Selection of Patients and Healthy Subjects
This study was conducted on a total of 816 healthy subjects distributed evenly according to age, sex, and educational background characteristics and 124 patients with a diagnosis of liver cirrhosis at the Gastroenterology Department of Ankara University Medical School from 01/01/2015 to 7/31/2015. Figure 1 shows the flow diagram of the study.
The study consisted of two parts; in the first part normal values of the PHES tests in the Turkish population based on age and educational level was determined, and compared to German norms [17]. In the second part, prevalence of mHE in compensated cirrhotic patients was explored. For the first study, healthy subjects, 18 to 70 years old, were enrolled (Suppl. Table 1). Subjects who had consumed alcohol or had used psychoactive drugs within the last 3 months were excluded. Further exclusion criteria were visual or hearing impairment, neurological diseases and illiteracy. For the cirrhotic patients, besides the listed exclusion criteria for healthy subjects, decompensated cirrhosis, a history of HE, esophageal variceal bleeding, hepatocellular carcinoma and any significant concomitant disease which might have affected the results of the study such as neuropsychiatric disease, respiratory, kidney or heart failure, or any malignant disease were exclusion criteria as well. The study was conducted in line with the Declaration of Helsinki. The study was approved by the University of Ankara Medical School Ethics Committee. All patients gave written informed consent.
Neuropsychometric Tests
The PHES test battery applied to patients and healthy subjects consisted of the DST, NCT-A, NCT-B, SDT and LTT. LTT was assessed separately based on time and the number of errors made.
PHES test score calculation was made according to the study of Weissenborn et al [17]. Thus, for each test the score was depicted as 0 when the test value was between -1 SD to +1 SD; the score was -1 for values between -1 SD to -2 SD; it was -2 for values between -2 SD to -3 SD, -3 for values below -3 SD, and the score was depicted as +1 when the values exceeded +1 SD. Thus, the total score interval ranged between -18 and +6. Scores equal to or lower than -5 were considered indicative of mHE based on the results in the norm population. The cut-off is the same as in the study by Weissenborn et al [17].
Critical flicker frequency
CFF measures the threshold at which fused light impression switched to flickering light impression perceived by the patient when frequency of light pulses from a light source decreased stepwise by 0.1 Hz from 60 Hz downwards. A total of nine measurements were recorded and the mean of those 9 measurements was calculated. CFF <39 Hz was taken to be the threshold for mHE, in line with previous studies [34]. CFF and the PHES test battery were administered at the same session.
Statistical Analysis
Data were evaluated with SPSS 15.0 statistical package program. Descriptive statistics were presented as mean (±) standard deviation, or median and percentiles. Statistical methods included the Chi-Square test and Fisher's test, for categorical variables and Student's t and Mann-Whitney U tests for continuous variables. Correlation between continuous variables was evaluated with Spearmen's correlation analysis for non-normal distribution and with Pearson's correlation analysis for normal distribution. P<0.05 was considered significant.
For determination of the Turkish norms for the PHES test, a transformation (log for NCT-A, log-log for NCT-B, log-log-log for SDT, square root for DST, log-log for LTT-time, and cube root for LTT-error) was performed for each sub-test as the data presented by healthy individuals did not conform to a normal distribution, assessed with the Kolmogorov-Smirnov test. In the transformed scales, norm limits were defined as the values of age dependent mean and of deviations of k = -1, +1, +2, +3 (NCTA, NCTB, LTT Time, SDT, LTT Error) or k = +1, -1, -2, -3 (DST) standard deviations from the mean value. Thus, deviances indicating worse performance of a subtest may be classified into three categories, while better performances form a single category. Retransformation for the log-transformation is based on the formula:
exp (a + b*age ± k * s).
For the iterated logarithm (log-log), the retransformation formula is:
exp exp (a + b*age ± k * s)
and for the threefold logarithm (log-log-log):
exp {exp exp (a + b*age ± k * s)}.
For the square root transformation it is:
(a + b*age ± k * s) 2
and
(a + b*age ± k * s) 3
for the cube root transformation.
Here, a denotes the constant (intercept) of the regression line in the transformed scale, b the slope of the line and s the standard deviation of the residuals. k is the integer value to be replaced by the defined number of standard deviations for the different norm limits.
The impact of covariates on the test results was analyzed as follows: for each subtest, age was always included into the regression model, applied to the transformed subtest results as described above. The two further covariates (education and gender) were included and handled in a stepwise procedure: after inclusion of both of them, at each step, the covariate with the highest p-value was excluded from the model as long as this p-value was > 0.05. Formal Education was handled as a categorical variable with contrast of “H” (for high school) and of “U” (university) vs. “P” (primary school). For each subtest, additional outliers were excluded from the model. The final regression model then contained the age together with the covariates relevant for this subtest. As a consequence, the norm values were recomputed for each subtest taking into account the relevant covariate values of each subject.
The homogeneity of variances among different groups was analyzed with the Levene test to ensure homogeneity. Individuals falling outside of ±3 standard deviation were excluded from the analysis. Distribution of test data was assessed according to educational background and age with a two-way variance analysis (two-way Anova test).
In analyzing German [17] and Turkish norms, in order to compare the regression characteristics for subtests with equal transformation, the datasets were combined, and a multiple backward procedure was applied allowing for different intercepts and slopes in the Turkish and the German populations. The differences were tested within the framework of the linear model and were eliminated from the model if p > 0.05. The standard deviations of the residuals were compared by applying Levene’s test of equal variances. The test results are given in the last column of table 1.
Finally, receiver operating characteristic (ROC) curves were created for assessing the diagnostic capabilities of single subtests of the PHES test. Then, ROC curves for combination of two single subtests were established. The area under ROCs (AUROC) of single or double subtests were compared using Hanley-McNeil tests [35]. A p-value of <0.05 was considered significant.