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AimTo test the psychometric properties of the Croatian version of the Chronic Venous Insufficiency Quality of Life (CIVIQ) Questionnaire and to assess the quality of life in patients with chronic venous disorders of all stages.MethodsThis cross-sectional study performed between 2014 and 2015 in a private family practice assessed the factorial validity, cross-sectional validity, and reliability of the Croatian CIVIQ 20-item questionnaire completed by 428 adult patients (78% women) with chronic venous disorders classified according to the Clinical-Etiologic-Anatomic-Pathophysiologic (CEAP) C classification as stages C1-C6.ResultsMedian patient age was 52 years (5th-95th percentile, 30-77). The distribution according to the clinical stages of chronic venous disorders was as follows: C1 (n = 78, 18%), C2 (n = 192, 45%), C3 (n = 53, 12%), C4 (n = 44, 10%), C5 (n = 13, 3%), and C6 (n = 48, 11%). The CIVIQ-20 factorial structure was unstable, and six items were excluded from the analysis to test the psychometric properties of the shortened version (CIVIQ-14). CIVIQ-14 has three dimensions (physical, psychological, and pain). Internal consistency reliability is high for the entire CIVIQ-14 (Cronbach α = 0.92) and for all CIVIQ-14 dimensions (α≥0.80). The median quality of life significantly decreased with higher CEAP C stages as follows: C1/C2 (86, 50-100); C3/C4 (75, 36-98); C5/C6 (67, 31-95) (P < 0.001). Post-hoc analysis showed a higher quality of life in C1/C2 than in other groups (P < 0.001).ConclusionThe shortened CIVIQ-14 version is useful for assessing the quality of life in patients with chronic venous disorders in everyday clinical practice. To achieve a stable validated instrument, we recommend a cross-cultural validation of items that have loadings on more than one factor.

Regression analysis is a widely used statistical technique to build a model from a set of data on two or more variables. Linear regression is based on linear correlation, and assumes that change in one variable is accompanied by a proportional change in another variable. Simple linear regression, or bivariate regression, is used for predicting the value of one variable from another variable (predictor); however, multiple linear regression, which enables us to analyse more than one predictor or variable, is more commonly used. This paper explains both simple and multiple linear regressions illustrated with an example of analysis and also discusses some common errors in presenting the results of regression, including inappropriate titles, causal language, inappropriate conclusions, and misinterpretation.