Milena Kostalova

Poststroke delirium incidence and outcomes: validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU).

Objective: To describe the epidemiology and time spectrum of delirium using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria and to validate a tool for delirium assessment in patients in the acute poststroke period. Design: A prospective observational cohort study. Setting: The stroke unit of a university hospital. Patients: A consecutive series of 129 patients with stroke (with infarction or intracerebral hemorrhage, 57 women and 72 men; mean age, 72.5 yrs; age range, 35–93 yrs) admitted to the stroke unit of a university hospital were evaluated for delirium incidence. Interventions: None. Measurements and Main Results: Criterion validity and overall accuracy of the Czech version of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) were determined using serial daily delirium assessments with CAM-ICU by a junior physician compared with delirium diagnosis by delirium experts using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria that began the first day after stroke onset and continued for at least 7 days. Cox regression models using time-dependent covariate analysis adjusting for age, gender, prestroke dementia, National Institutes of Stroke Health Care at admission, first-day Sequential Organ Failure Assessment, and asphasia were used to understand the relationships between delirium and clinical outcomes. An episode of delirium based on reference Diagnostic and Statistical Manual assessment was detected in 55 patients with stroke (42.6%). In 37 of these (67.3%), delirium began within the first day and in all of them within 5 days of stroke onset. A total of 1003 paired CAM-ICU/Diagnostic and Statistical Manual of Mental Disorders daily assessments were completed. Compared with the reference standard for diagnosing delirium, the CAM-ICU demonstrated a sensitivity of 76% (95% confidence interval [CI] 55% to 91%), a specificity of 98% (95% CI 93% to 100%), an overall accuracy of 94% (95% CI 88% to 97%), and high interrater reliability (&kgr; = 0.94; 95% CI 0.83–1.0). The likelihood ratio of the CAM-ICU in the diagnosis of delirium was 47 (95% CI 27–83). Delirium was an independent predictor of increased length of hospital stay (hazard ratio 1.63; 95% CI 1.11–2.38; p = .013). Conclusions: Poststroke delirium may frequently be detected provided that the testing algorithm is appropriate to the time profile of poststroke delirium. Early (first day after stroke onset) and serial screening for delirium is recommended. CAM-ICU is a valid instrument for the diagnosis of delirium and should be considered an aid in delirium screening and assessment in future epidemiologic and interventional studies in patients with stroke.

Robust and complex approach of pathological speech signal analysis

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to 100.0 ? 0.0 % ) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a length of sustained vowels, however in the case of Principe de Asturias (PdA) Hospital in Alcala de Henares of Madrid database we made improvements in classification accuracy ( 82.1 ? 3.3 % ) and specificity ( 83.8 ? 5.1 % ) when considering a single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann-Whitney U test as significant ( p < 0.05 ) when processing at least one of the mentioned databases. The largest discriminative power from these proposed features has a cepstral peak prominence extracted from the first intrinsic mode function ( p = 6.9443 i? 10 - 32 ) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The paper also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view.

Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson's disease

BACKGROUND AND OBJECTIVE Hypokinetic dysarthria (HD) is a frequent speech disorder associated with idiopathic Parkinsons disease (PD). It affects all dimensions of speech production. One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate, flat speech melody, and impairment of speech intensity control. Dysprosody has a detrimental impact on speech naturalness and intelligibility. METHODS This paper deals with quantitative prosodic analysis of neutral, stress-modified and rhymed speech in patients with PD. The analysis of prosody is based on quantification of monopitch, monoloudness, and speech rate abnormalities. Experimental dataset consists of 98 patients with PD and 51 healthy speakers. For the purpose of HD identification, sequential floating feature selection algorithm and random forests classifier is used. In this paper, we also introduce a concept of permutation test applied in the field of acoustic analysis of dysarthric speech. RESULTS Prosodic features obtained from stress-modified reading task provided higher classification accuracies compared to the ones extracted from reading task with neutral emotion demonstrating the importance of stress in speech prosody. Features calculated from poem recitation task outperformed both reading tasks in the case of gender-undifferentiated analysis showing that rhythmical demands can in general lead to more precise identification of HD. Additionally, some gender-related patterns of dysprosody has been observed. CONCLUSIONS This paper confirms reduced variation of fundamental frequency in PD patients with HD. Interestingly, increased variability of speech intensity compared to healthy speakers has been detected. Regarding speech rate disturbances, our results does not report any particular pattern. We conclude further development of prosodic features quantifying the relationship between monopitch, monoloudness and speech rate disruptions in HD can have a great potential in future PD analysis.

Small-nerve-fiber pathology in critical illness documented by serial skin biopsies

Introduction: Small‐fiber pathology can develop in the acute phase of critical illness and may explain chronic sensory impairment and pain in critical care survivors. Methods: Eleven adult ischemic stroke patients in a neurocritical care unit were enrolled in an observational cohort study. Intraepidermal nerve fiber density (IENFD) in the distal leg was assessed on admission to the intensive care unit and 10–14 days later, together with electrophysiological testing. Results: Of the 11 patients recruited, 9 (82%) had sepsis or multiple‐organ failure. Median IENFD on admission (5.05 fibers/mm) decreased significantly to 2.18 fibers/mm (P < 0.001), and abnormal IENFD was found in 6 patients (54.5%). Electrodiagnostic signs of large‐fiber neuropathy and/or myopathy were found in 6 patients (54.5%), and autonomic dysfunction was found in 2 patients (18.2%). Conclusion: Serial IENFD measurements confirmed the development of small‐fiber sensory involvement in the acute phase of critical illness. Muscle Nerve 52: 28–33, 2015

Towards a predictive model for post-stroke delirium

Primary objective: To assess predisposing and precipitating risk factors and create a predictive model for post-stroke delirium. Research design: A prospective observational study in a cohort of consecutive patients with ischemic stroke or intracerebral haematoma admitted within 24 hours of stroke onset. Methods: Patients were assessed daily for delirium during the first week by means of DSM-IV criteria and risk factors were recorded. Results: One hundred patients completed a 7-day evaluation (47 women and 53 men, median age 77 years). An episode of delirium was detected in 43 patients (43%). Using multivariate logistic regression, a predictive statistical model was developed that utilized independent risk factors: age (OR = 1.08; 95% CI = 1.02–1.15); intracerebral haemorrhage (OR = 6.11; 95% CI = 1.62–22.98), lesion volume > 40 ccm (OR = 3.99; 95% CI = 1.29–12.39) and either elevated gamma-glytamyl transferase (OR = 4.88; 95% CI = 1.45–16.35) and elevated serum bilirubin (OR = 3.70; 95% CI = 1.32–10.38) or maximum sequential organ failure assessment score >2 (OR = 3.33; 95% CI = 1.06–10.45) with acceptable sensitivity and specificity (69.0% and 80.7%). In ischemic strokes, total anterior circulation infarctions were more frequently associated with delirium (73.3% developed delirium) compared with the remainder of the groups combined (p = 0.004; OR = 6.66; 95% CI = 1.85–24.01). Conclusion: Higher age, metabolic disturbances, intracerebral haemorrhage and larger ischemic hemispheric strokes increase the risk of post-stroke delirium.

Addenbrooke's Cognitive Examination and Individual Domain Cut-Off Scores for Discriminating between Different Cognitive Subtypes of Parkinson's Disease

Objective. The main aim of this study was to verify the sensitivity and specificity of Addenbrookes Cognitive Examination-Revised (ACE-R) in discriminating between Parkinsons disease (PD) with normal cognition (PD-NC) and PD with mild cognitive impairment (PD-MCI) and between PD-MCI and PD with dementia (PD-D). We also evaluated how ACE-R correlates with neuropsychological cognitive tests in PD. Methods. We examined three age-matched groups of PD patients diagnosed according to the Movement Disorder Society Task Force criteria: PD-NC, PD-MCI, and PD-D. ROC analysis was used to establish specific cut-off scores of ACE-R and its domains. Correlation analyses were performed between ACE-R and its subtests with relevant neuropsychological tests. Results. Statistically significant differences between groups were demonstrated in global ACE-R scores and subscores, except in the language domain. ACE-R cut-off score of 88.5 points discriminated best between PD-MCI and PD-NC (sensitivity 0.68, specificity 0.91); ACE-R of 82.5 points distinguished best between PD-MCI and PD-D (sensitivity 0.70, specificity 0.73). The verbal fluency domain of ACE-R demonstrated the best discrimination between PD-NC and PD-MCI (cut-off score 11.5; sensitivity 0.70, specificity 0.73) while the orientation/attention subscore was best between PD-MCI and PD-D (cut-off score 15.5; sensitivity 0.90, specificity 0.97). ACE-R scores except for ACE-R language correlated with specific cognitive tests of interest.

Assessing progress of Parkinson's disease using acoustic analysis of phonation

This paper deals with a complex acoustic analysis of phonation in patients with Parkinsons disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales (e. g. Unified Parkinsons disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %). Finally, we proposed a binary classification based on random forests that is able to identify Parkinsons disease with sensitivity SEN = 92.86% (SPE = 85.71 %). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD.

Parkinson Disease Detection from Speech Articulation Neuromechanics

Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease. Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech. Methods: A dataset of sustained vowels recorded from Parkinson Disease patients is contrasted with similar recordings from normative subjects. The probability distribution of the absolute kinematic velocity of the jaw-tongue system is extracted from each utterance. A Random Least Squares Feed-Forward Network (RLSFN) has been used as a binary classifier working on the pathological and normative datasets in a leave-one-out strategy. Monte Carlo simulations have been conducted to estimate the influence of the stochastic nature of the classifier. Two datasets for each gender were tested (males and females) including 26 normative and 53 pathological subjects in the male set, and 25 normative and 38 pathological in the female set. Results: Male and female data subsets were tested in single runs, yielding equal error rates under 0.6% (Accuracy over 99.4%). Due to the stochastic nature of each experiment, Monte Carlo runs were conducted to test the reliability of the methodology. The average detection results after 200 Montecarlo runs of a 200 hyperplane hidden layer RLSFN are given in terms of Sensitivity (males: 0.9946, females: 0.9942), Specificity (males: 0.9944, females: 0.9941) and Accuracy (males: 0.9945, females: 0.9942). The area under the ROC curve is 0.9947 (males) and 0.9945 (females). The equal error rate is 0.0054 (males) and 0.0057 (females). Conclusions: The proposed methodology avails that the use of highly normalized descriptors as the probability distribution of kinematic variables of vowel articulation stability, which has some interesting properties in terms of information theory, boosts the potential of simple yet powerful classifiers in producing quite acceptable detection results in Parkinson Disease.

Non-invasive stimulation of the auditory feedback area for improved articulation in Parkinson's disease

INTRODUCTION Hypokinetic dysarthria (HD) is a common symptom of Parkinsons disease (PD) which does not respond well to PD treatments. We investigated acute effects of repetitive transcranial magnetic stimulation (rTMS) of the motor and auditory feedback area on HD in PD using acoustic analysis of speech. METHODS We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the left orofacial primary motor area, the right superior temporal gyrus (STG), and over the vertex (a control stimulation site) in 16 PD patients with HD. A cross-over design was used. Stimulation sites and protocols were randomised across subjects and sessions. Acoustic analysis of a sentence reading task performed inside the MR scanner was used to evaluate rTMS-induced effects on motor speech. Acute fMRI changes due to rTMS were also analysed. RESULTS The 1 Hz STG stimulation produced significant increases of the relative standard deviation of the 2nd formant (p = 0.019), i.e. an acoustic parameter describing the tongue and jaw movements. The effects were superior to the control site stimulation and were accompanied by increased resting state functional connectivity between the stimulated region and the right parahippocampal gyrus. The rTMS-induced acoustic changes were correlated with the reading task-related BOLD signal increases of the stimulated area (R = 0.654, p = 0.029). CONCLUSION Our results demonstrate for the first time that low-frequency stimulation of the temporal auditory feedback area may improve articulation in PD and enhance functional connectivity between the STG and the cortical region involved in an overt speech control.

Quantitative Analysis of Relationship Between Hypokinetic Dysarthria and the Freezing of Gait in Parkinson’s Disease

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson’s disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a mathematical model that would predict FOG deficits using acoustic analysis at baseline. We enrolled 75 PD patients who were assessed by 6 clinical scales including the Freezing of Gait Questionnaire (FOG–Q). We subsequently extracted 19 acoustic measures quantifying speech disorders in the fields of phonation, articulation and prosody. To identify the relationship between HD and FOG, we performed a partial correlation analysis. Finally, based on the selected acoustic measures, we trained regression models to predict the change in FOG during a 2-year follow-up. We identified significant correlations between FOG–Q scores and the acoustic measures based on formant frequencies (quantifying the movement of the tongue and jaw) and speech rate. Using the regression models, we were able to predict a change in particular FOG–Q scores with an error of between 7.4 and 17.0 %. This study is suggesting that FOG in patients with PD is mainly linked to improper articulation, a disturbed speech rate and to intelligibility. We have also proved that the acoustic analysis of HD at the baseline can be used as a predictor of the FOG deficit during 2 years of follow-up. This knowledge enables researchers to introduce new cognitive systems that predict gait difficulties in PD patients.