Performance of HIV Infection Prediction Models in Men Who Have Sex with Men: A Systematic Review and Meta-Analysis.

Autor: Luo Q; School of Nursing, Binzhou Medical University, 346 Guanhai Road, Laishan District, Yantai, 264003, China. luoqq@bzmc.edu.cn., Luo Y; Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China., Cui T; School of Nursing, Binzhou Medical University, 346 Guanhai Road, Laishan District, Yantai, 264003, China., Li T; School of Nursing, Binzhou Medical University, 346 Guanhai Road, Laishan District, Yantai, 264003, China.
Jazyk: angličtina
Zdroj: Archives of sexual behavior [Arch Sex Behav] 2023 Jul; Vol. 52 (5), pp. 2011-2023. Date of Electronic Publication: 2023 Mar 08.
DOI: 10.1007/s10508-023-02574-x
Abstrakt: Effective ways to identify and predict men who have sex with men (MSM) at substantial risk for HIV is a global priority. HIV risk assessment tools can improve individual risk awareness and subsequent health-seeking actions. We sought to identify and characterize the performance of HIV infection risk prediction models in MSM through systematic review and meta-analysis. PubMed, Embase, and The Cochrane Library were searched. Eighteen HIV infection risk assessment models with a total of 151,422 participants and 3643 HIV cases were identified, eight of which have been externally validated by at least one study (HIRI-MSM, Menza Score, SDET Score, Li Model, DHRS, Amsterdam Score, SexPro model, and UMRSS). The number of predictor variables in each model ranged from three to 12, age, the number of male sexual partners, unprotected receptive anal intercourse, recreational drug usage (amphetamines, poppers), and sexually transmitted infections were critical scoring variables. All eight externally validated models performed well in terms of discrimination, with the pooled area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95%CI: 0.51 to 0.73, SDET Score) to 0.83 (95%CI: 0.48 to 0.99, Amsterdam Score). Calibration performance was only reported in 10 studies (35.7%, 10/28). The HIV infection risk prediction models showed moderate-to-good discrimination performance. Validation of prediction models across different geographic and ethnic environments is needed to ensure their real-world application.
(© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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