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Review
. 2025 Aug 9:18:3943-3961.
doi: 10.2147/JPR.S513725. eCollection 2025.

Comparative Effectiveness of Different Frequencies of Spinal Cord Stimulation for the Treatment of Patients with Failed Back Surgery Syndrome: Systematic Review and Network Meta-Analysis

Affiliations
Review

Comparative Effectiveness of Different Frequencies of Spinal Cord Stimulation for the Treatment of Patients with Failed Back Surgery Syndrome: Systematic Review and Network Meta-Analysis

Yuan Yuan Qin et al. J Pain Res. .

Abstract

Purpose: Failed back surgery syndrome (FBSS) is a common and challenging complication after lumbar spine surgery, with around 30% of patients experiencing this condition post-surgery. Spinal cord stimulation (SCS) is a prevalent treatment for FBSS, yet there is a lack of systematic comparisons among different SCS frequencies. This first network meta-analysis (NMA) compared the effectiveness and superiority of different SCS frequencies for FBSS.

Material and methods: Adhering to PRISMA guidelines, we searched PubMed, Web of Science, Embase, and CENTRAL for RCTs. Bayesian random-effects network meta-analysis assessed outcomes including pain reduction, functional capacity, and health-related quality of life.

Results: This NMA (11 RCTs; n=2275) revealed efficacy variations among SCS modalities. Based on surface under the cumulative ranking (SUCRA) rankings, subperception SCS (500-1200 Hz) had the highest probability for global pain relief (SUCRA=64.0%) and ≥50% pain reduction (SUCRA=75.3%; P < 0.05 vs low-frequency SCS). High-frequency SCS (10 kHz) was associated with higher SUCRA values for back pain (99.7%; P < 0.05 vs comparators; consistency χ²=1.41, P = 0.703) and leg pain (93.2%; P < 0.05 vs low-frequency SCS), suggesting a potential advantage. For functional outcomes, high-frequency SCS correlated with better ODI scores (SUCRA=85.0%), while subperception SCS showed higher probability for improved EQ-5D metrics (SUCRA=80.3%). All networks satisfied transitivity assumptions without significant inconsistency (P > 0.05).

Conclusion: This NMA suggests potential differential therapeutic profiles among SCS modalities for FBSS. HF-SCS (10 kHz) showed relatively higher SUCRA values for back pain (99.7%), leg pain (93.2%), and disability improvement (ODI 85.0%). Subperception SCS (500-1200 Hz) was associated with better probability for global pain relief (64.0%), ≥50% pain reduction (75.3%), and HRQoL outcomes (EQ-5D 80.3%). These findings warrant validation in head-to-head RCTs.

Keywords: back pain; failed back surgery syndrome (FBSS); frequency; leg pain; spinal cord stimulation (SCS).

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Conflict of interest statement

All authors report no conflicts of interest in this work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1
Flow diagram of preferred reporting items identified, included, and excluded for systematic reviews and meta-analyses (PRISMA).
Figure 2
Figure 2
Plot of the risk of bias for each trial.
Figure 3
Figure 3
Network geometry of spinal cord stimulation (SCS) modalities for global pain.
Figure 4
Figure 4
Network geometry of spinal cord stimulation (SCS) modalities for back pain.
Figure 5
Figure 5
Network geometry of spinal cord stimulation (SCS) modalities for leg pain.
Figure 6
Figure 6
Network geometry of spinal cord stimulation (SCS) modalities for responder rate (50% of pain im-provement or 2-point decrease).
Figure 7
Figure 7
Network geometry of spinal cord stimulation (SCS) modalities for ODI (Oswestry Disability Index).
Figure 8
Figure 8
Network geometry of spinal cord stimulation (SCS) modalities for EQ-5D (used to describe health-related quality of life).
Figure 9
Figure 9
Forest plot of network effect sizes between different frequencies of SCS for global pain.
Figure 10
Figure 10
Forest plot of network effect sizes between different frequencies of SCS for back pain.
Figure 11
Figure 11
Forest plot of network effect sizes between different frequencies of SCS for leg pain.
Figure 12
Figure 12
Forest plot of network effect sizes between different frequencies of SCS for responder rate.
Figure 13
Figure 13
Forest plot of network effect sizes between different frequencies of SCS for ODI.
Figure 14
Figure 14
Forest plot of network effect sizes between different frequencies of SCS for EQ-5D.
Figure 15
Figure 15
Plots of SUCRA values for each SCS to global pain.
Figure 16
Figure 16
Plots of SUCRA values for each SCS to back pain.
Figure 17
Figure 17
Plots of SUCRA values for each SCS to leg pain.
Figure 18
Figure 18
Plots of SUCRA values for each SCS to responder rate.
Figure 19
Figure 19
Plots of SUCRA values for each SCS to ODI.
Figure 20
Figure 20
Plots of SUCRA values for each SCS to EQ-5D.

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