Simulation Training in Neuroangiography-Validation and Effectiveness.
Eye tracking
Neuroradiology
Training effect
Validity
Work load
Journal
Clinical neuroradiology
ISSN: 1869-1447
Titre abrégé: Clin Neuroradiol
Pays: Germany
ID NLM: 101526693
Informations de publication
Date de publication:
Jun 2021
Jun 2021
Historique:
received:
23
10
2019
accepted:
25
03
2020
pubmed:
19
4
2020
medline:
9
11
2021
entrez:
19
4
2020
Statut:
ppublish
Résumé
Simulators are increasingly used in the training of endovascular procedures; however, for the use of the Mentice vascular interventional system trainer (VIST) simulator in neuroradiology, the validity of the method has not yet been proven. The study was carried out to test the construct validity of such a simulator by demonstrating differences between beginner and expert neurointerventionalists and to evaluate whether a training effect can be demonstrated in repeated cases for different levels of experience. In this study 4 experts and 6 beginners performed 10 diagnostic angiographies on the VIST simulator (Mentice AB, Gothenburg, Sweden). Of the cases four were non-recurring, whereas three were repeated once and ten subjects performed all tasks. Additionally, another expert performed only five non-recurring cases. The simulator recorded total time, fluoroscopy time, amount of contrast medium and number of material changes. Furthermore, gaze direction and heart rate were recorded, and subjects completed a questionnaire on workload. Beginners and experts showed significant differences in total duration time, fluoroscopy time and amount of contrast agent (all p < 0.05). Gaze direction, dwell time and heart rate were similar between both groups. Only beginners improved during training with respect to total duration time, fluoroscopy time and amount of contrast agent. If a case was previously known to them, the total duration and fluoroscopy time were significantly shortened (p < 0.001). This study demonstrated both the construct validity of a diagnostic neuroangiography simulator as well as a significant training effect for beginners. Therefore, in particular beginner neurointerventionalists should use such simulation tools more extensively in their initial training.
Identifiants
pubmed: 32303789
doi: 10.1007/s00062-020-00902-5
pii: 10.1007/s00062-020-00902-5
pmc: PMC8211587
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
465-473Références
Berlis A, Morhard D, Weber W. On the basis of the DeGIR/DGNR register nationwide care for acute ischemic stroke patients in 2016 and 2017 using mechanical thrombectomy by radiologists and neuroradiologists. Rofo. 2019;191:613–7.
pubmed: 30947349
Saver JL, Goyal M, Bonafe A, Diener HC, Levy EI, Pereira VM, et al. Stent-retriever thrombectomy after intravenous t‑PA vs. t‑PA alone in stroke. N Engl J Med. 2015;372:2285–95.
pubmed: 25882376
Jovin TG, Chamorro A, Cobo E, de Miquel MA, Molina CA, Rovira A, et al. Thrombectomy within 8 hours after symptom onset in ischemic stroke. N Engl J Med. 2015;372:2296–306.
pubmed: 25882510
Goyal M, Demchuk AM, Menon BK, Eesa M, Rempel JL, Thornton J, et al. Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med. 2015;372:1019–30.
pubmed: 25671798
Berkhemer OA, Fransen PS, Beumer D, van den Berg LA, Lingsma HF, Yoo AJ, et al. A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med. 2015;372:11–20.
pubmed: 25517348
Campbell BC, Mitchell PJ, Kleinig TJ, Dewey HM, Churilov L, Yassi N, et al. Endovascular therapy for ischemic stroke with perfusion-imaging selection. N Engl J Med. 2015;372:1009–18.
pubmed: 25671797
Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med. 2018;378:11–21.
pubmed: 29129157
Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 2018;378:708–18.
pubmed: 29364767
pmcid: 6590673
Schimmel DR, Sweis R, Cohen ER, Davidson C, Wayne DB. Targeting clinical outcomes: Endovascular simulation improves diagnostic coronary angiography skills. Catheter Cardiovasc Interv. 2016;87:383–8.
pubmed: 26198625
Spiotta AM, Rasmussen PA, Masaryk TJ, Benzel EC, Schlenk R. Simulated diagnostic cerebral angiography in neurosurgical training: a pilot program. J Neurointerv Surg. 2013;5:376–81.
pubmed: 22576472
Chui CK, Li Z, Anderson JH, Murphy K, Venbrux A, Ma X, et al. Training and pretreatment planning of interventional neuroradiology procedures—initial clinical validation. Stud Health Technol Inform. 2002;85:96–102.
pubmed: 15458067
Wu X, Pegoraro V, Luboz V, Neumann PF, Bardsley R, Dawson S, et al. New approaches to computer-based interventional neuroradiology training. Stud Health Technol Inform. 2005;111:602–7.
pubmed: 15718806
Nicholson WJ, Cates CU, Patel AD, Niazi K, Palmer S, Helmy T, et al. Face and content validation of virtual reality simulation for carotid angiography: results from the first 100 physicians attending the Emory NeuroAnatomy Carotid Training (ENACT) program. Simul Healthc. 2006;1:147–50.
pubmed: 19088583
Coates PJ, Zealley IA, Chakraverty S. Endovascular simulator is of benefit in the acquisition of basic skills by novice operators. J Vasc Interv Radiol. 2010;21:130–4.
pubmed: 19931470
Aggarwal R, Black SA, Hance JR, Darzi A, Cheshire NJ. Virtual reality simulation training can improve inexperienced surgeons’ endovascular skills. Eur J Vasc Endovasc Surg. 2006;31:588–93.
pubmed: 16387517
Jensen UJ, Jensen J, Olivecrona GK, Ahlberg G, Tornvall P. Technical skills assessment in a coronary angiography simulator for construct validation. Simul Healthc. 2013;8:324–8.
pubmed: 23598862
Lipner RS, Messenger JC, Kangilaski R, Baim DS, Holmes DR Jr, Williams DO, et al. A technical and cognitive skills evaluation of performance in interventional cardiology procedures using medical simulation. Simul Healthc. 2010;5:65–74.
pubmed: 20661006
Berry M, Reznick R, Lystig T, Lonn L. The use of virtual reality for training in carotid artery stenting: a construct validation study. Acta Radiol. 2008;49:801–5.
pubmed: 18608009
Van Herzeele I, Aggarwal R, Choong A, Brightwell R, Vermassen FE, Cheshire NJ. Virtual reality simulation objectively differentiates level of carotid stent experience in experienced interventionalists. J Vasc Surg. 2007;46:855–63.
pubmed: 17980270
Nguyen N, Eagleson R, Boulton M, de Ribaupierre S. Realism, criterion validity, and training capability of simulated diagnostic cerebral angiography. Stud Health Technol Inform. 2014;196:297–303.
pubmed: 24732526
Ahmed K, Keeling AN, Fakhry M, Ashrafian H, Aggarwal R, Naughton PA, Darzi A, Cheshire N, Athanasiou T, Hamady M. Role of virtual reality simulation in teaching and assessing technical skills in endovascular intervention. J Vasc Interv Radiol. 2010;21:55–66.
pubmed: 20123191
Liebig T, Holtmannspötter M, Crossley R, Lindkvist J, Henn P, Lönn L, et al. Metric-based virtual reality simulation: a paradigm shift in training for mechanical thrombectomy in acute stroke. Stroke. 2018;49:e239–42.
pubmed: 29866758
Spiotta AM, Kellogg RT, Vargas J, Chaudry MI, Turk AS, Turner RD. Diagnostic angiography skill acquisition with a secondary curve catheter: phase 2 of a curriculum-based endovascular simulation program. J Neurointerv Surg. 2015;7:777–80.
pubmed: 25186445
Zaika O, Nguyen N, Boulton M, Eagleson R, de Ribaupierre S. Evaluation of user performance in simulation-based diagnostic cerebral angiography training. Stud Health Technol Inform. 2016;220:465–8.
pubmed: 27046624
Kaufmann T, Sütterlin S, Schulz SM, Vögele C. ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis. Behav Res Methods. 2011;43:1161–70.
pubmed: 21573720
Hart SG, Staveland LE. Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv Psychol. 1988;52:139–83.
Carroll JD, Messenger JC. Medical simulation: the new tool for training and skill assessment. Perspect Biol Med. 2008;51:47–60.
pubmed: 18192765
Jensen UJ, Lagerquist B, Jensen J, Tornvall P. The use of fluoroscopy to construct learning curves for coronary angiography. Catheter Cardiovasc Interv. 2012;80:564–9.
pubmed: 21805613
Marshall SP. The Index of Cognitive Activity: measuring cognitive workload. Proceedings of the IEEE 7th Conference on Human Factors and Power Plants, Scottsdale, AZ, USA, 2002, pp. 7–7.
Richstone L, Schwartz MJ, Seideman C, Cadeddu J, Marshall S, Kavoussi LR. Eye metrics as an objective assessment of surgical skill. Ann Surg. 2010;252:177–82.
pubmed: 20562602
Currie J, Bond RR, McCullagh P, Black P, Finlay DD, Gallagher S, et al. Wearable technology-based metrics for predicting operator performance during cardiac catheterisation. Int J Comput Assist Radiol Surg. 2019;14:645–57.
pubmed: 30730031
pmcid: 6420895
Martin J, Schneider F, Kowalewskij A, Jordan D, Hapfelmeier A, Kochs EF, et al. Linear and non-linear heart rate metrics for the assessment of anaesthetists’ workload during general anaesthesia. Br J Anaesth. 2016;117:767–74.
pubmed: 27956675
Weinger MB, Reddy SB, Slagle JM. Multiple measures of anesthesia workload during teaching and nonteaching cases. Anesth Analg. 2004;98:1419–25.
pubmed: 15105224
Schneider F, Martin J, Hapfelmeier A, Jordan D, Schneider G, Schulz CM. The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians. PLoS One. 2017;12:e188635.
pubmed: 29190808
pmcid: 5708782
Schneider F, Martin J, Schneider G, Schulz CM. The impact of the patient’s initial NACA score on subjective and physiological indicators of workload during pre-hospital emergency care. Plos One. 2018;13:e202215.
pubmed: 30092090
pmcid: 6084954
Mazur LM, Mosaly PR, Jackson M, Chang SX, Burkhardt KD, Adams RD, et al. Quantitative assessment of workload and stressors in clinical radiation oncology. Int J Radiat Oncol Biol Phys. 2012;83:e571–6.
pubmed: 22503527
Mansikka H, Virtanen K, Harris D. Comparison of NASATLX scale, modified cooper-harper scale and mean inter-beat interval as measures of pilot mental workload during simulated flight tasks. Ergonomics. 2019;62:246–54.
pubmed: 29708054
Hart SG. Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2006;50:904–8.
Dulan G, Rege RV, Hogg DC, Gilberg-Fisher KM, Arain NA, Tesfay ST, Scott DJ. Proficiency-based training for robotic surgery: construct validity, workload, and expert levels for nin inanimate exercises. Surg Endosc. 2012;26:1516–21.
pubmed: 22350226
Glaiberman CB, Jacobs B, Street M, Duncan JR, Scerbo MW, Pilgrim TK. Simulation in training: one-year experience using an efficiency index to assess interventional radiology fellow training status. J Vasc Interv Radiol. 2008;19:1366e71.
Kreiser K, Gehling K, Zimmer C. Simulation in angiography—experiences from 5 years teaching, training, and research. Rofo. 2019;191:547–52.
pubmed: 30754054
Dieckmann P, Wehner T. Über Grundsätze zur Gestaltung von Simulatorsettings für Forschung und Lehre. Harburger Beiträge zur Psychologie und Soziologie der Arbeit, Vol. 31. 2002.