Modelo matemático en MATLAB de parámetros ventilatorios para simular las ondas de presión, flujo y volumen en los modos controlados por presión y volumen
DOI:
https://doi.org/10.21754/tecnia.v33i2.1569Palabras clave:
Modo Respiratorio, Presión, Volumen, Flujo, Compliance, Simulación, ResistenciaResumen
Hoy en día las personas que presentan insuficiencia respiratoria aguda y que no responden a tratamientos no invasivos, requieren ventilación mecánica. Esto se vio incrementado en la pandemia del COVID 19, en donde los gobiernos y empresas incrementaron la producción, investigación de ventiladores mecánicos. Uno de los pasos principales para el desarrollo de un dispositivo médico es su simulación. El presente artículo muestra el procedimiento necesario para modelar matemáticamente ondas de volumen, flujo y presión en el modo ventilación mandatorio continúa controlada por presión (PC-CMV) y el modo ventilación mandatorio continúa controlado por volumen. Para su desarrollo se tomaron en cuenta los parámetros de entrada como la compliance, resistencia, frecuencia respiratoria, tiempo inspiratorio, tiempo espiratorio, tiempo pausa, PEEP, PIP y volumen tidal para su simulación en el software Matlab y obtención de parámetros ventilatorios en el modo PC-CMV y VC-CMV.
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Su Latt Phyu, C. D. Turnbull, and N. P. Talbot, “Basic respiratory physiology,” Medicine, vol. 51, no. 10, pp. 679–683, Oct. 2023, doi: 10.1016/j.mpmed.2023.07.006
P. M. Schlosser, B. A. Asgharian, and M. Medinsky, “Inhalation Exposure and Absorption of Toxicants,” Comprehensive Toxicology, vol. 10, pp. 75–109, 2010, doi: 10.1016/b978-0-08-046884-6.00104-4
N. J. Meyer, L. Gattinoni, and C. S. Calfee, “Acute respiratory distress syndrome,” The Lancet, vol. 398, pp. 622–637, Ag. 2021, doi: 10.1016/s0140-6736(21)00439-6
M. A. Matthay et al., “Acute respiratory distress syndrome,” Nature Reviews Disease Primers, vol. 5, no. 1, Mar. 2019, doi: 10.1038/s41572-019-0069-0
P. Aveyard et al., “Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study,” The Lancet Respiratory Medicine, vol. 9, no. 8, pp. 909–923, Ag. 2021, doi: 10.1016/s2213-2600(21)00095-3
H. F. Boncristiani, M. F. Criado, and E. Arruda, “Respiratory Viruses,” Encyclopedia of Microbiology, pp. 500–518, 2009, doi: 10.1016/b978-012373944-5.00314-x
E. C. Holmes et al., “The origins of SARS-CoV-2: A critical review,” Cell, vol. 184, no. 19, pp. 4848–4856, Sep. 2021, doi: 10.1016/j.cell.2021.08.017
J. A. Lednicky et al., “Emergence of porcine delta-coronavirus pathogenic infections among children in Haiti through independent zoonoses and convergent evolution,” medRxiv, Mar. 2021, doi: 10.1101/2021.03.19.21253391
A. N. Vlasova et al., “Novel Canine Coronavirus Isolated from a Hospitalized Patient With Pneumonia in East Malaysia,” Clinical Infectious Diseases, vol. 74, no. 3, pp. 446–454, May 2021, doi: 10.1093/cid/ciab456
Y. Alimohamadi, et al., “Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis,” Journal of preventive medicine and hygiene, vol. 61, no. 3, pp. E304–E312, 2020, doi: 10.15167/2421-4248/jpmh2020.61.3.1530.
Organización Mundial de la Salud, “Manejo clínico de la COVID-19 Orientaciones evolutivas,” Jan. 2021. [En línea]. Disponible: https://iris.who.int/bitstream/handle/10665/340629/WHO-2019-nCoV-clinical-2021.1-spa.pdf?sequence=1
J. N. Cronin, L. Camporota, and F. Formenti, “Mechanical ventilation in COVID‐19: A physiological perspective,” Experimental Physiology, vol. 107, no. 7, pp. 683–693, Sep. 2021, doi: 10.1113/ep089400
A. S. Tran, et al., “Design, Control, Modeling, and Simulation of Mechanical Ventilator for Respiratory Support,” Mathematical Problems in Engineering, vol. 2021, pp. 1–15, Nov. 2021, doi: 10.1155/2021/2499804
N. Q. Al-Naggar, “Modelling and Simulation of Pressure Controlled Mechanical Ventilation System,” Journal of Biomedical Science and Engineering, vol. 08, no. 10, pp. 707–716, 2015, doi: 10.4236/jbise.2015.810068
A. Peine et al., “Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care,” npj Digital Medicine, vol. 4, no. 1, Feb. 2021, doi: 10.1038/s41746-021-00388-6
F. Bautsch, G. Männel, and P. Rostalski, “Development of a Novel Low-cost Lung Function Simulator,” Current Directions in Biomedical Engineering, vol. 5, no. 1, pp. 557–560, Sep. 2019, doi: 10.1515/cdbme-2019-0140
Ministerio de Salud, “Tiempos de pandemia 2020-2021,” 2021. Accedido: Dic. 03, 2023. [En línea]. Disponible: http://bvs.minsa.gob.pe/local/MINSA/5485.pdf
J. Gallifant, et al., “Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias,” British Journal of Anaesthesia, vol. 128, no. 2, pp. 343–351, Feb. 2022, doi: 10.1016/j.bja.2021.09.025
A. Trikha, A. Borle, and P. Singh, “Newer nonconventional modes of mechanical ventilation,” Journal of Emergencies, Trauma, and Shock, vol. 7, no. 3, p. 222, 2014, doi: 10.4103/0974-2700.136869
E. Mireles-Cabodevila, A. Duggal, and R. L. Chatburn, “Modes of Mechanical Ventilation,” Mechanical Ventilation in Critically Ill Cancer Patients, pp. 177–188, 2018, doi: 10.1007/978-3-319-49256-8_17
R. L. Chatburn., M. El-Khatib &, R. Branson, (2006). Classification of Mechanical Ventilators. [En línea]. Disponible: https://www.researchgate.net/publication/291743072_Classification_of_Mechanical_Ventilators
A. L. Mora and J. I. Mora, “Ventilator Management.” Nih.gov, Accedido Dec. 06, 2023. https://www.ncbi.nlm.nih.gov/books/NBK448186
W. A. Carlo, N. Ambalavanan, and R. L. Chatburn, “Ventilator Parameters,” Manual of Neonatal Respiratory Care, pp. 81–85, 2006, doi: 10.1016/b978-032303176-9.50015-5
P. Michelet et al., “Effects of PEEP on oxygenation and respiratory mechanics during one-lung ventilation,” British Journal of Anaesthesia, Saunders vol. 95, no. 2, pp. 267–273, Ag. 2005, doi: 10.1093/bja/aei178
M. A. Warner and B. Patel, “Mechanical Ventilation,” in Benumof and Hagberg’s Airway Management, pp. 981-997, 2013, doi: 10.1016/b978-1-4377-2764-7.00048-8
G. Strauss-Blasche, M. Moser, M. Voica, D. McLeod, N. Klammer, and W. Marktl, “Relative Timing Of Inspiration And Expiration Affects Respiratory Sinus Arrhythmia,” Clinical and Experimental Pharmacology and Physiology, vol. 27, no. 8, pp. 601–606, Ag. 2000, doi: 10.1046/j.1440-1681.2000.03306.x
W. Woodward. “Airway Resistance.” TeachMePhysiology, Dec. 06, 2023. [En línea]. Disponible: https://teachmephysiology.com/respiratory-system/ventilation/airway-resistance/
D. A. Kaminsky, “What Does Airway Resistance Tell Us About Lung Function?,” Respiratory Care, vol. 57, no. 1, pp. 85–99, Jan. 2012, doi: 10.4187/respcare.01411
I. Singh and M. R. Pinsky, “Heart-Lung Interactions,” in Mechanical Ventilation, Saunders, pp. 173–184, 2008, doi: 10.1016/b978-0-7216-0186-1.50019-3
K. Tara, et al., “Detection of cardiac disorder using MATLAB based graphical user interface (GUI),” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dic. 2017, doi: 10.1109/r10-htc.2017.8288994
M. Ryzhii and E. Ryzhii, “Simulink heart model for simulation of the effect of external signals,” in 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Oct. 2016, doi: 10.1109/cibcb.2016.7758102
R. Davoodi, et al., “An integrated package of neuromusculoskeletal modeling tools in Simulink/spl trade/,” en 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2022, doi: 10.1109/iembs.2001.1020409.
A. S. Tran, et al., “Design, Control, Modeling, and Simulation of Mechanical Ventilator for Respiratory Support,” Mathematical Problems in Engineering, vol. 2021, pp. 1–15, Nov. 2021, doi: 10.1155/2021/2499804
K. Morales, S. Salinas, “Prototipo de simulador software electromecánico del pulmón prototipo de simulador software electromecánico del pulmón,” Puente. Revista científica, vol. 9, no. 1, 2015, [En línea]. Disponible: http://hdl.handle.net/20.500.11912/7277
Paolo Tamburrano, et al., “Validation of a Simulink Model for Simulating the Two Typical Controlled Ventilation Modes of Intensive Care Units Mechanical Ventilators,” Applied sciences, vol. 12, no. 4, pp. 2057–2057, Feb. 2022, doi: 10.3390/app12042057
M. Jaber, et al., “MATLAB/Simulink Mathematical Model for Lung and Ventilator,” in 2020 32nd International Conference on Microelectronics (ICM)¸ Dic. 2020, doi: https://doi.org/10.1109/icm50269.2020.9331820
J. Giri, Niraj Kshirsagar, and Aishwary Wanjari, “Design and simulation of AI-based low-cost mechanical ventilator: An approach,” Materials Today: Proceedings, vol. 47, pp. 5886–5891, Ene. 2021, doi: 10.1109/ICM50269.2020.9331820
E. Correger, et al., “Interpretation of ventilator curves in patients with acute respiratory failure,” Medicina Intensiva (English Edition), vol. 36, no. 4, 294–306, 2012, doi: 10.1016/j.medine.2012.06.001
R. Chatburn, “Simulation-Based Evaluation of Mechanical Ventilators,” Respiratory Care, vol. 63, no. 7, pp. 936–940, 2018, doi: 10.4187/respcare.06267
J. Park, “Monitoring and Interpretation of Mechanical Ventilator Waveform in the Neuro-Intensive Care Unit”, vol. 11, no. 2, pp. 63-70, 2018, doi: 10.18700/jnc.180069
S. Pearson, J. Koyner, and B. Patel, Management of Respiratory Failure, Clinical Journal of the American Society of Nephrology, vol. 17, no. 4, pp. 572–580, doi: 10.2215/cjn.13091021
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