Pharmacological Management of Ureteral Stent-related Symptoms: A Systematic Review, Bayesian Network Meta-analysis, and Meta-regression.

Purpose: Ureteral stents are commonly used for the treatment of ureteral obstruction, most often urolithiasis. Their use may be associated with significant bothersome symptoms and discomfort. Prior studies have examined the effects of various medication regimens on ureteral stent symptoms. This study utilized Bayesian network meta-analysis to analyze all available evidence on the pharmacological management of ureteral stent-related symptoms.

Materials And Methods: In December 2022 a systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines on randomized prospective studies on pharmacological management of ureteral stent-related symptoms reporting outcomes using the Ureteral Stent Symptom Questionnaire score on urinary symptoms and pain. The data were analyzed in Review Manager 5.3 and R Studio where a Bayesian network meta-analysis was performed. Treatments were ranked using surface under the cumulative ranking curve and mean difference vs placebo with 95% credible intervals.

Results: A total of 26 studies were analyzed. These were used to build networks which were modeled to run 100,000 Markov Chain Montecarlo simulations each. Drug-class analysis revealed the most effective class for each domain: for urinary symptoms, sexual performance, general health, and work performance-combined α-blocker and anticholinergic and phosphodiesterase 5 inhibitors; for pain-combined anticholinergic and pregabalin. The following were the most effective drugs and dosages for specific symptoms: for urinary symptoms-combined silodosin 8 mg+solifenacin 10 mg; for pain-combined silodosin 8 mg+solifenacin 10 mg; for sexual performance-tadalafil 5 mg. Combined silodosin 8 mg+solifenacin 10 mg+tadalafil 5 mg has the best general health scores while solifenacin 10 mg had the best work experience scores.

Conclusions: This network meta-analysis demonstrated that the most effective drug therapy is different for each symptom domain. It is important to consider a patient's chief complaint and domains in order to ascertain the optimal medication regimen for each patient. Further iterations of this analysis can be strengthened by trials that directly compare more of these drugs instead of relying on indirect evidence.