4.5 Article

Treatments for bipolar disorder: can number needed to treat/harm help inform clinical decisions?

Journal

ACTA PSYCHIATRICA SCANDINAVICA
Volume 123, Issue 3, Pages 175-189

Publisher

WILEY-BLACKWELL
DOI: 10.1111/j.1600-0447.2010.01645.x

Keywords

bipolar disorder; mania; depression; treatment efficacy; adverse effects

Categories

Funding

  1. Abbott Laboratories
  2. AstraZeneca Pharmaceuticals
  3. Astellas Pharmaceuticals
  4. Bristol-Myers Squibb Company
  5. Cephalon
  6. Dainippon Sumitomo Pharmaceuticals
  7. Eli Lilly and Company
  8. GlaxoSmithKline
  9. Janssen Pharmaceutica
  10. Jazz Pharmaceuticals
  11. Johnson Johnson
  12. Novartis Pharmaceuticals
  13. Noven Pharmaceuticals
  14. Organon International, Inc.
  15. Otsuka Pharmaceuticals
  16. Pfizer, Inc.
  17. Repligen Corporation
  18. Solvay Pharmaceuticals
  19. Valeant Pharmaceuticals
  20. Vanda Pharmaceuticals
  21. Wyeth Pharmaceuticals
  22. XenoPort, Inc.
  23. Azur Pharma Inc
  24. Barr Laboratories
  25. Bristol-Myers Squibb
  26. Forest Research Institute
  27. Janssen Pharmaceuticals
  28. Schering-Plough Corporation

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Objective: To compare bipolar treatment interventions, using number needed to treat (NNT) and number needed to harm (NNH). Method: Results of randomized controlled clinical trials were used to assess efficacy (NNT for response and relapse/recurrence prevention vs. placebo) and tolerability (e.g. NNH for weight gain and sedation vs. placebo). Results: United States Food and Drug Administration-approved bipolar disorder pharmacotherapies all have single-digit NNTs (i.e. > 10% advantage over placebo), but NNHs for adverse effects that vary widely. Some highly efficacious agents are as likely to yield adverse effects as therapeutic benefit, but may be interventions of choice in more acute severe illness. In contrast, some less efficacious agents with better tolerability may be interventions of choice in more chronic mild-moderate illness. Conclusion: Clinical trials can help inform clinical decision making by quantifying the likelihood of benefit vs. harm. Integrating such data with individual patient circumstances, values, and preferences can help optimize treatment choices.

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