Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness

BACKGROUND

In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations.

METHODS

We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome.

RESULTS

Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]).

CONCLUSIONS

Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Plasma Aβ biomarker for early diagnosis and prognosis of Alzheimer's disease - a systematic review

INTRODUCTION

Alzheimer's disease (AD) is the most common cause of dementia worldwide and a cost-effective diagnostic biomarker is needed. This systematic review provides an overview of the current research on plasma amyloid beta (Aβ) as a biomarker of AD and explores the clinical implications of this line of research.

METHODS

PubMed was searched using the keywords plasma Aβ and AD from 2017 to 2021. Only clinical studies involving amyloid PET (aPET) or cerebrospinal fluid (CSF) biomarker analysis (or both) were included. A meta-analysis of CSF Aβ42/40 ratio, aPET and plasma Aβ42/40 ratio was conducted when possible.

RESULTS

A total of 17 articles were identified. Plasma Aβ42/40 ratio was inversely correlated with aPET positivity r = -0.48 (95% confidence interval (CI): -0.65--0.31). In numerous studies, plasma Aβ42/40 ratio was also found to be directly correlated with CSF Aβ42 and CSF Aβ42/40 ratio r = 0.50 (95% CI: 0.30-0.69). Three studies found plasma Aβ42 to be positively associated with aPET positivity and CSF Aβ42; however, four other studies found no significant association between these variables. Seven studies reported no significant association of plasma Aβ40 with aPET or CSF Aβ40.

CONCLUSION

Plasma Aβ42/40 ratio seems as a promising plasma biomarker as it significantly correlates inversely with aPET positivity and directly with CSF Aβ42 and CSF Aβ42/40 ratio. However, more research is warranted, including validation studies, longitudinally clinical studies, studies comparing measurement methods and studies of Aβ kinetics.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Readiness among people with epilepsy to carry body-worn monitor devices in everyday life: A qualitative study

OBJECTIVE

There have been intensive efforts to design and develop new wearable technology for epileptic seizure detection. Several studies have focused on the technical aspects, but the readiness of patients with epilepsy (PWEs) to use wearables in everyday life, which is crucial, remains relatively unexplored.

METHODS

We conducted a qualitative interview study involving eight PWEs. The study was designed to provide insights into patient readiness to use wearables for home monitoring of epilepsy.

RESULTS

Three themes were identified: 1) making invisible situations visible, 2) having companionship within a troubled everyday life, and 3) sharing ownership of no recognizable moments. The analysis and interpretation revealed that the expectations of the participants for wearables were rooted in aspects that had a significant impact on their lives and self-image.

CONCLUSION

Patients with epilepsy disclosed that their readiness to use technology, specifically wearables, in everyday life relied on the assumption that they would provide an existential and comforting experience, in which the voids of their individual needs would be addressed in a more patient-friendly manner. Wearable design should consider the valuable insight that technology should be more than just technical tools that monitor symptoms; wearables are expected to be existential and esthetic artifacts that provide PWEs with meaningful experience.

Case comparison of sleep features from ear-EEG and scalp-EEG

Background

We investigate the potential usability of a novel in-the-ear electroencephalography recording device for sleep staging.

Methods

In one healthy subject we compare simultaneous earelectroencephalography to standard scalp EEG visually and using power spectrograms. Hypnograms independently derived from the records are compared.

Results

We find that alpha activity, K complexes, sleep spindles and slow wave sleep can be visually distinguished using earelectroencephalography. Spectral peaks are shared between the two records. Hypnograms are 90.9% similar.

Conclusion

The results indicate that ear-electroencephalography can be used for sleep staging.