This retrospective study in a tertiary hospital included ICS standard UDS[2] performed on adult patients using the Ellipse urodynamics machine with a water-filled system and AUDACT software (Andromeda Medizinische Systeme GmbH, Taufkirchen, Germany). All UDS performed between 2003 and 2008 were anonymized and loaded into MATLAB R2022b (The MathWorks Inc., Natick, MA). A subset of 1001 UDS was selected. All measurements contained vesical pressures (Pves) and abdominal pressures (Pabd) and contained no major artifacts in the filling phase.
One investigator analyzed the entire set of UDS and excluded all UDS without DO, with poor measurement quality, or with rectal contractions that would hamper parameter calculation. Each remaining DO pattern was visually scored based on the most recent definitions in literature[1], with some small additions by the investigators: 1: Phasic, cited: ‘characteristic waveform and may or may not lead to urinary incontinence’; 2: Terminal, cited: ‘detrusor contraction occurring near or at the maximum of cystometric capacity, which cannot be suppressed, and results in incontinence or even reflex bladder emptying’. We added that a terminal contraction may also result in the end of the filling phase, without incontinence.; 3: Compound, cited: ‘phasic detrusor contraction with a subsequent increase in detrusor and base pressure with each subsequent contraction’; 4: Sustained, cited: ‘continuous detrusor contraction without returning to the detrusor resting pressure’. There could be combined patterns of 1 & 2, 1 & 3, and 1 & 4. In all UDS with DO, the investigator used MATLAB to select each DO segment, which was defined as an elevation of detrusor pressure (Pdet) starting from the moment the pressure deviates from its baseline and ending the moment the pressure returns to its (original) baseline or with the end of the filling phase.
In all UDS with DO, the following parameters were calculated for each DO segment: duration, amplitude, the area under the curve (AUC), the gradient between the beginning of a DO segment and its amplitude, volume, and time at (the beginning of) a DO segment as absolute values and as a percentage of the total filling phase volume and time (V%, t%). In phasic patterns with multiple DO segments, the volume and time between DO segments were calculated and the abovementioned parameter values were averaged.
The values of the calculated parameters were described for each visually scored DO pattern. Differences in parameter values between DO patterns were tested using a Kruskal-Wallis test in IBM SPSS Version 27 (IBM Corp., Armonk, NY) with p<0.05 being significant.
The parameters described above were included in a SML model, using the naïve Bayes classifier, using Python 3.9.13 with the SKlearn package. The dataset was split using the random split function included in the KSlearn package, in a test and a training dataset, with 25% of the measurements in the test dataset. The SML training was repeated 100 times with different random splits of the dataset to reduce the dependency on a certain split. After each training, the accuracy of the SML was calculated and the mean accuracy for the 100 repetitions was given. In addition, this was repeated with only a selection of the parameters, to find the minimal amount of parameters needed.