Determinants and prediction of Chlamydia trachomatis re-testing and re-infection within 1 year among heterosexuals with chlamydia attending a sexual health clinic

Abstract

BackgroundChlamydia trachomatis (chlamydia) is one of the most common sexually transmitted infections (STI) globally, and re-infections are common. Current Australian guidelines recommend re-testing for chlamydia 3 months after treatment to identify possible re-infection. Patient-delivered partner therapy (PDPT) has been proposed to control chlamydia re-infection among heterosexuals. We aimed to identify determinants and the prediction of chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia to identify potential PDPT candidates.MethodsOur baseline data included 5,806 heterosexuals with chlamydia aged ≥18 years and 2,070 re-tested for chlamydia within 1 year of their chlamydia diagnosis at the Melbourne Sexual Health Center from January 2, 2015, to May 15, 2020. We used routinely collected electronic health record (EHR) variables and machine-learning models to predict chlamydia re-testing and re-infection events. We also used logistic regression to investigate factors associated with chlamydia re-testing and re-infection.ResultsAbout 2,070 (36%) of 5,806 heterosexuals with chlamydia were re-tested for chlamydia within 1 year. Among those retested, 307 (15%) were re-infected. Multivariable logistic regression analysis showed that older age (≥35 years old), female, living with HIV, being a current sex worker, patient-delivered partner therapy users, and higher numbers of sex partners were associated with an increased chlamydia re-testing within 1 year. Multivariable logistic regression analysis also showed that younger age (18–24 years), male gender, and living with HIV were associated with an increased chlamydia re-infection within 1 year. The XGBoost model was the best model for predicting chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia; however, machine learning approaches and these self-reported answers from clients did not provide a good predictive value (AUC < 60.0%).ConclusionThe low rate of chlamydia re-testing and high rate of chlamydia re-infection among heterosexuals with chlamydia highlights the need for further interventions. Better targeting of individuals more likely to be re-infected is needed to optimize the provision of PDPT and encourage the test of re-infection at 3 months.

Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data

“…Fieggen et al [ 8 ] discussed crucial considerations when selecting variables for model development and evaluating the performance of various machine learning algorithms, as well as the potential role of emerging tools such as Shapley Additive Explanations in understanding and decomposing these models in the context of HIV. Xu et al [ 9 ] sought to identify determinants and predict chlamydia re-testing and re-infection within one year among heterosexuals with chlamydia to pinpoint potential PDPT (Patient-Delivered Partner Therapy) candidates.…”

Building Gender-Specific Sexually Transmitted Infection Risk Prediction Models Using CatBoost Algorithm and NHANES Data

“…Fieggen et al[8] discussed crucial considerations when selecting variables for model development and evaluating the performance of various machine learning algorithms, as well as the potential role of emerging tools such as Shapley Additive Explanations in understanding and decomposing these models in the context of HIV. Xu et al [9] sought to identify determinants and predict chlamydia re-testing and re-infection within one year among heterosexuals with chlamydia to pinpoint potential PDPT candidates.…”