A digital tool for multidimensional assessment and prediction of treatment effectiveness in chronic pain management
2024

Digital Tool for Assessing Chronic Pain Treatment Effectiveness

Sample size: 200 publication 10 minutes Evidence: high

Author Information

Author(s): Rigoard Philippe, Ounajim Amine, Moens Maarten, Goudman Lisa, Roulaud Manuel, Naiditch Nicolas, Boukenna Raouf, Page Philippe, Bouche Bénédicte, Lorgeoux Bertille, Baron Sandrine, Nivole Kevin, Many Mathilde, Lampert Lucie, Brumauld de Montgazon Géraldine, Roy-Moreau Brigitte, David Romain, Billot Maxime

Primary Institution: CHU de Poitiers, PRISMATICS Lab

Hypothesis

Can a digital tool effectively assess and predict treatment outcomes for chronic pain management?

Conclusion

Neurostimulation significantly improves treatment outcomes for chronic pain compared to optimized medical management and reoperation.

Supporting Evidence

  • Neurostimulation showed significant improvements in pain, function, and quality of life at multiple follow-ups.
  • Optimized medical management and spinal reoperation did not show significant benefits.
  • The study identified predictors of treatment outcomes including pain surface, BMI, and smoking status.
  • The digital tool provided data-driven insights for personalized treatment recommendations.

Takeaway

This study shows that a special digital tool can help doctors find the best treatment for people with long-lasting back pain, and that a method called neurostimulation works better than other treatments.

Methodology

The study used a prospective observational design with a digital tool to assess treatment effectiveness over 12 months.

Potential Biases

Selection bias may have occurred due to the non-randomized nature of treatment allocation.

Limitations

The study was not randomized, and there was an uneven distribution of patients across treatment groups.

Participant Demographics

Patients included had persistent spinal pain syndrome type 2, with a mean age of approximately 50 years.

Statistical Information

P-Value

p<0.0001

Confidence Interval

[2.06, 3.65]

Statistical Significance

p<0.0001

Digital Object Identifier (DOI)

10.1016/j.isci.2024.111200

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