The predictive value of medical history taking and Chlamydia IgG ELISA antibody testing (CAT) in the selection of subfertile women for diagnostic laparoscopy: A clinical prediction model approach

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Abstract

Background: Medical history taking as well as Chlamydia antibody titre (CAT) testing are currently used in the selection of patients for diagnostic laparoscopy with tubal patency testing. Most research has focused on the predictive value of CAT in isolation from medical history. We assessed therefore whether the combination of medical history and CAT improves the efficiency of selecting patients for laparoscopy as compared to the use of either medical history or CAT. Methods: Data of 207 consecutive subfertile women were used to create multivariable logistic regression models for the prediction of tubal disease as diagnosed by diagnostic laparoscopy. Results: The model with data of medical history only had an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI 0.56-0.74). Addition of CAT increased the AUC to 0.70 (95% CI 0.62-0.78) (P = 0.065). CAT was positive in 40 women and showed a sensitivity of 0.37 (95% CI 0.26-0.49) for a specificity of 0.88 (95% CI 0.82-0.93). In CAT positive women, a blank medical history did not decrease the probability of tubal disease. Of the 167 women tested CAT negative, 23 (14%) still had a high probability of disease due to their medical history and 11 of them (48%) showed tubal abnormalities on diagnostic laparoscopy. Conclusions: CAT testing adds valuable information to a woman's risk profile based on her medical history. The combination of medical history taking and CAT testing has a better yield for diagnosing tubal disease than either of these alone. © 2007 Oxford University Press.

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Coppus, S. F. P. J., Opmeer, B. C., Logan, S., van der Veen, F., Bhattacharya, S., & Mol, B. W. J. (2007). The predictive value of medical history taking and Chlamydia IgG ELISA antibody testing (CAT) in the selection of subfertile women for diagnostic laparoscopy: A clinical prediction model approach. Human Reproduction, 22(5), 1353–1358. https://doi.org/10.1093/humrep/del521

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