Indian Journal of Critical Care Medicine

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VOLUME 24 , ISSUE 6 ( June, 2020 ) > List of Articles

Original Article

Simulation Training in Hemodynamic Monitoring and Mechanical Ventilation: An Assessment of Physician's Performance

Amarja A Havaldar, Sriram Sampath, Saravana K Paramasivam

Citation Information : Havaldar AA, Sampath S, Paramasivam SK. Simulation Training in Hemodynamic Monitoring and Mechanical Ventilation: An Assessment of Physician's Performance. Indian J Crit Care Med 2020; 24 (6):423-428.

DOI: 10.5005/jp-journals-10071-23458

License: CC BY-NC 4.0

Published Online: 30-07-2020

Copyright Statement:  Copyright © 2020; Jaypee Brothers Medical Publishers (P) Ltd.


Abstract

Background: Simulation is to imitate or replicate real-life scenarios in order to improve cognitive, diagnostic and therapeutic skills. An ideal model should be good enough to output realistic clinical scenarios and respond to interventions done by trainees in real time. Use of simulation-based training has been tried in various fields of medicine. The aim of our study was to prospectively evaluate the effectiveness of simulation model “CRITICA”™ (MEDUPLAY systems) in training critical care physicians. Materials and methods: The advanced intensive care unit (ICU) simulator “CRITICA”™ (MEDUPLAY systems) was developed as a joint collaboration between the Indian Institute of Science, Bengaluru and St John\'s Medical College, Bengaluru. Two-day workshop was conducted. Intensive didactic and case-based scenarios were simulated to formally teach principles of advanced ICU scenarios. The physicians were tested on clinical scenarios in hemodynamic monitoring and mechanical ventilation displayed on the simulator. Assessment of the analytical thinking and pattern recognition ability was carried out before and after the display of the scenarios. Pre- and posttest scores were collected. Results: The postsimulation test scores were higher than pretest scores and were statistically significant in hemodynamic monitoring and mechanical ventilation module. [Hemodynamic monitoring pre- and posttest scores 4.41 (2.06) vs 5.23 (2.22) p < 0.001] [Mechanical ventilation pre- and posttest scores 4 (2–5.5) vs 7.5 (6.5–8.5) p < 0.001]. A greater increase in posttest scores was seen in the mechanical ventilation module as compared to hemodynamic module. There was no effect of specialty or designation of a trainee on difference in pre- and posttest scores. Conclusion: Simulator-based training in hemodynamic monitoring and mechanical ventilation was effective. Comparison of routine classroom teaching and simulator-based training needs to be evaluated prospectively.


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  1. Gerlach H, Toussaint S. Between prediction, education, and quality control: Simulation models in critical care. Crit Care 2007;11(4):146. DOI: 10.1186/cc5950.
  2. Sevdalis N, Brett SJ. Improving care by understanding the way we work: Human factors and behavioural science in the context of intensive care. Crit Care 2009;13(2):139. DOI: 10.1186/cc7787.
  3. Good ML. Patient simulation for training basic and advanced clinical skills. Med Educ 2003;37(s1):14–21. DOI: 10.1046/j.1365-2923.37.s1.6.x.
  4. Fackler JC, Watts C, Grome A, Miller T, Crandall B, Pronovost P. Critical care physician cognitive task analysis: an exploratory study. Crit Care 2009;13(2):R33. DOI: 10.1186/cc7740.
  5. Sanri E, Karacabey S, Emre Eroglu SE, et al. The additional impact of simulation based medical training to traditional medical training alone in advanced cardiac life support: a scenario based evaluation. Signa vitae: Journal for Intensive care and Emergency Medicine 2018;14:68–72.
  6. Smith HL, Menon DK. Teaching difficult airway management: is virtual reality real enough? Intensive Care Med 2005;30(4):504–505. DOI: 10.1007/s00134-005-2576-6.
  7. Denadai R, Toledo AP, Bernades DM, Diniz FD, Eid FB, Lanfranchi LMMM, et al. Simulation-based ultrasound-guided central venous cannulation training program. Acta Cirurgica Brasileira 2014;29(2):132–144. DOI: 10.1590/S0102-86502014000200010.
  8. Vignon P, Pegot B, Dalmay F, Jean-Michel V, Bocher S, L'her E, et al. Acceleration of the learning curve for mastering basic critical care echocardiography using computerized simulation. Intensive Care Med 2018;44(7):1097–1105. DOI: 10.1007/s00134-018-5248-z.
  9. Ford DG, Seybert AL, Smithburger PL, Kobulinsky LR, Samosky JT, Kane-Gill SL. Impact of simulation-based learning on medication error rates in critically ill patients. Intensive Care Med 2010;36(9):1526–1531. DOI: 10.1007/s00134-010-1860-2.
  10. Yee J, Fuenning C, George R, Hejal R, Haines N, Dunn D, et al. Mechanical ventilation boot camp: a simulation-based pilot study. Crit Care Res Pract 2016;2016:1–7. DOI: 10.1155/2016/4670672.
  11. Saffaran S, Das A, Hardman JG, Yehya N, Bates DG. High-fidelity computational simulation to refine strategies for lung-protective ventilation in paediatric acute respiratory distress syndrome. Intensive Care Med 2019;14(7):1–3. DOI: 10.1007/s00134-019-05559-4.
  12. Stocker M, Allen M, Pool N, De Costa K, Combes J, West N, et al. Impact of an embedded simulation team training programme in a paediatric intensive care unit: a prospective, single-centre, longitudinal study. Intensive Care Med 2012;38(1):99–104. DOI: 10.1007/s00134-011-2371-5.
  13. Armenia S, Thangamathesvaran L, Caine A, King N, Kunac A, Merchant A. The role of high-fidelity team-based simulation in acute care settings: a systematic review. Surg J 2018;4(3):e136–e151. DOI: 10.1055/s-0038-1667315.
  14. Di Nardo M, David P, Stoppa F, Lorusso R, Raponi M, Amodeo A, et al. The introduction of a high-fidelity simulation program for training pediatric critical care personnel reduces the times to manage extracorporeal membrane oxygenation emergencies and improves teamwork. J Thorac Dis 2018;10(6):3409. DOI: 10.21037/jtd.2018.05.77.
  15. Saka G, Kreke JE, Schaefer AJ, Chang CC, Roberts MS, Angus DC, et al. Use of dynamic microsimulation to predict disease progression in patients with pneumonia-related sepsis. Crit Care 2007;11(3):R65. DOI: 10.1186/cc5942.
  16. Randall D, Garbutt D, Barnard M. Using simulation as a learning experience in clinical teams to learn about palliative and end-of-life care: a literature review. Death Stud 2018;42(3):172–183. DOI: 10.1080/07481187.2017.1334006.
  17. Garrouste-Orgeas M, Tabah A, Vesin A, Philippart F, Kpodji A, Bruel C, et al. The ETHICA study (part II): simulation study of determinants and variability of ICU physician decisions in patients aged 80 or over. Intensive Care Med 2013;39(9):1574–1583. DOI: 10.1007/s00134-013-2977-x.
  18. Abrahamson SD, Canzian S, Brunet F. Using simulation for training and to change protocol during the outbreak of severe acute respiratory syndrome. Crit Care 2005;10(1):R3. DOI: 10.1186/cc3916.
  19. Bruppacher HR, Alam SK, LeBlanc VR, Latter D, Naik VN, Savoldelli GL, et al. Simulation-based training improves physicians’ performance in patient care in high-stakes clinical setting of cardiac surgery. Anesthesiology: J Am Soc Anesthesiolog 2010;112(4):985–992. DOI: 10.1097/ALN.0b013e3181d3e31c.
  20. Hunziker S, Laschinger L, Portmann-Schwarz S, Semmer NK, Tschan F, Marsch S. Perceived stress and team performance during a simulated resuscitation. Intensive Care Med 2011;37(9):1473–1479. DOI: 10.1007/s00134-011-2277-2.
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