Dimensionality and scaling properties of the Patient Categorisation Tool in patients with complex rehabilitation needs following acquired brain injury

• Main Authors: Richard J. Siegert (Author), Oleg Medvedev (Author), Lynne Turner-Stokes (Author)
• Format: Book
• Published: Medical Journals Sweden, 2018-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Objective: To investigate the scaling properties of the Patient Categorisation Tool (PCAT) as an instrument to measure complexity of rehabilitation needs. Design: Psychometric analysis in a multicentre cohort from the UK national clinical database. Patients: A total of 8,222 patents admitted for specialist inpatient rehabilitation following acquired brain injury. Methods: Dimensionality was explored using principal components analysis with Varimax rotation, followed by Rasch analysis on a random sample of n = 500. Results: Principal components analysis identified 3 components explaining 50% of variance. The partial credit Rasch model was applied for the 17-item PCAT scale using a "super-items" methodology based on the principal components analysis results. Two out of 5 initially created super-items displayed signs of local dependency, which significantly affected the estimates. They were combined into a single super-item resulting in satisfactory model fit and unidimensionality. Differential item functioning (DIF) of 2 super-items was addressed by splitting between age groups (<65 and ≥ 65 years) to produce the best model fit (χ2/df = 54.72, p = 0.235) and reliability (Person Separation Index (PSI) = 0.79). Ordinal-to-interval conversion tables were produced. Conclusion: The PCAT has satisfied expectations of the unidimensional Rasch model in the current sample after minor modifications, and demonstrated acceptable reliability for individual assessment of rehabilitation complexity.

Item Description

1650-1977
1651-2081
10.2340/16501977-2327