Do pre-transplant cultural factors predict health-related quality of life after kidney transplantation?

BACKGROUND: Post-transplant health-related quality of life (HRQOL) is associated with health outcomes for kidney transplant (KT) recipients. However, pretransplant predictors of improvements in post-transplant HRQOL remain incompletely understood. Namely, important pretransplant cultural factors, such as experience of discrimination, perceived racism in healthcare, or mistrust of the healthcare system, have not been examined as potential HRQOL predictors. Also, few have examined predictors of decline in HRQOL post-transplant. METHODS: Using data from a prospective cohort study, we examined HRQOL change pre- to post-transplant, and novel cultural predictors of the change. We measured physical, mental, and kidney-specific HRQOL as outcomes, and used cultural factors as predictors, controlling for demographic, clinical, psychosocial, and transplant knowledge covariates. RESULTS: Among 166 KT recipients (57% male; mean age 50.6 years; 61.4% > high school graduates; 80% non-Hispanic White), we found mental and physical, but not kidney-specific, HRQOL significantly improved post-transplant. No culturally related factors outside of medical mistrust significantly predicted change in any HRQOL outcome. Instead, demographic, knowledge, and clinical factors significantly predicted decline in each HRQOL domain: physical HRQOL-older age, more post-KT complications, higher pre-KT physical HRQOL; mental HRQOL-having less information pre-KT, greater pre-KT mental HRQOL; and, kidney-specific HRQOL-poorer kidney functioning post-KT, lower expectations for physical condition to improve, and higher pre-KT kidney-specific HRQOL. CONCLUSIONS: Instead of cultural factors, predictors of HRQOL decline included demographic, knowledge, and clinical factors. These findings are useful for identifying patient groups that may be at greater risk of poorer post-transplant outcomes, in order to target individualized support to patients.

Comparison of raw and regression approaches to capturing change on patient-reported outcome measures.

PURPOSE: Patient-reported outcome (PRO) analyses often involve calculating raw change scores, but limitations of this approach are well documented. Regression estimators can incorporate information about measurement error and potential covariates, potentially improving change estimates. Yet, adoption of these regression-based change estimators is rare in clinical PRO research. METHODS: Both simulated and PROMIS® pain interference items were used to calculate change employing three methods: raw change scores and regression estimators proposed by Lord and Novick (LN) and Cronbach and Furby (CF). In the simulated data, estimators' ability to recover true change was compared. Standard errors of measurement (SEM) and estimation (SEE) with associated 95% confidence limits were also used to identify criteria for significant improvement. These methods were then applied to real-world data from the PROMIS® study. RESULTS: In the simulation, both regression estimators reduced variability compared to raw change scores by almost half. Compared to CF, the LN regression better recovered true simulated differences. Analysis of the PROMIS® data showed similar themes, and change score distributions from the regression estimators showed less dispersion. Using distribution-based approaches to calculate thresholds for significant within-patient change, smaller changes could be detected using both regression estimators. CONCLUSIONS: These results suggest that calculating change using regression estimates may result in more increased measurement sensitivity. Using these scores in lieu of raw differences can help better identify individuals who experience real underlying change in PROs in the course of a trial, and enhance the established methods for identifying thresholds for meaningful within-patient change in PROs.