Clinical Physiology of Circulation

Chief Editor

Leo A. Bockeria, MD, PhD, DSc, Professor, Academician of Russian Academy of Sciences, President of Bakoulev National Medical Research Center for Cardiovascular Surgery


Neural network technologies as a basis for strategy for preventing the complications after myocardial revascularization with cardiopulmonary bypass

Authors: A.S. Semenova 1, V.V. Agapov 1, V.A. Prelatov 1, A.V. Zhukov 1, M.Yu. Shigaev 2

Company:
1 Regional Center for Cardiac Surgery, ul. Krymskaya, 15, Saratov, 410039, Russia; 2 V.I. Razumovskiy Saratov State Medical University, Ministry of Health of the Russian Federation, ul. Bol'shaya Kazach'ya, 112, Saratov, 410012, Russia

Link: Clinical Physiology of Blood Circulaiton. 2013; (): -

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Abstract

Objective of the study – is to evaluate the possibilities of predicting the development of complications in early post-operative period after myocardial revascularization based on intra-operational parameters using artificial neural network technologies. Material and methods. The retrospective “case-control” study included data on 107 patients (mean age 58.4±8.5 years), who underwent isolated coronary artery bypass graft surgery. Development of organ dysfunctions (acute kidney, respiratory, cardiovascular, central nervous system dysfunction, myocardial infarction and paroxysmal atrial fibrillation) was considered a complication in an early postoperative period (0–6 days). Hemodynamic indicators and internal milieu parameters routinely monitored during surgical procedure with cardiopulmonary bypass were used as predictors of complications. Neural network method was applied for prediction, training and testing of artificial neural networks as well as the statistical analysis were performed using software package Statistica 6.0 StatSoft Inc. Results. 2 artificial neural networks that showed the best operating characteristics were selected after analysis of more than 3 thousand automatically constructed personal computer-based networks. After entering the values of predictors in binary format neural network gives an output on perspective outcome: presence or absence of complications. The area under the ROC curve was: for EuroSCORE – 0.611 (p=0.48), for artificial neural networks: 0.778 (p=0.027), 0.833 (p=0.034) and 0.944 (p=0.005). Conclusion. We obtained prognostic models – artificial neural networks with high predictive accuracy, suitable for the use in clinical practice.

References

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