Published in:
Open Access 01-12-2021 | Human Papillomavirus | Research
Development and validation of a clinical prediction model for endocervical curettage decision-making in cervical lesions
Published in: BMC Cancer | Issue 1/2021
Login to get accessAbstract
Background
In the absence of practical and reliable predictors for whether the endocervical curettage (ECC) procedure should be performed, decisions regarding patient selection are usually based on the colposcopists’ clinical judgment instead of evidence. We aimed to develop and validate a practical prediction model that uses available information to reliably estimate the need to perform ECC in patients suspected of having cervical lesions.
Methods
In this retrospective study, 2088 patients who underwent colposcopy, colposcopically directed biopsy (CDB) and ECC procedures between September 2019 and September 2020 at the Second Hospital of Shanxi Medical University were included. The data were analyzed with univariate and multivariable logistic regression. Least absolute shrinkage and selection operator (LASSO) was used to select predictors for ECC positivity. The ECC prediction model was presented as a nomogram and evaluated in terms of discrimination and calibration. Furthermore, this model was validated internally with cross-validation and bootstrapping.
Results
Significant trends were found for ECC positivity with increasing age (P = 0.001), menopause (P = 0.003), Human papillomavirus (HPV) status (P < 0.001), severity of ThinPrep Cytological Test (TCT) (P < 0.001), original squamous epithelium ectopia (P = 0.037) and colposcopy impression (P < 0.001) by multivariable logistic regression analysis. The ECC prediction model was developed based on the following predictors: age, menopause, symptom of contact bleeding, severity of TCT, HPV status, cervix visibility, original squamous epithelium ectopia, acetowhite changes and colposcopic impression. This model had satisfactory calibration and good discrimination, with an area under the receiver operator characteristic curve (AUC) of 0.869 (95% confidence interval 0.849 to 0.889).
Conclusions
A readily applicable clinical prediction model was constructed to reliably estimate the probability of ECC positivity in patients suspicious of having cervical lesions, which may help clinicians make decisions regarding the ECC procedure and possibly prevent adverse effects.