Development and validation of a lung cancer risk prediction model for African-Americans.
Development and validation of a lung cancer risk prediction model for African-Americans.
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DOI：
10.1158/1940-6207.capr-08-0082
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发表时间：
2008-09
期刊：
影响因子：
--
通讯作者：
Spitz MR
中科院分区：
文献类型：
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作者：
Etzel CJ;Kachroo S;Liu M;D'Amelio A;Dong Q;Cote ML;Wenzlaff AS;Hong WK;Greisinger AJ;Schwartz AG;Spitz MR
由于现有的肺癌风险预测模型是在白色人群中开发的，因此它们可能不适合预测非洲裔美国人的风险。因此，需要构建和验证特定于非洲裔美国人的肺癌风险预测模型。我们分析了491名非裔美国人肺癌患者和497名匹配的非裔美国人对照的数据，以确定特定的风险，并将其纳入肺癌的多变量风险模型，并估计肺癌的5年绝对风险。我们使用来自同一个正在进行的多种族/民族肺癌病例对照研究的额外病例和对照数据以及来自底特律大都市两个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。我们还将我们的非裔美国人模型与我们以前开发的白人风险预测模型进行了比较。最终风险模型包括吸烟相关变量[吸烟状态、吸烟包年数、戒烟年龄（既往吸烟者）和戒烟年数（既往吸烟者）]、自我报告的慢性阻塞性肺疾病或花粉热医生诊断以及石棉或木尘暴露。我们的非裔美国人风险预测模型对内部数据显示出良好的区分度[75%（95%置信区间，0.67 - 0.82）]，对外部数据组显示出中等区分度[63%（95%置信区间，0.57 - 0.69）]，这比Spitz模型对白色受试者的区分度有所提高。现有的肺癌预测模型可能不适合预测非洲裔美国人的风险，因为（a）它们是使用白色人群开发的，（B）非洲裔美国人与白人共享的风险因素的风险水平不同，以及（c）非洲裔美国人存在独特的群体特异性风险因素。这项研究开发并验证了一种针对非裔美国人的肺癌风险预测模型，从而更准确地预测他们的风险。这些发现突出了对疾病风险进行进一步种族特异性分析的重要性。
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.