Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept.

Artificial neural networks Closed loop Lactic acidosis Mechanical ventilation Translational model

Journal

Journal of clinical monitoring and computing
ISSN: 1573-2614
Titre abrégé: J Clin Monit Comput
Pays: Netherlands
ID NLM: 9806357

Informations de publication

Date de publication:
20 Aug 2024
Historique:
received: 22 05 2024
accepted: 07 08 2024
medline: 20 8 2024
pubmed: 20 8 2024
entrez: 20 8 2024
Statut: aheadofprint

Résumé

Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of metabolic acidosis. Data was collected from ten anesthetized, spontaneously breathing pigs divided randomly into two groups, one without dead space and the other with dead space at the beginning of the experiment. Each group underwent two equal sequences of pH lowering with pre-defined targets by continuous infusion of lactic acid. The inputs to ANNs were pH, ΔPaCO

Identifiants

DOI: 10.1007/s10877-024-01208-4 PMID: 39162839
pubmed: 39162839
doi: 10.1007/s10877-024-01208-4
pii: 10.1007/s10877-024-01208-4
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s).

Références

Hall J, Hall M. Pulmonary ventilation. Guyton and Hall Textbook of Medical Physiology. 14th ed. Elsevier; 2021. pp. 491–501.
Guyenet PG, Bayliss DA. Neural control of breathing and CO2 homeostasis. Neuron. 2015;87:946–61. https://doi.org/10.1016/j.neuron.2015.08.001 .
doi: 10.1016/j.neuron.2015.08.001 pubmed: 26335642 pmcid: 4559867
Ramirez J-M, Baertsch NA. The dynamic basis of respiratory rhythm generation: one breath at a time. Annu Rev Neurosci. 2018;41:475–99. https://doi.org/10.1146/annurev-neuro-080317-061756 .
doi: 10.1146/annurev-neuro-080317-061756 pubmed: 29709210 pmcid: 6548330
Otis AB, Fenn WO, Rahn H. Mechanics of Breathing in Man. J Appl Physiol. 1950;2:592–607. https://doi.org/10.1152/jappl.1950.2.11.592 .
doi: 10.1152/jappl.1950.2.11.592 pubmed: 15436363
Del Negro CA, Funk GD, Feldman JL. Breathing matters. Nat Rev Neurosci. 2018;19:351–67. https://doi.org/10.1038/s41583-018-0003-6 .
doi: 10.1038/s41583-018-0003-6 pubmed: 29740175 pmcid: 6636643
Napoli NJ, Rodrigues VR, Davenport PW. Characterizing and modeling breathing dynamics: flow rate, rhythm, period, and frequency. Front Physiol. 2022;12:1–12. https://doi.org/10.3389/fphys.2021.772295 .
doi: 10.3389/fphys.2021.772295
Arnal JM, Katayama S, Howard C. Closed-loop ventilation. Curr Opin Crit Care. 2023;29:19–25. https://doi.org/10.1097/MCC.0000000000001012 .
doi: 10.1097/MCC.0000000000001012 pubmed: 36484170
Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989;2:359–66. https://doi.org/10.1016/0893-6080(89)90020-8 .
doi: 10.1016/0893-6080(89)90020-8
Le TA, Baydin AG, Zinkov R, Wood F. (2017) Using synthetic data to train neural networks is model-based reasoning. In: International Joint Conference on Neural Networks (IJCNN). IEEE, pp 3514–3521.
Venkatesh B, Clutton Brock TH, Hendry SP. A multiparameter sensor for continuous intra-arterial blood gas monitoring: a prospective evaluation. Crit Care Med. 1994;22:588–94. https://doi.org/10.1097/00003246-199404000-00013 .
doi: 10.1097/00003246-199404000-00013 pubmed: 8143468
Shirer HW, Erichsen DF, Orr JA. Cardiorespiratory responses to HCl vs. lactic acid infusion. J Appl Physiol. 1988;65:534–40. https://doi.org/10.1152/jappl.1988.65.2.534 .
doi: 10.1152/jappl.1988.65.2.534 pubmed: 3170402
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.
doi: 10.1016/S0140-6736(86)90837-8 pubmed: 2868172
Lumb A, Thomas C. Control of Breathing. In: Lumb A, Thomas C, editors. Nunn and Lumb’s Applied Respiratory Physiology. 9th ed. Elsevier; 2020. pp. 42–58.
Sly PD, Bates JHT, Kochi T, Okubo S, Milic-Emili J. Frequency-dependent effects of hypercapnia on respiratory mechanics of cats. J Appl Physiol. 1987;62:444–50. https://doi.org/10.1152/jappl.1987.62.2.444 .
doi: 10.1152/jappl.1987.62.2.444 pubmed: 3104292
Ranieri VM, Giuliani R, Mascia L, Grasso S, Petruzzelli V, Puntillo N, Perchiazzi G, Fiore T, Brienza A. Patient-ventilator interaction during acute hypercapnia: pressure-support vs. proportional-assist ventilation. J Appl Physiol. 1996;81:426–36. https://doi.org/10.1152/jappl.1996.81.1.426 .
doi: 10.1152/jappl.1996.81.1.426 pubmed: 8828695
Clark FJ, von Euler C. On the regulation of depth and rate of breathing. J Physiol. 1972;222:267–95. https://doi.org/10.1113/jphysiol.1972.sp009797 .
doi: 10.1113/jphysiol.1972.sp009797 pubmed: 5033464 pmcid: 1331381
Berger AJ, Mitchell RA, Severinghaus JW. Regulation of respiration. N Engl J Med. 1977;297:194–201. https://doi.org/10.1056/NEJM197707282970406 .
doi: 10.1056/NEJM197707282970406 pubmed: 17834
Haldane JS, Priestley JG. The regulation of the lung-ventilation. J Physiol. 1905;32:225–66. https://doi.org/10.1113/jphysiol.1905.sp001081 .
doi: 10.1113/jphysiol.1905.sp001081 pubmed: 16992774 pmcid: 1465676
Rebuck AS, Kangalee M, Pengelly LD, Campbell EJ. Correlation of ventilatory responses to hypoxia and hypercapnia. J Appl Physiol. 1973;35:173–7. https://doi.org/10.1152/jappl.1973.35.2.173 .
doi: 10.1152/jappl.1973.35.2.173 pubmed: 4723024
Molkov YI, Rubin JE, Rybak IA, Smith JC. Computational models of the neural control of breathing. WIREs Syst Biol Med. 2017;9:1–22. https://doi.org/10.1002/wsbm.1371 .
doi: 10.1002/wsbm.1371
Walker RN, Heuberger RA. Predictive equations for energy needs for the critically ill. Respir Care. 2009;54:509–21.
pubmed: 19327188
Cherniack NS, Longobardo GS. Oxygen and carbon dioxide gas stores of the body. Physiol Rev. 1970;50:196–243. https://doi.org/10.1152/physrev.1970.50.2.196 .
doi: 10.1152/physrev.1970.50.2.196 pubmed: 4908089
Holmdahl MH, Wiklund L, Wetterberg T, Streat S, Wahlander S, Sutin K, Nahas G. The place of THAM in the management of acidemia in clinical practice. Acta Anaesthesiol Scand. 2000;44:524–7. https://doi.org/10.1034/j.1399-6576.2000.00506.x .
doi: 10.1034/j.1399-6576.2000.00506.x pubmed: 10786736
Lumb A, Thomas C. Changes in the carbon dioxide partial pressure. In: Lumb A, Thomas C, editors. Nunn and Lumb’s Applied Respiratory Physiology. 9th ed. Elsevier; 2020. pp. 268–72.
Larraza S, Dey N, Karbing DS, Jensen JB, Nygaard M, Winding R, Rees SE. A mathematical model approach quantifying patients’ response to changes in mechanical ventilation: evaluation in volume support. Med Eng Phys. 2015;37:341–9. https://doi.org/10.1016/j.medengphy.2014.12.006 .
doi: 10.1016/j.medengphy.2014.12.006 pubmed: 25686673
Kerlirzin P, Vallet F. Robustness in Multilayer Perceptrons. Neural Comput. 1993;5:473–82. https://doi.org/10.1162/neco.1993.5.3.473 .
doi: 10.1162/neco.1993.5.3.473
Ranieri VM. Optimization of patient-ventilator interactions: closed-loop technology to turn the century. Intensive Care Med. 1997;23:936–9. https://doi.org/10.1007/s001340050434 .
doi: 10.1007/s001340050434 pubmed: 9347363
Von Platen P, Pomprapa A, Lachmann B, Leonhardt S. The dawn of physiological closed-loop ventilation - A review. Crit Care. 2020;24:1–11. https://doi.org/10.1186/s13054-020-2810-1 .
doi: 10.1186/s13054-020-2810-1
Perchiazzi G, Rylander C, Pellegrini M, Larsson A, Hedenstierna G. Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks. J Clin Monit Comput. 2017;31. https://doi.org/10.1007/s10877-016-9874-0 .
Perchiazzi G, Hogman M, Rylander C, Giuliani R, Fiore T, Hedenstierna G. Assessment of respiratory system mechanics by artificial neural networks: an exploratory study. J Appl Physiol. 2001;90:1817–24.
doi: 10.1152/jappl.2001.90.5.1817 pubmed: 11299272
Perchiazzi G, Giuliani R, Ruggiero L, Fiore T, Hedenstierna G. Estimating respiratory system compliance during mechanical ventilation using artificial neural networks. Anesth Analg. 2003;97:1143–8. https://doi.org/10.1213/01.ANE.0000077905.92474.82 .
doi: 10.1213/01.ANE.0000077905.92474.82 pubmed: 14500172
Perchiazzi G, Rylander C, Pellegrini M, Larsson A, Hedenstierna G. Robustness of two different methods of monitoring respiratory system compliance during mechanical ventilation. Med Biol Eng Comput. 2017;55. https://doi.org/10.1007/s11517-017-1631-0 .
Chatburn RL. Computer control of mechanical ventilation. Respir Care. 2004;49:507–17.
pubmed: 15107139
Perchiazzi G. (2004) Artificial Neural Networks (ANN) in the Assessment of Respiratory Mechanics. Acta Universitatis Upsaliensis. Summaries of Uppsala Dissertations from the Faculty of Medicine NV-1389, Uppsala.
Loyola-Gonzalez O. Black-Box vs. White-Box: understanding their advantages and weaknesses from a practical point of View. IEEE Access. 2019;7:154096–113. https://doi.org/10.1109/ACCESS.2019.2949286 .
doi: 10.1109/ACCESS.2019.2949286

Auteurs

Gaetano Perchiazzi (G)

The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden. gaetano.perchiazzi@uu.se.
Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden. gaetano.perchiazzi@uu.se.
Hedenstierna Laboratoriet, Akademiska sjukhuset ing 40 3 tr, Uppsala, 75185, Sweden. gaetano.perchiazzi@uu.se.
ORCID: 0000-0001-6834-6399

Rafael Kawati (R)

Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden.

Mariangela Pellegrini (M)

The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden.

Jasmine Liangpansakul (J)

The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.

Roberto Colella (R)

Ministry of Education and Merit, Rome, Italy.

Paolo Bollella (P)

Department of Chemistry, University of Bari Aldo Moro, Bari, Italy.

Pramod Rangaiah (P)

Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden.

Annamaria Cannone (A)

Department of Anaesthesia and Intensive Care, "Madonna delle Grazie" Hospital, Matera, Italy.

Deepthi Hulithala Venkataramana (DH)

Department of Information Technology, Uppsala University, Uppsala, Sweden.

Mauricio Perez (M)

Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden.

Sebastiano Stramaglia (S)

Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy.
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy.

Luisa Torsi (L)

Department of Chemistry, University of Bari Aldo Moro, Bari, Italy.

Roberto Bellotti (R)

Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy.
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy.

Robin Augustine (R)

Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden.

Classifications MeSH