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

Abstract

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.

Coma Prognostication After Acute Brain Injury

“…Emerging methods of EEG acquisition and analysis have yielded novel insights into brain function in patients with DoC in acute and chronic settings. Whereas conventional EEG is typically interpreted subjectively, emerging techniques use quantitative interpretation (eg, machine learning) under task-based, stimulus-based, or resting-state conditions . CMD detected with task-based EEG has prognostic significance, predicting greater neurologic function at 3 months and 1 year (odds ratio, 4.6; 95% CI, 1.2-17.1) following injury as well as a more rapid recovery than patients without CMD.…”

“…While there are contextual features that influence recovery potential, eg, age, comorbidities, premorbid function, hospital complications, and brain injury etiology, multimodal prognostic markers that assess functional and structural brain integrity are critical in this process. Because no prognostic marker has perfect accuracy, markers likely have greater predictive value when combined, and thus guidelines urge clinicians to acquire multimodal assessments to more accurately predict recovery potential . Here, we review the state of the science in functional and structural prognostic markers, emphasizing emerging techniques that may be translated to ICU care.…”

“…Greater synchronization (or functional connectivity) between regions indicates greater functional network integrity. Preserved integrity of networks, such as the default mode network, as well as preserved interactions between networks predict a greater likelihood of recovering consciousness and neurologic function . Logistically, resting-state fMRI is simpler to acquire than other forms of fMRI, which require the delivery of stimuli.…”

The pupillary dark reflex in acute disorders of consciousness

“…Inclusion criteria were age ≥ 18 years, acute traumatic or non-traumatic brain injuries, and impaired consciousness as previously described. [11][12][13][14] Age-(± 5 years) and sex-matched healthy controls were recruited from the community through word-of-mouth advertisement. Written informed consent was obtained from all participants or their surrogate decision-makers.…”

“…Neurological assessments of consciousness were performed as described earlier, [11][12][13][14] including three clinical scales: the Glasgow Coma Scale (GCS), the Full Outline of Unresponsiveness (FOUR) score, 15 and the Simpli ed Evaluation of Consciousness Disorders (SECONDS). 16,17 Patients with a coma or UWS were categorized as clinically unresponsive; and patients in MCS or emerged from MCS were classi ed as being clinically low-responsive.…”