Tremor Stability Index: A New Tool for Differential Diagnosis in Tremor Syndromes

Lazzaro di Biase; John-Stuart Brittain; Syed Ahmar Shah; David J. Pedrosa; Hayriye Cagnan; Alexandre Mathy; Chiung Chu Chen; Juan Francisco Martín-Rodríguez; Pablo Mir; Lars Timmerman; Petra Schwingenschuh; Kailash Bhatia; Vincenzo Di Lazzaro; Peter Brown

Disclosures

Brain. 2017;140(7):1977-1986. 

In This Article

Abstract and Introduction

Abstract

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.

Introduction

Misdiagnosis in tremor syndromes is a common and often underestimated problem that can cause misleading results in clinical trials (Rizzo et al., 2016). At the clinical level misdiagnosis may lead to suboptimal treatment and incorrect prognosis. Central to this problem is the lack of accurate diagnostic tools that can distinguish different tremor aetiologies. Indeed, the diagnostic accuracy of Parkinson's disease turns out to be only moderate when assessed against the gold standard of post-mortem histology (Gibb and Lees, 1988). Overall, diagnostic accuracy has been estimated to be 80% amongst movement disorders experts, and 74% if the disease is diagnosed by a neurologist not expert in movement disorders (Rizzo et al., 2016). Thus, even using the UK Brain Bank criteria for Parkinson's disease (Gibb and Lees, 1988) as a proxy for post-mortem examination, about 2 in 10 patients with Parkinson's disease still receive a misdiagnosis, and this figure may be even higher in those presenting with Parkinson's disease tremor (Selikhova et al., 2013).

In essential tremor there is no gold standard diagnostic procedure, not even at post-mortem, and diagnosis is made purely on clinically-defined criteria (Deuschl et al., 1998). Thirty-seven per cent of essential tremor patients are misdiagnosed, with the most common misdiagnosis being Parkinson's disease tremor (Jain et al., 2006). The differential diagnosis of essential tremor and Parkinson's disease tremor is especially difficult early in the course of the disease when other parkinsonian signs may be absent and the clinician does not have the benefit of knowing the disease course (Deuschl et al., 1998; Jain et al., 2006; Bajaj et al., 2010; Rizzo et al., 2016). Moreover, patient age is not a discriminant factor, since early onset Parkinson's disease and late onset essential tremor are part of the spectrum of these two diseases, and this often underlies the cases in which differential diagnosis is most difficult (Lou and Jankovic, 1991; Schrag and Schott, 2006).

Clinically, Parkinson's disease tremor is present at rest, while tremor in essential tremor is postural and/or kinetic (Deuschl et al., 1998; Shahed and Jankovic, 2007). However, Parkinson's disease tremor may also manifest as a postural tremor, which generally appears a few seconds after assuming a posture—'re-emergent' tremor (Jankovic et al., 1999; Shahed and Jankovic, 2007; Belvisi et al., 2017). Nevertheless, this tremor onset delay can be absent in some patients with Parkinson's disease, showing a pure postural tremor (Shahed and Jankovic, 2007). Conversely, if patients with essential tremor are not fully relaxed during the muscular tone evaluation, tremor can lead to the false impression of a cogwheel phenomenon (Elble, 2002; Shahed and Jankovic, 2007).

Given these uncertainties, 123I-FP-CIT and 123I-β-CIT DAT-SPECT have been used to help discriminate between Parkinson's disease and essential tremor (Asenbaum et al., 1998; Benamer et al., 2000; Parkinson Study Group, 2000; Politis, 2014). However, the overall accuracy of nuclear imaging techniques for the Parkinson's disease diagnosis may not be different from that of clinical diagnosis established by a movement disorder expert (de la Fuente-Fernández, 2012). Moreover, the use of this diagnostic support tool has created a new diagnostic grouping, defined as SWEDD (scans without evidence of dopaminergic deficit). The latter consists of patients that present clinically with parkinsonian features but do not have evidence of a dopaminergic deficit on presynaptic PET or SPECT (single-photon emission computed tomography) studies (Erro et al., 2016). In clinical trials, the incidence of SWEDD is between 3.6 and 19.6% (Erro et al., 2016). Although the most probable diagnosis underlying SWEDD may not be Parkinson's disease, interpretation is confounded as some SWEDD patients do evolve to full-blown Parkinson's disease (Menéndez-González et al., 2014), and in the early stages of Parkinson's disease a nuclear imaging deficit may not always be evident (Erro et al., 2016). Meanwhile in essential tremor, there is insufficient evidence to support the use of nuclear imaging techniques for positive diagnosis (Colebatch et al., 1990; Jenkins et al., 1993; Wills et al., 1994). Thus, nuclear imaging techniques can help distinguish tremor in Parkinson's disease from that in other conditions, but are not perfect in this regard, and offer no help in distinguishing essential tremor from other non-parkinsonian forms of tremor such as that seen in dystonia. Finally, nuclear imaging techniques involve radiopharmaceutical agents, are expensive, time consuming, operator-dependent and not widely available.

In contrast, clinical neurophysiology is widely accessible, relatively inexpensive, and has also been explored as a diagnostic aid in tremor conditions (Deuschl et al., 1996). However, with one possible exception, no neurophysiological techniques have proven to have good classification properties for Parkinson's disease and essential tremor differential diagnosis, with confirmed validity in an independent cohort (Deuschl et al., 1996). The latter helps establish the robustness of any metric to small variations in data recording procedures and patient demographics. The possible exception to this rule is the mean harmonic power (MHP) of postural tremor harmonics (Muthuraman et al., 2011; Wile et al., 2014). This measure likely reflects differences in the structure of tremor EMG bursts between the two conditions. Nevertheless, its use has only been partially validated in two independent cohorts, as it proved necessary to have different cut-offs in the original and validation cohorts, perhaps because estimates of MHP rely on carefully calibrated accelerometer recordings (Muthuraman et al., 2011; Wile et al., 2014). The stability of tremor frequency over time has also recently been considered as the basis for a potential diagnostic aid. The instantaneous frequency of tremor and its temporal evolution is readily revealed by accelerometry—a cheap and simple means of recording tremor, and for this use devices do not require careful calibration across study populations. Brittain et al. (2015) analysed the variation in instantaneous tremor frequency over time, and showed that, in essential tremor, the frequency of tremor remains stable only over a narrow range of frequencies, whereas in Parkinson's disease tremor the frequency can remain stable over a much broader range. These authors defined a new index, the frequency tolerance of tremor, as the frequency range over which tremor could settle at a temporarily stable frequency. However, the range of frequency tolerance was essentially established by considering the behaviour of tremor oscillations at outlying frequencies and as such did not capitalize on the whole tremor time series, nor characterize overall tremor stability.

Here we analyse the overall tremor stability characteristics of Parkinson's disease and essential tremor, and use this information to develop a new measure, the tremor stability index (TSI), for the discrimination of these two tremor types. The utility and diagnostic performance of this index is analysed, and its performance validated in a separate patient cohort.

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