A New Approach to the Abstraction of Monitoring Data in Intensive Care

2005 ◽  
pp. 13-22 ◽  
Author(s):  
S. Sharshar ◽  
L. Allart ◽  
M. -C. Chambrin
Keyword(s):  
Intensive Care ◽  
Monitoring Data ◽  
New Approach

A new approach to train intensive care specialists using a full-scale anaesthesia simulator in severe sepsis management

2004 ◽  
Vol 21 (Supplement 32) ◽  
pp. 207
Author(s):  
G. H. Breuer ◽  
R. Riss ◽  
M. Cobas-Meyer ◽  
F. Bremer ◽  
M. Grapengeter ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Intensive Care ◽  
Full Scale ◽  
Sepsis Management ◽  
New Approach

Longitudinal Analysis of Continuous Pulse Oximetry as Prognostic Factor in Neonatal Respiratory Distress

2020 ◽  
Author(s):  
Gonzalo Solís-García ◽  
Elena Maderuelo-Rodríguez ◽  
Teresa Perez-Pérez ◽  
Laura Torres-Soblechero ◽  
Ana Gutiérrez-Vélez ◽  
...  
Keyword(s):  
Heart Rate ◽  
Intensive Care ◽  
Respiratory Distress ◽  
Oxygen Saturation ◽  
Continuous Monitoring ◽  
Vital Signs ◽  
Epidemiological Data ◽  
Monitoring Data ◽  
Joint Models ◽  
Neonatal Icu

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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