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

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


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.

Publication types

  • Validation Study

MeSH terms

  • Adult
  • Algorithms*
  • Chlamydia Infections / diagnosis*
  • Chlamydia Infections / epidemiology
  • Chlamydia Infections / microbiology
  • Chlamydia trachomatis*
  • Female
  • Gonorrhea / diagnosis*
  • Gonorrhea / epidemiology
  • Gonorrhea / microbiology
  • Humans
  • Logistic Models
  • Mass Screening
  • Neisseria gonorrhoeae*
  • Predictive Value of Tests
  • Prevalence
  • Risk Factors
  • Rwanda / epidemiology
  • Sensitivity and Specificity
  • Young Adult