Video-oculographic measures of eye movement control in the smooth pursuit neck torsion test can classify idiopathic neck pain patients from healthy individuals: A datamining based diagnostic accuracy study

Abstract

BackgroundIdiopathic neck pain patients frequently experience oculomotor disfunctions with deficits in eye movement control between neutral and neck torsion position (SPNT test) being commonly investigated in clinical and research settings. ObjectivesThe aim of the study was to determine accuracy of SPNT test in classifying idiopathic neck pain patients. Designa datamining based diagnostic accuracy study. MethodsThe study was conducted on a referred sample of 38 chronic neck pain patients from orthopaedic outpatient clinic and 40 healthy controls. Video-oculography was used to study gain and SPNTdiff during SPNT test under three target movement velocities and amplitudes and two different angles of neck torsion. A Naïve Bayesian predictive model was used to classify neck pain patients based on gain or SPNTdiff. ResultsGain during two target movement profiles at velocities of 30°s−1 and amplitudes of 30° and 40° under 45° of neck torsion presented with highest area under the curve (0.837), specificity (92%), sensitivity (94%), highest true positive and lowest false negative predicted value. Highest area under the curve (0.760), specificity (50%), sensitivity (71%), highest true positive and lowest false negative values were observed for SPNTdiff at velocities of 30°s−1 and amplitude of 30° applying 45° of neck torsion. ConclusionSPNT test provides useful diagnostic tool for classifying neck pain patients when using single or combination of two target movement profiles. Neck torsion of 45° as opposed to 30° should be used during SPNT test when investigating patients with neck pain disorders.