Düwel P, Ilgner J, Engelke JC, Westhofen M. Subclassification of vestibular disorders by means of statistical analysis in caloric labyrinth testing.
Acta Otolaryngol 2004;
124:595-602. [PMID:
15267178 DOI:
10.1080/00016480310015182]
[Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
OBJECTIVE
In the past, various attempts were made to perform a quantitative analysis of nystagmographic findings but their diagnostic value was limited. Therefore, the authors present a multivariate analysis of nystagmus findings with the aim of increasing the precision of diagnostic differentiation in cases of vestibular dysfunction.
MATERIAL AND METHODS
A group of 387 patients and 40 healthy volunteers were examined over a 14-month period using electronystagmography after stimulation by bithermal, bilateral irrigation of the labyrinth. Amplitude, slow-phase velocity, frequency and directional preponderance were evaluated.
RESULTS
No defined normal values for caloric nystagmus parameters could be obtained. However, by using the Mann Whitney U-test and logistic regression analysis a differentiation between pathological and healthy findings as well as between central and peripheral vestibular disorders and even between distinct vestibular disease entities is possible. Using these methods, the nystagmus amplitude was found to be the strongest discriminating parameter. Therefore, sole assessment of nystagmographic findings by selective calculation of the nystagmus slow-phase velocity falls short of the potential offered by electronystagmographic registration.
CONCLUSION
For daily clinical routine, counting nystagmus beats leads to the same diagnostic precision as the analysis of slow-phase velocities. In contrast, multivariate analysis of several nystagmus parameters can distinguish between distinct diseases with fairly high precision. This stepwise analysis of nystagmographic data could create the basis for an expert-system tool in the near future.
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