In:
American Journal of Perinatology, Georg Thieme Verlag KG, Vol. 39, No. 06 ( 2022-04), p. 677-682
Abstract:
Objective Analysis of longitudinal data can provide neonatologists with tools that can help predict clinical deterioration and improve outcomes. The aim of this study is to analyze continuous monitoring data in newborns, using vital signs to develop predictive models for intensive care admission and time to discharge. Study Design We conducted a retrospective cohort study, including term and preterm newborns with respiratory distress patients admitted to the neonatal ward. Clinical and epidemiological data, as well as mean heart rate and saturation, at every minute for the first 12 hours of admission were collected. Multivariate mixed, survival and joint models were developed. Results A total of 56,377 heart rate and 56,412 oxygen saturation data were analyzed from 80 admitted patients. Of them, 73 were discharged home and 7 required transfer to the intensive care unit (ICU). Longitudinal evolution of heart rate (p 〈 0.01) and oxygen saturation (p = 0.01) were associated with time to discharge, as well as birth weight (p 〈 0.01) and type of delivery (p 〈 0.01). Longitudinal heart rate evolution (p 〈 0.01) and fraction of inspired oxygen at admission at the ward (p 〈 0.01) predicted neonatal ICU (NICU) admission. Conclusion Longitudinal evolution of heart rate can help predict time to transfer to intensive care, and both heart rate and oxygen saturation can help predict time to discharge. Analysis of continuous monitoring data in patients admitted to neonatal wards provides useful tools to stratify risks and helps in taking medical decisions. Key Points
Type of Medium:
Online Resource
ISSN:
0735-1631
,
1098-8785
DOI:
10.1055/s-0040-1718877
Language:
English
Publisher:
Georg Thieme Verlag KG
Publication Date:
2022
detail.hit.zdb_id:
2042426-7
