Laser treatment for urinary incontinence in elite female athletes analyzed using a discrete mathematics approach

Abstract

Efficient treatment strategies for stress urinary incontinence (SUI) in elite female athletes (EFAs) are crucial for their timely return to sports. This study evaluates the effectiveness and potential drawbacks of non-ablative Er: YAG laser therapy combined with pelvic floor muscle training (PFMT) in treating SUI among EFAs. We employ a discrete mathematics analytical approach using network graphs to identify key factors influencing treatment outcomes and to address the challenges of small sample sizes and unknown variables in this population. Our results demonstrate significant improvements in urinary incontinence symptoms and increased return rates to elite sports activities in the laser treatment group compared to the PFMT-only group. The discrete mathematics approach effectively visualizes the complex relationships between variables and supports the development of personalized treatment plans. This study highlights the potential of laser therapy as an effective treatment option for SUI in EFAs while emphasizing the importance of tailored treatment strategies.

Keywords: Discrete mathematics; Discrete mathematics analytical approach; Elite female athletes; Network graphs; Non-ablative Er:YAG laser therapy; Pelvic floor muscle training; Personalized medicine; Return to sports; Stress urinary incontinence; Treatment outcomes.

Fig. 1

Flow diagram of patient selection and propensity score matching.

Fig. 2

Limitations of Propensity Score Matching in Evaluating the Effectiveness of Laser Treatment. (a) Comparison of 1-hour pad test (1HrPadTest) before and after treatment. The vertical axis represents the 1HrPadTest (g), and the horizontal axis shows the treatment groups (Treatment and Control) at pre and post-treatment time points. (b) Return to elite sports one year after treatment. The vertical axis represents the return rate (%), and the horizontal axis shows the treatment groups (Treatment and Control). (c) The distribution of treatment effects from different covariate sets in the sensitivity analysis. The horizontal axis represents the treatment effect, and the vertical axis shows the frequency of each treatment effect. The red dashed line indicates the mean treatment effect (Mean Effect: 11.00).

Fig. 3

Advanced Causal Inference Analysis Results for the Efficacy of Laser Treatment. (a) Regression Coefficients for Improvement in Dry Incontinence. The vertical axis represents the regression coefficients, and the horizontal axis shows various treatments and interventions. The blue bar (VEL + UEL group) indicates a statistically significant positive effect (p = 0.0001, denoted by ***). Other variables (testosterone, PFMT, core training, and T-cho) do not have statistically significant effects. (b) Variance Inflation Factor (VIF) Analysis. The vertical axis represents the VIF values, and the horizontal axis lists the analyzed variables (VEL + UEL, Testosterone, PFMT, Core Training, and T-cho). The red dashed line indicates the VIF threshold of 10. All variables have VIF values below this threshold, suggesting no multicollinearity concerns. (c) IPTW Analysis Results for Improvement Rates. The vertical axis represents the weighted mean improvement rate, and the horizontal axis displays the measures (1HrPadTest and ICIQ-SF) and their respective groups (Control and VEL + UEL). The VEL + UEL group shows higher improvement rates for both measures compared to the Control group.

Fig. 4

The network graph with node sizes based on VIF values. a: Patient Number, b: Bladder neck descent (BND) cm, c: Urinary incontinence since before childbirth, d: Age, f: Weekly training days, g: Duration of training (years), h: Number of births, i: ICIQ-SF, j: Return after 1 year, k: Pelvic floor muscle exercise frequency per week, l: Core Training, m: (1 year later) ICIQ-SF, n: (1 year later) 1HrPadTest, o: Has 1HrPadTest improved over 1 year?, p: Testosterone (resting, early morning), q: History of heart disease/arrhythmias, angina, r: Time since last race (years), z: Contractility of levator hiatus, aa: Hb, af: Uterine diseases/endometriosis, ag: Estradiol, ah: Pelvic floor muscle exercise duration per day, a.i.: Testosterone (resting, early morning), aj: Has ICIQ-SF improved over 1 year?, ak: T-cho, al: Distensibility of levator hiatus, am: Has pelvic floor muscle exercise decreased over 1 year? Frequency per week, an: Pelvic floor muscle exercise frequency per week, ao: 1HrPadTest, aq: VEL + UEL, ar: (3 months) 1HrPadTest, as: Has 1HrPadTest improved in 3 months?, at: Has ICIQ-SF improved in 3 months?, au: (3 months) ICIQ-SF, aw: (3 months) Pelvic floor muscle exercise frequency per week, ax: Years since treatment (from first consultation), ay: Has pelvic floor muscle exercise decreased in 3 months? Frequency per week, az: (3 months) Pelvic floor muscle exercise duration per day, bb: Has pelvic floor muscle exercise decreased over 1 year? Duration per day, bc: (1 year later) Pelvic floor muscle exercise duration per day, bd: (1 year later) Pelvic floor muscle exercise frequency per week, be: incontinence dry after 1 year, bf: Return after 1 year, bg: Has ICIQ-SF improved over 1 year?, bh: Has 1HrPadTest improved over 1 year?, bi: incontinence dry after 1 year, bj: Return after 1 year.

Fig. 5

Network Graph Analysis of Treatment Pathways and Outcomes. (a) This network graph represents the shortest path from the initial assessment (‘ap’) to return to elite competition (‘bj’) through intermediate nodes for three-month (‘av’) and one-year follow-ups (‘bi’). The path emphasizes the importance of pelvic floor muscle training (PFMT), highlighted by the node ‘an’. Edges are weighted based on inverse correlation values, and node sizes reflect their significance as determined by VIF values. (b) This scenario incorporates ‘q’ (history of heart disease/arrhythmias angina) into the treatment pathway. The inclusion of ‘q’ alters the path to include ‘aq’ (VEL + UEL treatment) due to the need for additional care in patients with heart conditions. (c) This analysis demonstrates the distinct pathways and success rates in overcoming incontinence, particularly highlighting the effectiveness of VEL + UEL treatment in severe cases. For patients with severe bladder neck descent (BND), the path includes both PFMT and VEL + UEL treatments. The graph underscores the significant improvement paths and the role of patient-specific factors in treatment success.