Dataset Information

Tremor stability index: a new tool for differential diagnosis in tremor syndromes.

ABSTRACT: See Vidailhet et al. (doi:10.1093/brain/awx140) for a scientific commentary on this article. Misdiagnosis among tremor syndromes is common, and can impact on both clinical care and research. To date no validated neurophysiological technique is available that has proven to have good classification performance, and the diagnostic gold standard is the clinical evaluation made by a movement disorders expert. We present a robust new neurophysiological measure, the tremor stability index, which can discriminate Parkinson’s disease tremor and essential tremor with high diagnostic accuracy. The tremor stability index is derived from kinematic measurements of tremulous activity. It was assessed in a test cohort comprising 16 rest tremor recordings in tremor-dominant Parkinson’s disease and 20 postural tremor recordings in essential tremor, and validated on a second, independent cohort comprising a further 55 tremulous Parkinson’s disease and essential tremor recordings. Clinical diagnosis was used as gold standard. One hundred seconds of tremor recording were selected for analysis in each patient. The classification accuracy of the new index was assessed by binary logistic regression and by receiver operating characteristic analysis. The diagnostic performance was examined by calculating the sensitivity, specificity, accuracy, likelihood ratio positive, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-validation. Tremor stability index with a cut-off of 1.05 gave good classification performance for Parkinson’s disease tremor and essential tremor, in both test and validation datasets. Tremor stability index maximum sensitivity, specificity and accuracy were 95%, 95% and 92%, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.916 (95% confidence interval 0.797–1.000) for the test dataset and a value of 0.855 (95% confidence interval 0.754–0.957) for the validation dataset. Classification accuracy proved independent of recording device and posture. The tremor stability index can aid in the differential diagnosis of the two most common tremor types. It has a high diagnostic accuracy, can be derived from short, cheap, widely available and non-invasive tremor recordings, and is independent of operator or postural context in its interpretation.

SUBMITTER: di Biase L

PROVIDER: S-EPMC5493195 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Tremor stability index: a new tool for differential diagnosis in tremor syndromes.

di Biase Lazzaro L Brittain John-Stuart JS Shah Syed Ahmar SA Pedrosa David J DJ Cagnan Hayriye H Mathy Alexandre A Chen Chiung Chu CC Martín-Rodríguez Juan Francisco JF Mir Pablo P Timmerman Lars L Schwingenschuh Petra P Bhatia Kailash K Di Lazzaro Vincenzo V Brown Peter P

Brain : a journal of neurology 20170701 7

See Vidailhet et al. (doi:10.1093/brain/awx140) for a scientific commentary on this article. Misdiagnosis among tremor syndromes is common, and can impact on both clinical care and research. To date no validated neurophysiological technique is available that has proven to have good classification performance, and the diagnostic gold standard is the clinical evaluation made by a movement disorders expert. We present a robust new neurophysiological measure, the tremor stability index, which can d ...[more]

PMID: 28459950

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Project description:PurposeLanguage decline has been associated with healthy aging and with various neurodegenerative conditions, making it challenging to differentiate among these conditions. This study examined the utility of linguistic measures derived from a short narrative language sample for 1) identifying language characteristics and cut-off scores to differentiate between healthy aging, Primary Progressive Aphasia (PPA), Mild Cognitive Impairment (MCI), and Alzheimer's dementia (AD); and 2) differentiating among PPA variants in which language is the primary impairment.MethodParticipants were 25 neurologically healthy English speakers, 20 individuals with MCI, 20 with AD, and 26 with PPA (non-fluent/agrammatic N = 10, logopenic N = 9, semantic N = 7). Narrative language samples of the Cookie Theft Picture of persons with healthy aging, MCI and AD were retrospectively obtained from the DementiaBank database (https://talkbank.org/DementiaBank/) and PPA samples were obtained from an ongoing research study. The language samples were analyzed for fluency, word retrieval success, grammatical accuracy, and errors using automated and manual analysis methods. The sensitivity and specificity of various language measures was computed.ResultsParticipants with PPA scored lower than neurologically healthy and MCI groups on fluency (words per minute and disfluencies), word retrieval (Correct Information Units and number of errors), and sentence grammaticality. PPA and AD groups did not differ on language measures. Agrammatic PPA participants scored lower than logopenic and semantic PPA groups on several measures, while logopenic and semantic PPA did not differ on any measures.ConclusionMeasures derived from brief language samples and analyzed using mostly automated methods are clinically useful in differentiating PPA from healthy aging and MCI, and agrammatic PPA from other variants. The sensitivity and specificity of these measures is modest and can be improved when coupled with clinical presentation.

Project description:Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

Dataset Information

Tremor stability index: a new tool for differential diagnosis in tremor syndromes.

Publications

Tremor stability index: a new tool for differential diagnosis in tremor syndromes.

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