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Measured versus predicted oxygen consumption in children with congenital heart disease
  1. P O Laitinena,
  2. J Räsänenb
  1. aDepartment of Anaesthesiology, Hospital for Children and Adolescents, University of Helsinki, Helsinki, Finland, bDepartment of Anesthesiology, Mayo Clinic, Rochester, Minnesota, USA
  1. Dr P O Laitinen, Hospital for Children and Adolescents, University of Helsinki, Stenbäckinkatu 11, FIN-00290, Helsinki, Finland.

Abstract

Objective To compare measured and predicted oxygen consumption (V˙o2) 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 V˙o2 was measured using indirect calorimetry; the predicted values were calculated from regression equations published by Lindahl, Wesselet 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 V˙o2. An artificial neural network was trained and used to produce an estimate of V˙o2 employing the same variables. The various estimates for V˙o2 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 V˙o2 was 3% (range −66% to 43%). When V˙o2 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 V˙o2 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 V˙o2 in patients with congenital heart disease. Measurement of V˙o2 is necessary in the preoperative evaluation of these patients.

  • oxygen consumption
  • congenital heart disease
  • neural networks

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