title: Semicontinuous cardiac output monitoring using a neural network.
abstract: OBJECTIVE
  This study compared 2-mL bolus thermodilution cardiac output measurements with standard 10-mL bolus measurements.
  
  
  METHODS
  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.
  
  
  METHODS
  Animal laboratory.
  
  
  METHODS
  Eight adult Duroc swine weighing between 38.0 and 57.5 kg.
  
  
  METHODS
  Once each minute, 2 mL 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.
  
  
  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 r2 = .92 and the SD of the difference was 1.30 L/min. For the neural network, the correlation coefficient was r2 = .94 and the SD of the difference was 0.88 L/min. Output filtering improved the results in both cases.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Anesthesiology, University of Utah, Salt Lake City 84132, USA.
authors.forename: C
authors.initials: C
authors.lastname: Healey
authors.forename: J
authors.initials: J
authors.lastname: Orr
authors.forename: D
authors.initials: D
authors.lastname: Westenskow
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D015982:Bias; D002302:Cardiac Output; D002407:Catheterization, Swan-Ganz / Q000191:economics / Q000295:instrumentation / Q000379:methods*; D003362:Cost-Benefit Analysis; D008991:Monitoring, Physiologic / Q000191:economics / Q000295:instrumentation / Q000379:methods*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013552:Swine; D013815:Thermodilution / Q000191:economics / Q000295:instrumentation / Q000379:methods*; D013997:Time Factors