This novel preliminary study sought to capture dynamic changes in heart rate variability (HRV) as a proxy for cognitive workload among perfusionists while operating the cardiopulmonary bypass (CPB) pump during real-life cardiac surgery.
Background
Estimations of operators’ cognitive workload states in naturalistic settings have been derived using noninvasive psychophysiological measures. Effective CPB pump operation by perfusionists is critical in maintaining the patient’s homeostasis during open-heart surgery. Investigation into dynamic cognitive workload fluctuations, and their relationship with performance, is lacking in the literature.
Method
HRV and self-reported cognitive workload were collected from three Board-certified cardiac perfusionists (N = 23 cases). Five HRV components were analyzed in consecutive nonoverlapping 1-min windows from skin incision through sternal closure. Cases were annotated according to predetermined phases: prebypass, three phases during bypass, and postbypass. Values from all 1min time windows within each phase were averaged.
Results
Cognitive workload was at its highest during the time between initiating bypass and clamping the aorta (preclamp phase during bypass), and decreased over the course of the bypass period.
Conclusion
We identified dynamic, temporal fluctuations in HRV among perfusionists during cardiac surgery corresponding to subjective reports of cognitive workload. Not only does cognitive workload differ for perfusionists during bypass compared with pre- and postbypass phases, but differences in HRV were also detected within the three bypass phases.
Application
These preliminary findings suggest the preclamp phase of CPB pump interaction corresponds to higher cognitive workload, which may point to an area warranting further exploration using passive measurement.
AvruninG. S.ChristovS. C.ClarkeL. A.ConboyH. M.OsterweilL. J.ZenatiM. A. (2018). Process driven guidance for complex surgical procedures. AMIA Annual Symposium Proceedings, 2018, 175–184.30815055
2.
BaekH. J.ChoC.-H.ChoJ.WooJ.-M. (2015). Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability. Telemedicine and E-Health, 21, 404–414.doi:10.1089/tmj.2014.010425807067
3.
BeckJ. R.FungK.LopezH.MongeroL. B.ArgenzianoM. (2015). Real-time data acquisition and alerts may reduce reaction time and improve perfusionist performance during cardiopulmonary bypass. Perfusion, 30, 41–44.doi:10.1177/026765911454825725138244
4.
CardS. K.MoranT. P.NewellA. (1983). The psychology of human-computer interaction. Lawrence Erlbaum Associates.
5.
DiasR. D.Ngo-HowardM. C.BoskovskiM. T.ZenatiM. A.YuleS. J. (2018). Systematic review of measurement tools to assess surgeons’ intraoperative cognitive workload. British Journal of Surgery, 105, 491–501.doi:10.1002/bjs.1079529465749
6.
DiasR. D.ZenatiM. A.ConboyH. M.ClarkeL. A.OsterweilL. J.AvruninG. S.YuleS. J. (2019). Dissecting cardiac surgery: A video-based recall protocol to elucidate team cognitive processes in the operating room. Annals of Surgery, 1–8.doi:10.1097/SLA.000000000000348931348036
FrazierS. E.ParkerS. H. (2019). Measurement of physiological responses to acute stress in multiple occupations: A systematic review and implications for front line healthcare providers. Translational Behavioral Medicine, 9, 158–166.doi:10.1093/tbm/iby01929522140
9.
HartS. G.StavelandL. E. (1988). Development of NASA-TLX (task load index): Results of empirical and theoretical research. Advances in psychology, 52, 139–183.
10.
HazlehurstB.McMullenC. K.GormanP. N. (2007). Distributed cognition in the heart room: How situation awareness arises from coordinated communications during cardiac surgery. Journal of Biomedical Informatics, 40, 539–551.doi:10.1016/j.jbi.2007.02.00117368112
11.
HughesA. M.HancockG. M.MarlowS. L.StowersK.SalasE. (2019). Cardiac measures of cognitive workload: A meta-analysis. Human Factors: The Journal of the Human Factors and Ergonomics Society, 61, 393–414.doi:10.1177/001872081983055330822151
12.
KennedyL.LauN.PappadaS.ParkerS. H. (2018). Selecting, evaluating, and translating psychophysiological measures into clinical settings: From lab to practice. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 7, 232–236.doi:10.1177/2327857918071054
13.
KennedyL.ParkerS. H.Henrickson ParkerS. (2019). Biofeedback as a stress management tool: A systematic review. Cognition, Technology & Work, 21, 161–190.doi:10.1007/s10111-018-0487-x
14.
KierasD. E.HelanderM. (1988). Towards a practical GOMS model methodology for user interface design. InHelanderM. (Ed.), Handbook of human-computer interaction (pp.135–157).doi:https://doi.org/10.1016/b978-0-444-47970536-5.50012-9
15.
KimH.-G.CheonE.-J.BaiD.-S.LeeY. H.KooB.-H. (2018). Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15, 235–245.doi:10.30773/pi.2017.08.1729486547
16.
Luque-CasadoA.PeralesJ. C.CárdenasD.SanabriaD. (2016). Heart rate variability and cognitive processing: The autonomic response to task demands. Biological Psychology, 113, 83–90.doi:10.1016/j.biopsycho.2015.11.01326638762
17.
MalikM.BiggerJ. T.CammA. J.KleigerR. E.MallianiA.MossA. J.SchwartzP. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17, 354–381.doi:10.1093/oxfordjournals.eurheartj.a014868
18.
McCratyR.ShafferF. (2015). Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and Medicine, 4, 46–61.doi:10.7453/gahmj.2014.07325694852
19.
McDuffD.GontarekS.PicardR. (2014). Remote measurement of cognitive stress via heart rate variability[Conference session]. Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2957–2960.doi:10.1109/EMBC.2014.694424325570611
20.
MerkleF.KurtovicD.StarckC.PawelkeC.GierigS.FalkV. (2019). Evaluation of attention, perception, and stress levels of clinical cardiovascular perfusionists during cardiac operations: A pilot study. Perfusion, 34, 544–551.doi:10.1177/026765911982856330868941
21.
MunozM. L.van RoonA.RieseH.ThioC.OostenbroekE.WestrikI.de GeusE. J. C.GansevoortR.LefrandtJ.NolteI. M.SniederH. (2015). Validity of (ultra-)short recordings for heart rate variability measurements. Plos One, 10, e0138921.doi:10.1371/journal.pone.013892126414314
22.
MuscaS. C.KamiejskiR.NugierA.MéotA.Er-RafiyA.BrauerM. (2011). Data with hierarchical structure: Impact of intraclass correlation and sample size on type-I error. Frontiers in Psychology, 2, 1–6.doi:10.3389/fpsyg.2011.00074
23.
NussinovitchU.ElishkevitzK. P.KatzK.NussinovitchM.SegevS.VolovitzB.NussinovitchN. (2011). Reliability of ultra-short ECG indices for heart rate variability. Annals of Noninvasive Electrocardiology, 16, 117–122.doi:10.1111/j.1542-474X.2011.00417.x21496161
24.
OlsonJ. R.OlsonG. M. (1990). The growth of cognitive modelling since GOMS. Human-Computer Interaction, 5, 221–265.
25.
OttensJ.TubleS. C.SandersonA. J.KnightJ. L.BakerR. A. (2010). Improving cardiopulmonary bypass: Does continuous blood gas monitoring have a role to play?The Journal of Extra-Corporeal Technology, 42, 191–198.21114221
26.
PeughJ. L. (2010). A practical guide to multilevel modeling. Journal of School Psychology, 48, 85–112.doi:10.1016/j.jsp.2009.09.00220006989
27.
Rajendra AcharyaU.Paul JosephK.KannathalN.LimC. M.SuriJ. S. (2006). Heart rate variability: A review. Medical & Biological Engineering & Computing, 44, 1031–1051.doi:10.1007/s11517-006-0119-017111118
28.
SalahuddinL.ChoJ.JeongM. G.KimD. (2007). Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings[Conference session]. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,4656–4659.doi:https://doi.org/10.1109/IEMBS.2007.4353378
29.
SchaaffK.AdamM. T. P. (2013). Measuring emotional arousal for online applications: Evaluation of ultra-short term heart rate variability measures[Conference session]. Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 362–368.doi:https://doi.org/10.1109/ACII.2013.66
30.
SegerstromS. C.NesL. S. (2007). Heart rate variability reflects self-regulatory strength, effort, and fatigue. Psychological Science, 18, 275–281.doi:10.1111/j.1467-9280.2007.01888.x17444926
31.
SextonK.JohnsonA.GotschA.HusseinA. A.CavuotoL.GuruK. A. (2018). Anticipation, teamwork and cognitive load: Chasing efficiency during robot-assisted surgery. BMJ Quality & Safety, 27, 148–154.doi:10.1136/bmjqs-2017-00670128689193
32.
ShafferF.GinsbergJ. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 1–17.doi:10.3389/fpubh.2017.00258
33.
ShafferF.ShearmanS.MeehanZ. M. (2016). The promise of ultra-short-term (UST) heart rate variability measurements. Biofeedback, 44, 229–233.doi:10.5298/1081-5937-44.3.09
34.
TarvainenM. P.NiskanenJ.-P.LipponenJ. A.Ranta-AhoP. O.KarjalainenP. A. (2014). Kubios HRV–heart rate variability analysis software. Computer Methods and Programs in Biomedicine, 113, 210–220.doi:10.1016/j.cmpb.2013.07.02424054542
35.
ThayerJ. F.HansenA. L.Saus-RoseE.JohnsenB. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health. Annals of Behavioral Medicine, 37, 141–153.doi:10.1007/s12160-009-9101-z19424767
36.
ThayerJ. F.ÅhsF.FredriksonM.SollersJ. J.WagerT. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience & Biobehavioral Reviews, 36, 747–756.doi:10.1016/j.neubiorev.2011.11.00922178086
37.
ThongT.LiK.McNamesJ.AboyM.GoldsteinB. (2003). Accuracy of ultra-short heart rate variability measures. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3, 2424–2427.
38.
WadheraR. K.ParkerS. H.BurkhartH. M.GreasonK. L.NealJ. R.LevenickK. M.WiegmannD. A.SundtT. M. (2010). Is the “sterile cockpit” concept applicable to cardiovascular surgery critical intervals or critical events? The impact of protocol-driven communication during cardiopulmonary bypass. The Journal of Thoracic and Cardiovascular Surgery, 139, 312–319.doi:10.1016/j.jtcvs.2009.10.04820106395
39.
WilsonM. R.PooltonJ. M.MalhotraN.NgoK.BrightE.MastersR. S. W. (2011). Development and validation of a surgical workload measure: The surgery task load index (SURG-TLX). World Journal of Surgery, 35, 1961–1969.doi:10.1007/s00268-011-1141-421597890
40.
YuD.LowndesB.ThielsC.BingenerJ.AbdelrahmanA.LyonsR.HallbeckS. (2016). Quantifying intraoperative workloads across the surgical team roles: Room for better balance?World Journal of Surgery, 40, 1565–1574.doi:10.1007/s00268-016-3449-626952115
41.
YuleS.FlinR.MaranN.RowleyD.YoungsonG.Paterson-BrownS. (2008). Surgeons’ non-technical skills in the operating room: Reliability testing of the NOTSS behavior rating system. World Journal of Surgery, 32, 548–556.doi:10.1007/s00268-007-9320-z18259809
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
