Study design
The current study was planned as a non-interventional within-subject design and can be considered as part II of the recent publication [15], which mainly analysed the diagnostic values of the ACSS as compared to the recently published draft guidelines of FDA and EMA [16, 17].
Study tool
The ACSS is composed of the “Diagnostic” and “Follow-up” forms (part A and part B). Each of these forms consists of four domains: (1) typical symptoms, (2) differential symptoms, (3) quality of life (QoL), (4) additional medical conditions. Besides the four mentioned domains, the “Follow-up” part B of the ACSS contains the “Dynamics” domain to assess the overall clinical outcome reported by the patient [10].
The “Typical” domain of the ACSS contains six patient-reported items corresponding to (1) urination frequency, (2) urination urgency, (3) burning pain during urination (dysuria), (4) suprapubic pain, (5) incomplete bladder emptying, vi) visible blood in the urine.
The “QoL” domain is composed of three items concerning (1) overall discomfort (bothersomeness) caused by the symptoms and their severity, (2) impact on daily work/activities, and (3) impact on social activities.
The items of the “Typical” and “QoL” domains were scored according to severity: none, mild, moderate, and severe.
The “Differential” domain of the ACSS contains items concerning differential diagnostic considerations, such as female genital infections and upper UTI symptoms. The “Additional” domain contains questions concerning important medical conditions, such as menstruation, premenstrual syndrome (PMS), postmenopause, pregnancy, and diabetes mellitus.
The “Dynamics” domain of the ACSS is composed of five grades concerning overall changes of the symptomatology: Feeling (1) normal (all symptoms have gone away); (2) much better (most of the symptoms has gone away); (3) somewhat better (only some symptoms have gone away); (4) no changes; (5) worse.
The data from both “Diagnostic” and “Follow-up” forms of the ACSS questionnaire were used in this study.
From the draft guidelines proposed by FDA, the four (dysuria, urinary frequency, urinary urgency, and suprapubic pain) or by EMA, the three (frequency, urgency and dysuria) symptoms mentioned in the corresponding draft guidelines—all included also in the ACSS questionnaire—were analysed accordingly [16, 17]. All items were dichotomized (s. below) as “Positive” or “Negative”, depending on the presence or absence of the symptom, and their severity was also considered.
Data acquisition
The e-USQOLAT database, containing relevant clinical information and laboratory data of women with and without AC was selected as a primary source for data mining [18]. These data were obtained from female respondents at baseline and follow-up visits during clinical validation of the ACSS in several countries. All relevant data were acquired from the database at its state on the access date of January 1, 2019.
Data processing
Of among 517 female respondents, described in our recent publication [15], we have selected patients with AC according to the diagnosis made by the treating physician with the following inclusion criteria: (1) summary score of “typical symptoms” of 6 and more; (2) at least one follow-up evaluation after the initial “diagnostic” visit; (3) no missing values in the ACSS questionnaire data, including the “Dynamics” domain of the “follow-up Part B” of the questionnaire (Fig. 1).
Patients were supposed to receive appropriate medical treatment according to the national and international guidelines and therapeutic standards [1, 2, 19]. However, only outcome and not therapy modalities were included in the further analysis of this non-interventional study.
Patients, who have filled up more than 1 “follow-up Part B” of the ACSS were added as new cases per each available follow-up form (visit). Visits were grouped depending on the time difference between the first diagnostic visit and further “follow-up” evaluation visits.
The “Dynamics” domain of the “follow-up Part B” form of the ACSS was also considered for evaluation of overall clinical outcome determined by the patient. In the purpose of dichotomization, items “Yes I feel normal” and “Yes, I feel much better” were merged and classified as “clinical cure”, whereas the three remaining items (“Yes, I feel somewhat better”, “No, there are barely any changes”, and “Yes, I feel worse”) were merged to “failure”. The procedures of dichotomization were described previously [15].
In general, relative variables were labelled as “0” for “negative,”/“not match”, and “1” for “positive”/“match”.
Thresholds and terms
The evaluation terms or “visits” were classified according to the time difference (in days) between the “diagnostic” and “follow-up” evaluations.
To determine meaningful thresholds for clinical cure, typical symptoms, QoL and overall clinical assessments (“Dynamic” domain) were evaluated, combined and/or weighed against each other.
Statistical analysis
Two-by-two contingency tables were used for the statistical analysis of the bivariate (dichotomized) variables, where the thresholds in different times of the evaluation were considered as the test variable (exposure), and efficacy of the therapy was taken as an outcome.
The validity of the predetermined thresholds was evaluated by the assessment of their relations with the overall clinical outcome as reported by the patients in the “Dynamics” domain of the “follow-up” form of the ACSS.
Such values as sensitivity, specificity, positive and likelihood ratios, Youden’s J-index, odds ratio (OR), positive and negative predictive values (PPV and NPV respectively), positive and negative likelihood ratios (+LR and −LR respectively) were calculated. ROC-curve analysis was used for the assessment of area under the curve (AUC). The strength of associations between test variables and the outcome was measured using Pearson’s product–moment correlation coefficient.
Tests of the comparative analyses were performed in dependence of normality and homoscedasticity of distributions which in turn were assessed using normality tests (Shapiro–Wilk’s) [20], histograms and normal Q–Q plots (see Suppl. Figures 1 and 2).
For the comparison of independent, homoscedastic and normally distributed variables, Student’s two-sided t test was used. For normally distributed heteroscedastic independent variables, Welch’s two-sided modified t test was used. Non-parametric tests such as Kruskal–Wallis rank-sum test [21] and Wilcoxon/Mann–Whitney rank-sum test for pairwise comparisons [22] were used when parametric tests were considered inappropriate. A p value of less than 0.05 was considered statistically significant.
R v.3.5.2 with in-built and additional packages was used for the statistical analysis and graphical representation of the results [23,24,25,26].