Medical aspects of the persistent vegetative state (second of two parts)

Outcome from coma caused by cerebral hypoxia-ischemia (eg, cardiac arrest) was compared with serial neurological findings in 210 patients. Thirteen percent of patients regained independent function at some point during the first postarrest year. Computer application of new multivariate techniques to the prospectively observed findings generated easily utilized rules that classified patients by likely outcome. At the time of initial examination, 52 patients (one fourth of the total population) had absent pupillary light reflexes, and none of these patients ever regained independent daily function. By contrast, the initial presence of pupillary light reflexes, the development of spontaneous eye movements that were roving conjugate or better, and the findings of extensor, flexor, or withdrawal responses to pain identified a smaller group of 27 patients, 11 (41%) of whom regained independence in their daily lives. By 24 hours after onset, 93 poor-outcome patients were identified by motor responses that were absent, extensor, or flexor and by spontaneous eye movements that were neither orienting nor roving conjugate; only one regained independent function. This contrasts with recovery in 19 (63%) of 30 patients who at that time showed improvement in their eye-opening responses and obeyed commands or had motor responses that were withdrawal or localizing. Similarly simple rules distinguished between good- and poor-prognosis patients on postarrest days 3, 7, and 14.

https://jamanetwork.com/journals/JAMA/articlepdf/397254/jama_253_10_020.pdf

Predicting Outcome From Hypoxic-Ischemic Coma

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(06)68508-5.pdf

Chronic disorders of consciousness

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitie-Salpetriere and Liege). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liege datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liege remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.

Robust EEG-based cross-site and cross-protocol classification of states of consciousness.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(97)06447-7.pdf

Persistent vegetative state

A 43-year-old man remained in a vegetative state for one and one-half years after resuscitation from cardiac arrest. During the vegetative period neurological examinations provided little indication of recovery. Initially, his EEG showed activity of delta and theta frequencies. After eighteen months intellectual functions partially recoverd; his verbal WAIS score is now 100, although he remains severely physically disabled. The usual prognostic criteria did not predict the cognitive outcome in this patient with prolonged medical coma.

Stephen F. Johnson

Papers

Recovery of cognition after prolonged vegetative state

Transderm pupil and confusion in a 10 year old.