JMIR出版物1年肺癌发生风险预测:卡塔尔世界杯8强波胆分析使用缅因州电子健康记录的前瞻性研究%A Wang， %A Zhang,Yan %A Hao， %A Zheng， % Le %A Liao,Jiayu %A Ye,Chengyin %A Xia,Minjie %A Wang,Oliver %A Liu,Modi %A Weng,Ching Ho %A Duong,Son Q %A Jin,Bo %A Alfreds,Shaun T %A Stearns,Frank %A Kanov,Laura %A Sylvester,Karl G %A Widen,Eric %A McElhinney,Doff B %A Ling,Xuefeng B %+斯坦福大学外科，美国加利福尼亚州斯坦福大学Grant大厦S370, 1 650 427 9198，bxling@stanford.edu %K肺癌%K风险预测模型%K电子健康记录%K前瞻性研究%D 2019 %7 16.05.2019 %9原论文%J J医学互联网研究%G英文%X背景:肺癌是全球癌症死亡的主要原因。早期发现有肺癌风险的个体对于降低死亡率至关重要。目的:本研究的目的是建立并验证一种前瞻性风险预测模型，以识别普通人群中未来1年内有新发肺癌风险的患者。方法:从缅因州健康信息交换网络中提取个人患者电子健康记录(EHRs)的数据。研究人群包括2016年4月1日至2018年3月31日期间至少有一次电子病历的患者，且无肺癌病史。建立回顾性队列(N=873,598)和前瞻性队列(N=836,659)进行模型构建和验证。采用极限梯度增强(XGBoost)算法建立模型。它给每个人打分，以量化2016年10月1日至2017年9月31日期间新发肺癌诊断的概率。 The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018. Results: The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer. Conclusions: We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance. %M 31099339 %R 10.2196/13260 %U //www.mybigtv.com/2019/5/e13260/ %U https://doi.org/10.2196/13260 %U http://www.ncbi.nlm.nih.gov/pubmed/31099339