Michael B Rothberg1,2, Bo Hu3, Laura Lipold4, Sarah Schramm5, Xian Wen Jin6, Andrea Sikon6, Glen B Taksler5. 1. Department of Internal Medicine, Medicine Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH, 44195, USA. rothbem@ccf.org. 2. Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, Cleveland, OH, USA. rothbem@ccf.org. 3. Quantitative Health Sciences Institute, Cleveland Clinic, Cleveland, OH, USA. 4. Department of Family Medicine, Medicine Institute, Cleveland Clinic, Cleveland, OH, USA. 5. Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, Cleveland, OH, USA. 6. Department of Internal Medicine, Medicine Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH, 44195, USA.
Abstract
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.
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.
Authors: Geir Severin R E Langberg; Jan F Nygård; Vinay Chakravarthi Gogineni; Mari Nygård; Markus Grasmair; Valeriya Naumova Journal: Sci Rep Date: 2022-07-15 Impact factor: 4.996
Authors: Mindaugas Stankūnas; Kersti Pärna; Anna Tisler; Anda Ķīvīte-Urtāne; Una Kojalo; Jana Zodzika; Nicholas Baltzer; Jan Nygard; Mari Nygard; Anneli Uuskula Journal: Acta Med Litu Date: 2022-06-29