Hypothesis / aims of study
The prevalence of low urinary tract symptoms (LUTS) is about 62% in men at any age but it increases consistently if we focus in the group over 60, where it reaches the 80.7% (1).
Since uroflowmetry (UF) was first introduced in 1957 by Von Garrelts (2), it has become a useful, inexpensive and non-invasive tool to study objectively the voiding symptoms in patients complaining of LUTS. Those symptoms are the usual presentation of benign prostatic hyperplasia (BPH) and bladder outlet obstruction (BOO) (3). Among the parameters offered by the UF, the maximum flow (Qmax) is the most valuable parameter and it puts in objective terms the urine volume per time unit.
It is because daily use of UF that we consider helpful the fact of creating a visual tool that corresponds with the Q(max) and supplements or replaces the need of a classical UF in selected situations. In this scenario, urologists and other specialists may assess voiding dynamics by offering a pictogram (ANUF) in the office, emergency room or wherever it is difficult to find an uroflowmeter. As well, primary care physicians (PCP) could complete their first assessment to LUTS in their own day clinic.
Our aim is to create an analogical tool and check its correspondence with Q(max) measured with an objective flow rate instrument like classical UF in men being studied because of their LUTS.
Study design, materials and methods
At first, we configured an original four-image pictogram (Figure 1) representing four standing men with decreasing urine streams, being the left image (A) the strongest (reaching longest distance and with increased stream caliber) and the image of the right (D) the weakest (lowest distance reached and minimum continuous stream caliber).
Once the pictogram was finished, a prospective study was designed. The patients were selected consecutively between August 2018 and January 2019.
Inclusion criteria were: male over 18 years of age, being in study for his LUTS and referred for UF. Patients with mobility difficulties, impossibility for stand micturition or blindness were excluded, as well as those UF curves labelled by the patient as “non-representative” or presenting a voided volume (VV) under 150 mL.
Informed consent was obtained from all individual participants included in the study and was registered in each clinical report. All procedures performed were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
We invited the patient to drink 0’5 L of water. When the patient wanted to urinate, an UF was performed using a single uroflowmeter (Mediwatch Urodyn+®). Right at this moment, we showed our pictogram to the patient and indicated that he should select the image that most corresponds to the recent urination. After that, the postvoid residual (PVR) was calculated automatically by ultrasound system using the ellipsoid formula.
The variables collected were: age, ability to choose an image without help, UF pattern (arc-shaped, flattened, plateau, oscillating, interrupted and superflow), Qmax, average flow (Qave), voided volume (VV), ultrasound PVR and selected image.
For the statistical analysis, R software was used. Spearman’s rank test was used for correlations. To evaluate the possible association between Qmax and the selected image, ANOVA and Tukey Test (or multiple comparisons) as well as lineal regression were performed. To validate the lineal model, the transformation of Qmax to logarithmic scale was needed to compliment the assumptions of the model. For a better interpretation of the results, we transformed the logarithmic values to its original scale. Outliers were not excluded for the analysis as they correspond to real values. We consider p<0.005 as statistically significant.
A total of 358 patients were included with a mean age of 64.6 ±12 years.
The UF global mean values are represented in Table 1. Arc-shaped UF pattern was the most commonly found UF pattern (45.3%), followed by the flattened one (31.3%) and plateau (9.2%). Remaining 15% was composed of oscillating, interrupted and superflow patterns.
All patients were able to complete the questionnaire by choosing a representative image without help. Distribution of selected image was as follows: 6.4% chose ImageA, 24.6% ImageB, 54.2% ImageC and 14.8% ImageD. Mean value and standard deviation of the Qmax were 20.4 ± 10.5 mL/s for ImageA; 15.5 ± 6.4 mL/s for ImageB; 13.5 ± 6.0 mL/s for ImageC and 10.4 ± 5.4 mL/s for Image4D.
A lineal regression model was performed to calculate the confident intervals for the Qmax of each image. The results were: ImageA) 17.8, CI95% [14.9-21.5] mL/s; ImageB) 14.3, CI95% [13.0-15.7] mL/s; ImageC) 12.3, CI95% [11.5-13.1] mL/s and ImageD) 9.1, CI95% [8.1-10.3] mL/s.
Statistically significant negative correlations were found between ANUF and Qmax (r= - 0.317; p<0.0001). no statistically significant differences were found in the age or PVR distribution among the selected images (p>.05 for both).
Interpretation of results
The feasibility of ANUF was optimal. Our 100% of ANUF completed without assistance is probably due to the fact that choosing a picture out of four is not as difficult as other more complex questionnaires.That is important when those tests are supposed to be administered mainly to patients between 60 and 80 years of age, who might present visual disabilities or cognitive difficulties.
A correspondence exists between the selected image and Qmax. As well, we have been capable to establish confident intervals to predict approximately the range of Qmax of each patient depending on the selected image. It is not our focus to substitute the UF neither to give a diagnosis based on the image selected, but to add a supplementary tool which offers an estimation of the Qmax value. In the scenario of the primary care physician attaining an uroflowmeter may be difficult and impractical. With the ANUF we are providing a really simple instrument in this scenario to complete the patient’s evaluation.
Some limitations of this study need to be taken into account. Firstly, obtaining data from a single institution might provide a selection bias. Secondly, it would be interesting to analyze if ANUF can be useful to analyze the patient’s voiding pattern during the last month. This is the second phase of the study, in which we are working now.