Irritable bowel syndrome diagnosis and management: A simplified algorithm for clinical practice Journal Articles uri icon

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abstract

  • BackgroundEffective management of irritable bowel syndrome (IBS), a common functional gastrointestinal disorder, can be challenging for physicians because of the lack of simple diagnostic tests and the wide variety of treatment approaches available.ObjectiveThe objective of this article is to outline a simple algorithm for day‐to‐day clinical practice to help physicians navigate key stages to reaching a positive IBS diagnosis and guidance on how to prioritise the use of specific management strategies.MethodsThis algorithm was based on the opinion of an expert panel evaluating current evidence.ResultsThe key principles forming the foundation of this evidence‐supported algorithm are: confidently naming and explaining an IBS diagnosis for the patient, followed by assessment of key patient characteristics likely to influence the choice of therapy, such as predominant symptoms, and exploring the patient agenda and preferences. Consultation should always include education and reassurance with an explanatory model of IBS tailored to the patient. Individualised lifestyle changes, dietary modifications, pharmacological therapies, psychological strategies or a combination of interventions may be used to optimise treatment for each patient.ConclusionThe simple visual tools developed here navigate the key stages to reaching a positive diagnosis of IBS, and provide a stepwise approach to patient‐centred management targeted towards the most bothersome symptoms. Establishing a strong patient‐physician relationship is central to all stages of the patient journey from diagnosis to effective management.

authors

  • Moayyedi, Paul
  • Mearin, Fermín
  • Azpiroz, Fernando
  • Andresen, Viola
  • Barbara, Giovanni
  • Corsetti, Maura
  • Emmanuel, Anton
  • Hungin, A Pali S
  • Layer, Peter
  • Stanghellini, Vincenzo
  • Whorwell, Peter
  • Zerbib, Frank
  • Tack, Jan

publication date

  • October 2017