Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury

  1. Abel Torres Espín
  2. Jenny Haefeli
  3. Reza Ehsanian
  4. Dolores Torres
  5. Carlos A de Almeida
  6. J Russell Huie
  7. Austin Chou
  8. Dmitriy Morozov
  9. Nicole Sanderson
  10. Benjamin Dirlikov
  11. Catherine G Suen
  12. Jessica L Nielson
  13. Nikolaos Kyritsis
  14. Debra D Hemmerle
  15. Jason Talbott
  16. Geoff T Manley
  17. Sanjay S Dhall
  18. William D Whetstone
  19. Jacqueline C Bresnahan
  20. Michael S Beattie
  21. Stephen L McKenna
  22. Jonathan Z Pan  Is a corresponding author
  23. Adam Ferguson  Is a corresponding author
  1. University of California, San Francisco, United States
  2. University of California San Francisco, United States
  3. University of New Mexico School of Medicine, United States
  4. Lawrence Berkley National Lab, United States
  5. Santa Clara Valley Medical Center, United States
  6. University of Minnesota, United States

Abstract

Background:

Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.

Methods:

Intra-operative monitoring records and neurological outcome data were extracted (n=118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.

Results:

Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery.

Conclusion:

We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.

Funding:

NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ARF)(ATE); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Data availability

Source data has been deposited to the Open Data Commons for Spinal Cord Injury (odc-sci.org; RRID:SCR_016673) under the accession number ODC-SCI:245 (doi: 10.34945/F5R59) and ODC-SCI:246 (doi: 10.34945/F5MG68)

The following data sets were generated

Article and author information

Author details

  1. Abel Torres Espín

    Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Jenny Haefeli

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Reza Ehsanian

    Neurosurgery, University of New Mexico School of Medicine, Alburquerque, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Dolores Torres

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Carlos A de Almeida

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. J Russell Huie

    Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Austin Chou

    Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Dmitriy Morozov

    Data analytics and Visualization group, Lawrence Berkley National Lab, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Nicole Sanderson

    Data analytics and Visualization group, Lawrence Berkley National Lab, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Benjamin Dirlikov

    Rehabilitation Research Center, Santa Clara Valley Medical Center, San Jose, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Catherine G Suen

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Jessica L Nielson

    Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Nikolaos Kyritsis

    Neurological Surgery, University of California, San Francisco, San Francsico, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7801-5796
  14. Debra D Hemmerle

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2796-6107
  15. Jason Talbott

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Geoff T Manley

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. Sanjay S Dhall

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  18. William D Whetstone

    Neurological surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  19. Jacqueline C Bresnahan

    Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  20. Michael S Beattie

    Neurological Surgery, University of California San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  21. Stephen L McKenna

    Rehabilitation Research Center, Santa Clara Valley Medical Center, San Jose, United States
    Competing interests
    The authors declare that no competing interests exist.
  22. Jonathan Z Pan

    Neurological surgery, University of California San Francisco, San Francisco, United States
    For correspondence
    jonathan.pan@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
  23. Adam Ferguson

    Neurological Surgery, University of California, San Francisco, San Francisco, United States
    For correspondence
    adam.ferguson@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7102-1608

Funding

National Institute of Neurological Disorders and Stroke (R01NS088475)

  • Adam Ferguson

National Institute of Neurological Disorders and Stroke (UG3NS106899)

  • Adam Ferguson

U.S. Department of Veterans Affairs (1I01RX002245)

  • Adam Ferguson

U.S. Department of Veterans Affairs (I01RX002787)

  • Adam Ferguson

Wings for Life Foundation

  • Abel Torres Espín

Wings for Life Foundation

  • Adam Ferguson

Craig H. Neilsen Foundation

  • Adam Ferguson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Arduino A Mangoni, Flinders Medical Centre, Australia

Ethics

Human subjects: This study constitutes a retrospective data analysis. All data was de-identified before pre-processing and analysis. Protocols for retrospective data extraction were approved by Institutional Research Board (IRB) under protocol numbers 11-07639 and 11-06997.

Version history

  1. Received: March 2, 2021
  2. Accepted: October 23, 2021
  3. Accepted Manuscript published: November 16, 2021 (version 1)
  4. Accepted Manuscript updated: November 17, 2021 (version 2)
  5. Version of Record published: December 2, 2021 (version 3)
  6. Version of Record updated: December 3, 2021 (version 4)

Copyright

© 2021, Torres Espín et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Abel Torres Espín
  2. Jenny Haefeli
  3. Reza Ehsanian
  4. Dolores Torres
  5. Carlos A de Almeida
  6. J Russell Huie
  7. Austin Chou
  8. Dmitriy Morozov
  9. Nicole Sanderson
  10. Benjamin Dirlikov
  11. Catherine G Suen
  12. Jessica L Nielson
  13. Nikolaos Kyritsis
  14. Debra D Hemmerle
  15. Jason Talbott
  16. Geoff T Manley
  17. Sanjay S Dhall
  18. William D Whetstone
  19. Jacqueline C Bresnahan
  20. Michael S Beattie
  21. Stephen L McKenna
  22. Jonathan Z Pan
  23. Adam Ferguson
(2021)
Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury
eLife 10:e68015.
https://doi.org/10.7554/eLife.68015

Share this article

https://doi.org/10.7554/eLife.68015

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