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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Model-based synthesis of aircraft noise to quatify the subjective human perception

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Behaviour of well-mixed zones (WMZs) in an imperfectly mixed ventilated room

In this paper the behaviour of 3D well-mixed zones (WMZs) of temperature is studied using numerically generated data. A WMZ of temperature is a 3D zone of improved mixing whereby an acceptably low spatial temperature difference occurs. Here it was anticipated to set a criterion to define a WMZ that takes into account the comfort and health of building occupants. It was found that WMZs are irregular in shape and unpredictable in size in imperfectly mixed ventilated rooms. Furthermore airflow exchange between the considered WMZ and its surrounding was quantified. The results of the analysis show the potential of computational fluid dynamics (CFD) in quantifying the shape, size and volume of WMZs that may not be possible to perform using experimental methods because of the practical limitation of placing thousands of sensors inside a ventilated room. (C) 2007 Elsevier Ltd. All rights reserved.status: publishe

Simultaneous response of stem diameter, sap flow rate and leaf temperature of tomato plants to drought stress

One of the many opportunities of direct crop monitoring is the ability to detect suboptimal growth conditions in a very early stage, so that both productivity and quality can still be guaranteed. In this study, eight tomato plants (Lycopersicon esculentum Mill. 'Clothilde') were hydroponically grown in order to modulate the root zone. Four of these plants were subjected to drought stress by adding 200 g/L poly-ethylene glycol 6000 to the nutrient solution. This treatment immediately resulted in a considerable reduction in sap flow rate, thereby forcing the plants to use their internal water storage to support transpiration. Consequently, a stem diameter shrinkage of 0.1 mm was measured before visual symptoms of turgidity loss were observed. At leaf level, the leaf temperature increased to a level above the predicted leaf temperature, which indicated that the stomata were gradually closing. Measurements of stomatal resistance confirmed this hypothesis. The lack of transpiration and, hence, the ceased cooling of the leaves during the treatment caused permanent damage to the leaves, which was also confirmed by a permanent reduction in sap flow rate. In conclusion, stem diameter, sap flow rate and leaf temperature measurements detected drought stress before visual symptoms were observed, so they can be implemented in an early-warning system. A combination of these plant responses gives a good overall picture of the water status of a tomato plant