{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Torres-Espin A"],"funding":["National Institute of Neurological Disorders and Stroke","Department of Energy","Foundation for Anesthesia Education and Research","RRD VA","NIMH NIH HHS","Wings for Life","NINDS NIH HHS","U.S. Department of Veterans Affairs","Department of Defense","Craig H. Neilsen Foundation"],"pagination":["e68015"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8639149"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension"],"journal":["eLife"],"pubmed_title":["Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury."],"pmcid":["PMC8639149"],"funding_grant_id":["SC190233","R01 NS122888","R01NS088475","U24 NS122732","U24NS122732","R01NS122888","R01 NS088475","I01RX002787","A123320","R01 MH116156","I01 RX002787","I01 RX002245","DE-AC02-05CH11231","UH3NS106899","SC150198","UH3 NS106899","1I01RX002245"],"pubmed_authors":["Manley GT","TRACK-SCI Investigators","Ehsanian R","Chou A","Hemmerle DD","Kyritsis N","de Almeida CA","Torres-Espin A","Torres D","Doung-Fernandez X","Pan JZ","Singh V","Huie JR","Dhall SS","Burke JF","Weinstein P","McKenna SL","Haefeli J","Nielson JL","Moncivais S","Pascual LU","Whetstone WD","Omondi C","Almeida CA","Bresnahan JC","Beattie MS","Sanderson N","Suen CG","Morozov D","Dirlikov B","Thomas LH","Talbott JF","Ferguson AR","DiGiorgio AM"],"additional_accession":[]},"is_claimable":false,"name":"Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury.","description":"<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Nov","modification":"2026-05-08T19:09:00.453Z","creation":"2022-02-11T14:18:39.106Z"},"accession":"S-EPMC8639149","cross_references":{"pubmed":["34783309"],"doi":["10.7554/eLife.68015"]}}