Predicción de mortalidad en recién nacidos prematuros. Revisión sistemática actualizada

Ruth del Río, Marta Thió, Mattia Bosio, Josep Figueras, Martín Iriondo
2020 Anales de Pediatría  
Extreme prematurity is associated with high mortality rates. The probability of death at different points in time is a priority for professionals and parents, and needs to be established on an individual basis. The aim of this study is to carry out a systematic review of predictive models of mortality in premature infants that have been published recently. A double search was performed for article published in PubMed on models predicting mortality in premature neonates. The population studied
more » ... opulation studied were premature neonates with a gestational age of ≤30 weeks and / or a weight at birth of ≤1500g. Works published with new models from June 2010 to July 2019 after a systematic review by Medlock (2011) were included. An assessment was made of the population, characteristics of the model, variables used, measurements of functioning, and validation. Of the 7744 references (1st search) and 1435 (2nd search) found, 31 works were selected, with 8 new models finally being included. Five models (62.5%) were developed in North America and 2 (25%) in Europe. A sequential model (Ambalavanan) enables predictions of mortality to be made at birth, 7, 28 days of life, and 36 weeks post-menstrual. A multiple logistic regression analysis was performed on 87.5% of the models. The population discrimination was measured using Odds Ratio (75%) and the area under the curve (50%). "Internal Validation" had been carried out on 5 models. Three models can be accessed on-line. There are no predictive models validated in Spain. The making of decisions based on predictive models can lead to the care given to the premature infant being more individualised and with a better use of resources. Predictive models of mortality in premature neonates in Spain need to be developed.
doi:10.1016/j.anpedi.2019.11.003 pmid:31926888 fatcat:rr5rzr4fkvcozesghi5hg3fxxu