Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury
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...
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eLife Sciences Publications Ltd
2021
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oai:doaj.org-article:deaeee6d6a7c4743a5ba3373d4ca53da2021-12-03T17:17:15ZTopological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury10.7554/eLife.680152050-084Xe68015https://doaj.org/article/deaeee6d6a7c4743a5ba3373d4ca53da2021-11-01T00:00:00Zhttps://elifesciences.org/articles/68015https://doaj.org/toc/2050-084XBackground: 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 (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery. Conclusions: 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 (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).Abel Torres-EspínJenny HaefeliReza EhsanianDolores TorresCarlos A AlmeidaJ Russell HuieAustin ChouDmitriy MorozovNicole SandersonBenjamin DirlikovCatherine G SuenJessica L NielsonNikos KyritsisDebra D HemmerleJason F TalbottGeoffrey T ManleySanjay S DhallWilliam D WhetstoneJacqueline C BresnahanMichael S BeattieStephen L McKennaJonathan Z PanAdam R FergusonThe TRACK-SCI InvestigatorseLife Sciences Publications Ltdarticletopological networks analysisspinal cord injuryblood pressuremachine learningsurgeryMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021) |
| institution |
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DOAJ |
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| topic |
topological networks analysis spinal cord injury blood pressure machine learning surgery Medicine R Science Q Biology (General) QH301-705.5 |
| spellingShingle |
topological networks analysis spinal cord injury blood pressure machine learning surgery Medicine R Science Q Biology (General) QH301-705.5 Abel Torres-Espín Jenny Haefeli Reza Ehsanian Dolores Torres Carlos A Almeida J Russell Huie Austin Chou Dmitriy Morozov Nicole Sanderson Benjamin Dirlikov Catherine G Suen Jessica L Nielson Nikos Kyritsis Debra D Hemmerle Jason F Talbott Geoffrey T Manley Sanjay S Dhall William D Whetstone Jacqueline C Bresnahan Michael S Beattie Stephen L McKenna Jonathan Z Pan Adam R Ferguson The TRACK-SCI Investigators Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury |
| description |
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 (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery.
Conclusions: 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 (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM). |
| format |
article |
| author |
Abel Torres-Espín Jenny Haefeli Reza Ehsanian Dolores Torres Carlos A Almeida J Russell Huie Austin Chou Dmitriy Morozov Nicole Sanderson Benjamin Dirlikov Catherine G Suen Jessica L Nielson Nikos Kyritsis Debra D Hemmerle Jason F Talbott Geoffrey T Manley Sanjay S Dhall William D Whetstone Jacqueline C Bresnahan Michael S Beattie Stephen L McKenna Jonathan Z Pan Adam R Ferguson The TRACK-SCI Investigators |
| author_facet |
Abel Torres-Espín Jenny Haefeli Reza Ehsanian Dolores Torres Carlos A Almeida J Russell Huie Austin Chou Dmitriy Morozov Nicole Sanderson Benjamin Dirlikov Catherine G Suen Jessica L Nielson Nikos Kyritsis Debra D Hemmerle Jason F Talbott Geoffrey T Manley Sanjay S Dhall William D Whetstone Jacqueline C Bresnahan Michael S Beattie Stephen L McKenna Jonathan Z Pan Adam R Ferguson The TRACK-SCI Investigators |
| author_sort |
Abel Torres-Espín |
| title |
Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury |
| title_short |
Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury |
| title_full |
Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury |
| title_fullStr |
Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury |
| title_full_unstemmed |
Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury |
| title_sort |
topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury |
| publisher |
eLife Sciences Publications Ltd |
| publishDate |
2021 |
| url |
https://doaj.org/article/deaeee6d6a7c4743a5ba3373d4ca53da |
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