Subjective visual vertical imprecision during lateral head tilt in patients with chronic dizziness.

Most prior studies of the subjective visual vertical (SVV) focus on inaccuracy of subjects' SVV responses with the head in an upright position. Here we investigated SVV imprecision during lateral head tilt in patients with chronic dizziness compared to healthy controls. Forty-five dizzy patients and 45 healthy controls underwent SVV testing wearing virtual reality (VR) goggles, sitting upright (0°) and during head tilt in the roll plane (± 30°). Ten trials were completed in each of three static head positions. The SVV inaccuracy and SVV imprecision were analyzed and compared between groups, along with systematic errors during head tilt, i.e., A-effect and E-effect (E-effect is a typical SVV response during head tilts of ± 30°). The SVV imprecision was found to be affected by head position (upright/right head tilt/left head tilt, p < 0.001) and underlying dizziness (dizzy patients/healthy controls, p = 0.005). The SVV imprecision during left head tilt was greater in dizzy patients compared to healthy controls (p = 0.04). With right head tilt, there was a trend towards greater SVV imprecision in dizzy patients (p = 0.08). Dizzy patients were more likely to have bilateral (6.7%) or unilateral (22.2%) A-effect during lateral head tilt than healthy controls (bilateral (0%) or unilateral (6.7%) A-effect, p < 0.01). Greater SVV imprecision in chronically dizzy patients during head tilts may be attributable to increased noise of vestibular sensory afferents or disturbances of multisensory integration. Our findings suggest that SVV imprecision may be a useful clinical parameter of underlying dizziness measurable with bedside SVV testing in VR.

The bucket test differentiates patients with MRI confirmed brainstem/cerebellar lesions from patients having migraine and dizziness alone.

BACKGROUND: Amongst the most challenging diagnostic dilemmas managing patients with vestibular symptoms (i.e. vertigo, nausea, imbalance) is differentiating dangerous central vestibular disorders from benign causes. Migraine has long been recognized as one of the most common causes of vestibular symptoms, but the clinical hallmarks of vestibular migraine are notoriously inconsistent and thus the diagnosis is difficult to confirm. Here we conducted a prospective study investigating the sensitivity and specificity of combining standard vestibular and neurological examinations to determine how well central vestibular disorders (CVD) were distinguishable from vestibular migraine (VM). METHOD: Twenty-seven symptomatic patients diagnosed with CVD and 36 symptomatic patients with VM underwent brain imaging and clinical assessments including; 1) SVV bucket test, 2) ABCD2, 3) headache/vertigo history, 4) presence of focal neurological signs, 5) nystagmus, and 6) clinical head impulse testing. RESULTS: Mean absolute SVV deviations measured by bucket testing in CVD and VM were 4.8 ± 4.1° and 0.7 ± 1.0°, respectively. The abnormal rate of SVV deviations (> 2.3°) in CVD was significantly higher than VM (p < 0.001). Using the bucket test alone to differentiate CVD from VM, sensitivity was 74.1%, specificity 91.7%, positive likelihood ratio (LR+) 8.9, and negative likelihood ratio (LR-) 0.3. However, when we combined the SVV results with the clinical exam assessing gaze stability (nystagmus) with an abnormal focal neurological exam, the sensitivity (92.6%) and specificity (88.9%) were optimized (LR+ (8.3), LR- (0.08)). CONCLUSION: The SVV bucket test is a useful clinical test to distinguish CVD from VM, particularly when interpreted along with the results of a focal neurological exam and clinical exam for nystagmus.

Sudden Hearing Loss with Vertigo Portends Greater Stroke Risk Than Sudden Hearing Loss or Vertigo Alone.

BACKGROUND: Because it is unknown whether sudden hearing loss (SHL) in acute vertigo is a "benign" sign (reflecting ear disease) or a "dangerous" sign (reflecting stroke), we sought to compare long-term stroke risk among patients with (1) "SHL with vertigo," (2) "SHL alone," and (3) "vertigo alone" using a large national health-care database. METHODS: Patients with first-incident SHL (International Classification of Diseases, Ninth Edition, Clinical Modification [ICD-9-CM] 388.2) or vertigo (ICD-9-CM 386.x, 780.4) were identified from the National Health Insurance Research Database of Taiwan (2002-2009). We defined SHL with vertigo as a vertigo-related diagnosis ±30 days from the index SHL event. SHL without a temporally proximate vertigo diagnosis was considered SHL alone. The vertigo-alone group had no SHL diagnosis. All the patients were followed up until stroke, death, withdrawal from the database, or current end of the database (December 31, 2012) for a minimum period of 3 years. The hazards of stroke were compared across groups. RESULTS: We studied 218,656 patients (678 SHL with vertigo, 1998 with SHL alone, and 215,980 with vertigo alone). Stroke rates at study end were 5.5% (SHL with vertigo), 3.0% (SHL alone), and 3.9% (vertigo alone). Stroke hazards were higher in SHL with vertigo than in SHL alone (hazard ratio [HR], 1.93; 95% confidence interval [CI], 1.28-2.91) and in vertigo alone (HR, 1.63; 95% CI, 1.18-2.25). Defining a narrower window between SHL and vertigo (±3 days) increased the hazards. CONCLUSIONS: The combination of SHL plus vertigo in close temporal proximity is associated with increased subsequent stroke risk over SHL alone and vertigo alone. This suggests that SHL in patients with vertigo is not necessarily a benign peripheral vestibular sign.

Automated nystagmus detection: Accuracy of slow-phase and quick-phase algorithms to determine the presence of nystagmus.

PURPOSE: To verify the accuracy of automated nystagmus detection algorithms. METHOD: Video-oculography (VOG) plots were analyzed from consecutive patients with dizziness presenting to a neurology clinic. Data were recorded for 30 s in upright position with fixation block. For automated nystagmus detection, slow-phase algorithm parameters included mean and median slow-phase velocity (SPV), and slow-phase duration ratio. Quick-phase algorithm parameters included saccadic difference and saccadic ratio. For verification, two independent blinded assessors reviewed VOG traces and videos and coded presence or absence of nystagmus. Assessor consensus was used as reference standard. Accuracy of slow-phase and quick-phase algorithm parameters were compared, and ROC analysis was performed. RESULTS: Among 524 analyzed VOG traces, 99 were verified as nystagmus present and 425 were verified as nystagmus absent. Prevalence of nystagmus in the sample population was 18.9%. In ROC analysis, areas under the curve of individual algorithm parameters were 0.791-0.896. With optimal thresholds for determining presence or absence of nystagmus, algorithm sensitivity (70.7-87.9%), specificity (71.8-84.0%), and negative predictive value (91.7-96.4%) were ideal, but positive predictive value (38.8-53.4%) was not ideal. Combining algorithm parameters using logistic regression models mildly improved detection accuracy. CONCLUSION: Both slow-phase and fast-phase algorithms were accurate for detecting nystagmus. Due to low positive predictive value, the utility of independent automated nystagmus detection systems is limited in clinical settings with low prevalence of nystagmus. Combining parameters using logistic regression models appears to improve detection accuracy, indicating that machine learning may potentially optimize the accuracy of future automated nystagmus detection systems.

Test-retest reliability of subjective visual vertical measurements with lateral head tilt in virtual reality goggles.

OBJECTIVE: The objective is to investigate the test-retest reliability of subjective visual vertical (SVV) in the upright position and with lateral head tilts through a computerized SVV measuring system using virtual reality (VR) goggles. MATERIALS AND METHODS: Thirty healthy controls underwent SVV test in upright position, with the head tilted to the right 30°, and with the head tilted to the left 30°. Subjects wore SVV VR goggles, which contained a gyroscope for monitoring the angle of head tilt. Each subject completed 10 adjustments in each head position. The mean value of SVV deviations and SVV imprecision (the intra-individual variability of SVV deviations from the 10 adjustments) were recorded and compared across different head positions. The participants then repeated the same SVV protocol at least 1 week later. The test-retest reliability of SVV deviation and SVV imprecision were analyzed. RESULTS: The SVV deviation (mean ± standard deviation) was 0.22° ± 1.56° in upright position, -9.64° ± 5.91° in right head tilt, and 7.20° ± 6.36° in left head tilt. The test-retest reliability of SVV deviation was excellent in upright position (intra-class correlation coefficient [ICC] = 0.77, P < 0.001), right head tilt (ICC = 0.83, P < 0.001) and left head tilt (ICC = 0.84, P < 0.001). The SVV values from the 10 adjustments made during right and left head tilts were less precise than when measured at upright (P < 0.001). The test-retest reliability of SVV imprecision was poor at upright (ICC = 0.21, P = 0.26) but fair-to-good in right head tilt (ICC = 0.72, P < 0.001) and left head tilt (ICC = 0.44, P = 0.04). CONCLUSION: The test-retest reliability of SVV deviation during lateral head tilts via VR goggles is excellent, which supports further research into the diagnostic value of head-tilt SVV in various vestibular disorders. In addition, the degree of SVV imprecision during head tilt has fair-to-good test-retest reliability, which suggests SVV imprecision may have clinical applicability.