Validation of a clinical prediction rule to predict asymptomatic chlamydia and gonorrhoea infections among internet-based testers

Background: Clinical prediction rules (CPRs) can be used in STI testing environments to prioritize individuals at highest risk of infection and optimize resource allocation. We previously derived a CPR to predict asymptomatic chlamydia and/or gonorrhea (CT/NG) infection among women and heterosexual men at in-person STI clinics based on five predictors. Population differences between clinic-based and internet-based testers may limit the tool’s application across settings. The primary objective of this study was to assess the validity, sensitivity, and overall performance of this CPR within an internet-based testing environment (GetCheckedOnline.com).

Methods: We analyzed GetCheckedOnline online risk assessment and laboratory data from 10/2015-06/2019. We compared the STI clinic population used for CPR derivation (data previously published) and the GetCheckedOnline validation population using chi-square tests. Calibration and discrimination were assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test and the Area Under the Receiver Operating Curve (AUC), respectively. Sensitivity and the fraction of total screening tests offered were quantified for CPR-predicted risk scores.

Results: Asymptomatic CT/NG infection prevalence in the GetCheckedOnline population (n=5,478) was higher than in the STI clinic population (n=10,437; 2.4% vs. 1.8%, p=0.007). When applied to GetCheckedOnline, the CPR had reasonable calibration (H-L p=0.90) and discrimination (AUC=0.64). By screening only individuals with total risk scores ≥4, we would detect 97% of infections and reduce screening by 14%.

Conclusions: The application of an existing CPR to detect asymptomatic CT/NG infection is valid within an internet-based STI testing environment. CPRs applied online can reduce unnecessary STI testing and optimize resource allocation within publicly-funded health systems.