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

被引:10
|
作者
Latt, Phyu M. [1 ,2 ]
Soe, Nyi N. [1 ,2 ]
Xu, Xianglong [1 ,3 ]
Ong, Jason J. [4 ]
Chow, Eric P. F. [2 ,4 ,5 ]
Fairley, Christopher K. [2 ,4 ]
Zhang, Lei [1 ,2 ,6 ,7 ]
机构
[1] Alfred Hlth, Melbourne Sexual Hlth Ctr, Artificial Intelligence & Modelling Epidemiol Prog, Melbourne, Australia
[2] Monash Univ, Fac Med Nursing & Hlth Sci, Cent Clin Sch, 99 Commercial Rd, Melbourne, Vic 3004, Australia
[3] Shanghai Univ Tradit Chinese Med, Sch Publ Hlth, Shanghai, Peoples R China
[4] Alfred Hlth, Melbourne Sexual Hlth Ctr, Melbourne, Vic, Australia
[5] Univ Melbourne, Ctr Epidemiol & Biostat, Melbourne Sch Populat & Global Hlth, Melbourne, Australia
[6] Nanjing Med Univ, Childrens Hosp, Clin Med Res Ctr, Nanjing 210008, Jiangsu, Peoples R China
[7] Alfred Hlth, Melbourne Sexual Hlth Ctr, Artificial Intelligence & Modelling Epidemiol Prog, 580 Swanston St, Carlton, Vic 3053, Australia
来源
OPEN FORUM INFECTIOUS DISEASES | 2024年 / 11卷 / 03期
基金
英国医学研究理事会;
关键词
HIV; machine learning; risk assessment tool; sexually transmitted infections; STIs; YOUDEN INDEX; MEN; SEX;
D O I
10.1093/ofid/ofae011
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
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.
引用
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页数:10
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