biostudies-literatureTorres-Espin ANational Institute of Neurological Disorders and StrokeDepartment of EnergyFoundation for Anesthesia Education and ResearchRRD VANIMH NIH HHSWings for LifeNINDS NIH HHSU.S. Department of Veterans AffairsDepartment of DefenseCraig H. Neilsen Foundatione68015https://www.ebi.ac.uk/biostudies/studies/S-EPMC8639149biostudies-literatureUnknown10<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 hypertensioneLifeTopological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury.PMC8639149SC190233R01 NS122888R01NS088475U24 NS122732U24NS122732R01NS122888R01 NS088475I01RX002787A123320R01 MH116156I01 RX002787I01 RX002245DE-AC02-05CH11231UH3NS106899SC150198UH3 NS1068991I01RX002245Manley GTTRACK-SCI InvestigatorsEhsanian RChou AHemmerle DDKyritsis Nde Almeida CATorres-Espin ATorres DDoung-Fernandez XPan JZSingh VHuie JRDhall SSBurke JFWeinstein PMcKenna SLHaefeli JNielson JLMoncivais SPascual LUWhetstone WDOmondi CAlmeida CABresnahan JCBeattie MSSanderson NSuen CGMorozov DDirlikov BThomas LHTalbott JFFerguson ARDiGiorgio AMfalseTopological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury.<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 hypertension2021-01-01T00:00:00Z2021 Nov2026-05-08T19:09:00.453Z2022-02-11T14:18:39.106ZS-EPMC86391493478330910.7554/eLife.68015