Patient-tailored healthcare and tibial nerve neuromodulation in the treatment of patients with overactive bladder symptoms
SourceNeurourology and Urodynamics, 41, 2, (2022), pp. 679-684
Article / Letter to editor
Display more detailsDisplay less details
Neurourology and Urodynamics
SubjectRadboudumc 10: Reconstructive and regenerative medicine RIMLS: Radboud Institute for Molecular Life Sciences
PURPOSE: The aim of this study was to demonstrate features predictive of treatment response for patient-tailored overactive bladder (OAB) intervention with an implantable tibial neurostimulator using patient and technical prediction factors. MATERIALS AND METHODS: This study was designed as a follow-up study based on parameter settings and patients' preferences during the pilot and extended study of the implantable tibial nerve stimulator (RENOVA™ iStim system). For this study, we compared all treatment parameters (stimulation amplitude, frequency, and pulse width) and usage data (duration of treatment) during the different follow-up visits. RESULTS: We obtained usage data from a total of 32 patients who were implanted with the system between February and September 2015. Age, sex, body mass index (BMI) and previous experience with percutaneous tibial nerve stimulation (PTNS) treatment were considered as possible prediction factors for treatment success. However, only BMI was considered a statistically significant prediction factor (p = 0.042). A statistically significant increase in mean treatment level was seen in the responder group during the 3 month follow-up visit (mean: 6.7 mA, SD 0.416) as compared with the initial system activation visit (mean: 5.8 mA, SD 0.400) (p = 0.049). No other visits demonstrated statistically significant changes in both groups (responders and nonresponders) during the defined timepoints. CONCLUSION: This data underscores the need to use patient-tailored OAB treatment. BMI was found to be a negative predictive factor for treatment success. However, it was not possible to develop a specific responder model. A model predicting response to treatment could be useful for implementing shared decision making.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.