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


Применение метода логистической регрессии для факторов риска, влияющих на исход операции в условиях искусственного кровообращения

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

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Abstract

Objective. To estimate the probability of lethal outcome of operations in the condition of cardiopulmonary bypass. Resolving this task for operations in the condition of extracorporeal circulation is associated with determining main risk factors of lethal outcome. Having substantiated the choice of important factors from medical point of view the subsequent stage of task resolving was performed using statistical calculation.

Material and methods. Within the period 2006 to 2009 in V. I. Shumakov Federal Scientific Center for Transplantation and Artificial Organs the data base was formed concerning the cardiac surgery patients who were operated in the condition of cardiopulmonary bypass. The analysis was focused on 1731 patients. The following factors were outlined: a) preoperative - presence/absence of repeated operations and bacterial endocarditis, age; b) intraoperative - presence/absence of chronic source of infection, performance of re-thoracotomy and blood loss more than 500 ml and cardiopulmonary bypass duration; c) postoperative - presence/absence of polyorganic insufficiency developing in the first days after the surgery, performance of intra-aortic balloon contrarpulsation and duration of artificial ventilation of lungs that was performed within days. Preliminary statistical analysis was performed with the help of non-parametrical criteria. Evaluation of intensity of connections between the outlined risk factors influencing on operation outcome was performed according to V-coefficient of Cramer.The subsequent statistical analysis was performed with the help of logistic regression model.

Results. The adequate equations of logistic regression were received for intra-aortic balloon contrarpulsation factor and polyorganic insufficiency factor in which the preoperative and intraoperative risk factors of the lethal outcome represent the variables of the equation. In 78.3 % of cases it is possible to predict the probability of presence of intra-aortic balloon contrarpulsation factor and in 84.6 % - polyorganic insufficiency factor. These figures represent the percentage of concordance. The adequate model with concordance percentage of 93.9 % was received for the death factor. The importance is not only receiving the equation in accordance with which it is possible to accurately compute the probability of death but also establishing connections between risk factors leading to it.

Conclusion. Long length artificial lungs ventilation has been a statistically proven risk factor for development of polyorganic insufficiency. In order to decrease the probability of intra-aortic balloon contrarpulsation performance it is necessary not allowing the re-thoracotomy. The same might permit decreasing the probability of presence of polyorganic insufficiency factor as it depends on the performed re-thoracotomy via the factor when the blood loss is more than 500 ml. The precursor factor affecting the surgery outcome is the polyorganic insufficiency which is often associated with the artificial lungs ventilation performed longer than 2 days. The factor of duration of cardiopulmonary bypass in its quantity form plays the same role. The third place is occupied by the group of factors regarding the patient's age expressed in quantity form and the intraoperative associated factors such as performance of re-thoracotomy and blood loss more than 500 ml.

References

Афифи А., Эйзен С. Статистический анализ: подход с использованием ЭВМ. М.: Мир, 1982. 488 с.
Габриэлян Н.И. Гнойно-септические осложнения в трансплантологии и кардиохирургии: эпидемиология и профилактика: Автореф. дис. … д-ра мед. наук. М., 2011.
Кендалл М., Стьюарт А. Статистические выводы и связи / Пер. с англ. М.: Главная редакция физ.-мат. литературы, 1973. 899 с.
Ланг Т. А., Сесик М. Как описывать статистику в медицине: Руководство для авторов, редакторов и рецензентов / Пер. c англ. под ред. В.П. Леонова. М.: Практическая медицина, 2011. 480 с.
Леонов В. Логистическая регрессия в медицине и биологии. URL: http://www.biometrica.tomsk.ru/logit_1.htm.
Леонов В.П. Обработка экспериментальных данных на программируемых микрокалькуляторах. Томск: Изд-во ТГУ, 1990. 376 с.
Симанков Д.С., Савостьянова О.Н. Статистические методы для анализа значимости факторов риска послеоперационной летальности у кардиохирургических пациентов, прооперированных в условиях искусственного кровообращения // Вестник трансплантологии и искусственных органов. Т. 14. Материалы 6 Всероссийского съезда трансплантологов. М., 2012. С. 234-235.
Симанков Д.С., Савостьянова О.А., Мелемука И.В. и др. Статистическая модель прогноза летальности пациентов кардиохирургического профиля, оперированных в условиях искусственного кровообращения // Тезисы 14 съезда сердечно-сосудистых хирургов. М.: НЦССХ им. А.Н. Бакулева РАМН, 2008.
Справочник по прикладной статистике. В 2-х т.: Пер. с англ. / Под ред. Э. Ллойда, У. Ледермана, Ю.Н. Тюрина. М.: Финансы и статистика, 1989, 1990.
Hosmer D. W. Jr, Lemeshow S. Applied logistic regression. 2nd ed. Hoboken, New Jersey: John Wiley & Sons, Inc., 2000. 397 p.

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