An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care
Open Access
- 1 January 2010
- journal article
- research article
- Published by Springer Science and Business Media LLC in BioMedical Engineering OnLine
- Vol. 9 (1), 62
- https://doi.org/10.1186/1475-925x-9-62
Abstract
In the intensive care unit (ICU), clinical staff must stay vigilant to promptly detect and treat hypotensive episodes (HEs). Given the stressful context of busy ICUs, an automated hypotensive risk stratifier can help ICU clinicians focus care and resources by prospectively identifying patients at increased risk of impending HEs. The objective of this study was to investigate the possible existence of discriminatory patterns in hemodynamic data that can be indicative of future hypotensive risk. Given the complexity and heterogeneity of ICU data, a machine learning approach was used in this study. Time series of minute-by-minute measures of mean arterial blood pressure, heart rate, pulse pressure, and relative cardiac output from 1,311 records from the MIMIC II Database were used. An HE was defined as a 30-minute period during which the mean arterial pressure was below 60 mmHg for at least 90% of the time. Features extracted from the hemodynamic data during an observation period of either 30 or 60 minutes were analyzed to predict the occurrence of HEs 1 or 2 hours into the future. Artificial neural networks (ANNs) were trained for binary classification (normotensive vs. hypotensive) and regression (estimation of future mean blood pressure). The ANNs were successfully trained to discriminate patterns in the multidimensional hemodynamic data that were predictive of future HEs. The best overall binary classification performance resulted in a mean area under ROC curve of 0.918, a sensitivity of 0.826, and a specificity of 0.859. Predicting further into the future resulted in poorer performance, whereas observation duration minimally affected performance. The low prevalence of HEs led to poor positive predictive values. In regression, the best mean absolute error was 9.67%. The promising pattern recognition performance demonstrates the existence of discriminatory patterns in hemodynamic data that can indicate impending hypotension. The poor PPVs discourage a direct HE predictor, but a hypotensive risk stratifier based on the pattern recognition algorithms of this study would be of significant clinical value in busy ICU environments.Keywords
This publication has 27 references indexed in Scilit:
- Swallow segmentation with artificial neural networks and multi-sensor fusionMedical Engineering & Physics, 2009
- Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimatorBioMedical Engineering OnLine, 2009
- Robust parameter extraction for decision support using multimodal intensive care dataPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2008
- Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filterPhysiological Measurement, 2007
- The Role of Continuous Glucose Monitoring in Clinical Decision-Making in Diabetes in PregnancyObstetrical & Gynecological Survey, 2007
- Dimension Reduction for Classification with Gene Expression Microarray DataStatistical Applications in Genetics and Molecular Biology, 2006
- Wavelet packet feature extraction for vibration monitoringIEEE Transactions on Industrial Electronics, 2000
- The use of the area under the ROC curve in the evaluation of machine learning algorithmsPattern Recognition, 1997
- Spectral Analysis of Blood Pressure and Heart Rate Variability in Evaluating Cardiovascular RegulationHypertension, 1995
- On the approximate realization of continuous mappings by neural networksNeural Networks, 1989