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Open Access Research article

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

Geert Meyfroidt1*, Fabian Güiza1, Dominiek Cottem1, Wilfried De Becker1, Kristien Van Loon3, Jean-Marie Aerts3, Daniël Berckmans3, Jan Ramon2, Maurice Bruynooghe2 and Greet Van den Berghe1

Author Affiliations

1 Department of Intensive Care Medicine, Katholieke Universiteit Leuven; Herestraat 49, B-3000 Leuven, Belgium

2 Declarative Languages and Artificial Intelligence (DTAI), Department of Computer Science, Katholieke Universiteit Leuven; Celestijnenlaan 200a - bus 2402, B-3001 Heverlee, Belgium

3 M3-BIORES, Department of Biosystems, Katholieke Universiteit Leuven; Kasteelpark Arenberg 30 - bus 2456, B-3001 Heverlee, Belgium

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BMC Medical Informatics and Decision Making 2011, 11:64  doi:10.1186/1472-6947-11-64

Published: 25 October 2011

Abstract

Background

The intensive care unit (ICU) length of stay (LOS) of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP), a machine learning technique.

Methods

Non-interventional study. Predictive modeling, separate development (n = 461) and validation (n = 499) cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task). GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF) ((actual-predicted)/actual) and calculating root mean squared relative errors (RMSRE).

Results

Median (P25-P75) ICU length of stay was 3 (2-5) days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%), which was significantly better than the EuroSCORE (p < 0.001) and nurses (p = 0.044) but equivalent to physicians. GP had the lowest RMSRE (0.408) of all predictive models.

Conclusions

A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a classification as well as a regression task. The GP model demonstrated a significantly better discriminative power than the EuroSCORE and the ICU nurses, and at least as good as predictions done by ICU physicians. The GP model was the only well calibrated model.