Publication:

A Cervical Abnormality Risk Prediction Model

Loading...
Thumbnail Image

Date

2013

Journal Title

Journal ISSN

Volume Title

Publisher

Ovid Technologies (Wolters Kluwer Health)
The Harvard community has made this article openly available. Please share how this access benefits you.

Research Projects

Organizational Units

Journal Issue

Citation

Charlton, Brittany M., Jenny L. Carwile, Karin B. Michels, and Sarah Feldman. 2013. “A Cervical Abnormality Risk Prediction Model.” Journal of Lower Genital Tract Disease 17 (3) (July): 242–247. doi:10.1097/lgt.0b013e3182730fec.

Abstract

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.

Description

Research Data

Keywords

Uterine Cervical Dysplasia, Vaginal Smears, Colposcopy, Decision Support Techniques

Terms of Use

This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service

Endorsement

Review

Supplemented By

Related Stories