P O Laitinen1, J Räsänen. 1. Department of Anaesthesiology, Hospital for Children and Adolescents, University of Helsinki, Finland.
Abstract
OBJECTIVE: To compare measured and predicted oxygen consumption (VO2) in children with congenital heart disease. DESIGN: Retrospective study. SETTING: The cardiac catheterisation laboratory in a university hospital. PATIENTS: 125 children undergoing preoperative cardiac catheterisation. INTERVENTIONS: VO2 was measured using indirect calorimetry; the predicted values were calculated from regression equations published by Lindahl, Wessel et al, and Lundell et al. Stepwise linear regression and analysis of variance were used to evaluate the influence of age, sex, weight, height, cardiac malformation, and heart failure on the bias and precision of predicted VO2. An artificial neural network was trained and used to produce an estimate of VO2 employing the same variables. The various estimates for VO2 were evaluated by calculating their bias and precision values. RESULTS: Lindahl's equation produced the highest precision (+/- 42%) of the regression based estimates. The corresponding average bias of the predicted VO2 was 3% (range -66% to 43%). When VO2 was predicted according to regression equations by Wessel and Lundell, the bias and precision were 0% and +/- 44%, and -16% and +/- 51%, respectively. The neural network predicted VO2 from variables included in the regression equations with a bias of 6% and precision +/- 29%; addition of further variables failed to improve this estimate. CONCLUSIONS: Both regression based and artificial intelligence based techniques were inaccurate for predicting preoperative VO2 in patients with congenital heart disease. Measurement of VO2 is necessary in the preoperative evaluation of these patients.
OBJECTIVE: To compare measured and predicted oxygen consumption (VO2) in children with congenital heart disease. DESIGN: Retrospective study. SETTING: The cardiac catheterisation laboratory in a university hospital. PATIENTS: 125 children undergoing preoperative cardiac catheterisation. INTERVENTIONS: VO2 was measured using indirect calorimetry; the predicted values were calculated from regression equations published by Lindahl, Wessel et al, and Lundell et al. Stepwise linear regression and analysis of variance were used to evaluate the influence of age, sex, weight, height, cardiac malformation, and heart failure on the bias and precision of predicted VO2. An artificial neural network was trained and used to produce an estimate of VO2 employing the same variables. The various estimates for VO2 were evaluated by calculating their bias and precision values. RESULTS: Lindahl's equation produced the highest precision (+/- 42%) of the regression based estimates. The corresponding average bias of the predicted VO2 was 3% (range -66% to 43%). When VO2 was predicted according to regression equations by Wessel and Lundell, the bias and precision were 0% and +/- 44%, and -16% and +/- 51%, respectively. The neural network predicted VO2 from variables included in the regression equations with a bias of 6% and precision +/- 29%; addition of further variables failed to improve this estimate. CONCLUSIONS: Both regression based and artificial intelligence based techniques were inaccurate for predicting preoperative VO2 in patients with congenital heart disease. Measurement of VO2 is necessary in the preoperative evaluation of these patients.
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