Multidomain patient-reported outcomes of irritable bowel syndrome: exploring person-centered perspectives to better understand symptom severity scores

Abstract

Objectives: Patient-reported outcomes assessing multiple gastrointestinal symptoms are central to characterizing the therapeutic benefit of novel agents for irritable bowel syndrome (IBS). Common approaches that sum or average responses across different illness components must be unidimensional and have small unique variances to avoid aggregation bias and misinterpretation of clinical data. This study sought to evaluate the unidimensionality of the IBS Symptom Severity Scale (IBS-SSS) and to explore person-centered cluster analytic methods for characterizing multivariate-based patient profiles.

Methods: Ninety-eight Rome-diagnosed patients with IBS completed the IBS-SSS and a single, global item of symptom severity (UCLA Symptom Severity Scale) at pretreatment baseline of a clinical trial funded by the National Institutes of Health. k-means cluster analyses were performed on participants' symptom severity scores.

Results: The IBS-SSS was not unidimensional. Exploratory cluster analyses revealed four common symptom profiles across five items of the IBS-SSS. One cluster of patients (25%) had elevated scores on pain frequency and bowel dissatisfaction, with less elevated but still high scores on life interference and low pain severity ratings. A second cluster (19%) was characterized by intermediate scores on both pain dimensions but more elevated scores on bowel dissatisfaction. A third cluster (18%) had elevated scores across all IBS-SSS subcomponents. The fourth and the most common cluster (37%) had relatively low scores on all dimensions except bowel dissatisfaction and life interference due to IBS symptoms.

Conclusions: Patient-reported outcome end points and research on IBS more generally relying on multicomponent assessments of symptom severity should take into account the multidimensional structure of symptoms to avoid aggregation bias and to optimize the sensitivity of detecting treatment effects.