Relationship Between Baseline Patient-reported Outcomes and Demographic, Psychosocial, and Clinical Characteristics:...

Alternative payment models in total lower extremity joint replacement (TJR) increasingly emphasize patient-reported outcomes (PROs) to link the latter to value-based payments. It is unclear to what extent demographic, psychosocial, and clinical characteristics are related to PROs measured preoperatively with the commonly used Hip/Knee Osteoarthritis Outcome Scores (HOOS/KOOS) and the Veterans RAND 12-Item Health Survey (VR-12) questionnaires. We aim to identify (1) the preoperative relationship between HOOS/KOOS and VR-12 scores and several demographic, psychosocial, and clinical patient characteristics and (2) the best modifiable factors for optimization, which may result in improved baseline PROs before TJR.

All TJR cases performed in 2017 at the two highest-volume hospitals within an urban academic health system were queried. Preoperative HOOS/KOOS and VR-12 surveys were administered through an e-collection platform. VR-12 physical and mental component scores (PCS, MCS) were generated. Patient information was extracted from the electronic health record. Bivariate and multivariate regression analyses were performed. Odds ratios (ORs) and 95% confidence intervals were reported.

In univariate analysis, patients with HOOS/KOOS, VR-12 PCS, and MCS in the ≤25th percentile group were more likely to have an ASA score of ≥3 compared with those with higher scores. In multivariate analysis, increased and decreased odds of low HOOS/KOOS were associated with a one-unit increase in Charlson Comorbidity Index (OR, 1.16) and VR-12 MCS (OR, 0.97), respectively. Increased odds of low baseline VR-12 PCS and MCS were associated with ASA class ≥3 (OR, 1.65 and 1.40). Decreased odds of a low MCS were associated with an increase in HOOS/KOOS (OR, 0.98) (P ≤ 0.05 for all).

Of the factors that are associated with low baseline PRO scores, preoperatively addressing mismanaged comorbidities, mental health, and physical function were identified as the best modifiable factors for optimization, which may result in improved baseline PROs before TJR.