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

Phyu M Latt; Nyi N Soe; Xianglong Xu; Jason J Ong; Eric P F Chow; Christopher K Fairley; Lei Zhang

doi:10.1093/ofid/ofae011

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

Open Forum Infect Dis. 2024 Jan 8;11(3):ofae011. doi: 10.1093/ofid/ofae011. eCollection 2024 Mar.

Authors

Phyu M Latt^{1

2}, Nyi N Soe^{1

2}, Xianglong Xu^{1

3}, Jason J Ong^{2

4}, Eric P F Chow^{2

4

5}, Christopher K Fairley^{2

4}, Lei Zhang^{1

2

6}

Affiliations

¹ Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
² Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
³ School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁴ Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
⁵ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
⁶ Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210008, China.

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.

Keywords: HIV; STIs; machine learning; risk assessment tool; sexually transmitted infections.