Etzel Carol J, Kachroo Sumesh, Liu Mei, D'Amelio Anthony, Dong Qiong, Cote Michele L, Wenzlaff Angela S, Hong Waun Ki, Greisinger Anthony J, Schwartz Ann G, Spitz Margaret R
Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
Cancer Prev Res (Phila). 2008 Sep;1(4):255-65. doi: 10.1158/1940-6207.CAPR-08-0082.
Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self-reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67-0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57-0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.
由于现有的肺癌风险预测模型是在白人人群中开发的，它们可能不适用于预测非裔美国人的风险。因此，有必要构建并验证一个针对非裔美国人的肺癌风险预测模型。我们分析了491名患有肺癌的非裔美国人以及497名匹配的非裔美国对照的数据，以确定特定风险，并将其纳入肺癌多变量风险模型中，同时估计肺癌的5年绝对风险。我们使用来自同一正在进行的多种族/民族肺癌病例对照研究（该研究提供了模型构建数据）中的额外病例和对照数据，以及底特律大都市地区两项不同肺癌研究的数据，分别对风险模型进行了内部和外部验证。我们还将针对非裔美国人的模型与我们之前为白人开发的风险预测模型进行了比较。最终的风险模型包括与吸烟相关的变量[吸烟状况、吸烟包年数、戒烟年龄（既往吸烟者）以及戒烟后的年数（既往吸烟者）]、自我报告的医生诊断的慢性阻塞性肺疾病或花粉症，以及石棉或木尘暴露情况。我们针对非裔美国人的风险预测模型对内部数据表现出良好的区分度[75%（95%置信区间，0.67 - 0.82）]，对外部数据组表现出中等区分度[63%（95%置信区间，0.57 - 0.69）]，这比针对白人受试者的斯皮茨模型有所改进。现有的肺癌预测模型可能不适用于预测非裔美国人的风险，原因如下：（a）它们是使用白人人群开发的；（b）非裔美国人与白人共有的风险因素的风险水平不同；（c）非裔美国人存在独特的群体特异性风险因素。本研究开发并验证了一个针对非裔美国人的肺癌风险预测模型，因此在预测他们的风险方面更为精确。这些发现凸显了对疾病风险进行进一步种族特异性分析的重要性。
