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

Bringing algorithms to Flemish classrooms: Teaching the teachers, and some students

Computer science (CS) is currently not yet part of the official cur- riculum imposed by the Flemish government for secondary edu- cation. However, an increasing number of schools offer the topic, or elements of it, in a “free” course, especially in grades 7 and 8, and also in scientific and/or technical profiles in grades 9 to 12. The teachers who develop and teach these courses usually do so with great effort and enthusiasm, but a limited background in CS. The universities of Leuven, Gent and Hasselt, are organising several series of workshops in which teachers’ skills and knowledge on Physical Computing and Algorithms are enhanced during in-service training. The approach taken is presented, as well as the results of a small scale teaching experiment on algorithmics in grade 12, and plans for future activities and research in this area.status: publishe

DYNAMIC AUTOREGRESSIVE MODELLING OF CRITICAL CARE PATIENTS AS A BASIS FOR HEALTH MONITORING

Real-time modelling techniques could be valuable to continuously evaluate individual critically ill patients and to help the medical staff with estimation of prognosis. This preliminary study examines the possibilities to distinguish survivors from non-survivors on the basis of instabilities in the dynamics of daily measured variables. A data set, containing 140 patients, was generated in the intensive care unit (ICU) of the university hospital of Leuven. First and second order dynamic auto-regression (DAR) models were used to quantify the stability of time series of three physiological variables as a criterion to distinguish survivors from non-survivors. The best results were found for blood urea concentration with true negative fractions of 45/72 (63%) and true positive fractions of 43/68 (62%). The results indicate that the dynamics of time series of laboratory parameters from critically ill patients are indicative for their clinical condition and outcome.status: publishe

Determination of boundary conditions for passive schools : impact on heating/cooling demand (case study)

In Flanders (Belgium), the evolution towards more energy efficient school buildings started in 2009 by the approval and subsidizing of 24 passive schools, covering up to 65.000 m². As of December 7, 2007, the criteria for Flemish passive school were set forward by the government: 1° annual net heating demand ≤ 15 kWh/m².a 2° annual net cooling demand ≤ 15 kWh/m².a 3° n50 ≤ 0,6 h-1 4° maximum E-level = 55 (energy performance) To evaluate the performance of a design, monthly energy balances methods partially based on EN ISO 13790 are used. The results of these calculations are strongly influenced by the users profiles and boundary conditions. In school buildings, these boundary conditions differ strongly from the well known characteristics of residential and office buildings. Schools typically have a discontinuous user profile, higher occupancy rates, higher internal heat gains and ventilation flow rates and a large percentage of glass surface. In conclusion, a set of boundary conditions need to be defined to guarantee a uniform and objective evaluation of the design of all passive school building in Flanders. On the one hand, boundary conditions, meeting the specific characteristics typical of schools, are developed based on the existing European, Belgian, German and Dutch standards concerning energy performance, ventilation and comfort. Moreover, these boundary conditions are tested by the assumptions in the existing German passive schools and the design of 6 pilot projects in Flanders. On the other hand, these boundary conditions are implemented in the existing monthly calculation methods PHPP and EPBD. The impact of these characteristics on the energy demand for heating and cooling is studied. Moreover, these calculations are compared to the results of dynamic building simulations in TRNSYS. The paper will give an overview of the developed boundary conditions and clearly amplify the effects of these properties on the energy demand using both static and dynamic tools. A comparison will be made between the currently applied boundary conditions and the newly defined properties. This analysis is applied on the kindergarten of Etterbeek (Brussels), a design of EVR-architects, engineering office 3E (energy),Fraeye and Partners (structural engineering) and Stockman (HVAC systems). This paper is the result of the participation of the Catholic University College Ghent in a research study ‘Development of the specific boundary conditions for schools built by the passive house standard’ by Flemish government order (AGIOn).status: publishe

Combination of Dynamic Analysis and data mining for stability prediction after cardiac surgery

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Dynamic information improves discharge prediction after cardiac surgery

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Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis

This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the time they need to reach a stable state after coronary bypass surgery: less or more than 9 h. On the basis of five physiological variables (heart rate, systolic arterial blood pressure, systolic pulmonary pressure, blood temperature and oxygen saturation), different dynamic features were extracted, namely the means and standard deviations at different moments in time, coefficients of multivariate autoregressive models and cepstral coefficients. These sets of features served subsequently as inputs for a Gaussian process and the prediction results were compared with the case where only admission data was used for the classification. The dynamic features, especially the cepstral coefficients (aROC: 0.749, Brier score: 0.206), resulted in higher performances when compared to static admission data (aROC: 0.547, Brier score: 0.247). The differences in performance are shown to be significant. In all cases, the Gaussian process classifier outperformed to logistic regression.Published online: 6 January 2009status: publishe

Dynamic data analysis and data mining for prediction of clinical stability

This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the time they need to reach a stable state after coronary bypass surgery: less or more than nine hours. On the basis of five physiological variables different dynamic features were extracted. These sets of features served subsequently as inputs for a Gaussian process and the prediction results were compared with the case where only admission data was used for the classification. The dynamic features, especially the cepstral coefficients (aROC: 0.749, Brier score: 0.206), resulted in higher performances when compared to static admission data (aROC: 0.547, Brier score: 0.247). In all cases, the Gaussian process classifier outperformed logistic regression.status: publishe