{"title":"PR13:吸烟者肺癌风险预测模型的外部验证","authors":"L. Sakoda, L. Habel, K. Thai, C. Quesenberry","doi":"10.1158/1538-7755.CARISK16-PR13","DOIUrl":null,"url":null,"abstract":"Early detection strategies for lung cancer may be improved by using valid risk prediction models to identify persons at highest risk for the disease. However, external validation of lung cancer risk prediction models has been limited. We sought to externally validate the PLCOM2012 model, which predicts the probability of lung cancer within six years on the basis of age, race, education, body mass index, chronic obstructive pulmonary disease, personal history of cancer, family history of lung cancer, and smoking status, quantity, duration, and quit years, in the Kaiser Permanente Northern California (KPNC) Research Program on Genes, Environment, and Health (RPGEH) cohort. To increase comparability to the populations of smokers used to initially develop and validate the PLCOM2012 model, we restricted our analysis to the 28,757 ever smokers ages 55 to 74 with no history of lung cancer, no history of other non-melanoma skin cancers in the prior five years, and complete data on all model predictors. For each person, the predicted probability of lung cancer risk was estimated with data ascertained from the RPGEH survey on all predictors except quit years, which was ascertained from electronic health records. Using KPNC Cancer Registry data, we identified 672 diagnosed with lung cancer within six years post-survey. Both calibration and discrimination were examined to assess model performance. Calibration was assessed by determining the mean absolute difference in observed and predicted probabilities of lung cancer for each decile of predicted risk. Discrimination was assessed by estimating the area under curve (AUC). The absolute difference in observed and predicted probabilities of lung cancer risk was generally small: Citation Format: Lori C. Sakoda, Laurel A. Habel, Khanh K. Thai, Charles P. Quesenberry, Jr. External validation of a risk prediction model for lung cancer among smokers. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR13.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Abstract PR13: External validation of a risk prediction model for lung cancer among smokers\",\"authors\":\"L. Sakoda, L. Habel, K. Thai, C. 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引用次数: 0
摘要
通过使用有效的风险预测模型来识别疾病风险最高的人,可以改进肺癌的早期检测策略。然而,肺癌风险预测模型的外部验证一直有限。在Kaiser Permanente北加州(KPNC)基因、环境与健康研究项目(RPGEH)队列中,我们试图对PLCOM2012模型进行外部验证,该模型基于年龄、种族、教育程度、体重指数、慢性阻塞性肺病、个人癌症史、肺癌家族史、吸烟状况、数量、持续时间和戒烟年限预测六年内肺癌的概率。为了增加与用于最初开发和验证PLCOM2012模型的吸烟者人群的可比性,我们将分析限制在28,757名年龄在55至74岁之间的吸烟者,他们没有肺癌史,在过去五年内没有其他非黑色素瘤皮肤癌史,并完成所有模型预测因子的数据。对于每个人,肺癌风险的预测概率是用RPGEH调查中确定的数据来估计的,除了戒烟年限,戒烟年限是从电子健康记录中确定的。使用KPNC癌症登记处的数据,我们在调查后的六年内确定了672名被诊断为肺癌的患者。对模型的校准和判别进行了检验,以评估模型的性能。通过确定预测风险的每十分位数中观察到的肺癌概率和预测的肺癌概率的平均绝对差来评估校准。通过估计曲线下面积(AUC)来评估鉴别性。观察到的肺癌风险概率和预测的肺癌风险概率的绝对差异通常很小:引文格式:Lori C. Sakoda, Laurel a . Habel, Khanh K. Thai, Charles P. Quesenberry, Jr.吸烟者肺癌风险预测模型的外部验证。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr PR13。
Abstract PR13: External validation of a risk prediction model for lung cancer among smokers
Early detection strategies for lung cancer may be improved by using valid risk prediction models to identify persons at highest risk for the disease. However, external validation of lung cancer risk prediction models has been limited. We sought to externally validate the PLCOM2012 model, which predicts the probability of lung cancer within six years on the basis of age, race, education, body mass index, chronic obstructive pulmonary disease, personal history of cancer, family history of lung cancer, and smoking status, quantity, duration, and quit years, in the Kaiser Permanente Northern California (KPNC) Research Program on Genes, Environment, and Health (RPGEH) cohort. To increase comparability to the populations of smokers used to initially develop and validate the PLCOM2012 model, we restricted our analysis to the 28,757 ever smokers ages 55 to 74 with no history of lung cancer, no history of other non-melanoma skin cancers in the prior five years, and complete data on all model predictors. For each person, the predicted probability of lung cancer risk was estimated with data ascertained from the RPGEH survey on all predictors except quit years, which was ascertained from electronic health records. Using KPNC Cancer Registry data, we identified 672 diagnosed with lung cancer within six years post-survey. Both calibration and discrimination were examined to assess model performance. Calibration was assessed by determining the mean absolute difference in observed and predicted probabilities of lung cancer for each decile of predicted risk. Discrimination was assessed by estimating the area under curve (AUC). The absolute difference in observed and predicted probabilities of lung cancer risk was generally small: Citation Format: Lori C. Sakoda, Laurel A. Habel, Khanh K. Thai, Charles P. Quesenberry, Jr. External validation of a risk prediction model for lung cancer among smokers. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR13.