Beaman, Lindsay

Background: Platelet-rich plasma (PRP) is an emerging therapy that demonstrates potential benefits for knee osteoarthritis (OA); however, the accessibility of PRP remains limited, in part, because its use is not covered by insurance. Purpose: To identify factors contributing to health disparities in PRP treatment for knee OA using machine learning models. Study Design: Cross-sectional study; Level of evidence, 3. Methods: The authors collected data from electronic medical records at 2 upper Midwest medical centers (Mayo Clinic and Detroit Medical Center) from 2022 to 2023 encompassing demographic, socioeconomic, and clinical factors. Data collection encompassed age, sex, race, ethnicity, area deprivation index, marital status, income, rurality, insurance, employment, language, religion, treatment site, and region. Logistic regression and random forest models were applied to 3667 samples (80% training, 20% testing), comparing PRP recipients against a standard care group. Models were evaluated using 10-fold cross-validation to ensure robust performance assessment. Results: Logistic regression revealed that increased age, widowed status, lower income, rural residence, unemployment/disability, and Medicaid/Medicare insurance were associated with lower odds of receiving PRP. Upper-middle income, contracted insurance, and treatment at the Mayo-Arizona and Mayo-Florida sites were associated with higher odds. The model achieved areas under the curve of 0.83 (training set) and 0.82 (testing set). Random forest analysis identified distance to care, age, area deprivation index, income, and treatment site as top predictors, with areas under the curve of 0.92 (training) and 0.87 (testing). Conclusion: The current study demonstrated that both models discerned key barriers (income, distance to care, retirement status) and facilitators (regional differences) affecting PRP accessibility. These findings provide potential policy initiatives aimed at improving PRP access for underserved demographics, offering a quantitative blueprint to foster equitable access to regenerative therapies for knee OA.

Multi-listing (ML) increases the odds of transplantation compared with single-center listing. However, not all patients have equivalent access to ML. The recent allocation policy change prioritized distance from the donor hospital in an attempt to decrease geographic disparities. This study assessed access to liver transplant (LT) given ML practices before and after the acuity circles policy change. United Network for Organ Sharing data were queried for LTs between January 1, 2016, and November 1, 2024. Patients were grouped by listing at one versus multiple centers and by acuity circle era (ACE). Cohort characteristics and comparisons were described using chi-square and Wilcoxon rank tests. Multivariable logistic regressions and propensity score match (PSM) were performed to assess associations between ML and LT. The final cohort consisted of 105,030 patients; 4084 (3.9%) were multi-listed. Racial/ethnic minority groups, less educated, and publicly insured candidates were less often multi-listed. ML was positively associated with LT in all analyses (overall: OR=1.180, p <0.001; PSM: OR=1.164, p =0.001; pre-ACE: OR=1.127, p =0.015; post-ACE: OR=1.218, p <0.001). Education, race/ethnicity, and insurance type were also associated with LT across all analyses. ML patients were more often transplanted at their primary center with a significant increase in primary center LT post-ACE (53.4% vs. 63.4%, p <0.001). ML and LT had a positive correlation. There was a negative association between minority groups and ML as well as LT. Although acuity circles decreased geographic disparities, they did not change ML accessibility across candidates.