Semicontinuous Cardiac Output Monitoring Using A Neural Network

Objectives: This study compared 2-mL bolus thermodilution cardiac output measurements with standard 10-mL bolus measurements,Design: Cardiac output was measured with the new 2-mL bolus technique and the 10-mL standard thermodilution technique in a perspective series. We describe a system that automatically cools and injects 2-mL boluses of saline into a standard pulmonary artery catheter, It uses a Peltier effect solid-state cooler and pneumatically driven syringe injector to measure cardiac output once per minute,Setting: Animal laboratory,Animals: Eight adult Duroc swine weighing between 38.0 and 57.5 kg,Interventions., Once each minute, 2 mt of cooled 5% dextrose was injected through the pulmonary catheter, Once every 8 mins, four sequential measurements of cardiac output were made using 10-mL injections,Measurements and Main Results: A total of 1249 paired waveforms were processed with both a conventional algorithm and with a neural network, For the conventional algorithm, the correlation coefficient was r(2) = .92 and the so of the difference was 1.30 L/min. For the neural network, the correlation coefficient was r(2) = .94 and the so of the difference was 0.88 L/min, Output filtering improved the results in both cases.Conclusion: Neural networks accurately derive cardiac output from 2-mL bolus thermodilution injections, allowing cardiac output to be monitored automatically once per minute in many patients, The technique is convenient and uses standard low-cost catheters.