Intraocular pressure increases after complex simulated surgical procedures in residents: an experimental study
Surgeons’ overload is one of the main causes of medical errors that might compromise patient safety. Due to the drawbacks of current options to monitor surgeons’ load, new, sensitive, and objective indices of task (over)load need to be considered and tested. In non-health-care scenarios, intraocular pressure (IOP) has been proved to be an unbiased physiological index, sensitive to task complexity (one of the main variables related to overload), and time on task. In the present study, we assessed the effects of demanding and complex simulated surgical procedures on surgical and medical residents’ IOP.
Thirty-four surgical and medical residents and healthcare professionals took part in this study (the experimental group, N = 17, and the control group, N = 17, were matched for sex and age). The experimental group performed two simulated bronchoscopy procedures that differ in their levels of complexity. The control group mimicked the same hand-eye movements and posture of the experimental group to help control for the potential effects of time on task and re-measurement on IOP. We measured IOP before and after each procedure, surgical performance during procedures, and perceived task complexity.
IOP increased as consequence of performing the most complex procedure only in the experimental group. Consistently, residents performed worse and reported higher perceived task complexity for the more complex procedure.
Our data show, for the first time, that IOP is sensitive to residents’ task load, and it could be used as a new index to easily and rapidly assess task (over)load in healthcare scenarios. An arousal-based explanation is given to describe IOP variations due to task complexity.
KeywordsCognitive load Mental workload Patient safety Neuroergonomics Ocular biomarkers
Research by LLDS was supported by the BBVA Foundation, Madrid, Spain (Grant No. 2015-2) and is currently supported by the Ramón y Cajal fellowship program (RYC-2015-17483). We thank IAVANTE staff (Andalusian Public Foundation for Progress and Health) for their help during data collection. We thank Dr. G. A. Koulieris (Inria, Université Côte d’Azur, France) for proofreading the paper.
This study was funded by the Campus of International Excellence (BioTic Granada) Research Programme (Research Project V7-2015 to CDP). The funding source had no role in the design or conduct of this study.
Compliance with ethical standards
Drs. Vera, Diaz-Piedra, Jiménez, Sanchez-Carrion, and Di Stasi have no conflicts of interest or financial ties to disclose.
- 8.Ruiz-Rabelo JF, Navarro-Rodriguez E, Di-Stasi LL, Diaz-Jimenez N, Cabrera-Bermon J, Diaz-Iglesias C, Gomez-Alvarez M, Briceño-Delgado J (2015) Validation of the NASA-TLX score in ongoing assessment of mental workload during a laparoscopic learning curve in bariatric surgery. Obes Surg 25:2451–2456. https://doi.org/10.1007/s11695-015-1922-1 CrossRefGoogle Scholar
- 27.Morales JM, Díaz-Piedra C, Rieiro H, Roca-González J, Romero S, Catena A, Fuentes LJ, Di Stasi LL (2017) Monitoring driver fatigue using a single-channel electroencephalographic device: a validation study by gaze-based, driving performance, and subjective data. Accid Anal Prev 109:62–69. https://doi.org/10.1016/j.aap.2017.09.025 CrossRefGoogle Scholar
- 33.Apfelbaum JL, Hagberg CA, Caplan RA, Blitt CD, Connis RT, Nickinovich DG, Benumof JL, Berry FA, Bode RH, Cheney FW (2013) Practice guidelines for management of the difficult airwayan updated report by the American Society of Anesthesiologists Task Force on management of the difficult airway. J Am Soc Anesthesiol 118:251–270CrossRefGoogle Scholar
- 34.Manski CF (2013) Diagnostic testing and treatment under ambiguity: using decision analysis to inform clinical practice. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.1221405110/-/DCSupplemental Google Scholar
- 38.Hoddes E, Zarcone V, Dement W (1972) Development and use of Stanford Sleepiness scale (SSS). Psychophysiology 9:150Google Scholar
- 39.Hart SG, Staveland LE (1988) Development of NASA-TLX (task load index): Results of empirical and theorical research. In: Hum. Ment. Workload. pp 139–183Google Scholar
- 50.Jackson ML, Kennedy GA, Clarke C, Gullo M, Swann P, Downey LA, Hayley AC, Pierce RJ, Howard ME (2016) The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness. Accid Anal Prev 87:127–133. https://doi.org/10.1016/j.aap.2015.11.033 CrossRefGoogle Scholar
- 55.Stefanidis D, Korndorffer JR, Black FW, Dunne JB, Sierra R, Touchard CL, Rice DA, Markert RJ, Kastl PR, Scott DJ (2006) Psychomotor testing predicts rate of skill acquisition for proficiency-based laparoscopic skills training. Surgery 140:252–262. https://doi.org/10.1016/j.surg.2006.04.002 CrossRefGoogle Scholar
- 57.Yamaguchi S, Konishi K, Yasunaga T, Yoshida D, Kinjo N, Kobayashi K, Ieiri S, Okazaki K, Nakashima H, Tanoue K, Maehara Y, Hashizume M (2007) Construct validity for eye-hand coordination skill on a virtual reality laparoscopic surgical simulator. Surg Endosc Other Interv Tech 21:2253–2257. https://doi.org/10.1007/s00464-007-9362-1 CrossRefGoogle Scholar