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

To determine if 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 one-year following knee arthroplasty (KA).We used data from KASTPain, a one-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 two exemplar outcome measures (EQ VAS single-item self-rated health and 4-Item pain ratings) and compared to 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 to 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 PROMs of KA outcome. Clinicians and researchers should rely on latent class analyses over PASS to differentiate between outcome subgroups following KA.