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. 2021 Apr 28;21(1):392.
doi: 10.1186/s12879-021-06073-z.

Developing and validating a risk algorithm to diagnose Neisseria gonorrhoeae and Chlamydia trachomatis in symptomatic Rwandan women

Kristin M Wall  1 , Julien Nyombayire  2 , Rachel Parker  3 , Rosine Ingabire  2 , Jean Bizimana  2 , Jeannine Mukamuyango  2 , Amelia Mazzei  2 , Matt A Price  4 , Marie Aimee Unyuzimana  2 , Amanda Tichacek  3 , Susan Allen  3 , Etienne Karita  2
Affiliations

Developing and validating a risk algorithm to diagnose Neisseria gonorrhoeae and Chlamydia trachomatis in symptomatic Rwandan women

Kristin M Wall et al. BMC Infect Dis. .

Abstract

Background: Algorithms that bridge the gap between syndromic sexually transmitted infection (STI) management and treatment based in realistic diagnostic options and local epidemiology are urgently needed across Africa. Our objective was to develop and validate a risk algorithm for Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) diagnosis among symptomatic Rwandan women and to compare risk algorithm performance to the current Rwandan National Criteria for NG/CT diagnosis.

Methods: The risk algorithm was derived in a cohort (n = 468) comprised of symptomatic women in Kigali who sought free screening and treatment for sexually transmitted infections and vaginal dysbioses at our research site. We used logistic regression to derive a risk algorithm for prediction of NG/CT infection. Ten-fold cross-validation internally validated the risk algorithm. We applied the risk algorithm to an external validation cohort also comprised of symptomatic Rwandan women (n = 305). Measures of calibration, discrimination, and screening performance of our risk algorithm compared to the current Rwandan National Criteria are presented.

Results: The prevalence of NG/CT in the derivation cohort was 34.6%. The risk algorithm included: age < =25, having no/primary education, not having full-time employment, using condoms only sometimes, not reporting genital itching, testing negative for vaginal candida, and testing positive for bacterial vaginosis. The model was well calibrated (Hosmer-Lemeshow p = 0.831). Higher risk scores were significantly associated with increased prevalence of NG/CT infection (p < 0.001). Using a cut-point score of > = 5, the risk algorithm had a sensitivity of 81%, specificity of 54%, positive predictive value (PPV) of 48%, and negative predictive value (NPV) of 85%. Internal and external validation showed similar predictive ability of the risk algorithm, which outperformed the Rwandan National Criteria. Applying the Rwandan National Criteria cutoff of > = 2 (the current cutoff) to our derivation cohort had a sensitivity of 26%, specificity of 89%, PPV of 55%, and NPV of 69%.

Conclusions: These data support use of a locally relevant, evidence-based risk algorithm to significantly reduce the number of untreated NG/CT cases in symptomatic Rwandan women. The risk algorithm could be a cost-effective way to target treatment to those at highest NG/CT risk. The algorithm could also aid in sexually transmitted infection risk and prevention communication between providers and clients.

Keywords: Chlamydia trachomatis; Neisseria gonorrhoeae; Risk algorithm; Rwanda.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Receiver operating curves companing the risk algorithm and Rwandan National Criteria (derivation cohort, n = 468 women)
Fig. 2
Fig. 2
Prevalence of CT/NG and population distribution within exact risk score categories comparing the risk algorithm (panel a) and the 2019 Rwandan National Guidelines Criteria (panel b) applied to the derivation cohort (n = 468 women)
See this image and copyright information in PMC

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