A risk prediction model to allow personalized screening for cervical cancer
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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 ≥ 30 years.
Main outcome and measures
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%).
Conclusions and relevance
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.
KeywordsCervical cancer Screening Guidelines
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