A risk prediction model to allow personalized screening for cervical cancer
A risk prediction model to allow personalized screening for cervical cancer
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DOI：
10.1007/s10552-018-1013-4
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发表时间：
2018-03-01
影响因子：
2.3
通讯作者：
Taksler, Glen B.
中科院分区：
文献类型：
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作者：
Rothberg, Michael B.;Hu, Bo;Taksler, Glen B.
宫颈癌筛查指南正在演变中。目前的指南并没有根据个体患者的风险来区分建议。为了推导并验证一种工具，用于预测单个时间点宫颈上皮内瘤变2级或更高（CIN2+）的个体化概率，基于人口统计学因素和病史。研究设计包括一个观察性队列，采用分层广义线性回归模型。研究在33个主要研究对象中进行。研究对象为年龄在30岁以上的女性，CIN2+是活检的主要结果，预测因素包括：年龄、种族、婚姻状况、保险类型、吸烟史、基于邮政编码的中位收入、既往人乳头瘤病毒（HPV）检测结果。其中，745例（0.75%）为CIN 2+。多变量模型的C统计量为0.81。除种族外，所有因素均与CIN2+独立相关。该模型将女性分类为低于平均值的CIN2+风险（0.15%预测与0.12%观察风险），平均CIN2+风险（0.42%预测与0.36%观察）和高于平均值的CIN2+风险（1.76%预测与1.85%观察）。在筛查前，低于平均风险的女性患CIN2+的风险远低于ASCUS和HPV阴性的女性（0.12%对0.20%）。使用电子健康记录数据的多变量模型能够将女性分为50倍CIN2+风险梯度。经过进一步验证，使用类似的模型可以实现更有针对性的宫颈癌筛查。
Cervical cancer screening guidelines are in evolution. Current guidelines do not differentiate recommendations based on individual patient risk.To derive and validate a tool for predicting individualized probability of cervical intraepithelial neoplasia grade 2 or higher (CIN2+) at a single time point, based on demographic factors and medical history.The study design consisted of an observational cohort with hierarchical generalized linear regression modeling.The study was conducted in a setting of 33 primary care practices from 2004 to 2010.The participants of the study were women aged ae 30 years.CIN2+ was the main outcome on biopsy, and the following predictors were included: age, race, marital status, insurance type, smoking history, median income based on zip code, prior human papilloma virus (HPV) results.The final dataset included 99,319 women. Of these, 745 (0.75%) had CIN2+. The multivariable model had a C-statistic of 0.81. All factors but race were independently associated with CIN2+. The model categorized women as having below-average CIN2+ risk (0.15% predicted vs. 0.12% observed risk), average CIN2+ risk (0.42% predicted vs. 0.36% observed), and above-average CIN2+ risk (1.76% predicted vs. 1.85% observed). Before screening, women at below-average risk had a risk of CIN2+ well below that of women with ASCUS and HPV negative (0.12 vs. 0.20%).A multivariable model using data from the electronic health record was able to stratify women across a 50-fold gradient of risk for CIN2+. After further validation, use of a similar model could enable more targeted cervical cancer screening.