G146(P) Is death a poor man’s currency? A time-series analysis of resource use and outcomes in a paediatric intensive care unit

2016 ◽  
Vol 101 (Suppl 1) ◽  
pp. A76.1-A76
Author(s):  
S Ray ◽  
C Pierce ◽  
J Brierley
Keyword(s):  
Intensive Care Unit ◽  
Time Series ◽  
Intensive Care ◽  
Time Series Analysis ◽  
Resource Use ◽  
Paediatric Intensive Care Unit ◽  
Paediatric Intensive Care ◽  
Series Analysis

scholarly journals Predicting hospital mortality for intensive care unit patients: Time-series analysis

2019 ◽  
Vol 26 (2) ◽  
pp. 1043-1059 ◽  
Author(s):  
Aya Awad ◽  
Mohamed Bader-El-Den ◽  
James McNicholas ◽  
Jim Briggs ◽  
Yasser El-Sonbaty
Keyword(s):  
Intensive Care Unit ◽  
Time Series ◽  
Intensive Care ◽  
Time Series Analysis ◽  
Intensive Care Unit Admission ◽  
Hospital Mortality ◽  
Prediction Models ◽  
Scoring Systems ◽  
Machine Learning Classification ◽  
Series Analysis

Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.

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