期刊
EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY
卷 30, 期 5, 页码 716-721出版社
OXFORD UNIV PRESS INC
DOI: 10.1016/j.ejcts.2006.08.009
关键词
aortic valve replacement; mortality; risk factors
Objective: Haematological and biochemical measurements are performed routinely before surgery to exclude organ malfunction and blood cell and coagulation abnormalities. We aimed to test routinely obtained laboratory data as factors predicting operative risk. Methods: Between 1996 and 2003, 2198 patients underwent aortic valve replacement (AVR) (908 of them with concomitant CABG) in our institute. The mean age of the study population was 69 +/- 11 years (range 13-91, 43% female). Clinical and laboratory parameters based on the consolidated data mart set were evaluated by multiple logistic regression analysis. Results: The overall operative mortality (within 30 days) was 3.8% and the mortality after 3 months was 5.9%. In addition to clinical characteristics, the following laboratory values were identified as independent predictors of 30-day mortality: fasting blood glucose, antithrombine III, partial thromboplastine time and creatinine kinase. As independent predictors of 3-month mortality, the following laboratory values were indentified: fasting blood glucose, serum creatinine, antithrombine III, partial thromboplastine time, lactate dehydrogenase, sodium concentration and serum proteins. The discriminative power of the models increased if laboratory parameters were included in addition to preoperative clinical characteristics (from 0.75 to 0.79 and from 0.75 to 0.78 for 30-day and 3-month mortality, respectively). The discriminative power using the logistic EuroScore was tower (0.71 and 0.7, for 30-day and 3-month mortality, respectively). Conclusions: Laboratory parameters as objective markers for organ function and nutritional status are useful data for the prediction of 30-day and 3-month mortality after aortic valve replacement. Using modern methods of information technology, these valuable data which are stored electronically in most hospitals, can be used efficiently for research and quality control. (c) 2006 Elsevier B.V. All rights reserved.
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