Cancer causes & control : CCC · 2018
A risk prediction model to allow personalized screening for cervical cancer.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Rothberg Michael B, Hu Bo, Lipold Laura, Schramm Sarah, Jin Xian Wen, Sikon Andrea, Taksler Glen B
Аннотация
IMPORTANCE: Cervical cancer screening guidelines are in evolution. Current guidelines do not differentiate recommendations based on individual patient risk.
OBJECTIVE: 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.
DESIGN: The study design consisted of an observational cohort with hierarchical generalized linear regression modeling.
SETTING: The study was conducted in a setting of 33 primary care practices from 2004 to 2010.
PARTICIPANTS: 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.
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.
OBJECTIVE: 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.
DESIGN: The study design consisted of an observational cohort with hierarchical generalized linear regression modeling.
SETTING: The study was conducted in a setting of 33 primary care practices from 2004 to 2010.
PARTICIPANTS: 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.
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.