Perceptual and qualitative voice alterations detected by GIRBAS in patients with Parkinson's disease: is there a relation with lung function and oxygenation?

BACKGROUND: Impairments in respiration, voice and speech are common in people with Parkinson's disease (PD). AIMS: To evaluate the prevalence of dysphonia, assessed by a specific acoustic evaluation and description of the voice by the speech therapist (GIRBAS), and its relation with lung function and oxygenation, in particular cough ability and during the night or exercise desaturation. METHODS: This is a posthoc analysis of a prospective cross-sectional observational study on PD patients collecting anthropometric and clinical data, comorbidities, PD severity, motor function and balance, respiratory function at rest, during exercise and at night, voice function with acoustic analysis and presence of speech disorders, in addition to the GIRBAS scale. Based on GIRBAS Global dysphonia ('G') score, we divided patients into dysphonic (moderate-to-severe deviance from the euphonic condition) vs. no/mild dysphonic and analyzed the relations with respiratory impairments. RESULTS: We analyzed 55 patients and found significant impairments in both respiratory and voice/speech functions. Most patients (85.5%) presented mild-to-severe deviance from the euphonic condition in at least one GIRBAS perceptual element (80% of cases for Global dysphonia) and only 14.5% did not show deviance in all elements simultaneously. At Odds Ratio analysis, the risk of presenting nocturnal desaturation and reduced peak cough expiratory flow was approximately 24 and 8 times higher, respectively, in dysphonic patients vs. those with no/mild dysphonia. CONCLUSION: Perceptual and qualitative evaluation of the voice with GIRBAS showed that mild-to-severe dysphonia was highly prevalent in PD patients, and associated with nocturnal oxygen desaturation and poor cough ability.

Voice Alterations, Dysarthria, and Respiratory Derangements in Patients With Parkinson's Disease.

PURPOSE: Almost 90% of people with Parkinson's disease (PD) develop voice and speech disorders during the course of the disease. Ventilatory dysfunction is one of the main causes. We aimed to evaluate relationships between respiratory impairments and speech/voice changes in PD. METHOD: At Day 15 from admission, in consecutive clinically stable PD patients in a neurorehabilitation unit, we collected clinical data as follows: comorbidities, PD severity, motor function and balance, respiratory function at rest (including muscle strength and cough ability), during exercise-induced desaturation and at night, voice function (Voice Handicap Index [VHI] and acoustic analysis [Praat]), speech disorders (Robertson Dysarthria Profile [RDP]), and postural abnormalities. Based on an arbitrary RDP cutoff, two groups with different dysarthria degree were identified-moderate-severe versus no-mild dysarthria-and compared. RESULTS: Of 55 patients analyzed (median value Unified Parkinson's Disease Rating Scale Part II 9 and Part III 17), we found significant impairments in inspiratory and expiratory muscle pressure (> 90%, both), exercise tolerance at 6-min walking distance (96%), nocturnal (12.7%) and exercise-induced (21.8%) desaturation, VHI (34%), and Praat Shimmer% (89%). Patients with moderate-severe dysarthria (16% of total sample) had more comorbidities/disabilities and worse respiratory pattern and postural abnormalities (camptocormia) than those with no-mild dysarthria. Moreover, the risk of presenting nocturnal desaturation, reduced peak expiratory flow, and cough ability was about 11, 13, and 8 times higher in the moderate-severe group. CONCLUSIONS: Dysarthria and respiratory dysfunction are closely associated in PD patients, particularly nocturnal desaturation and reduced cough ability. In addition, postural condition could be at the base of both respiratory and voice impairments. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21210944.

Walking function determinants in parkinson patients undergoing rehabilitation.

BACKGROUND: To date little is known about factors affecting walking rehabilitation in Parkinson's disease (PD) patients. OBJECTIVE: This prospective observational cohort study evaluated the walking distance covered in 6 minutes (6 MWD) before and after conventional rehabilitation and verified which among PD motor disorders was the most important determinant of walking distance in PD patients undergoing rehabilitation. METHODS: Data were collected from 55 PD patients, performing a conventional outpatient motor rehabilitation program. The 6MWD at the end of rehabilitation and gain in 6MWD were the outcome measures. The Unified Parkinson's Disease Rating Scale, Hoehn and Yahr scale, Nine Hole Peg Test, Grip and Pinch test, ROM wrist motility, and Berg Balance Scale (BBS) were used to assess PD patients at admission and were considered as dependent variables. Backward multiple regression analyses identified the determinants of 6MWD outcomes. RESULTS: The 6MWD was 246.58±115 meters at admission and 286.90±116 at the end of rehabilitation. At end of rehabilitation, the 6MWD was 42.32±47 meters greater than admission (p < 0.001), corresponding to an increase of +17.16%. At the end of rehabilitation, the 6MWD was significantly longer in PD patients with stages 1-3 of the Hoehn and Yahr scale. Berg Balance Scale (ß= 0.47, p < 0.001) and right Grip and Pinch at admission (ß= 0.36, p = 0.001) were the only determinants of final 6MWD. The R2 value of the model was 0.47 (R2 adjusted 0.45). No variable was a determinant of gain in 6MWD. CONCLUSIONS: The study indicates that balance and generalized muscle strength are important determinants of walking rehabilitation in PD patients, in whom it is essential to maintain high levels of balance and muscle strength for a time as long as possible. These findings suggest planning more intensive rehabilitation treatments in PD patients with low levels of balance and muscle strength.

Neuropsychological Pattern in a Series of Post-Acute COVID-19 Patients in a Rehabilitation Unit: Retrospective Analysis and Correlation with Functional Outcomes.

Intensive Care Unit delirium, insomnia, anxiety, and frontal/dysexecutive disorders have been described following COVID-19 infection. The aim of this case study was to re-evaluate the neuropsychological pattern in a series of patients with COVID-19 outcomes. We retrospectively evaluated 294 patients admitted to the Istituti Clinici Scientifici Maugeri of Lumezzane (Brescia) (May-September 2020). Neuropsychological assessment was available for 12 patients. We extracted clinical, functional data (FIM and Barthel Index score) and neuropsychological tests (MMSE, Trail making a-b, verbal fluency test, digit span, prose memory test, Frontal Assessment Battery, clock drawing test, Rey-Osterrieth complex figure, Tower of London test). The results were analyzed by Spearman (rho) correlation. Six patients presented dysexecutive alterations even in the presence of normal overall cognitive functioning. Forward digit span score was directly correlated to FIM value at admission (p = 0.015) and inversely correlated to delta FIM (p = 0.030) and delta Barthel Index (p = 0.025). In our experience, subclinical cognitive alterations were present in 4% of patients recovering from COVID-19 pneumonia. The possible correlation between verbal memory and frontal functions, and the degree of functional impairment at admission and its subsequent improvement, underscores the importance of an adequate cognitive evaluation and rehabilitation.

Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening.

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM-recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.