Log in

Automated Pupillometry for Assessment of Treatment Success in Nonconvulsive Status Epilepticus

  • Original work
  • Published:
Neurocritical Care Aims and scope Submit manuscript

An Invited Commentary to this article was published on 30 July 2021

Abstract

Background

Altered pupillary function may reflect nonconvulsive status epilepticus (NCSE). Neurological pupil index (NPi) assessed by automated pupillometry is a surrogate marker of global pupillary function. We aimed to assess NPi changes in relation to NCSE treatment response.

Methods

In this prospective observational study, serial automated pupillometry was performed in 68 NCSE episodes. In accordance with local standards, patients were treated with clonazepam (1–2 mg), levetiracetam (40 mg/kg), and lacosamide (5 mg/kg) in a stepwise approach under continuous electroencephalography monitoring until NCSE was terminated. Patients with refractory NCSE received individualized regimens. NPi was assessed bilaterally before and after each treatment step. For statistical analysis, the lower NPi of both sides (minNPi) was used. Nonparametric testing for matched samples and Cohen’s d to estimate effect size were performed. Principal component analysis was applied to assess the contribution of baseline minNPi, age, sex, and NCSE duration to treatment outcome.

Results

In 97.1% of 68 episodes, NCSE could be terminated; in 16.2%, NCSE was refractory. In 85.3% of episodes, an abnormal baseline minNPi ≤ 4.0 was obtained. After NCSE termination, minNPi increased significantly (p < 0.001). Cohen’s d showed a strong effect size of 1.24 (95% confidence interval 0.88–1.61). Baseline minNPi was higher in clonazepam nonresponders vs. responders (p = 0.008), minNPi increased in responders (p < 0.001) but not in nonresponders. NCSE refractivity was associated with normal baseline minNPi (principal component analysis, component 1, 32.6% of variance, r = 0.78), male sex, and longer NCSE duration (component 2, 27.1% of variance, r = 0.62 and r = 0.78, respectively).

Conclusions

Automated pupillometry may be a helpful noninvasive neuromonitoring tool for the assessment of patients with NCSE and response to treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Hantus S. Monitoring for seizures in the intensive care unit. Handb Clin Neurol. 2019;161:103–7.

    Article  Google Scholar 

  2. Meierkord H, Holtkamp M. Non-convulsive status epilepticus in adults: clinical forms and treatment. Lancet Neurol. 2007;6(4):329–39.

    Article  Google Scholar 

  3. Rohracher A, et al. Status epilepticus in the elderly-a retrospective study on 120 patients. Epilepsy Res. 2016;127:317–23.

    Article  Google Scholar 

  4. Kellinghaus C, et al. Sustained effort network for treatment of status epilepticus (SENSE)—a multicenter prospective observational registry. Epilepsy Behav. 2019;101(Part B):553.

    Google Scholar 

  5. Sadek AR, et al. Seizure-induced miosis. Epilepsia. 2011;52(12):e199-203.

    Article  Google Scholar 

  6. Shirozu K, et al. The relationship between seizure in electroconvulsive therapy and pupillary response using an automated pupilometer. J Anesth. 2018;32(6):866–71.

    Article  Google Scholar 

  7. Shirozu K, Murayama K, Yamaura K. Pupillary response as assessment of effective seizure induction by electroconvulsive therapy. J Vis Exp. 2019(146).

  8. Miller NR, Walsh FB, Hoyt WF. Walsh and Hoyt’s clinical neuro-ophthalmology. Baltimore: Lippincott Williams & Wilkins; 2005.

    Google Scholar 

  9. Fernández-Torre JL, et al. Pupillary hippus as clinical manifestation of refractory autonomic nonconvulsive status epilepticus: pathophysiological implications. Seizure. 2018;63:102–4.

    Article  Google Scholar 

  10. Schnell D, et al. Pupillary hippus in nonconvulsive status epilepticus. Epileptic Disord. 2012;14(3):310–2.

    Article  Google Scholar 

  11. Turnbull PR, et al. Origins of pupillary hippus in the autonomic nervous system. Invest Ophthalmol Vis Sci. 2017;58(1):197–203.

    Article  Google Scholar 

  12. Devinsky O. Effects of seizures on autonomic and cardiovascular function. Epilepsy Curr. 2004;4(2):43–6.

    Article  Google Scholar 

  13. Ong C, Hutch M, Smirnakis S (2018) The effect of ambient light conditions on quantitative pupillometry. Neurocrit Care

  14. Olson DM, et al. Interrater reliability of pupillary assessments. Neurocrit Care. 2016;24(2):251–7.

    Article  Google Scholar 

  15. Olson DM, Fishel M. The use of automated pupillometry in critical care. Crit Care Nurs Clin North Am. 2016;28(1):101–7.

    Article  Google Scholar 

  16. Chen JW, et al. Infrared pupillometry, the neurological pupil index and unilateral pupillary dilation after traumatic brain injury: implications for treatment paradigms. Springerplus. 2014;3:548.

    Article  Google Scholar 

  17. Jahns FP, et al. Quantitative pupillometry for the monitoring of intracranial hypertension in patients with severe traumatic brain injury. Crit Care. 2019;23(1):155.

    Article  Google Scholar 

  18. Stevens AR, et al. Optical pupillometry in traumatic brain injury: neurological pupil index and its relationship with intracranial pressure through significant event analysis. Brain Inj. 2019;33(8):1032–8.

    Article  CAS  Google Scholar 

  19. Aoun SG, et al. Detection of delayed cerebral ischemia using objective pupillometry in patients with aneurysmal subarachnoid hemorrhage. J Neurosurg. 2019;132(1):27–32.

    Article  Google Scholar 

  20. Kim TJ, et al. Neurological pupil index as an indicator of neurological worsening in large hemispheric strokes. Neurocrit Care. 2020;33(2):575–81.

    Article  Google Scholar 

  21. Solari D, et al. Early prediction of coma recovery after cardiac arrest with blinded pupillometry. Ann Neurol. 2017;81(6):804–10.

    Article  Google Scholar 

  22. Obling L, et al. Prognostic value of automated pupillometry: an unselected cohort from a cardiac intensive care unit. Eur Heart J Acute Cardiovasc Care. 2020;9(7):779–87.

    Article  Google Scholar 

  23. Oddo M, et al. Quantitative versus standard pupillary light reflex for early prognostication in comatose cardiac arrest patients: an international prospective multicenter double-blinded study. Intensive Care Med. 2018;44(12):2102–11.

    Article  Google Scholar 

  24. Soeken TA, et al. Quantitative pupillometry for detection of intracranial pressure changes during head-down tilt. Aerosp Med Hum Perform. 2018;89(8):717–23.

    Article  Google Scholar 

  25. Yang E, et al. Infrared pupillometry helps to detect and predict delirium in the post-anesthesia care unit. J Clin Monit Comput. 2018;32(2):359–68.

    Article  Google Scholar 

  26. Godau J, et al. Quantitative infrared pupillometry in nonconvulsive status epilepticus. Neurocrit Care. 2020. https://doi.org/10.1007/s12028-020-01149-1

    Article  PubMed  Google Scholar 

  27. Leitinger M, et al. Salzburg consensus criteria for non-convulsive status epilepticus–approach to clinical application. Epilepsy Behav. 2015;49:158–63.

    Article  CAS  Google Scholar 

  28. Shorvon S, Ferlisi M. The treatment of super-refractory status epilepticus: a critical review of available therapies and a clinical treatment protocol. Brain. 2011;134(Pt 10):2802–18.

    Article  Google Scholar 

  29. Shorvon S, et al. The drug treatment of status epilepticus in Europe: consensus document from a workshop at the first London colloquium on status epilepticus. Epilepsia. 2008;49(7):1277–85.

    PubMed  Google Scholar 

  30. Kapur J, et al. Randomized trial of three anticonvulsant medications for status epilepticus. N Engl J Med. 2019;381(22):2103–13.

    Article  CAS  Google Scholar 

  31. Holtkamp M, et al. Predictors and prognosis of refractory status epilepticus treated in a neurological intensive care unit. J Neurol Neurosurg Psychiatry. 2005;76(4):534–9.

    Article  CAS  Google Scholar 

  32. Chen JW, Naylor DE, Wasterlain CG. Advances in the pathophysiology of status epilepticus. Acta Neurol Scand Suppl. 2007;186:7–15.

    Article  CAS  Google Scholar 

  33. Niquet J, et al. Early polytherapy for benzodiazepine-refractory status epilepticus. Epilepsy Behav. 2019;101(Pt B):106367.

    Article  Google Scholar 

  34. Wasterlain CG, Chen JW. Mechanistic and pharmacologic aspects of status epilepticus and its treatment with new antiepileptic drugs. Epilepsia. 2008;49(Suppl 9):63–73.

    Article  CAS  Google Scholar 

  35. Wasterlain CG, et al. Trafficking of NMDA receptors during status epilepticus: therapeutic implications. Epilepsia. 2013;54(Suppl 6):78–80.

    Article  CAS  Google Scholar 

  36. Büki A, et al. Impaired pupillary control in “schizophrenia-like” WISKET rats. Auton Neurosci. 2018;213:34–42.

    Article  Google Scholar 

  37. Eilers H, Larson MD. The effect of ketamine and nitrous oxide on the human pupillary light reflex during general anesthesia. Auton Neurosci. 2010;152(1–2):108–14.

    Article  CAS  Google Scholar 

  38. Kim J, et al. Quantitative assessment of pupillary light reflex in normal and anesthetized dogs: a preliminary study. J Vet Med Sci. 2015;77(4):475–8.

    Article  CAS  Google Scholar 

  39. Osman M, et al. Correlation of objective pupillometry to midline shift in acute stroke patients. J Stroke Cerebrovasc Dis. 2019;28(7):1902–10.

    Article  Google Scholar 

  40. McNett M, et al. Correlations between hourly pupillometer readings and intracranial pressure values. J Neurosci Nurs. 2017;49(4):229–34.

    Article  Google Scholar 

  41. Miroz JP, et al. Neurological pupil index for early prognostication after venoarterial extracorporeal membrane oxygenation. Chest. 2020;157(5):1167–74.

    Article  Google Scholar 

Download references

Funding

This study received no funding.

Author information

Authors and Affiliations

Authors

Contributions

JG is corresponding author and responsible for manuscript submission. JG and JB contributed to conceptualization, review, editing, figure and table creation. JR, KB, GN and SK contributed to review and editing of text, tables and figures. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Jana Godau.

Ethics declarations

Conflicts of interest

JR reports personal fees from Eisai GmbH, outside the submitted work. JB reports personal fees from Medtronic, personal fees from Zoll, personal fees from Böhringer Ingelheim, grants from German Neurocritical Care Society (DGNI), grants from Patient-Centered Outcomes Research Institute (PCORI), outside the submitted work. All remaining authors have no conflicts to disclose.

Ethical approval/informed consent

The study was performed in adherence to ethical guidelines. Ethical approval including a formal consent waiver for observational pupillometry was granted by the Hesse Medical Association Ethical Board (FF 20/2018).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is related to the Invited Commentary available at https://springer.longhoe.net/article/10.1007/s12028-021-01274-5

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Godau, J., Bharad, K., Rösche, J. et al. Automated Pupillometry for Assessment of Treatment Success in Nonconvulsive Status Epilepticus. Neurocrit Care 36, 148–156 (2022). https://doi.org/10.1007/s12028-021-01273-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12028-021-01273-6

Keywords

Navigation