Authors: Hussain N et al.
Anesthesiology 144(4):898–912, April 2026
Summary:
This study evaluates the strength of evidence supporting spinal cord stimulation (SCS) for chronic pain by applying the fragility index—a statistical measure that assesses how many outcome changes would be needed to overturn a trial’s results. Unlike traditional P values, the fragility index provides insight into how stable or “fragile” a study’s conclusions actually are.
Analyzing 30 randomized controlled trials, the authors found a median fragility index of approximately 5. This means that, on average, changing the outcomes of about five patients would eliminate statistical significance. Importantly, many trials demonstrated higher fragility scores, suggesting relatively robust findings overall.
The robustness of results was consistent across different types of outcomes, trial designs, and the presence or absence of conflicts of interest. Additionally, certain pain conditions—such as persistent spinal pain syndrome, complex regional pain syndrome, and diabetic neuropathy—showed particularly strong fragility scores, reinforcing confidence in SCS effectiveness for these indications.
The study highlights an important limitation of relying solely on P values, which do not reflect how sensitive results are to small changes. By contrast, the fragility index offers a more intuitive and clinically meaningful way to interpret trial reliability.
Overall, the findings support the general robustness of the evidence base for SCS in chronic pain, while also encouraging broader adoption of fragility analysis to improve interpretation of clinical trials.
Key Points:
- Fragility index measures how easily trial results can lose statistical significance
- Median fragility index across SCS trials was ~5, indicating moderate to strong robustness
- Findings were consistent across outcomes, trial types, and conflict-of-interest status
- Certain pain conditions showed particularly strong evidence for SCS
- Fragility index adds meaningful context beyond traditional P values
What You Should Know:
Not all “statistically significant” studies are equal. This gives you a way to see how solid the data really is. For SCS, the evidence holds up better than many assume—but this also shows why we should start looking beyond P values when judging studies.
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