Abstract
We attempted to find a way to distinguish survivors and non-survivors on the basis of the differences in the dynamics in both patient classes using multivariate autoregressive (MAR) time series analysis techniques. Time series data of 11 physiological variables were used to calculate MAR models. Data were taken from a subset of patients, with an intensive care unit length of stay of at least 20 days, from a database of a previously published randomized controlled trial [1]. The methodology was developed on 20 and validated on 16 patients. Based on the MAR coefficients, impulse response curves were simulated to describe the contributions of a single variable to fluctuations in another. The impulse responses of non-survivors had a tendency to be either more instable or to return to the initial level after a longer time than the responses of survivors did. This allowed us to distinguish survivors from non-survivors in the development cohort with a sensitivity of 0.70 and a selectivity of 1.00. This result was confirmed in the validation set where a sensitivity of 0.63 and a selectivity of 1.00 were reached.
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Van Den Berghe, G., Wouters, P., Weekers, F., Verwaest, C., Bruyninckx, F., Schetz, M., Vlasselaers, D., Ferdinande, P., Lauwers, P., Bouillon, R.: Intensive Insulin Therapy in Critically Ill Patients. New Engl. J. Med. 345, 1359–1367 (2001)
Knaus, W.A., Draper, E.A., Wagner, D.P., Zimmerman, J.E.: Apache II: a severity of disease classification system. Crit. Care Med. 13, 818–829 (1985)
Knaus, W.A., Wagner, D.P., Draper, E.A., Zimmerman, J.E., Bergner, M., Bastos, P.G., Sirio, C.A., Murphy, D.J., Lotring, T., Damiano, A., Harrell, F.E.: The APACHE III Prog-nostic system. Chest, 1619–1636 (1991)
Wyatt, J.: Nervous About Artificial Neural Networks. Lancet 346, 1175–1177 (1995)
Dybowski, R., Weller, P., Chang, R., Gant, V.: Prediction of Outcome in Critically Ill Patients Using Artificial Neural Network Synthesised by Genetic Algorithm. Lancet 347, 1146–1150 (1996)
Frize, M., Ennett, C.M., Stevenson, M., Trigg, H.C.E.: Clinical Decision Support Systems for Intensive Care Units: Using Artificial Neural Networks. Med. Eng. Phys. 23, 217–225 (2001)
Ennett, C.M., Frize, M., Charette, E.: Improvement and Automation of Artificial Neural Networks to Estimate Medical Outcomes. Med. Eng. Phys. 26, 321–328 (2004)
Sierra, B., Serrano, N., Larranaga, P., Plasencia, E.J., Inza, I., Jimenez, J.J., Revuelta, P., Mora, M.L.: Using Bayesian Networks in the Construction of a Bi-Level Multi-Classifier. A Case Study Using Intensive Care Unit Patients Data. Artif. Intell. Med. 22, 233–248 (2001)
Frize, M., Walker, R.: Clinical Decision-Support Systems for Intensive Care Units Using Case-Based Reasoning. Med. Eng. Phys. 22, 671–677 (2000)
Hanson, C.W., Marshall, B.E.: Artificial Intelligence Applications in the Intensive Care Unit. Crit. Care Med. 29, 427–435 (2001)
Lambert, C.R., Raymenants, E., Pepine, C.J.: Time-Series Analysis of Long-Term Ambu-latory Myocardial-Ischemia - Effects of Beta-Adrenergic and Calcium-Channel Blockade. Am. Heart J. 129, 677–684 (1995)
Imhoff, M., Bauer, M., Gather, U., Lohlein, D.: Statistical Pattern Detection in Univariate Time Series of Intensive Care on-Line Monitoring Data. Intens. Care Med. 24, 1305–1314 (1998)
Akaike, H.: On the use of a linear model for the identification of feedback systems. Ann. I. Stat. Math. 20, 425–439 (1968)
Jones, R.W. (ed.): Principles of biological regulation: an introduction to feedback systems. Academic Press Inc, New York (1973)
Box, G.E., Jenkins, G.M., Reinsel, G.C. (eds.): Time series analysis: forecasting and control. Prentice-Hall International, New Jersey (1994)
Wada, T., Akaike, H., Yamada, Y., Udagawa, E.: Application of Multivariate Autoregressive Modeling for Analysis of Immunological Networks in Man. Comput. Math. Appl. 15, 713–722 (1988)
Wada, T., Yamada, H., Inoue, H., Iso, T., Udagawa, E., Kuroda, S.: Clinical Usefulness of Multivariate Autoregressive (Ar) Modeling as a Tool for Analyzing Lymphocyte-T Subset Fluctuations. Math. Comput. Model 14, 610–613 (1990)
Wada, T., Sato, S., Matsuo, N.: Application of Multivariate Autoregressive Modeling for Analyzing Chloride Potassium Bicarbonate Relationship in the Body. Med. Biol. Eng. Comput. 31, S99–S107 (1993)
Miwakeichi, F., Galka, A., Uchida, S., Arakaki, H., Hirai, N., Nishida, M., Maehara, T., Kawai, K., Sunaga, S., Shimizu, H.: Impulse Response Function Based on Multivariate Ar Model Can Differentiate Focal Hemisphere in Temporal Lobe Epilepsy. Epilepsy Res. 61, 73–78 (2004)
Tschacher, W., Scheier, C., Hashimoto, Y.: Dynamical Analysis of Schizophrenia Courses. Biol. Psychiat. 41, 428–437 (1997)
Clermont, G., Neugebauer, E.A.M.: Systems Biology and Translational Research. J. Crit. Care 20, 381–382 (2005)
Kitano, H.: Computational Systems Biology. Nature 420, 206–210 (2002)
Seely, A.J.E., Macklem, P.T.: Complex Systems and the Technology of Variability Analysis. Crit. Care 8, R367–R384 (2004)
Buchman, T.G.: Nonlinear dynamics, complex systems, and the pathobiology of critical illness. Curr. Opin. Crit. Care 10, 378–382 (2004)
Glass, L.: Synchronization and Rhythmic Processes in Physiology. Nature 410, 277–284 (2001)
Lipsitz, L.A.: Dynamics of Stability: the Physiologic Basis of Functional Health and Frailty. J Gerontol. A-Biol. 57, 115–125 (2002)
Poon, C.S., Merrill, C.K.: Decrease of Cardiac Chaos in Congestive Heart Failure. Nature 389, 492–495 (1997)
Ivanov, P.C., Amaral, L.A.N., Goldberger, A.L., Havlin, S., Rosenblum, M.G., Struzik, Z.R., Stanley, H.E.: Multifractality in Human Heartbeat Dynamics. Nature 399, 461–465 (1999)
Bruhn, J., Ropcke, H., Hoeft, A.: Approximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect During Desflurane Anesthesia. Anesthesiology 92, 715–726 (2000)
Pincus, S.M.: Approximate Entropy as a Measure of System-Complexity. P. Natl. Acad. Sci. USA 88, 2297–2301 (1991)
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Van Loon, K., Aerts, JM., Meyfroidt, G., Van den Berghe, G., Berckmans, D. (2006). The Use of Multivariate Autoregressive Modelling for Analyzing Dynamical Physiological Responses of Individual Critically Ill Patients. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_26
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DOI: https://doi.org/10.1007/11946465_26
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