Person:

Feldman, Sarah

Objective—HPV infections and abnormal Pap tests are common, and most do not progress to cervical cancer. Since it is difficult to predict which mild Pap abnormalities will develop into precancerous lesions, many women undergo painful and costly evaluations, and even unnecessary treatment. The objective of this study was to develop a risk prediction model based on clinical and demographic information to identify women most likely to develop significant precancerous lesions (CIN2/3 or AIS) among women with mild Pap abnormalities (ASCUS/LSIL).

Materials and Methods—The Abnormal Pap Smear Registry includes women who received treatment at the Brigham and Women’s Hospital/Dana Farber Cancer Institute Pap Smear Evaluation Center beginning in 2006. It includes 1,072 women with mild cervical dysplasia (ASCUS or LSIL Pap tests) on their referral Pap test. We derived a clinical prediction model to predict the probability of developing CIN2/3 or AIS using multivariate logistic regression with a split-sample approach.

Results—By the end of follow-up, 93 of the 1,072 women developed CIN2/3 or AIS (8.7%). There were several differences between women who developed CIN2/3 or AIS and women who did not. However, once we put these into the regression model, the only variable that was significantly associated with CIN2/3 or AIS was having a prior history of an abnormal Pap or biopsy [OR=2.44, 95% CI (1.03 to 5.76)]. The resulting prediction model had poor discriminative ability and was poorly calibrated.

Conclusions—Despite accounting for known risk factors, we were unable to predict individual patients’ probability for progression on the basis of available data.

Background High-grade cervical dysplasia or cervical intraepithelial neoplasia (CIN) grade 2 or worse has been widely used as a surrogate endpoint in cervical cancer screening or prevention trials.

Methods To identify high-grade cervical dysplasia and cervical cancer, we developed claims-based algorithms that incorporated a combination of diagnosis and procedure codes using the billing data in an electronic medical records (EMR) database and assessed the validity of the algorithms in an independent administrative claims database. We calculated the positive predictive value (PPV) with the 95% confidence interval (CI) of each algorithm, using new cytologic or pathologic diagnosis of CIN 2 or 3, carcinoma in situ, or cervical cancer as the gold standard.

Results Having ≥1 diagnosis code for high-grade cervical dysplasia or cervical cancer had a PPV of 57.1% (95%CI 54.7–59.5%). By requiring ≥2 diagnoses for high-grade cervical dysplasia or cervical cancer, separated by 7 to 30 days, the PPV increased to 60.2% (95%CI 53.9–66.1%). At least 2 diagnoses and a procedure code within a month from the first diagnosis date yielded a PPV of 80.7% (95%CI 73.6–86.2%). The algorithms had greater PPVs in identifying prevalent high-grade cervical dysplasia or cervical cancer. Overall, the PPVs of these algorithms were similar or slightly lower in the external claims data than in the sample used to derive the algorithms.

Conclusions Use of ≥ 2 diagnosis codes in combination with a procedure code appears to be a valid tool for studying high-grade cervical dysplasia and cervical cancer in both EMR and administrative claims databases.