Dataset Information

Quantitative EEG signatures of delirium and coma in mechanically ventilated ICU patients.

ABSTRACT:

Objective

To identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients.

Methods

We prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional connectivity and complexity of 5-minute EEG segments after each assessment. We used penalized regression to select EEG metrics associated with delirium or coma, and compared mixed-effects models predicting delirium with and without the selected EEG metrics.

Results

Delta variability, high-beta variability, relative theta power, and relative alpha power contributed independently to EEG-based identification of delirium or coma. A model with these metrics achieved better prediction of delirium or coma than a model with clinical variables alone (Akaike Information Criterion: 36 vs 43, p = 0.006 by likelihood ratio test). The area under the receiver operating characteristic curve for an ad hoc hypothetical delirium score using these metrics was 0.94 (95%CI 0.83-0.99).

Conclusions

We identified four EEG metrics that, in combination, provided excellent discrimination between delirious/comatose and non-delirious mechanically ventilated ICU patients.

Significance

Our findings give insight to neurophysiologic changes underlying delirium and provide a basis for pragmatic, EEG-based delirium monitoring technology.

SUBMITTER: Williams Roberson S

PROVIDER: S-EPMC9889081 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Quantitative EEG signatures of delirium and coma in mechanically ventilated ICU patients.

Williams Roberson Shawniqua S Azeez Naureen A NA Fulton Jenna N JN Zhang Kevin C KC Lee Aaron X T AXT Ye Fei F Pandharipande Pratik P Brummel Nathan E NE Patel Mayur B MB Ely E Wesley EW

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 20221201

<h4>Objective</h4>To identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients.<h4>Methods</h4>We prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional c ...[more]

PMID: 36529066

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Project description:BackgroundMechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts.MethodsWe developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV. Using the MIMIC-III database, we identified 25,202 eligible patients through ICD-9 codes. We employed backward elimination and the Lasso method, selecting 32 features based on clinical insights and literature. Data preprocessing included eliminating columns with over 90% missing data and using mean imputation for the remaining missing values. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE). We evaluated several ML models, including CatBoost, XGBoost, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using a 70/30 train-test split. The CatBoost model was chosen for its superior performance in terms of accuracy, precision, recall, F1-score, AUROC metrics, and calibration plots.ResultsThe study involved a cohort of 25,202 patients on MV. The CatBoost model attained an AUROC of 0.862, an increase from an initial AUROC of 0.821, which was the best reported in the literature. It also demonstrated an accuracy of 0.789, an F1-score of 0.747, and better calibration, outperforming other models. These improvements are due to systematic feature selection and the robust gradient boosting architecture of CatBoost.ConclusionThe preprocessing methodology significantly reduced the number of relevant features, simplifying computational processes, and identified critical features previously overlooked. Integrating these features and tuning the parameters, our model demonstrated strong generalization to unseen data. This highlights the potential of ML as a crucial tool in ICUs, enhancing resource allocation and providing more personalized interventions for MV patients.

Project description:ObjectiveDelirium is common in intensive care patients and is associated with short- and long-term adverse outcomes. We investigated the long-term risk of cognitive impairment and post-traumatic stress disorder (PTSD) in intensive care patients with and without delirium.MethodsThis is a prospective cohort study in ICUs in two Australian university-affiliated hospitals. Patients were eligible if they were older than 18 years, mechanically ventilated for more than 24 h and did not meet exclusion criteria. Delirium was assessed using the Confusion Assessment Method for Intensive Care Unit. Variables assessing cognitive function and PTSD symptoms were collected at ICU discharge, after 6 and 12 months: Mini-Mental State Examination, Telephone Interview for Cognitive Status, Impact of Events Scale-Revised and Informant Questionnaire for Cognitive Decline (caregiver).Results103 participants were included of which 36% developed delirium in ICU. Patients with delirium were sicker and had longer duration of mechanical ventilation and ICU length of stay. After 12 months, 41/60 (68.3%) evaluable patients were cognitively impaired, with 11.6% representing the presence of symptoms consistent with dementia. When evaluated by the patient's caregiver, the patient's cognitive function was found to be severely impaired in a larger proportion of patients (14/60, 23.3%). Delirium was associated with worse cognitive function at ICU discharge, but not with long-term cognitive function. IES-R scores, measuring PTSD symptoms, were significantly higher in patients who had delirium compared to patients without delirium. In regression analysis, delirium was independently associated with cognitive function at ICU discharge and PTSD symptoms at 12 months.ConclusionsIntensive care survivors have significant rates of long-term cognitive decline and PTSD symptoms. Delirium in ICU was independently associated with short-term but not long-term cognitive function, and with long-term PTSD symptoms. Trial registration Australian New Zealand Clinical Trials Registry, ACTRN12616001116415, 15/8/2016 retrospectively registered, https://www.anzctr.org.au.

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Project description:BackgroundBiomarkers can be used to detect the presence of endothelial and/or alveolar epithelial injuries in case of ARDS. Angiopoietin-2 (Ang-2), soluble intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion protein-1 (VCAM-1), P-selectin and E-selectin are biomarkers of endothelial injury, whereas the receptor for advanced glycation end-products (RAGE) reflects alveolar epithelial injury. The aims of this study were to evaluate whether the plasma concentration of the above-mentioned biomarkers was different 1) in survivors and non-survivors of COVID-19-related ARDS and 2) in COVID-19-related and classical ARDS.MethodsThis prospective study was performed in two COVID-19-dedicated Intensive Care Units (ICU) and one non-COVID-19 ICU at Ferrara University Hospital. A cohort of 31 mechanically ventilated patients with COVID-19 ARDS and a cohort of 11 patients with classical ARDS were enrolled. Ang-2, ICAM-1, VCAM-1, P-selectin, E-selectin and RAGE were determined with a bead-based multiplex immunoassay at three time points: inclusion in the study (T1), after 7 ± 2 days (T2) and 14 ± 2 days (T3). The primary outcome was to evaluate the plasma trend of the biomarker levels in survivors and non-survivors. The secondary outcome was to evaluate the differences in respiratory mechanics variables and gas exchanges between survivors and non-survivors. Furthermore, we compared the plasma levels of the biomarkers at T1 in patients with COVID-19-related ARDS and classical ARDS.ResultsIn COVID-19-related ARDS, the plasma levels of Ang-2 and ICAM-1 at T1 were statistically higher in non-survivors than survivors, (p = 0.04 and p = 0.03, respectively), whereas those of P-selectin, E-selectin and RAGE did not differ. Ang-2 and ICAM-1 at T1 were predictors of mortality (AUROC 0.650 and 0.717, respectively). At T1, RAGE and P-selectin levels were higher in classical ARDS than in COVID-19-related ARDS. Ang-2, ICAM-1 and E-selectin were lower in classical ARDS than in COVID-19-related ARDS (all p < 0.001).ConclusionsCOVID-19 ARDS is characterized by an early pulmonary endothelial injury, as detected by Ang-2 and ICAM-1. COVID-19 ARDS and classical ARDS exhibited a different expression of biomarkers, suggesting different pathological pathways. Trial registration NCT04343053 , Date of registration: April 13, 2020.

Project description:PurposeWhile fever may be a presenting symptom of COVID-19, fever at hospital admission has not been identified as a predictor of mortality. However, hyperthermia during critical illness among ventilated COVID-19 patients in the ICU has not yet been studied. We sought to determine mortality predictors among ventilated COVID-19 ICU patients and we hypothesized that fever in the ICU is predictive of mortality.Materials and methodsWe conducted a retrospective cohort study of 103 ventilated COVID-19 patients admitted to the ICU between March 14 and May 27, 2020. Final follow-up was June 5, 2020. Patients discharged from the ICU or who died were included. Patients still admitted to the ICU at final follow-up were excluded.Results103 patients were included, 40 survived and 63(61.1%) died. Deceased patients were older {66 years[IQR18] vs 62.5[IQR10], (p = 0.0237)}, more often male {48(68%) vs 22(55%), (p = 0.0247)}, had lower initial oxygen saturation {86.0%[IQR18] vs 91.5%[IQR11.5], (p = 0.0060)}, and had lower pH nadir than survivors {7.10[IQR0.2] vs 7.30[IQR0.2] (p < 0.0001)}. Patients had higher peak temperatures during ICU stay as compared to hospital presentation {103.3°F[IQR1.7] vs 100.0°F[IQR3.5], (p < 0.0001)}. Deceased patients had higher peak ICU temperatures than survivors {103.6°F[IQR2.0] vs 102.9°F[IQR1.4], (p = 0.0008)}. Increasing peak temperatures were linearly associated with mortality. Febrile patients who underwent targeted temperature management to achieve normothermia did not have different outcomes than those not actively cooled. Multivariable analysis revealed 60% and 75% higher risk of mortality with peak temperature greater than 103°F and 104°F respectively; it also confirmed hyperthermia, age, male sex, and acidosis to be predictors of mortality.ConclusionsThis is one of the first studies to identify ICU hyperthermia as predictive of mortality in ventilated COVID-19 patients. Additional predictors included male sex, age, and acidosis. With COVID-19 cases increasing, identification of ICU mortality predictors is crucial to improve risk stratification, resource management, and patient outcomes.

Dataset Information

Quantitative EEG signatures of delirium and coma in mechanically ventilated ICU patients.

Objective

Methods

Results

Conclusions

Significance

Publications

Quantitative EEG signatures of delirium and coma in mechanically ventilated ICU patients.

Similar Datasets

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