Colegate Stephen P, Palipana Anushka, Gecili Emrah, Szczesniak Rhonda D, Brokamp Cole
Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
School of Nursing, Duke University, Durham, NC, USA.
J Clin Transl Sci. 2024 May 7;8(1):e94. doi: 10.1017/cts.2024.532. eCollection 2024.
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.
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.
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.
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.
囊性纤维化（CF）患者经常经历肺功能急性下降的发作，称为肺部加重（PEx）。一种现有的基于临床和地点的精准医学算法能够准确预测PEx，但在临床和地理空间训练数据中可能存在种族和民族偏见，从而导致健康不平等现象意外加剧。
我们基于美国CF基金会患者登记处26392名6岁及以上（2003 - 2017年）个体的PEx非平稳高斯随机过程模型预测结果，估计了受试者工作特征曲线。我们筛选预测因素以确定模型性能存在歧视性的原因。
在所有三个预测期内，与白人患者或其他种族患者相比，精准医学算法对黑人患者PEx的预测效果更差。在相同预测期内，西班牙裔和非西班牙裔患者的预测准确率几乎没有差异。F508del、吸烟家庭、二手烟暴露、主次道路密度、到CF中心的距离和驾车时间以及临床评估平均次数的差异是与种族相关的关键因素。
我们的PEx精准医学算法在预测准确率上存在种族差异。未来PEx的错误分类归因于几个与种族相关的潜在因素：CF突变、患者居住地点以及临床认知。我们所采用的与CF相关健康结果的种族指标之间的关联，可能会在数据收集以及由此构建的精准医学算法的预测准确率方面导致系统性种族主义。
