| Autor: |
Lin J; Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand. chl29@student.canterbury.ac.nz, Lee D, Chase JG, Shaw GM, Le Compte A, Lotz T, Wong J, Lonergan T, Hann CE |
| Jazyk: |
angličtina |
| Zdroj: |
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2008 Feb; Vol. 89 (2), pp. 141-52. Date of Electronic Publication: 2007 Jun 04. |
| DOI: |
10.1016/j.cmpb.2007.04.006 |
| Abstrakt: |
Targeted, tight model-based glycemic control in critical care patients that can reduce mortality 18-45% is enabled by prediction of insulin sensitivity, S(I). However, this parameter can vary significantly over a given hour in the critically ill as their condition evolves. A stochastic model of S(I) variability is constructed using data from 165 critical care patients. Given S(I) for an hour, the stochastic model returns the probability density function of S(I) for the next hour. Consequently, the glycemic distribution following a known intervention can be derived, enabling pre-determined likelihoods of the result and more accurate control. Cross validation of the S(I) variability model shows that 86.6% of the blood glucose measurements are within the 0.90 probability interval, and 54.0% are within the interquartile interval. "Virtual Patients" with S(I) behaving to the overall S(I) variability model achieved similar predictive performance in simulated trials (86.8% and 45.7%). Finally, adaptive control method incorporating S(I) variability is shown to produce improved glycemic control in simulated trials compared to current clinical results. The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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