Validation of the Symptom Questionnaire for Visual Dysfunctions (SQVD): A Questionnaire to Evaluate Symptoms of any Type of Visual Dysfunctions

Abstract

Purpose: To assess psychometric properties of the Symptom Questionnaire for Visual Dysfunctions (SQVD) questionnaire, including accuracy, validity, and reliability, in a clinical sample of patients having any type of visual dysfunction.

Methods: A clinical sample of 306 patients self-administered the SQVD. Rasch analysis was performed to analyze the functionality of the response categories, fit statistics, differential item functioning (DIF), person and item reliability, targeting, local dependency, unidimensionality, and transformation table. Accuracy was assessed by means of receiver operating characteristic (ROC) curves, using symptoms reported in each patient's clinical record as the gold standard for classifying patients with and without symptoms. The concurrent validity, known group validity, and test-retest reliability (repeatability, using the intraclass correlation coefficient [ICC]) were also examined.

Results: SQVD showed orderly category responses. The 14 items fit the Rasch model without significant DIF for gender, presbyopia, and dysfunctions. Person and item reliabilities were 0.81 and 0.85, respectively. Targeting was -1.49 logits. Yen's Q3 statistic showed no local dependency. SQVD was unidimensional (first contrast of the residual = 1.852 eigenvalue with a variance explained by measures of 52.23%). The area under the ROC curve was 0.836 (95% confidence interval [CI], 0.792-0.879) with a cutoff of ≥6 showing good accuracy (sensitivity = 0.759; specificity = 0.783). SQVD showed good concurrent and known group validity and high repeatability (ICC, 0.857; 95% CI, 0.710-0.933) when administered twice 1 week apart.

Conclusions: SQVD has shown good psychometric properties. It can be considered an accurate, valid, and reliable questionnaire to detect visual symptoms related to any type of refractive, accommodative, and binocular dysfunction.

Translational relevance: SQVD may be used for diagnostic purposes, as it can accurately detect symptoms related to any sort of visual dysfunction. It may also be useful to monitor the treatment outcomes of these conditions.

Figure 1.

Category probability curves (CPCs) for the SQVD. Each curve in the CPC graph represents one response category (no = 0; occasionally/often = 1; almost always = 2). The figure shows the performance of the three response categories of the SQVD, which asked about the frequency of each of the symptoms under evaluation. The point where two adjacent curves overlap is the threshold, and the two corresponding categories have equal likelihood of being chosen.

Figure 2.

Person–item map for the SQVD. Patients (person ability) are represented on the left of the dashed line. The # symbol represents three subjects, and the • symbol indicates one or two subjects. The items of SQVD with their number (Pnumber of item) are shown on the right of the dashed line of the figure. “M” indicates the mean measure (left, person ability; right, item difficulty). “S” shows 1 SD from the mean, and “T” indicates 2 SDs, all expressed in logits. The person–item map orders the symptoms of the patients and the item difficulty. Higher ability for persons (in this study, higher frequency of symptoms) and more difficult items are at the top of the figure.

Figure 3.

ROC curve for the SQVD questionnaire. The ROC curve summarizes the results of sensitivity and specificity analyses for various cutoff points. The greater the area under the curve, the better the accuracy of the SQVD to detect symptoms.