Research article

Predicting ICU survival: A meta-level approach

Lefteris G Gortzis¹ , Filippos Sakellaropoulos¹ , Ioannis Ilias² , Konstantinos Stamoulis³ and Ioanna Dimopoulou³

¹Telemedicine Unit, School of Medicine, University of Patras, Patras, Greece

²Department of Endocrinology, Elena Venizelou Hospital, Athens, Greece

³Second Department of Critical Care Medicine, Attikon Hospital, Medical School, University of Athens, Athens, Greece

author email corresponding author email

BMC Health Services Research 2008, 8:157doi:10.1186/1472-6963-8-157

Published:	26 July 2008

Abstract

Background

The performance of separate Intensive Care Unit (ICU) status scoring systems vis-à-vis prediction of outcome is not satisfactory. Computer-based predictive modeling techniques may yield good results but their performance has seldom been extensively compared to that of other mature or emerging predictive models. The objective of the present study was twofold: to propose a prototype meta-level predicting approach concerning Intensive Care Unit (ICU) survival and to evaluate the effectiveness of typical mining models in this context.

Methods

Data on 158 men and 46 women, were used retrospectively (75% of the patients survived). We used Glasgow Coma Scale (GCS), Acute Physiology And Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA) and Injury Severity Score (ISS) values to structure a decision tree (DTM), a neural network (NNM) and a logistic regression (LRM) model and we evaluated the assessment indicators implementing Receiver Operating Characteristics (ROC) plot analysis.

Results

Our findings indicate that regarding the assessment of indicators' capacity there are specific discrete limits that should be taken into account. The Az score ± SE was 0.8773± 0.0376 for the DTM, 0.8061± 0.0427 for the NNM and 0.8204± 0.0376 for the LRM, suggesting that the proposed DTM achieved a near optimal Az score.

Conclusion

The predicting processes of ICU survival may go "one step forward", by using classic composite assessment indicators as variables.