Developing and validating a Bayesian clinical risk prediction model for three sexually transmitted infections in key populations from two Canadian provinces.

objectivesAcross Canada, in the last decade, incidence rates of sexually transmitted and blood-borne infections (STBBI) have peaked (syphilis) or plateaued (hepatitis C virus (HCV) and HIV). Key populations (gay, bisexual and other men who have sex with men, trans and gender-diverse people, and people who use injection drugs) are at greater risk for these STBBIs, so correctly predicting risk before screening potentially infected individuals is crucial. We developed and validated a diagnostic clinical risk prediction model (CRPM) estimating HIV, HCV and syphilis risk for two key populations in two Canadian provinces.

methodsWe used 20 variables and STBBI test results from a cross-sectional study evaluating multiplexed testing (detection of coinfections) in New Brunswick and Quebec (n=400) to develop our CRPM. We randomly split the data into development (n=300) and validation (n=100) datasets using clinic-stratified sampling. We used Bayesian predictive projection with development data to select ranked STBBI predictors. We obtained the ORs of the highest performing submodel measured as area under the receiver operating curve (AUC), sensitivity and specificity with 89% credible intervals (89% CrI) using validation data. Analyses were performed in R (≥V.4.2.3).

resultsOut of 400 participants, 73 were infected with HIV (n=16), HCV (n=60), and/or syphilis (n=5). An internally validated submodel with two predictors (

conclusionsThis Bayesian-based CRPM is the first to identify high-risk individuals for HIV, HCV and syphilis with an overall good performance that minimises case missing. After additional validation, it could serve as a promising novel tool for prescreening key populations and improve Canadian STBBI multiplexed screening strategies.