Skip to main content

Advertisement

SpringerLink
Log in

The Neurological Examination Improves Cranial Accelerometry Large Vessel Occlusion Prediction Accuracy

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

Abstract

Background/Objective

We combined cranial accelerometry, a device-based approach to large vessel occlusion (LVO) prediction, with neurological examination findings to determine if this improves diagnostic accuracy compared to either alone.

Methods

Cranial accelerometry recordings and NIHSS scores were obtained during stroke codes and thrombectomy transfers at an academic medical center using convenience sampling. The reference standard was discharge diagnosis of LVO stroke. We compared accuracy statistics between machine learning models trained using cranial accelerometry alone, with asymmetric arm weakness added, with NIHSS scores added, and retrospective examination only LVO prediction scales. An exploratory analysis required asymmetric arm weakness prior to model training or scale testing.

Results

Of 68 patients, there were 23 LVO strokes. Cranial accelerometry was 65% sensitive (95% CI 43–84%) and 87% specific (95% CI 73–95%). Adding asymmetric arm weakness increased specificity to 91% (95% CI 79–98%). Adding asymmetric arm weakness and the NIHSS increased sensitivity to 74% (95% CI 52–90%) and decreased specificity to 89% (95% CI 76–96%). LVO prediction scales had wide sensitivity and specificity ranges. The exploratory analysis improved sensitivity to 91% (95% CI 72–99%) and specificity to 93% (95% CI 92–99%) with only three false positives and two false negatives.

Conclusions

Cranial accelerometry models are improved by various additions of asymmetric arm weakness and the NIHSS. An exploratory analysis requiring asymmetric arm weakness prior to cranial accelerometry model training minimized false positives and negatives.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Saver JL, Goyal M, van der Lugt A, et al. Time to treatment with endovascular thrombectomy and outcomes from ischemic stroke: a meta-analysis. JAMA. 2016;316(12):1279–88.

    Article  Google Scholar 

  2. Goyal M, Menon BK, van Zwam WH, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet. 2016;387(10029):1723–31.

    Article  Google Scholar 

  3. Meretoja A, Keshtkaran M, Tatlisumak T, Donnan GA, Churilov L. Endovascular therapy for ischemic stroke: Save a minute-save a week. Neurology. 2017;88(22):2123–7.

    Article  Google Scholar 

  4. Keenan KJ, Kircher C, McMullan JT. Prehospital prediction of large vessel occlusion in suspected stroke patients. Curr Atheroscler Rep. 2018;20(7):x.

    Article  Google Scholar 

  5. Smith E, Kent D, Bulsara K, et al. Accuracy of prediction instruments for diagnosing large vessel occlusion in individuals with suspected stroke: a systematic review for the 2018 guidelines for the early management of patients with acute ischemic stroke. Stroke. 2018;49(3):e111–22.

    PubMed  Google Scholar 

  6. Carrera D, Gorchs M, Querol M, et al. Revalidation of the RACE scale after its regional implementation in Catalonia: a triage tool for large vessel occlusion. J Neurointerv Surg. 2019;11(8):751–6.

    Article  Google Scholar 

  7. Smith WS, Keenan KJ, Lovoi PA. A unique signature of cardiac-induced cranial forces during acute large vessel stroke and development of a predictive model. Neurocrit Care. 2019. https://doi.org/10.1007/s12028-019-00845-x.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11):e012799.

    Article  Google Scholar 

  9. Richards CT, Huebinger R, Tataris KL, et al. Cincinnati prehospital stroke scale can identify large vessel occlusion stroke. Prehospital Emerg Care. 2018;22(3):312–8.

    Article  Google Scholar 

  10. McMullan JT, Katz B, Broderick J, Schmit P, Sucharew H, Adeoye O. Prospective prehospital evaluation of the Cincinnati stroke triage assessment tool. Prehospital Emerg Care. 2017;21(4):481–8.

    Article  Google Scholar 

  11. Gropen TI, Boehme A, Martin-Schild S, et al. Derivation and validation of the emergency medical stroke assessment and comparison of large vessel occlusion scales. J Stroke Cerebrovasc Dis. 2017;7(3):806–15.

    Article  Google Scholar 

  12. Lima FO, Silva GS, Furie KL, et al. Field assessment stroke triage for emergency destination: a simple and accurate prehospital scale to detect large vessel occlusion strokes. Stroke. 2016;47(8):1997–2002.

    Article  Google Scholar 

  13. Scheitz JF, Abdul-Rahim AH, MacIsaac RL, et al. Clinical selection strategies to identify ischemic stroke patients with large anterior vessel occlusion: results from SITS-ISTR (Safe Implementation of Thrombolysis in Stroke International Stroke Thrombolysis Registry). Stroke. 2017;48(2):290–7.

    Article  Google Scholar 

  14. Mulkerin W, Spokoyny I, Corry M, et al. Standardized prehospital education and modified national institutes of health stroke scale training for paramedics: a pilot study. Acad Emerg Med. 2018;25:S259.

    Google Scholar 

  15. Hastrup S, Damgaard D, Johnsen SP, Andersen G. Prehospital acute stroke severity scale to predict large artery occlusion: design and comparison with other scales. Stroke; a journal of cerebral circulation. 2016;47(7):1772–6.

    Article  Google Scholar 

  16. Keenan KJ, Smith WS. The Speech Arm Vision Eyes (SAVE) scale predicts large vessel occlusion stroke as well as more complicated scales. J Neurointerv Surg. 2018;11(7):659–63.

    Article  Google Scholar 

  17. Teleb MS, Hage AV, Carter J, Jayaraman MV, McTaggart RA. Stroke vision, aphasia, neglect (VAN) assessment-a novel emergent large vessel occlusion screening tool: pilot study and comparison with current clinical severity indices. J Neurointerv Surg. 2016;9(2):122–6.

    Article  Google Scholar 

  18. Singer OC, Dvorak F, du Mesnil de Rochemont R, Lanfermann H, Sitzer M, Neumann-Haefelin T. A simple 3-item stroke scale: comparison with the National Institutes of Health Stroke Scale and prediction of middle cerebral artery occlusion. Stroke. 2005;36(4):773–6.

    Article  Google Scholar 

  19. Meyer BC, Lyden PD. The modified National Institutes of Health Stroke Scale: its time has come. Int J Stroke. 2009;4(4):267–73.

    Article  CAS  Google Scholar 

  20. Keenan KJ, Lovoi PA, Smith WS. Abstract TMP68: simple neurological exam combined with headpulse accurately predicts large vessel occlusion stroke. Stroke. 2019;50(Suppl 1):ATMP68–ATMP68.

    Google Scholar 

  21. Kellner CP, Sauvageau E, Snyder KV, et al. The VITAL study and overall pooled analysis with the VIPS non-invasive stroke detection device. J NeuroInterv Surg. 2018;10(11):1079–84.

    Article  Google Scholar 

  22. Neural Analytics, Inc. Announces the First Site Initiation and First Subject Enrollment into the CODEX Study, Utilizing Autonomous Robotic Transcranial Doppler Technology [Internet]. 2019 [cited 2019 Aug 22]; https://www.businesswire.com/news/home/20190318005101/en/Neural-Analytics-Announces-Site-Initiation-Subject-Enrollment

  23. EEG Controlled Triage in the Ambulance for Acute Ischemic Stroke - Full Text View - ClinicalTrials.gov [Internet]. [cited 2019 Aug 22]; https://clinicaltrials.gov/ct2/show/NCT03699397

  24. Stroke Detector | Pitt Med | University of Pittsburgh [Internet]. [cited 2019 Aug 22]; https://www.pittmed.health.pitt.edu/story/stroke-detector

  25. Pilot Study of Acute Stroke Using the BrainpulseTM [Internet]. https://clinicaltrials.gov/ct2/show/NCT03235271

Download references

Acknowledgements

The authors would like to acknowledge UCSF Clinical Research Coordinators Dominica Randazzo, BS, Tina Rothschild, RN, and Jeany Duncan, CCRP, and Maximilian Vuong, BS for their steadfast effort in obtaining acute recordings, engaging patients in the informed consent process, data entry, study logistics, and IRB management. We would also like to acknowledge UCSF Stroke Coordinator Mark Ciano, RN for his contributions to Figure 1 and our neurointerventional radiology colleagues.

Funding

This study was funded thanks to philanthropic support to the University of California, San Francisco (UCSF) Department of Neurology. Kevin J. Keenan’s research effort was supported by the NIH StrokeNet Fellowship through grants U10NS086494 and U24NS107229.

Author information

Authors and Affiliations

  1. Department of Neurology, University of California, Davis, 4860 Y Street, Suite 3700, Sacramento, CA, 95817, USA

    Kevin J. Keenan

  2. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Kevin J. Keenan, Paul A. Lovoi & Wade S. Smith

Authors
  1. Kevin J. Keenan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul A. Lovoi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wade S. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Kevin J. Keenan, MD contributed to the literature search, study design, study enrollment, data analysis, data interpretation, table and figure design, and manuscript writing. Paul A. Lovoi, PhD contributed to the data analysis, data interpretation, and manuscript writing. Wade S. Smith, MD, PhD contributed to the literature search, study design, study enrollment, data interpretation, table and figure design, and manuscript writing.

Corresponding author

Correspondence to Kevin J. Keenan.

Ethics declarations

Conflict of interest

Dr. Keenan has nothing to disclose. Dr. Lovoi reports he has been issued patents US10307065, US10092195, US10076274, and US08905932 that are relevant to this work. In addition, he Co-founded MindRhythm, Inc. based in part on the technology used in this manuscript. Dr. Smith reports ownership interest in MindRhythm, Inc, hold stock options in Cerebrotech, Inc. This technology has been submitted for patent protection by the UC Regents and Dr. Smith and Dr. Lovoi are co-inventors. UC Regents is Dr. Smith's employer.

Informed Consent

This study adhered to ethical guidelines, had IRB approval, and used informed consent.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (DOCX 989 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keenan, K.J., Lovoi, P.A. & Smith, W.S. The Neurological Examination Improves Cranial Accelerometry Large Vessel Occlusion Prediction Accuracy. Neurocrit Care 35, 103–112 (2021). https://doi.org/10.1007/s12028-020-01144-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12028-020-01144-6

Keywords

Search

Navigation