Abstract
Recognition of mortality-related factors in intensive care units (ICUs) could increase the efficiency and effectiveness of these units. The purpose of this research is to investigate the recorded data of the patients admitted in ICU with clinical analysis, in order to find indices of mortality. The long-term goal of this study is to develop an algorithm that is able to anticipate the mortality risk of ICU patients. Extracted features included time and frequency domain analysis of ECG and some of the physiological variables. The results showed that heart rate variability (HRV) and blood pressure are the most important parameters in ICU mortality risk assessment and anticipation for cardiovascular patients.
Highlights
Heart rate variability (HRV), a marker of autonomic tone, has been extensively studied after myocardial infarction and has been established as a potent predictor of mortality [1].The primary role of the intensive care units is to monitor and stabilize the vital functions of the patients with life-threatening conditions
In order to aid intensive care units (ICUs) nurses and intensities with this work, scoring systems have been developed to express the overall state of an ICU patient as a numerical value, that is used to develop a classification rule, which classifies a patient as being at risk or not
We collect a database of patients that were admitted in ICU
Summary
Heart rate variability (HRV), a marker of autonomic tone, has been extensively studied after myocardial infarction and has been established as a potent predictor of mortality [1].The primary role of the intensive care units is to monitor and stabilize the vital functions of the patients with life-threatening conditions. In order to aid ICU nurses and intensities with this work, scoring systems have been developed to express the overall state of an ICU patient as a numerical value, that is used to develop a classification rule, which classifies a patient as being at risk or not Such scoring systems typically depend on the parameters that are estimated from a database of cases, but one of their features is that, often, they have missing values [2]. One suggestion as to why a patient attributes remains unrecorded is that an intensity assumes the variable to be clinically normal on the basis of other observations, not worthy of confirmation This clinical-normality assumption has been criticized [3], the mortality rate is higher in those patients with completed records. We suspect that there are some random omissions due to the pressure of work within an ICU; it may be the case that the incompleteness of an ICU data set is due to a mixture of different missing-data mechanisms
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