Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy*
OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroen...
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ovid Technologies (Wolters Kluwer Health)
2021
|
| Online Access: | https://hdl.handle.net/1721.1/132385 |
| _version_ | 1811081412151869440 |
|---|---|
| author | Ghassemi, Mohammad M Amorim, Edilberto Alhanai, Tuka Lee, Jong W Herman, Susan T Sivaraju, Adithya Gaspard, Nicolas Hirsch, Lawrence J Scirica, Benjamin M Biswal, Siddharth Moura Junior, Valdery Cash, Sydney S Brown, Emery N Mark, Roger G Westover, M Brandon |
| author_facet | Ghassemi, Mohammad M Amorim, Edilberto Alhanai, Tuka Lee, Jong W Herman, Susan T Sivaraju, Adithya Gaspard, Nicolas Hirsch, Lawrence J Scirica, Benjamin M Biswal, Siddharth Moura Junior, Valdery Cash, Sydney S Brown, Emery N Mark, Roger G Westover, M Brandon |
| author_sort | Ghassemi, Mohammad M |
| collection | MIT |
| description | OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. DESIGN: Retrospective. SETTING: ICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.None. MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance. |
| first_indexed | 2024-09-23T11:46:16Z |
| format | Article |
| id | mit-1721.1/132385 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:46:16Z |
| publishDate | 2021 |
| publisher | Ovid Technologies (Wolters Kluwer Health) |
| record_format | dspace |
| spelling | mit-1721.1/1323852021-09-21T03:40:00Z Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy* Ghassemi, Mohammad M Amorim, Edilberto Alhanai, Tuka Lee, Jong W Herman, Susan T Sivaraju, Adithya Gaspard, Nicolas Hirsch, Lawrence J Scirica, Benjamin M Biswal, Siddharth Moura Junior, Valdery Cash, Sydney S Brown, Emery N Mark, Roger G Westover, M Brandon OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. DESIGN: Retrospective. SETTING: ICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.None. MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance. 2021-09-20T18:22:08Z 2021-09-20T18:22:08Z 2020-10-19T14:01:29Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/132385 en 10.1097/CCM.0000000000003840 Critical Care Medicine Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Ovid Technologies (Wolters Kluwer Health) PMC |
| spellingShingle | Ghassemi, Mohammad M Amorim, Edilberto Alhanai, Tuka Lee, Jong W Herman, Susan T Sivaraju, Adithya Gaspard, Nicolas Hirsch, Lawrence J Scirica, Benjamin M Biswal, Siddharth Moura Junior, Valdery Cash, Sydney S Brown, Emery N Mark, Roger G Westover, M Brandon Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy* |
| title | Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy* |
| title_full | Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy* |
| title_fullStr | Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy* |
| title_full_unstemmed | Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy* |
| title_short | Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy* |
| title_sort | quantitative electroencephalogram trends predict recovery in hypoxic ischemic encephalopathy |
| url | https://hdl.handle.net/1721.1/132385 |
| work_keys_str_mv | AT ghassemimohammadm quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT amorimedilberto quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT alhanaituka quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT leejongw quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT hermansusant quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT sivarajuadithya quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT gaspardnicolas quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT hirschlawrencej quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT sciricabenjaminm quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT biswalsiddharth quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT mourajuniorvaldery quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT cashsydneys quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT brownemeryn quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT markrogerg quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy AT westovermbrandon quantitativeelectroencephalogramtrendspredictrecoveryinhypoxicischemicencephalopathy |