Proescholdt, Martin A. and Faltermeier, Rupert and Bele, Sylvia and Brawanski, Alexander (2017) Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE: 8454527. ISSN 1748-670X, 1748-6718
Full text not available from this repository. (Request a copy)Abstract
Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | TRAUMATIC BRAIN-INJURY; CEREBROVASCULAR PRESSURE-REACTIVITY; NEUROCRITICAL CARE; PERFUSION-PRESSURE; HEAD-INJURY; DECOMPRESSIVE CRANIECTOMY; INTRACRANIAL-PRESSURE; UNIT; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Neurochirurgie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Dec 2018 12:58 |
| Last Modified: | 19 Feb 2019 15:06 |
| URI: | https://pred.uni-regensburg.de/id/eprint/102 |
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