RT Journal Article
T1 Tremor stability index: a new tool for differential diagnosis in tremor syndromes.
A1 di Biase, Lazzaro
A1 Brittain, John-Stuart
A1 Shah, Syed Ahmar
A1 Pedrosa, David J
A1 Cagnan, Hayriye
A1 Mathy, Alexandre
A1 Chen, Chiung Chu
A1 Martín-Rodríguez, Juan Francisco
A1 Mir, Pablo
A1 Timmerman, Lars
A1 Schwingenschuh, Petra
A1 Bhatia, Kailash
A1 Di Lazzaro, Vincenzo
A1 Brown, Peter
K1 Parkinson’s disease
K1 clinical neurophysiology
K1 movement disorders
K1 neurophysiology
K1 tremor
AB 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.
YR 2017
FD 2017
LK http://hdl.handle.net/10668/11150
UL http://hdl.handle.net/10668/11150
LA en
DS RISalud
RD Apr 7, 2025