Pain medicine

Do psychological assessment tools predict spinal cord stimulation treatment outcomes?

Reg Anesth Pain MedPublished Retrospective cohort study

Authors' conclusion

Psychological measures were not strong independent predictors of binary SCS success. Instead, depressive burden influenced early pain-response coupling, while cognitive-rigidity traits were associated with smaller long-term improvement.

Study context

Population
Patients undergoing SCS implantation153 at 6 months, 155 at 12 months followed up
Participants
217 patients
Outcome
Treatment success (≥30% reduction in NRS pain from baseline)
Follow-up
6 and 12 months
Methodological detailscomplete-case analysis
  • Complete-case analysis

Main finding

As reported in the abstract

Treatment success
Success rate at 6 months (59/153)
38.6%
Success rate at 12 months (47/155)
30.3%
6 months: baseline Numeric Rating Scale (NRS) borderline associated with success, OR 1.39, 95% CI 0.99 to 2.01
p=0.056
12 months: 'denial of depression' trend toward lower odds of success, OR 0.34, 95% CI 0.09 to 1.11
p=0.074
12 months: age borderline association
p=0.056

Observational data: associations, not effects.

Further findings

Depression-by-pain interaction (linear models)

Baseline–follow-up pain relationship strongest in minimal Beck Depression Inventory (BDI), weaker in higher BDI categories; overall model significant (R²=0.145)
P = 0.005
Individual interaction terms not significant

BDI vs ΔNRS (partial Spearman)

6 months, ρ=−0.199, n=133
P = 0.023
12 months, ρ=−0.060, n=137
P = 0.493

Perfectionism and resistance-to-change vs 12-month improvement

Perfectionism, unadjusted
P = 0.0177
Resistance-to-change, unadjusted
P = 0.0378
Perfectionism, multivariable β = −1.195
P = 0.010
Resistance-to-change, multivariable β = −1.101
P = 0.009
Multivariable model R2=0.077
P = 0.0025
  • Associated with smaller improvement

Original publication

Do psychological assessment tools predict spinal cord stimulation treatment outcomes? Reg Anesth Pain Med. Published 3 June 2026. doi:10.1136/rapm-2025-107329

Composed by AI from the published abstract and checked against it. How Briefs are made →

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