Brimonidine eye drops reveal diminished sympathetic pupillary tone in comatose patients with brain injury.

BACKGROUND: There is an urgent need for easy-to-perform bedside measures to detect residual consciousness in clinically unresponsive patients with acute brain injury. Interestingly, the sympathetic control of pupil size is thought to be lost in states of unconsciousness. We therefore hypothesized that administration of brimonidine (an alpha-2-adrenergic agonist) eye drops into one eye should produce a pharmacologic Horner's syndrome if the clinically unresponsive patient is conscious, but not if the patient is unconscious. Here, in a first step to explore this hypothesis, we investigated the potential of brimonidine eye drops to distinguish preserved sympathetic pupillary function in awake volunteers from impairment of sympathetic tone in patients in a coma. METHODS: We enrolled comatose patients admitted for acute brain injury to one of the intensive care units (ICU) of a tertiary referral center, in whom EEG and/or neuroimaging for all practical purposes had ruled out residual consciousness. Exclusion criteria were deep sedation, medications with known drug interactions with brimonidine, and a history of eye disease. Age- and sex-matched healthy and awake volunteers served as controls. We measured pupils of both eyes, under scotopic conditions, at baseline and five times 5-120 min after administering brimonidine into the right eye, using automated pupillometry. Primary outcomes were miosis and anisocoria at the individual and group levels. RESULTS: We included 15 comatose ICU patients (seven women, mean age 59 ± 13.8 years) and 15 controls (seven women, mean age 55 ± 16.3 years). At 30 min, miosis and anisocoria were seen in all 15 controls (mean difference between the brimonidine-treated pupil and the control pupil: - 1.31 mm, 95% CI [- 1.51; - 1.11], p < 0.001), but in none (p < 0.001) of the 15 ICU patients (mean difference: 0.09 mm, 95% CI [- 0.12;0.30], p > 0.99). This effect was unchanged after 120 min and remained robust in sensitivity analyses correcting for baseline pupil size, age, and room illuminance. CONCLUSION: In this proof-of-principle study, brimonidine eye drops produced anisocoria in awake volunteers but not in comatose patients with brain injury. This suggests that automated pupillometry after administration of brimonidine can distinguish between the extremes of the spectrum of consciousness (i.e., fully conscious vs. deeply comatose). A larger study testing the "intermediate zone" of disorders of consciousness in the ICU seems warranted.

Understanding, detecting, and stimulating consciousness recovery in the ICU.

Coma is a medical and socioeconomic emergency. Although underfunded, research on coma and disorders of consciousness has made impressive progress. Lesion-network-mapping studies have delineated the precise brainstem regions that consistently produce coma when damaged. Functional neuroimaging has revealed how mechanisms like "communication through coherence" and "inhibition by gating" work in synergy to enable cortico-cortical processing and how this information transfer is disrupted in brain injury. On the cellular level, break-down of intracellular communication between the layer 5 pyramidal cell soma and the apical dendritic part impairs dendritic information integration, with up-stream effects on microcircuits in local neuronal populations and on large-scale fronto-parietal networks, which correlates with loss of consciousness. A breakthrough in clinical concepts occurred when fMRI, and later EEG, studies revealed that 15% of clinically unresponsive patients in acute and chronic settings are in fact awake and aware, as shown by their command following abilities revealed by brain activation during motor and locomotion imagery tasks. This condition is now termed "cognitive motor dissociation." Furthermore, epidemiological data on coma were literally non-existent until recently because of difficulties related to case ascertainment with traditional methods, but crowdsourcing of family observations enabled the first estimates of how frequent coma is in the general population (pooled annual incidence of 201 coma cases per 100,000 population in the UK and the USA). Diagnostic guidelines on coma and disorders of consciousness by the American Academy of Neurology and the European Academy of Neurology provide ambitious clinical frameworks to accommodate these achievements. As for therapy, a broad range of medical and non-medical treatment options is now being tested in increasingly larger trials; in particular, amantadine and transcranial direct current stimulation appear promising in this regard. Major international initiatives like the Curing Coma Campaign aim to raise awareness for coma and disorders of consciousness in the public, with the ultimate goal to make more brain-injured patients recover consciousness after a coma. To highlight all these accomplishments, this paper provides a comprehensive overview of recent progress and future challenges related to understanding, detecting, and stimulating consciousness recovery in the ICU.

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.