Decannulation and improvement of responsiveness in patients with disorders of consciousness

Hakiki, Bahia; Pancani, Silvia; Draghi, Francesca; Portaccio, Emilio; Tofani, Ariela; Binazzi, Barbara; Maria, Romoli Anna; Scarpino, Maenia; Macchi, Claudio; Cecchi, Francesca

doi:10.1080/09602011.2020.1833944

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…These authors affirmed that a more accurate assessment of responsiveness and level of consciousness could better predict the probability of decannulation in ABI patients. This evidence was recently confirmed by Hikiki et al [ 21 ], demonstrating that the highest CRS-r scores at admission corresponded to the highest probability of decannulation at discharge.…”

Section: Discussionsupporting

confidence: 64%

External Validation and Calibration of the DecaPreT Prediction Model for Decannulation in Patients with Acquired Brain Injury

et al. 2021

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We propose a new set of clinical variables for a more accurate early prediction of safe decannulation in patients with severe acquired brain injury (ABI), during a post-acute rehabilitation course. Starting from the already validated DecaPreT scale, we tested the accuracy of new logistic regression models where the coefficients of the original predictors were reestimated. Patients with tracheostomy were retrospectively selected from the database of the neurorehabilitation unit at the S. Anna Institute of Crotone, Italy. New potential predictors of decannulation were screened from variables collected on admission during clinical examination, including (a) age at injury, (b) coma recovery scale-revised (CRS-r) scores, and c) length of ICU period. Of 273 patients with ABI (mean age 53.01 years; 34% female; median DecaPreT = 0.61), 61.5% were safely decannulated before discharge. In the validation phase, the linear logistic prediction model, created with the new multivariable predictors, obtained an area under the receiver operating characteristics curve of 0.901. Our model improves the reliability of simple clinical variables detected at the admission of the post-acute phase in predicting decannulation of ABI patients, thus helping clinicians to plan better rehabilitation.

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Section: Discussionsupporting

confidence: 64%

External Validation and Calibration of the DecaPreT Prediction Model for Decannulation in Patients with Acquired Brain Injury

et al. 2021

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“…For this reason, prognostication on neurological outcomes in patients with DoC remains a challenging task. A conspicuous number of predictive parameters in rehabilitation of patients with DoC have been reported both from the clinical world [3]- [5] and the instrumental world [6]- [8]. Multiple electroencephalography (EEG) descriptors as reactivity, alpha waves and an anteroposterior gradient (APG) have been shown to be predictive of a possible consciousness recovery [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Liuzzi

Grippo

Campagnini

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Liuzzi et al. Merging clinical and EEG biomarkers in an Elastic-Net regression for disorder of consciousness prognosis prediction Abstract-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 databased 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. Datadriven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decisionmaking process the practitioners must face.

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“…Along with the fluctuation of the consciousness level itself, some concomitant clinical states, such as aphasia, apraxia, object, or visual agnosia, marked spasticity ( 43 ), and hyper or hypotonus was reported to produce impossible or improbable CRS-R scores ( 44 ). Additionally, some associated pathologies or clinical conditions, such as the presence of a CINMP ( 22 ) or the presence of tracheostomy ( 45 , 46 ), may limit the recognition of voluntary behavior and, consequently, hinder a correct diagnosis of consciousness ( 42 ). This risk of misdiagnosis has serious ethical concerns and deep implications for medical management and the decisions of the patient' families ( 47 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study

et al. 2022

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BackgroundDue 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.MethodsAll 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.StatisticsAdvanced 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.DiscussionIdentifying 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.ConclusionsThe 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.

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Decannulation and improvement of responsiveness in patients with disorders of consciousness

Cited by 8 publications

References 37 publications

External Validation and Calibration of the DecaPreT Prediction Model for Decannulation in Patients with Acquired Brain Injury

External Validation and Calibration of the DecaPreT Prediction Model for Decannulation in Patients with Acquired Brain Injury

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study

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