在临床预测模型中使用种族和族裔，可能会减少或无意间增加医疗决策中的种族和族裔差异。为了比较现代美国代表性人口中符合肺癌筛查资格的情况，将筛查计算机断层扫描（LYFS-CT）模型重新拟合，排除种族和族裔，与假设情景资格方法进行对比，该方法重新计算具有相同协变量的种族和族裔少数派个体的预期寿命，将白人种族替代，并使用更高的预期寿命，以确保历史上服务不足的群体不受处罚。LYFS-CT NoRace由两个子模型组成，没有考虑种族和族裔进行了重新拟合和外部验证：参与大型临床试验（招募于1993-2001年，随访至2009年12月31日）的吸烟者中的肺癌死亡子模型（n = 39180），以及1997-2001年参与全国健康访谈调查（NHIS）的吸烟者的全部死因病例子模型（n = 74842，随访至2006年12月31日），在曾吸烟的年龄在40至80岁之间的RHIS2015-2018参与者中研究了筛查资格。数据分析期为2021年6月至2022年9月。在每个LYFS-CT子模型中包括和排除种族和族裔（非裔美国人、亚裔美国人、拉美裔美国人和白人）。按种族和族裔分：LYFS-CT NoRace模型和反事实方法（预期与观察到的结局比率 [E/O]）的校准，美国符合筛查资格的个体，在LYFS-CT模型下预测的筛查获益天数。NHIS 2015-2018包括25601名年龄在50至80岁之间的曾经吸烟者（其中非裔美国人2769人、亚裔美国人649人、拉美裔美国人1855人、白人20328人）。从子模型中排除种族和族裔导致在非裔美国人中低估了肺癌死亡风险（预期/观测 [E/O] = 0.72，95% CI，0.52-1.00），以及在非裔美国人中低估了全因死亡率（E/O = 0.90，95% CI，0.86-0.94）。在拉美裔美国人（E/O = 1.08，95% CI，1.00-1.16）和亚裔美国人（E/O = 1.14，95% CI，1.01-1.30）中高估了死亡率。因此，LYFS-CT NoRace模型将拉美裔美国人的资格增加了108%，亚裔美国人的资格增加了73%，而将非裔美国人的资格减少了39%。使用LYFS-CT与反事实全因死亡率模型能更好地保持组间的校准，并增加非裔美国人的资格13%，而不减少拉美裔美国人和亚裔美国人的资格。在这项研究中，排除种族和族裔使LYFS-CT子模型校准错误，并严重减少了非裔美国人进行肺癌筛查的资格。在反事实的资格定义下，没有人变得不符合资格，而非裔美国人的资格增加了，展示了在减少差异的情况下保持模型准确性的潜力。

Using race and ethnicity in clinical prediction models can reduce or inadvertently increase racial and ethnic disparities in medical decisions.To compare eligibility for lung cancer screening in a contemporary representative US population by refitting the life-years gained from screening-computed tomography (LYFS-CT) model to exclude race and ethnicity vs a counterfactual eligibility approach that recalculates life expectancy for racial and ethnic minority individuals using the same covariates but substitutes White race and uses the higher predicted life expectancy, ensuring that historically underserved groups are not penalized.The 2 submodels composing LYFS-CT NoRace were refit and externally validated without race and ethnicity: the lung cancer death submodel in participants of a large clinical trial (recruited 1993-2001; followed up until December 31, 2009) who ever smoked (n = 39 180) and the all-cause mortality submodel in the National Health Interview Survey (NHIS) 1997-2001 participants aged 40 to 80 years who ever smoked (n = 74 842, followed up until December 31, 2006). Screening eligibility was examined in NHIS 2015-2018 participants aged 50 to 80 years who ever smoked. Data were analyzed from June 2021 to September 2022.Including and removing race and ethnicity (African American, Asian American, Hispanic American, White) in each LYFS-CT submodel.By race and ethnicity: calibration of the LYFS-CT NoRace model and the counterfactual approach (ratio of expected to observed [E/O] outcomes), US individuals eligible for screening, predicted days of life gained from screening by LYFS-CT.The NHIS 2015-2018 included 25 601 individuals aged 50 to 80 years who ever smoked (2769 African American, 649 Asian American, 1855 Hispanic American, and 20 328 White individuals). Removing race and ethnicity from the submodels underestimated lung cancer death risk (expected/observed [E/O], 0.72; 95% CI, 0.52-1.00) and all-cause mortality (E/O, 0.90; 95% CI, 0.86-0.94) in African American individuals. It also overestimated mortality in Hispanic American (E/O, 1.08, 95% CI, 1.00-1.16) and Asian American individuals (E/O, 1.14, 95% CI, 1.01-1.30). Consequently, the LYFS-CT NoRace model increased Hispanic American and Asian American eligibility by 108% and 73%, respectively, while reducing African American eligibility by 39%. Using LYFS-CT with the counterfactual all-cause mortality model better maintained calibration across groups and increased African American eligibility by 13% without reducing eligibility for Hispanic American and Asian American individuals.In this study, removing race and ethnicity miscalibrated LYFS-CT submodels and substantially reduced African American eligibility for lung cancer screening. Under counterfactual eligibility, no one became ineligible, and African American eligibility increased, demonstrating the potential for maintaining model accuracy while reducing disparities.