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This retrospective study evaluated windowed time-lagged cross-correlation (WTLCC) matrices for describing cerebral hemodynamics\u2013ANS interactions to predict outcome, enabling identifying high-risk patients who may benefit from enhanced monitoring to prevent complications.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>The first experiment aimed to predict short-term outcome using WTLCC-based convolution neural network models on the Wroclaw University Hospital (WUH) database (P<jats:sub>training<\/jats:sub>\u2009=\u200931 with 1,079 matrices, P<jats:sub>val<\/jats:sub>\u2009=\u200916 with 573 matrices). The second experiment predicted long-term outcome, training on the CENTER-TBI database (P<jats:sub>training<\/jats:sub>\u2009=\u2009100 with 17,062 matrices) and validating on WUH (P<jats:sub>val<\/jats:sub>\u2009=\u200947 with 6,220 matrices). Cerebral hemodynamics was characterized using intracranial pressure (ICP), cerebral perfusion pressure (CPP), pressure reactivity index (PRx), while ANS metrics included low-to-high-frequency heart rate variability (LF\/HF) and baroreflex sensitivity (BRS) over 72\u00a0h. Short-term outcome at WUH was assessed using the Glasgow Outcome Scale (GOS) at discharge. Long-term outcome was evaluated at 3\u00a0months at WUH and 6\u00a0months at CENTER-TBI using GOS and GOS-Extended, respectively. The XGBoost model was used to compare performance of WTLCC-based model and averaged neuromonitoring parameters, adjusted for age, Glasgow Coma Scale, major extracranial injury, and pupil reactivity in outcome prediction.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>For short-term outcome prediction, the best-performing WTLCC-based model used ICP-LF\/HF matrices. It had an area under the curve (AUC) of 0.80, vs. 0.71 for averages of ANS and cerebral hemodynamics metrics, adjusted for clinical metadata. For long-term outcome prediction, the best-score WTLCC-based model used ICP-LF\/HF matrices. It had an AUC of 0.63, vs. 0.66 for adjusted neuromonitoring parameters.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Among all neuromonitoring parameters, ICP and LF\/HF signals were the most effective in generating the WTLCC matrices. WTLCC-based model outperformed adjusted neuromonitoring parameters in short-term but had moderate utility in long-term outcome prediction.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00701-024-06375-6","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T07:36:16Z","timestamp":1734334576000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploration of simultaneous transients between cerebral hemodynamics and the autonomic nervous system using windowed time-lagged cross-correlation matrices: a CENTER-TBI study"],"prefix":"10.1007","volume":"166","author":[{"given":"Agnieszka","family":"Uryga","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cyprian","family":"Mataczy\u0144ski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam 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Ethical approval was obtained for each recruiting site from the appropriate local ethics committee, and the full list of approvals is available on the website: . The retrospective analysis of data recorded at Wroclaw University Hospital (WUH) was approved by the bioethical committee of WUH (approval KB-133\/2023). All volunteers were required to provide written consent before entering the study. The data were fully anonymised, and no data protection issues were involved.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The manuscript has not been submitted elsewhere or published elsewhere in whole or in part. Some of the results have been accepted as poster presentations for the 2024 meeting of the International NeuroTrauma Society (INTS) conference, which took place in September 2024 in Cambridge, UK. 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