Evidence map›Paper›PMID 42835062›Full record

SynthesisFrontiers in neurology2026

Efficacy differences between SNM and various peripheral nerve electrical stimulation modalities for NLUTD: a Bayesian network meta-analysis.

Fei Miao, Xinhao Wang, Jiong Zhang, Jipeng Wang, Jinfeng Wu, Yong Xia, Jiawen Wang

Abstract readNetwork Meta-AnalysisSystematic Review
In one paragraph

Synthesis in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Fei Miao *Department of Obstetrics and Gynecology, Fuzhou First General Hospital Affiliated with Fujian Medical University, Fuzhou, China.
Xinhao Wang *Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Beijing, China.
Jiong ZhangDepartment of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Beijing, China.
Jipeng WangDepartment of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Beijing, China.
Jinfeng WuDepartment of Urology, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Fuzhou, China.
Yong XiaDepartment of Obstetrics and Gynecology, Fuzhou First General Hospital Affiliated with Fujian Medical University, Fuzhou, China.
Jiawen WangDepartment of Urology, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Neurogenic lower urinary tract dysfunction (NLUTD) severely affects patients' quality of life, and conventional therapies have limited efficacy. Direct comparisons between SNM and peripheral nerve electrical stimulation are lacking. This study employed a network meta-analysis to evaluate the efficacy of these two approaches on voiding-related parameters. Methods: A computerized search of PubMed, Embase, Scopus, Web of Science, VIP, Wanfang, and CNKI databases was conducted for publicly available randomized controlled trials (RCTs) on sacral neuromodulation and peripheral nerve electrical stimulation for neurogenic lower urinary tract dysfunction, up to May 2026. The Cochrane Handbook version 5.4 (RoB 2) was used for quality assessment of the included studies. Bayesian network meta-analysis was performed using Rstudio (version 4.4.1) to compare and rank the outcomes. Results: The network meta-analysis showed that for maximum flow rate (Qmax), neuromuscular electrical stimulation (NMES) combined with conventional therapy (SUCRA = 90.5%) ranked highest, significantly better than placebo (MD = 6.5 mL/s, 95% CrI: 0.38, 13) and conventional therapy (MD = 5.6 mL/s, 95% CrI: 3.1, 8.3). For improvement in maximum cystometric capacity (MCC), sacral neuromodulation (SNM) combined with conventional therapy (SUCRA = 96.2%) demonstrated the best efficacy; however, because only six SNM-related RCTs were included, this result should be considered exploratory and cannot yet confirm its superiority over other interventions. In terms of post-void residual (PVR) volume, reduction in 24-h voiding frequency, and improvement in 24-h incontinence episodes, NMES combined with conventional therapy ranked first (SUCRA = 90, 89.5, and 91%, respectively). For mean voided volume (MVV), tibial nerve stimulation (TNS) combined with conventional therapy (SUCRA = 88.6%) performed best. Overall, combination treatment strategies were generally superior to monotherapies, with different techniques showing differential advantages in improving various dimensions of lower urinary tract function. Conclusion: For improving maximum flow rate, post-void residual volume, 24-h voiding frequency, and incontinence episodes, NMES combined with conventional therapy ranked highest; SNM combined with conventional therapy ranked highest for maximum cystometric capacity; TNS combined with conventional therapy ranked highest for mean voided volume. However, these rankings only suggest potential relative advantages and do not establish definitive superiority; in particular, the evidence for SNM is limited, and the results should be considered exploratory. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261324281.

Indexed as

Electric Stimulation TherapyLower Urinary Tract SymptomsPeripheral NervesBayes TheoremHumansBayesian modelnetwork meta-analysisneuromuscular electrical stimulationSNMtibial nerve stimulationtranscutaneous electrical nerve stimulation

Identifiers

PMID42835062
PMCPMC13634872

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.