16 research outputs found
Comparative Analysis of Qualitative Verbal Fluency Performance in Normal Elderly and Demented Populations
Psychometric Evaluation of the Montreal Cognitive Assessment (MoCA) in Three Diverse Samples
Validation of the New Interpretive Guidelines for the Clinical Dementia Rating Scale Sum of Boxes Score in the National Alzheimer's Coordinating Center Database
Traumatic brain injury history is associated with earlier age of onset of Alzheimer disease
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A novel speech analysis algorithm to detect cognitive impairment in a Spanish population
Peer reviewed: TrueObjectiveEarly detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.MethodData were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic “F” fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.ResultsMean logit algorithm scores were significantly different across groups in the testing sample (p &lt; 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.ConclusionFindings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.</jats:sec
Developing a Deep Brain Stimulation Neuromodulation Network for Parkinson Disease, Essential Tremor, and Dystonia: Report of a Quality Improvement Project
<div><p>Objective</p><p>To develop a process to improve patient outcomes from deep brain stimulation (DBS) surgery for Parkinson disease (PD), essential tremor (ET), and dystonia.</p><p>Methods</p><p>We employed standard quality improvement methodology using the Plan-Do-Study-Act process to improve patient selection, surgical DBS lead implantation, postoperative programming, and ongoing assessment of patient outcomes.</p><p>Results</p><p>The result of this quality improvement process was the development of a neuromodulation network. The key aspect of this program is rigorous patient assessment of both motor and non-motor outcomes tracked longitudinally using a REDCap database. We describe how this information is used to identify problems and to initiate Plan-Do-Study-Act cycles to address them. Preliminary outcomes data is presented for the cohort of PD and ET patients who have received surgery since the creation of the neuromodulation network.</p><p>Conclusions</p><p>Careful outcomes tracking is essential to ensure quality in a complex therapeutic endeavor like DBS surgery for movement disorders. The REDCap database system is well suited to store outcomes data for the purpose of ongoing quality assurance monitoring.</p></div