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DOI 10.1097/olq.0000000000001468

Медицинская библиотека · Sexually transmitted diseases · 2021

Predicting the Probability of Chlamydia Reinfection in African American Women Using Immunologic and Genetic Determinants in a Bayesian Model.

Olson Kristin M, Geisler William M, Bakshi Rakesh K, Gupta Kanupriya, Tiwari Hemant K

СтатьяПоддержка NIH: внешняя программаПеревод в очереди

Аннотация

BACKGROUND

African Americans have the highest rates of Chlamydia trachomatis (CT) infection in the United States and also high reinfection rates. The primary objective of this study was to develop a Bayesian model to predict the probability of CT reinfection in African American women using immunogenetic data.

METHODS

We analyzed data from a cohort of CT-infected African American women enrolled at the time they returned to a clinic in Birmingham, AL, for the treatment of a positive routine CT test result. We modeled the probability of CT reinfection within 6 months after treatment using logistic regression in a Bayesian framework. Predictors of interest were presence or absence of an HLA-DQB1*06 allele and CT-specific CD4+ IFN-γ response, both of which we had previously reported were independently associated with CT reinfection risk.

RESULTS

Among 99 participants evaluated, the probability of reinfection for those with a CT-specific CD4+ IFN-γ response and no HLA-DQB1*06 alleles was 14.1% (95% credible interval [CI], 3.0%-45.0%), whereas the probability of reinfection for those without a CT-specific CD4+ IFN-γ response and at least one HLA-DQB1*06 allele was 61.5% (95% CI, 23.1%-89.7%).

CONCLUSIONS

Our model demonstrated that presence or absence of an HLA-DQB1*06 allele and CT-specific CD4+ IFN-γ response can have an impact on the predictive probability of CT reinfection in African American women.

Для цитирования: Olson Kristin M, Geisler William M, Bakshi Rakesh K, Gupta Kanupriya, Tiwari Hemant K. Predicting the Probability of Chlamydia Reinfection in African American Women Using Immunologic and Genetic Determinants in a Bayesian Model.. Sexually transmitted diseases. 2021. DOI: 10.1097/olq.0000000000001468.

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