180: Racial and ethnic disparity in FEV1-indicated exacerbation signal (FIES) events in children with cystic fibrosis

Bichl, S.; Rangaraj, V.; O'Malley, Catherine; Rogers, Troy D.; Ward, Sheila A.; Palla, John

doi:10.1016/s1569-1993(21)01605-2

Cited by 1 publication

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“…Rapid lung function decline is defined by a decrease in FEV 1 of more than 10% predicted from the maximum observed FEV 1 within the past 12 months [32,33]. We identified a PEx using predictions of rapid lung function decline based on FIES [34][35][36]. FIES is determined by first defining the patient's baseline FEV 1 as the average of the highest two FEV 1 measurements in the past 12 months when not under intravenous antibiotic treatment [37].…”

Section: Methodsmentioning

confidence: 99%

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

Colegate,

Palipana,

Gecili

et al. 2024

J. Clin. Trans. Sci.

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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)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) from the US CF Foundation Patient Registry. We screened predictors to identify reasons for discriminatory model performance. Results: The precision medicine algorithm performed worse predicting a PEx among Black patients when compared with White patients or to patients of another race for all three prediction horizons. There was little to no difference in prediction accuracies among Hispanic and non-Hispanic patients for the same prediction horizons. Differences in F508del, smoking households, secondhand smoke exposure, primary and secondary road densities, distance and drive time to the CF center, and average number of clinical evaluations were key factors associated with race. Conclusions: Racial differences in prediction accuracies from our PEx precision medicine algorithm exist. Misclassification of future PEx was attributable to several underlying factors that correspond to race: CF mutation, location where the patient lives, and clinical awareness. Associations of our proxies with race for CF-related health outcomes can lead to systemic racism in data collection and in prediction accuracies from precision medicine algorithms constructed from it.

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