit; length of stay; prematurity; linear regression

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