Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence–Based Risk Assessment Tool

Abstract Background We have previously developed an artificial intelligence–based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresh...

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Published in:Open forum infectious diseases Vol. 11; no. 3; p. ofae011
Main Authors: Latt, Phyu M, Soe, Nyi N, Xu, Xianglong, Ong, Jason J, Chow, Eric P F, Fairley, Christopher K, Zhang, Lei
Format: Journal Article
Language:English
Published: US Oxford University Press 01-03-2024
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Summary:Abstract Background We have previously developed an artificial intelligence–based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresholds to identify individuals at high risk for HIV/STIs. Methods Using 2008–2022 data from 216 252 HIV, 227 995 syphilis, 262 599 gonorrhea, and 320 355 chlamydia consultations at a sexual health center, we applied MySTIRisk machine learning models to estimate infection risk scores. Optimal cutoffs for determining high-risk individuals were determined using Youden's index. Results The HIV risk score cutoff for high risk was 0.56, with 86.0% sensitivity (95% CI, 82.9%–88.7%) and 65.6% specificity (95% CI, 65.4%–65.8%). Thirty-five percent of participants were classified as high risk, which accounted for 86% of HIV cases. The corresponding cutoffs were 0.49 for syphilis (sensitivity, 77.6%; 95% CI, 76.2%–78.9%; specificity, 78.1%; 95% CI, 77.9%–78.3%), 0.52 for gonorrhea (sensitivity, 78.3%; 95% CI, 77.6%–78.9%; specificity, 71.9%; 95% CI, 71.7%–72.0%), and 0.47 for chlamydia (sensitivity, 68.8%; 95% CI, 68.3%–69.4%; specificity, 63.7%; 95% CI, 63.5%–63.8%). High-risk groups identified using these thresholds accounted for 78% of syphilis, 78% of gonorrhea, and 69% of chlamydia cases. The odds of positivity were significantly higher in the high-risk group than otherwise across all infections: 11.4 (95% CI, 9.3–14.8) times for HIV, 12.3 (95% CI, 11.4–13.3) for syphilis, 9.2 (95% CI, 8.8–9.6) for gonorrhea, and 3.9 (95% CI, 3.8–4.0) for chlamydia. Conclusions Risk scores generated by the AI-based risk assessment tool MySTIRisk, together with Youden's index, are effective in determining high-risk subgroups for HIV/STIs. The thresholds can aid targeted HIV/STI screening and prevention.
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Potential conflicts of interest. All authors: no reported conflicts.
ISSN:2328-8957
2328-8957
DOI:10.1093/ofid/ofae011