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.

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.

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.

Metrics

  • 1,933
    views
  • 279
    downloads
  • 22
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Computational and Systems Biology
    Nobuhisa Umeki, Yoshiyuki Kabashima, Yasushi Sako
    Research Article

    The RAS-MAPK system plays an important role in regulating various cellular processes, including growth, differentiation, apoptosis, and transformation. Dysregulation of this system has been implicated in genetic diseases and cancers affecting diverse tissues. To better understand the regulation of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements in cells stimulated with EGF: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis allows for model-free assessment of the timing, direction, and strength of the information flow regulating the system response. We detected significant amounts of TE in both directions between SOS and RAF, indicating feedback regulation. Importantly, the amount of TE did not simply follow the input dose or the intensity of the causal reaction, demonstrating the uniqueness of TE. TE analysis proposed regulatory networks containing multiple tracks and feedback loops and revealed temporal switching in the reaction pathway primarily responsible for reaction control. This proposal was confirmed by the effects of an MEK inhibitor on TE. Furthermore, TE analysis identified the functional disorder of a SOS mutation associated with Noonan syndrome, a human genetic disease, of which the pathogenic mechanism has not been precisely known yet. TE assessment holds significant promise as a model-free analysis method of reaction networks in molecular pharmacology and pathology.

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.