Participants and study design
In this retrospective study, approved by the local ethical committee, anonymized patient charts were reviewed and analyzed. Patients referred to a tertiary otorhinolaryngology department at the local university hospital between 2001 and 2007 for an additional physiotherapeutic assessment because of complaints of instability or dizziness. To be included in the analyses, data on both vestibular function and functional balance testing had to be available. From the available patient files (n = 175), results from 62 patients were eligible for analyses [mean age (SD) 52.3 (11.9) years; 35 males]. Reason for exclusion were: incomplete ENG data (n = 60), balance testing more than two weeks after ENG assessment (n = 46) and a central lesion (n = 7).
Procedures
Caloric testing
The external ear canals were consecutively irrigated with 180 ml of warm (44 °C) and cold (30 °C) water for 30 s with open loop irrigation (warm right, warm left, cold right, cold left). In a nearly complete darkened room patients closed the eyes during testing and performed mental tasks. To allow temperature stabilization in the labyrinth, a pause of 5 min was introduced between irrigations of the same ear. For both binaural bithermal caloric testing and the SHA test computerized electronystagmography was used to register eye movements. A detailed description on the applied methodology has been described elsewhere [7].
Based on the slow component velocity (SCV, °/s), obtained during the maximal response of a caloric irrigation, Jongkees’ formula was used to calculate the percentage of labyrinth asymmetries. This labyrinth asymmetry is considered normal if the difference between both ears was less than 19% [7], based on a normative study performed in the same clinical setting, with exactly the same ENG procedure. Caloric testing has shown to be a valid measure to assess labyrinth asymmetries [20].
Sinusoidal Harmonic Acceleration test (SHA)
Subjects were seated in a servo-controlled motorized chair that was rotated sinusoidally around an earth vertical axis [7]. The chair was accelerated to a peak velocity of 50°/s and the frequency of the sinusoidal acceleration was 0.05 Hz. The test duration was 2 min. An angular rate sensor, attached to the subject’s head, recorded the head movements. The test was performed in total darkness with the eyes closed and patients were requested to perform mental tasks for mental alertness. Based on the slow phase velocity component, VOR gain, -asymmetry and -phase were calculated. The VOR gain is a measure of VOR performance calculated by the velocity of the correcting eye movement divided by the velocity of the head (normal values between 0.29 and 0.87) [7]. The VOR asymmetry stands for the percentage difference between the peak slow component eye velocities of the nystagmi to the left and right (normal value < 22%) [7]. The phase comprises the angle of the response which is a representation of the time difference between the eye and the head velocity and therefore a measure of the delay in the vestibular system (normal values between − 1° and 18°) [7]. The SHA is a valid VOR test that is clinically useful for the observation of a patient’ s progress through central compensation after unilateral dysfunction [20,21,22].
Based on the results on the caloric test and the SHA, three groups were composed. Group 1 consisted of patients that presented with a normal ENG, i.e. caloric symmetry and VOR gain, -asymmetry, and phase values within normal limits. If patients exhibited caloric asymmetry and normal VOR gain, asymmetry and -phase, they were classified in group 2 (peripheral loss, compensated). If patients showed caloric asymmetry and an abnormal result in either VOR gain, -asymmetry or -phase, they were assigned to group 3 (peripheral loss, uncompensated). For the logistic regression, groups 1 and 2 were merged.
Dizziness Handicap Inventory
The Dizziness Handicap Inventory (DHI) was used to assess the degree to which vestibular dysfunction subjectively affects overall activities of daily life [23, 24]. The questionnaire comprises 25 questions that the patient must answer with “always” (4 points), “sometimes” (2 points) or “no” (0 points) [23, 24]. The ordinal item scores were added up resulting in a maximum of 100, with 0–30 points indicating a mild handicap as a consequence of dizziness and instability, 31–60 points moderate handicap and 61–100 severe handicap [23]. The DHI is the most commonly used patient-reported outcome measure in clinical vestibular research [25]. A validated Flemish version of the DHI was used [26,27,28].
Standing balance
In clinical practice many stand-alone clinical tests such as classic Romberg with Jendrassik maneuver (RJ), standing on foam (SOF), tandem stance (TS) and single les stance (SLS) with eyes open (EO) and/or eyes closed (EC) are currently used to assess standing balance in vestibular patients. As the effect of age on performance, when using one single balance test, might interfere with vestibular pathology we investigated both the single tests and a combination of these tests [29]. A standardized foam pad with medium density (60 kg/cm3) was used (45 × 45 × 12 cm, NeuroCom International Inc. Clackamas, USA).
Patients were instructed to stand for 30s in each of the following seven conditions: RJ EC, SOF EO, SOF EC, TS EO, TS EC, SLS EO, SLS EC [29]. A digital stopwatch was used for time measurements. The best of three trials was considered for analysis.
For each single balance test scores (seconds) varied between 0 and 30 with higher scores indicating better balance. The scores of the single balance tests in the EC conditions were summed, resulting in the standing balance sum eyes closed (SBS-EC). This variable was selected for logistic regression, because it was more sensitive for vestibular disorders. Hence, the SBS-EC scores ranged between 0 and 120 s, with higher scores indicating better standing balance [29].
The Timed Up and Go Test (TUG)
The TUG was administered following the protocol by Podsiadlo and Richardson [30] using a standard chair with arm- and back rests and seat height of 46 cm. Subjects performed the TUG as fast as possible but safely. Timing was started at the cue “go” and stopped when the patient sat down again on the chair with their back against the back rest after walking for 3 m and turning back. A digital stopwatch was used for time measurements. All patients performed the TUG three times when turning in the preferred direction and again three times when turning in the opposite direction [29]. The best time was used as the final result and was considered normal if it was less than 10s [30]. Slower scores on the TUG (> 11.1 s) correlated with reports of falls in persons with vestibular dysfunction [31]. The TUG has moderate sensitivity and specificity in identifying individuals with disequilibrium due to vestibular impairments [18, 31].
The Dynamic Gait Index (DGI)
During DGI assessment, the patient performs eight walking conditions [32]: walking on a level surface, walking with changing walking speed, walking with horizontal and vertical head turns, making a 180° turn and stop after walking, stepping over and around objects and stair climbing. Each condition is rated with a 4-point rating scale (minimum 0 and maximum 3 points), in which three points represents best performance, resulting in a total score of maximum 24 points [32]. A score of less than 19 points indicates risk of falling [31, 33]. Lower scores on the DGI correlated with reports of falls in persons with vestibular dysfunction [31]. The DGI’s sensitivity and specificity is moderate to identify individuals with disequilibrium due to vestibular impairments [18, 31].
Data analysis and statistical analysis
Statistical analyses were performed with SPSS 25.0 for windows. Normal distribution was verified using the Kolmogorov–Smirnov test. The sample was described with minima, maxima, medians and interquartile ranges (IQR) of age, body mass index (BMI), the caloric SCV asymmetry (%), SHA VOR gain, SHA VOR asymmetry (%) and SHA VOR phase (°), DHI (points), RJ EC (s), SOF EO (s), SOF EC (s), TS EO (s), TS EC (s), SLS EO (s), SLS EC (s), SBS-EC (s), TUG (s), DGI (points) and the distribution of sex, side of the lesion and the etiology.
To determine how balance performance relates to VOR recovery in patients with PVD, Spearman’s rho correlations (ρ) were calculated between variables. Correlation coefficients were interpreted as: very high (0.9–1.00), high (0.7–0.9), moderate (0.5–0.7), low (0.3–0.5) or negligible (< 0.3) [34].
Investigations on the most suited balance test to identify uncompensated PVD patients were performed in two steps. First, differences in balance performance between the three groups were investigated with the Kruskal–Wallis test, followed by pairwise comparison with the Mann Whitney U test using Bonferroni correction for multiple comparisons. Level of significance was set at α = 0.05.
If present, the differences in vestibular and balance function between the groups indicated group 3 (PVD uncompensated) was distinguishable from group 1 (normal) and/or group 2 (PVD compensated). Therefore group 1 and 2 were considered one group (group 1 and 2) in further analyses. Subsequently, stepwise backward logistic regression analysis was performed to determine the optimal combination of balance measures that separates uncompensated PVD patients from those who have normal vestibular function or are compensated. All balance measures were used as input variables. The outcome resulting in the highest sensitivity and specificity was selected. A standard logistic regression procedure results in a function f = c0 + c1 × X1 + c2 × X2 + ⋯ + cn × Xn. In this function, the coefficients c1, c2 etc. are determined such that the combination with the variables X1, X2 etc. yield the best classification matrix with the highest sensitivity and specificity. When the logistic regression is performed stepwise, only those variables X1, X2 that are contributing to the best classification are selected. Hence, the selection in our study of SBS-EC and age, although many other as well as more variables could have been retained. When the logistic regression yields for example f = − 6.9 + 0.07 × age + 0.06 × SBS-EC, this means that for a given patient who is 60 years old and has a SBS-EC of 120 s (the best performance), the function f = −6.9 + 0.07 × 60 + 0.06 × 120 = 4.5. Then, the logistic regression transforms this value into F = 1/(1 + exp(− f)), and this yields then f = 0.99. Whereas f can vary between − infinity to + infinity, the function f varies between 0 and 1. If for a given patient f > 0, this yields that f > 0.5, and this patient is attributed to the compensated group. If however f < 0, and hence f < 0.5, this patient is classified to the uncompensated group. This model is compared with the actual status of the patient, and based on iterations, the best combination of variables and coefficients is determined. Optimally, a 100% sensitivity and specificity would be ideal, but this is seldom the reality. Rather than focusing on classification, we introduce an index which is based on the function f. We calculated the average f for each group and rescaled that to − 5 and + 5 for the both uncompensated and compensated group. This yields the final equation.