An enhanced cerebral recovery index for coma prognostication following cardiac arrest
Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In thi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/109707 https://orcid.org/0000-0001-5135-8588 https://orcid.org/0000-0002-6318-2978 https://orcid.org/0000-0003-2668-7819 |
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| author | Amorim, Edilberto Pati, Sandipan B. Purdon, Patrick L. Westover, M. Brandon Ghassemi, Mohammad Mahdi Mark, Roger G Brown, Emery Neal |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Amorim, Edilberto Pati, Sandipan B. Purdon, Patrick L. Westover, M. Brandon Ghassemi, Mohammad Mahdi Mark, Roger G Brown, Emery Neal |
| author_sort | Amorim, Edilberto |
| collection | MIT |
| description | Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27. |
| first_indexed | 2024-09-23T10:25:44Z |
| format | Article |
| id | mit-1721.1/109707 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:25:44Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1097072022-09-30T21:04:05Z An enhanced cerebral recovery index for coma prognostication following cardiac arrest Amorim, Edilberto Pati, Sandipan B. Purdon, Patrick L. Westover, M. Brandon Ghassemi, Mohammad Mahdi Mark, Roger G Brown, Emery Neal Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Ghassemi, Mohammad Mahdi Mark, Roger G Brown, Emery Neal Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27. 2017-06-07T15:43:22Z 2017-06-07T15:43:22Z 2015-11 2015-08 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-9271-8 1094-687X 1558-4615 http://hdl.handle.net/1721.1/109707 Ghassemi, Mohammad M.; Amorim, Edilberto; Pati, Sandipan B.; Mark, Roger G.; Brown, Emery N.; Purdon, Patrick L. and Westover, M. Brandon. “An Enhanced Cerebral Recovery Index for Coma Prognostication Following Cardiac Arrest.” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 25-29 2015, Milan, Italy, Institute of Electrical and Electronics Engineers (IEEE), November 2015 https://orcid.org/0000-0001-5135-8588 https://orcid.org/0000-0002-6318-2978 https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1109/EMBC.2015.7318417 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) PMC |
| spellingShingle | Amorim, Edilberto Pati, Sandipan B. Purdon, Patrick L. Westover, M. Brandon Ghassemi, Mohammad Mahdi Mark, Roger G Brown, Emery Neal An enhanced cerebral recovery index for coma prognostication following cardiac arrest |
| title | An enhanced cerebral recovery index for coma prognostication following cardiac arrest |
| title_full | An enhanced cerebral recovery index for coma prognostication following cardiac arrest |
| title_fullStr | An enhanced cerebral recovery index for coma prognostication following cardiac arrest |
| title_full_unstemmed | An enhanced cerebral recovery index for coma prognostication following cardiac arrest |
| title_short | An enhanced cerebral recovery index for coma prognostication following cardiac arrest |
| title_sort | enhanced cerebral recovery index for coma prognostication following cardiac arrest |
| url | http://hdl.handle.net/1721.1/109707 https://orcid.org/0000-0001-5135-8588 https://orcid.org/0000-0002-6318-2978 https://orcid.org/0000-0003-2668-7819 |
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