Decision support tools in low back pain

Veerle M.H. Coupé*, Miranda L. van Hooff, Marinus de Kleuver, Ewout W. Steyerberg, Raymond W.J.G. Ostelo

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Information from individual classification systems or clinical prediction rules that aim to facilitate stratified care in low back pain is important but often not comprehensive enough to be used to support clinical decision-making. The development and implementation of a clinically useful decision support tool (DST) that considering all key features is a challenging enterprise, requiring a multidisciplinary approach. Key features are inclusion of all relevant treatment options, patient characteristics, and benefits and harms and presentation as an accessible and easy to use toolkit. To be of clinical value, a DST should (1) be based on large numbers of high-quality data, allowing robust estimation of benefits and harms; (2) be presented using visually attractive and easy-to-use software; (3) be externally validated with a clinical beneficial impact established; and (4) include a procedure for regular updating and monitoring. As an illustration, we describe the development; presentation; and plans for further validation, implementation, and updating of the Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP).

Original languageEnglish
Pages (from-to)1084-1097
Number of pages14
JournalBest Practice and Research: Clinical Rheumatology
Issue number6
Publication statusPublished - 1 Dec 2016


  • Clinical prediction rules
  • Decision support tool
  • Low back pain


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