PLoS ONE (Jan 2018)

Automated quantification of low amplitude rhythmic contractions (LARC) during real-world urodynamics identifies a potential detrusor overactivity subgroup.

  • Zachary E Cullingsworth,
  • Brooks B Kelly,
  • Nicholas A Deebel,
  • Andrew F Colhoun,
  • Anna S Nagle,
  • Adam P Klausner,
  • John E Speich

DOI
https://doi.org/10.1371/journal.pone.0201594
Journal volume & issue
Vol. 13, no. 8
p. e0201594

Abstract

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OBJECTIVES:Detrusor overactivity (DO) is characterized by non-voiding detrusor smooth muscle contractions during the bladder filling phase and often contributes to overactive bladder. In some patients DO is observed as isolated or sporadic contractions, while in others DO is manifested as low amplitude rhythmic contractions (LARC). The aim of this study was to develop an objective method to quantify LARC frequencies and amplitudes in urodynamic studies (UDS) and identify a subgroup DO of patients with LARC. METHODS:An automated Fast Fourier Transform (FFT) algorithm was developed to analyze a 205-second region of interest of retrospectively collected "real-world" UDS ending 30 seconds before voiding. The algorithm was designed to identify the three largest rhythmic amplitude peaks in vesical pressure (Pves) in the 1.75-6 cycle/minute frequency range. These peak Pves amplitudes were analyzed to determine whether they were 1) significant (above baseline Pves activity) and 2) independent (distinct from any in abdominal pressure (Pabd) rhythm). RESULTS:95 UDS met criteria for inclusion and were analyzed with the FFT algorithm. During a blinded visual analysis, a neurourologist/urodynamicist identified 52/95 (55%) patients as having DO. The FFT algorithm identified significant and independent (S&I) LARC in 14/52 (27%) patients with DO and 0/43 patients (0%) without DO, resulting in 100% specificity and a significant association (Fischer's exact test, p<0.0001). The average slowest S&I LARC frequency in this DO subgroup was 3.20±0.34 cycles/min with an amplitude of 8.40±1.30 cm-H2O. This algorithm can analyze individual UDS in under 5 seconds, allowing real-time interpretation. CONCLUSIONS:An FFT algorithm can be applied to "real-world" UDS to automatically characterize the frequency and amplitude of underlying LARC. This algorithm identified a potential subgroup of DO patients with LARC.