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

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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Detalles Bibliográficos
Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: eLife Sciences Publications Ltd 2021
Materias:
topological networks analysis
spinal cord injury
blood pressure
machine learning
surgery
Medicine
R
Science
Q
Biology (General)
QH301-705.5
Acceso en línea:https://doaj.org/article/deaeee6d6a7c4743a5ba3373d4ca53da
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Sumario: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).

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