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Care pathways models and clinical outcomes in Disorders of consciousness.

ABSTRACT: Patients with Disorders of consciousness, are persons with extremely low functioning levels and represent a challenge for health care systems due to their high needs of facilitating environmental factors. Despite a common Italian health care pathway for these patients, no studies have analyzed information on how each region have implemented it in its welfare system correlating data with patients' clinical outcomes.A multicenter observational pilot study was realized. Clinicians collected data on the care pathways of patients with Disorder of consciousness by asking 90 patients' caregivers to complete an ad hoc questionnaire through a structured phone interview. Questionnaire consisted of three sections: sociodemographic data, description of the care pathway done by the patient, and caregiver evaluation of health services and information received.Seventy-three patients were analyzed. Length of hospital stay was different across the health care models and it was associated with improvement in clinical diagnosis. In long-term care units, the diagnosis at admission and the number of caregivers available for each patient (median value = 3) showed an indirect relationship with worsening probability in clinical outcome. Caregivers reported that communication with professionals (42%) and the answer to the need of information were the most critical points in the acute phase, whereas presence of Non-Governmental Organizations (25%) and availability of psychologists for caregivers (21%) were often missing during long-term care. The 65% of caregivers reported they did not know the UN Convention on the Rights of Persons with Disabilities.This study highlights relevant differences in analyzed models, despite a recommended national pathway of care. Future public health considerations and actions are needed to guarantee equity and standardization of the care process in all European countries.

SUBMITTER: Sattin D

PROVIDER: S-EPMC5561306 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Care pathways models and clinical outcomes in Disorders of consciousness.

Sattin Davide D Morganti Laura L De Torres Laura L Dolce Giuliano G Arcuri Francesco F Estraneo Anna A Cardinale Viviana V Piperno Roberto R Zavatta Elena E Formisano Rita R D'Ippolito Mariagrazia M Vassallo Claudio C Dessi Barbara B Lamberti Gianfranco G Antoniono Elena E Lanzillotti Crocifissa C Navarro Jorge J Bramanti Placido P Corallo Francesco F Zampolini Mauro M Scarponi Federico F Avesani Renato R Salvi Luca L Ferro Salvatore S Mazza Luigi L Fogar Paolo P Feller Sandro S De Nigris Fulvio F Martinuzzi Andrea A Buffoni Mara M Pessina Adriano A Corsico Paolo P Leonardi Matilde M

Brain and behavior 20170721 8

<h4>Objective</h4>Patients with Disorders of consciousness, are persons with extremely low functioning levels and represent a challenge for health care systems due to their high needs of facilitating environmental factors. Despite a common Italian health care pathway for these patients, no studies have analyzed information on how each region have implemented it in its welfare system correlating data with patients' clinical outcomes.<h4>Materials and methods</h4>A multicenter observational pilot ...[more]

PMID: 28828206

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Project description:Patients with severe acquired brain injury and prolonged disorders of consciousness (pDoC) are characterized by high clinical complexity and high risk to develop medical complications. The present multi-center longitudinal study aimed at investigating the impact of medical complications on the prediction of clinical outcome by means of machine learning models. Patients with pDoC were consecutively enrolled at admission in 23 intensive neurorehabilitation units (IRU) and followed-up at 6 months from onset via the Glasgow Outcome Scale-Extended (GOSE). Demographic and clinical data at study entry and medical complications developed within 3 months from admission were collected. Machine learning models were developed, targeting neurological outcomes at 6 months from brain injury using data collected at admission. Then, after concatenating predictions of such models to the medical complications collected within 3 months, a cascade model was developed. One hundred seventy six patients with pDoC (M: 123, median age 60.2 years) were included in the analysis. At admission, the best performing solution (k-Nearest Neighbors regression, KNN) resulted in a median validation error of 0.59 points [IQR 0.14] and a classification accuracy of dichotomized GOS-E of 88.6%. Coherently, at 3 months, the best model resulted in a median validation error of 0.49 points [IQR 0.11] and a classification accuracy of 92.6%. Interpreting the admission KNN showed how the negative effect of older age is strengthened when patients' communication levels are high and ameliorated when no communication is present. The model trained at 3 months showed appropriate adaptation of the admission prediction according to the severity of the developed medical complexity in the first 3 months. In this work, we developed and cross-validated an interpretable decision support tool capable of distinguishing patients which will reach sufficient independence levels at 6 months (GOS-E > 4). Furthermore, we provide an updated prediction at 3 months, keeping in consideration the rehabilitative path and the risen medical complexity.

Project description:BackgroundIn 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.MethodsWe 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.ResultsBetween 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]).ConclusionsOur 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.

Project description:IntroductionDisorders of consciousness (DoCs) are a frequent complication of brain injury disease, and effective treatments are currently lacking. Transauricular vagus nerve stimulation (tVNS) has been proposed as a promising therapeutic method for neurological disorders such as epilepsy and depression. In our previous study, we demonstrated that vagus nerve stimulation promoted recovery in rats with DoCs caused by traumatic brain injury. However, the clinical effect of vagus nerve stimulation on consciousness disorders is unclear. We aimed to investigate the therapeutic efficacy and safety of tVNS in patients with DoCs.MethodsWe conducted a randomized, double-blinded, sham-controlled trial. Patients (N = 60) with DoCs, including minimally conscious state (MCS) and vegetative state/unresponsive wakefulness syndrome, were enrolled and randomized to groups receiving either active or sham tVNS. A frequency of 20 Hz and pulse wave of 200 us was used in the active-tVNS protocol, which was performed in the auricular branch of the vagus nerve in the left outer ear. The sham-tVNS protocol was the same as the active-tVNS protocol although without current input. Both groups of patients also received conventional treatments. Consciousness was evaluated according to the Coma Recovery Scale-Revised before and after the 4-week intervention. We also recorded the type and number of behavioral responses. Safety was primarily assessed according to the incidence of treatment-emergent adverse events. Each patient's heart rate and blood pressure were monitored during all treatment sessions.ResultsUltimately, 57 patients completed the study: 28 patients underwent active tVNS and 29 patients underwent sham tVNS. No significant differences were observed in Coma Recovery Scale-Revised scores between the active- and sham-tVNS groups before the tVNS sessions. Compared with patients in the sham-tVNS group (9.28 ± 4.38), patients with DoCs treated with active tVNS showed improved consciousness (10.93 ± 4.99), although not statistically significant. Further analysis revealed obvious differences between patients with MCS receiving active and sham tVNS, but no significant difference in patients with vegetative state/unresponsive wakefulness syndrome in both groups. All side effects were considered common medical conditions with no obvious correlation to tVNS.ConclusionThese preliminary data provide early evidence that tVNS may be an effective and safe approach for promoting the recovery of consciousness, especially in patients with MCS.Clinical trial registrationhttps://www.chictr.org.cn/edit.aspx?pid=175938&htm=4, identifier: ChiCTR2200066629.

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Care pathways models and clinical outcomes in Disorders of consciousness.

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Care pathways models and clinical outcomes in Disorders of consciousness.

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