Journal of Medical Systems

, Volume 24, Issue 3, pp 141–146 | Cite as

Development of a Decision Support System to Assist Anesthesiologists in Operating Room

  • Marina Krol
  • David L. Reich
Article

Abstract

The complexity of modern anesthesia procedures requires the development of decision-support systems functioning in a smart-alarm capacity. We developed computer algorithms to detect critical conditions during surgery (light anesthesia or unstable blood pressure), based on computerized anesthesia records containing hemodynamic data (heart rate, mean arterial pressure and systolic arterial pressure). Our analysis indicated that a ≥12% change in mean arterial blood pressure (MAP), compared with the median value of MAP over the preceding 10-min interval, may be chosen as the criterion for detecting LA, with a sensitivity of 96% and a specificity of 91%. The best agreement between human and computer ratings of blood pressure lability (correlation coefficient 0.78) was achieved when we used the absolute value of the fractional change of the mean arterial pressure (|FCM|) between one 2-min epoch and the next 2-min epoch. Work is under progress to develop a decision-support system to alert clinicians in the operating room environment to critical events.

modern anesthesia smart-alarm capacity computer algorithms 

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REFERENCES

  1. 1.
    Hunter, J., Decision support in the operating theatre and intensive care: A personal view. Artificial Intelligence in Medicine 11(2):93–96, 1997.Google Scholar
  2. 2.
    Groth, T., et al., KBSIM/FLUIDTHERAPY: A system for optimized design of fluid resuscitation in trauma. Comput. Methods Programs Biomed 34(2-3):163–173, 1991Google Scholar
  3. 3.
    Schecke, T., et al., Knowledge-based decision support for patient monitoring in cardioanesthesia. Int. J. Clin. Monit. Comput., 9(1):1–11, 1992.Google Scholar
  4. 4.
    Larsen, V.H., and Siggaard-Andersen, O., The oxygen status algorithm on-line with the pH-blood gas analyzer. Scand. J. Clin. Lab. Invest. Suppl. 224:9–19, 1996.Google Scholar
  5. 5.
    Sukuvaara, T., et al., A knowledge-based alarm system for monitoring cardiac operated patients— technical construction and evaluation. Int. J. Clin. Monit. Comput. 10(2):117–126, 1993.Google Scholar
  6. 6.
    Gehin, A., et al., Application of the smart sensor concept to the anesthesia supervision. Proc. 1993 IEEE Engineering in Medicine and Biology 15th Annual Conference, vol. 2, pp. 616–7, 1993.Google Scholar
  7. 7.
    Reich, D.L., Bodian, C., Krol, M., Osinski, T., and Lansman, S., Intraoperative hemodynamics and mortality stroke and myocardial infarction following coronary artery bypass surgery. Anesthesiology 87:A125, 1997.Google Scholar
  8. 8.
    Reich, D., Krol, M., Bodian, C., Grubb, C., and McConville J., Inadequate intraoperative anesthetic depth: Derivation of an algorithm to predict the relative importance of various anesthetic agents. Anesthesiology. In press. (abstract).Google Scholar

Copyright information

© Plenum Publishing Corporation 2000

Authors and Affiliations

  • Marina Krol
    • 1
  • David L. Reich
    • 1
  1. 1.Department of AnesthesiologyThe Mount Sinai-School of MedicineNew York

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