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
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Continuous monitoring of vital signs can help predict and prevent clinical deterioration
in neonatal patients.
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In our study, longitudinal analysis of heart rate and oxygen saturation predicted
time to discharge and intensive care admission.
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More studies are needed to prospectively prove that these models can helpmake clinical
decisions and stratify patients' risks.
Keywords
respiratory distress - longitudinal analysis - joint models - big data
