Evaluating precision medicine tools in cystic fibrosis for racial and ethnic fairness.

ABSTRACT:

Introduction

Patients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities.

Methods

We estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry. We screened predictors to identify reasons for discriminatory model performance.

Results

The precision medicine algo

SUBMITTER: Colegate SP 

PROVIDER: S-EPMC11362628 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature