A prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2, based on simple clinical parameters.

Abstract:

:This study aims to develop a prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2 (CIN 2) lesions based on simple clinicopathological parameters. The study was conducted at Maastricht University Medical Center, the Netherlands. The prediction model was developed in a retrospective cohort of 129 women with a histologic diagnosis of CIN 2 who were managed by watchful waiting for 6 to 24months. Five potential predictors for spontaneous regression were selected based on the literature and expert opinion and were analyzed in a multivariable logistic regression model, followed by backward stepwise deletion based on the Wald test. The prediction model was internally validated by the bootstrapping method. Discriminative capacity and accuracy were tested by assessing the area under the receiver operating characteristic curve (AUC) and a calibration plot. Disease regression within 24months was seen in 91 (71%) of 129 patients. A prediction model was developed including the following variables: smoking, Papanicolaou test outcome before the CIN 2 diagnosis, concomitant CIN 1 diagnosis in the same biopsy, and more than 1 biopsy containing CIN 2. Not smoking, Papanicolaou class <3, concomitant CIN 1, and no more than 1 biopsy containing CIN 2 were predictive of disease regression. The AUC was 69.2% (95% confidence interval, 58.5%-79.9%), indicating a moderate discriminative ability of the model. The calibration plot indicated good calibration of the predicted probabilities. This prediction model for spontaneous regression of CIN 2 may aid physicians in the personalized management of these lesions.

journal_name

Hum Pathol

journal_title

Human pathology

authors

Koeneman MM,van Lint FHM,van Kuijk SMJ,Smits LJM,Kooreman LFS,Kruitwagen RFPM,Kruse AJ

doi

10.1016/j.humpath.2016.09.012

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

62-69

eissn

0046-8177

issn

1532-8392

pii

S0046-8177(16)30232-5

journal_volume

59

pub_type

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