Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a ...complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical data-based and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 IQR = 4.56, 3.39 IQR = 4.36, 3.16 IQR = 4.13 for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Data-driven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decision-making process the practitioners must face.
Due to continuous advances in intensive care technology and neurosurgical procedures, the number of survivors from severe acquired brain injuries (sABIs) has increased considerably, raising several ...delicate ethical issues. The heterogeneity and complex nature of the neurological damage of sABIs make the detection of predictive factors of a better outcome very challenging. Identifying the profile of those patients with better prospects of recovery will facilitate clinical and family choices and allow to personalize rehabilitation. This paper describes a multicenter prospective study protocol, to investigate outcomes and baseline predictors or biomarkers of functional recovery, on a large Italian cohort of sABI survivors undergoing postacute rehabilitation.
All patients with a diagnosis of sABI admitted to four intensive rehabilitation units (IRUs) within 4 months from the acute event, aged above 18, and providing informed consent, will be enrolled. No additional exclusion criteria will be considered. Measures will be taken at admission (T0), at three (T1) and 6 months (T2) from T0, and follow-up at 12 and 24 months from onset, including clinical and functional data, neurophysiological results, and analysis of neurogenetic biomarkers.
Advanced machine learning algorithms will be cross validated to achieve data-driven prediction models. To assess the clinical applicability of the solutions obtained, the prediction of recovery milestones will be compared to the evaluation of a multiprofessional, interdisciplinary rehabilitation team, performed within 2 weeks from admission.
Identifying the profiles of patients with a favorable prognosis would allow customization of rehabilitation strategies, to provide accurate information to the caregivers and, possibly, to optimize rehabilitation outcomes.
The application and validation of machine learning algorithms on a comprehensive pool of clinical, genetic, and neurophysiological data can pave the way toward the implementation of tools in support of the clinical prognosis for the rehabilitation pathways of patients after sABI.
Disorders of consciousness (DoCs) include unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Critical illness polyneuropathy and myopathy (CIPNM) is frequent in severe ...acquired brain injuries and impacts functional outcomes at discharge from the intensive rehabilitation unit (IRU). We investigated the prevalence of CIPNM in DoCs and its relationship with the consciousness assessment.
Patients with DoCs were retrospectively selected from the database including patients admitted to the IRU of the IRCCS Don Gnocchi Foundation, Florence, from August 2012 to May 2020. Electroneurography/electromyography was performed at admission. Consciousness was assessed using the Coma Recovery Scale-Revised (CRS-R) at admission and discharge. Patients transitioning from a lower consciousness state to a higher one were classified as improved responsiveness (IR).
A total of 177 patients were included (UWS: 81 (45.8%); MCS: 96 (54.2%); 78 (44.1%) women; 67 years (IQR: 20). At admission, 108 (61.0%) patients had CIPNM. At discharge, 117 (66.1%) patients presented an IR. In the multivariate analysis, CRS-R at admission (
= 0.006; OR: 1.462) and CIPNM (
= 0.039; OR: -1.252) remained significantly associated with IR only for the UWS patients.
CIPNM is frequent in DoCs and needs to be considered during the clinical consciousness assessment, especially in patients with UWS.
Objective
According to electroencephalogram (EEG) descriptors included in the American Clinical Neurophysiology Society (ACNS) terminology, we generated a score, and we compared it to the EEG scores ...previously proposed in order to identify the one with the best prognostic power for neurological outcome at post‐acute stages in patients with severe disorders of consciousness (DoC).
Materials and methods
Patients included in the analysis were clinically evaluated with the Coma Recovery Scale‐Revised (CRS‐R). An EEG was performed within the first week after admission to Intensive Rehabilitation Unit (IRU). EEGs were classified according to the ACNS terminology and to the scores of Bagnato and Estraneo.
Results
A total of 260 patients admitted to the IRU were analysed. A total of 160 patients (61%) improved their consciousness level during IRU stay. EEG score based on the ANCS terminology showed higher overall performance (receiver‐operating area under the curve = 0.79) and greater sensitivity (65%), at comparable specificities (80%), for clinical improvement as compared to both CRS‐R admission score and other EEG scores. Combining our EEG score with CRS‐R score at admission, the cumulative sensitivity increased to 76% when at least one good prognostic index test was present in the same patient, whereas specificity increased up to 93% if both the good prognostic patterns of clinical and instrumental parameters were simultaneously present.
Conclusion
The EEG scored according to the ACNS terminology is the best among those looked at for the prediction of short‐term clinical improvement in patients with DoC and represents a useful instrumental test, complementary to clinical evaluation at admission, to be added in post‐acute neurological prognostication methods.
To evaluate whether electroencephalographic (EEG) features recorded during the post-acute stage in patients with severe disorders of consciousness (DoC) after acute brain injury (ABI), contribute to ...neurological outcome prediction of these patients at discharge from the intensive rehabilitation unit (IRU).
We retrospectively evaluated all patients consecutively admitted to the IRU from August 2012 to December 2016. Inclusion criteria were: 1) age >18years, 2) patients with unresponsive wakefulness syndrome (UWS) or in a minimally conscious state (MCS), and 3) EEG and a coma recovery scale-revised (CRS-R) score available within the first week after admission. Clinical evaluation was performed using the Italian version of the CRS-R score. EEGs were classified according to American Clinical Neurophysiology Society (ACNS) terminology. Clinical state at final discharge was evaluated using the CRS-R score.
In total, 102 patients were included in the analysis. After a mean of five months of IRU stay, among the 61 UWS subjects, 19 transitioned to MCS and 11 recovered to exit-MCS (E-MCS); twenty-three of the 41 subjects in MCS progressed to E-MCS. Using logistic regression, consciousness level (UWS/MCS-OR=13.4), CRS-R score at admission (OR=1.33) and use of activating drugs (OR=4.7) were significant predictors of clinical improvement. Multivariable analysis showed that specific EEG patterns were independent predictors of improved consciousness at discharge in UWS patients.
EEG performed within the first week after IRU admission, classified according to ACNS-terminology in patients with UWS at admission, can provide useful prognostic contribution.
•EEG-derived symmetry indexes are markers of recovery for patients with acquired brain injury.•Machine learning algorithms can be crossvalidated to automatically extract symmetry index.•The solution ...detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%)•It can speed up analysis and improve quality of care in settings lacking skilled staff.
Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff.
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