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1.
Neurocrit Care ; 2024 May 29.
Article in English | MEDLINE | ID: mdl-38811512

ABSTRACT

BACKGROUND: Resting-state electroencephalography (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI). We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in patients with TBI. METHODS: This is a retrospective study of comatose patients with TBI who were admitted to a trauma center (October 2013 to January 2022). Demographics, basic clinical data, imaging characteristics, and EEGs were collected. We calculated the following using 10-min rsEEGs: power spectral density, permutation entropy (complexity measure), weighted symbolic mutual information (wSMI, global information sharing measure), Kolmogorov complexity (Kolcom, complexity measure), and heart-evoked potentials (the averaged EEG signal relative to the corresponding QRS complex on electrocardiography). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, and rsEEG data via a support vector machine. RESULTS: We studied 113 of 134 (84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50 years, p = 0.01). Patients who recovered also had higher Kolcom (U = 1688, p = 0.01), increased beta power (U = 1,652 p = 0.003) with higher variability across channels (U = 1534, p = 0.034) and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04), and higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = 0.026; U = 1639, p = 0.024) than those who did not recover. The area under the receiver operating characteristic curve for rsEEG was higher than that for clinical data (using age, motor response, pupil reactivity) and higher than that for the Marshall computed tomography classification (0.69 vs. 0.66 vs. 0.56, respectively; p < 0.001). CONCLUSIONS: We describe the rsEEG signature in recovery of consciousness prior to discharge in comatose patients with TBI. rsEEG measures performed modestly better than the clinical and imaging data in predicting recovery.

2.
Article in English | MEDLINE | ID: mdl-38673294

ABSTRACT

(1) Background: Climate change is increasing the already frequent diverse extreme weather events (EWE) across geographic locations, directly and indirectly impacting human health. However, current ongoing research fails to address the magnitude of these indirect impacts, including healthcare access. Vulnerable populations such as persons with spinal cord injury (pSCI) face added physiologic burden such as thermoregulation or mobility challenges like closure of public transportation. Our exploratory research assessed commute and transport to healthcare facilities as well as the knowledge, attitudes and behaviors (KAB) of pSCI regarding EWE and climate change when compared to pSCI caregivers (CG) and the general public (GP). (2) Methods: A KAB survey was employed to conduct a cross-sectional assessment of pSCI, CG, and GP in Miami from October through November 2019 using snowball sampling. Descriptive and logistic regression statistical analyses were used. (3) Results: Of 65 eligible survey respondents, 27 (41.5%) were pSCI, 11 (17%) CG, and 27 (41.5%) GP. Overall, pSCI reported EWE, particularly flooding and heavy rain, affecting their daily activities including healthcare appointments, more frequently than CG or GP. The overall models for logistic regression looking at commute to and attendance of healthcare appointments were statistically significant. pSCI self-report being less vulnerable than others, and a large proportion of each group was not fully convinced climate change is happening. (4) Conclusions: This study provided insight to the KAB of 3 population subgroups in Miami, Florida. pSCI are significantly more vulnerable to the effects of regional weather events yet exhibit disproportionate self-perception of their vulnerability. Continued and more comprehensive research is needed to characterize the barriers that vulnerable populations face during weather events.


Subject(s)
Caregivers , Climate Change , Spinal Cord Injuries , Florida , Humans , Spinal Cord Injuries/psychology , Adult , Female , Male , Middle Aged , Cross-Sectional Studies , Caregivers/statistics & numerical data , Caregivers/psychology , Survivors/psychology , Survivors/statistics & numerical data , Weather , Young Adult , Aged , Health Knowledge, Attitudes, Practice
3.
Res Sq ; 2024 Jan 31.
Article in English | MEDLINE | ID: mdl-38352430

ABSTRACT

Background Resting-state electroencephalogram (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI) patients. We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in comatose TBI patients. Methods This is a retrospective study of comatose TBI patients who were admitted to a level-1 trauma center (10/2013-1/2022). Demographics, basic clinical data, imaging characteristics, and EEG data were collected. We calculated using 10-minute rsEEGs: power spectral density (PSD), permutation entropy (PE - complexity measure), weighted symbolic-mutual-information (wSMI - global information sharing measure), Kolmogorov complexity (Kolcom - complexity measure), and heart-evoked potentials (HEP - the averaged EEG signal relative to the corresponding QRS complex on electrocardiogram). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, rsEEG data via Support Vector Machine with a linear kernel (SVM). Results We studied 113 (out of 134, 84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50, p .01). Patients who recovered consciousness had higher Kolcom (U = 1688, p = 0.01,), increased beta power (U = 1652 p = 0.003), with higher variability across channels ( U = 1534, p = 0.034), and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04) and showed higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = .026, U = 1639, p = 0.024) than those who didn't recover. The ROC-AUC improved from 0.66 (using age, motor response, pupils' reactivity, and CT Marshall classification) to 0.69 (p < 0.001) when adding rsEEG measures. Conclusion We describe the rsEEG EEG signature in recovery of consciousness prior to discharge in comatose TBI patients. Resting-state EEG measures improved prediction beyond the clinical and imaging data.

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