Patient Acceptable Symptom State Versus Latent Class Analysis Outcome Classification: A Comparative Longitudinal Study of Knee Arthroplasty.

Abstract

To determine whether Patient Acceptable Symptom State (PASS), a single-item deterministic binary measure of pain and function outcome satisfaction, leads to better differentiation of outcome classification versus latent class analysis probability-based outcome subgroups 1 year after knee arthroplasty (KA). We used data from Knee Arthroplasty Skills Training for Pain (KASTPain), a 1-year no-effect multicenter randomized clinical trial of participants with KA, along with prior work that developed and externally validated good and poor outcome trajectories. Confirmatory latent class analyses were conducted on 2 exemplar outcome measures (Euroquol visual analog scale single-item self-rated health and 4-item pain ratings) and compared with PASS scores. Separation of trajectories were used to compare good and poor latent class self-rated health/4-item pain trajectories and PASS score trajectories. Prevalence rates for poor outcomes were 10% for self-rated health and 20% for 4-item pain and PASS. Probabilistic latent class-derived classifications of self-rated health and 4-item pain outcomes outperformed PASS in separating growth trajectories. The effect size point estimates for 12-month 4-item pain scale score separation was approximately 3 times larger for latent class analyses as compared with PASS. When used for outcome classification, observed PASS scores consistently underperform relative to probabilistic latent class-derived subgroups of pain and self-rated health outcome. PASS is a weak substitute for probabilistic classification of other patient-reported outcome measures of KA outcome. Clinicians and researchers should rely on latent class analyses over PASS to differentiate between outcome subgroups after KA.