Adpative Neuro-Fuzzy Inference System Estimation Propofol dose in the induction phase during anesthesia; case study

Document Type : Original Article

Authors

1 Faculty of Industrial Engineering, Yazd University, Yazd, Iran

2 Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

In this study, the anesthetic drug dose estimation due to the physiological patients' parameters is considered. The most critical anesthetic drug, propofol, is considered in this modeling. Among the intravenous anesthetic drugs, propofol is one of the most widely used during surgery in the induction and maintenance phase of anesthesia. The effect of propofol as an intravenous anesthetic agent is as well as sedate in/outside the operation theatres. In this work, the adaptive neuro-fuzzy inference system estimation model is applied to calculate the drug dose to administrate anesthesia safety. The model estimates the propofol dose during the induction phase based on the physiological parameters (age, weight, height, gender), blood pressure, heart rate, and the depth of anesthesia of real patients. The sensitivity analysis was applied to evaluate the validity of the estimation model, so the appropriate agreement is obtained. In the end, the proposed estimation model's performance is compared to the classical model and the actual data obtained from patients undergoing surgery. The results show that the ANFIS estimation model by 0.999 accuracies reduces the total amount of propofol dose. The proposed model not only controls the patient's depth of anesthesia accurately but also obtained outcomes in practice successfully.

Keywords

References

  1. Mu, J., Jiang, T., Xu, X., Yuen, V. and Irwin, M., "Comparison of target-controlled infusion and manual infusion for propofol anaesthesia in children", British Journal of Anaesthesia, Vol. 120, No. 5, (2018), 1049-1055, DOI: 10.1016/j.bja.2017.11.102.
  2. Zhang, J. and Huang, C., "Dynamics analysis on a class of delayed neural networks involving inertial terms", Advances in Difference Equations, Vol. 2020, No. 1, (2020), 1-12, DOI: 10.1186/s13662-020-02566-4.
  3. Van Den Berg, J., Vereecke, H., Proost, J., Eleveld, D., Wietasch, J., Absalom, A. and Struys, M., "Pharmacokinetic and pharmacodynamic interactions in anaesthesia. A review of

 

 

current knowledge and how it can be used to optimize anaesthetic drug administration", British Journal of Anaesthesia,  Vol. 118, No. 1, (2017), 44-57, DOI: 10.1093/bja/aew312.

  1. Sahinovic, M.M., Struys, M.M. and Absalom, A.R., "Clinical pharmacokinetics and pharmacodynamics of propofol", Clinical Pharmacokinetics, Vol. 57, No. 12, (2018), 1539-1558, DOI: 10.1007/s40262-018-0672-3.
  2. van Heusden, K., Soltesz, K., Cooke, E., Brodie, S., West, N., Gorges, M., Ansermino, J.M. and Dumont, G., "Optimizing robust pid control of propofol anesthesia for children; design and clinical evaluation", IEEE Transactions on Biomedical Engineering, (2019), doi: 10.1109/TBME.2019.2898194.
  3. Wei, Z.-X., Doctor, F., Liu, Y.-X., Fan, S.-Z. and Shieh, J.-S., "An optimized type-2 self-organizing fuzzy logic controller applied in anesthesia for propofol dosing to regulate bis", IEEE Transactions on Fuzzy Systems, Vol. 28, No. 6, (2020), 1062-1072, doi: 10.1109/TFUZZ.2020.2969384.
  4. Jin, W., Zucker, M. and Pralle, A., "Membrane nanodomains homeostasis during propofol anesthesia as function of dosage and temperature", Biochimica et Biophysica Acta (BBA)-Biomembranes, Vol. 1863, No. 2, (2021), 183511, doi: 10.1016/j.bbamem.2020.183511.
  5. Hsieh, M.-L., Lu, Y.-T., Lin, C.-C. and Lee, C.-P., "Comparison of the target-controlled infusion and the manual infusion of propofol anesthesia during electroconvulsive therapy: An open-label randomized controlled trial", BMC Psychiatry, Vol. 21, No. 1, (2021), 1-10, doi: 10.1186/s12888-021-03069-6.
  6. Lai, H.-C., Lee, M.-S., Lin, K.-T., Huang, Y.-H., Chen, J.-Y., Lin, Y.-T., Hung, K.-C. and Wu, Z.-F., "Propofol-based total intravenous anesthesia is associated with better survival than desflurane anesthesia in robot-assisted radical prostatectomy", PloS One, Vol. 15, No. 3, (2020), e0230290, doi: 10.1371/journal.pone.0230290 .
  7. West, N., van Heusden, K., Görges, M., Brodie, S., Rollinson, A., Petersen, C.L., Dumont, G.A., Ansermino, J.M. and Merchant, R.N., "Design and evaluation of a closed-loop anesthesia system with robust control and safety system", Anesthesia & Analgesia, Vol. 127, No. 4, (2018), 883-894, doi: 10.1213/ANE.0000000000002663.
  8. Kodama, M., Higuchi, H., Ishii-Maruhama, M., Nakano, M., Honda-Wakasugi, Y., Maeda, S. and Miyawaki, T., "Multi-drug therapy for epilepsy influenced bispectral index after a bolus propofol administration without affecting propofol’s pharmacokinetics: A prospective cohort study", Scientific Reports, Vol. 10, No. 1, (2020), 1-9, doi: 10.1038/s41598-020-58460-2.
  9. Araújo, A.M., Machado, H., de Pinho, P.G., Soares‐da‐Silva, P. and Falcão, A., "Population pharmacokinetic‐pharmacodynamic modeling for propofol anesthesia guided by the bispectral index (bis)", The Journal of Clinical Pharmacology, Vol. 60, No. 5, (2020), 617-628, doi: 10.1002/jcph.1560.
  10. Samadi, F. and Moghadam-Fard, H., "Active suspension system control using adaptive neuro fuzzy (anfis) controller", International Journal of Engineering, Vol. 28, No. 3, (2015), 396-401, doi: 10.5829/idosi.ije.2015.28.03c.08.
  11. Lashkenari, M., KhazaiePoul, A., Ghasemi, S. and Ghorbani, M., "Adaptive neuro-fuzzy inference system prediction of zn metal ions adsorption by γ-Fe2O3/polyrhodanine nanocomposite in a fixed bed column", International Journal of Engineering, Vol. 31, No. 10, (2018), 1617-1623, doi: 10.5829/ije.2018.31.10a.02.
  12. Bahadori-Chinibelagh, S., Fathollahi-Fard, A.M. and Hajiaghaei-Keshteli, M., "Two constructive algorithms to address a multi-depot home healthcare routing problem", IETE Journal of Research, (2019), 1-7, doi: 10.1080/03772063.2019.1642802.
  13. Sigl, J.C. and Chamoun, N.G., "An introduction to bispectral analysis for the electroencephalogram", Journal of Clinical Monitoring, Vol. 10, No. 6, (1994), 392-404, doi: 10.1007/BF01618421.
  14. Jamali, N., Sadegheih, A., Lotfi, M., Wood, L.C. and Ebadi, M., "Estimating the depth of anesthesia during the induction by a novel adaptive neuro-fuzzy inference system: A case study", Neural Processing Letters, (2020), 1-45, doi: 10.1007/s11063-020-10369-7.
International Journal of Engineering
Volume 34, Issue 9
TRANSACTIONS C: Aspects
September 2021
Pages 2148-2156

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