Updated Risk Score Algorithms for Acute HIV Infection Detection at a Sexually Transmitted Infections Clinic in Lilongwe, Malawi

Griffin J Bell; Jane S Chen; Courtney N Maierhofer; Mitch Matoga; Sarah E Rutstein; Kathryn E Lancaster; Maganizo B Chagomerana; Edward Jere; Pearson Mmodzi; Naomi Bonongwe; Esther Mathiya; Beatrice Ndalama; Mina C Hosseinipour; Michael Emch; Ann M Dennis; Myron S Cohen; Irving F Hoffman; William C Miller; Kimberly A Powers

doi:10.1097/QAI.0000000000003519

Updated Risk Score Algorithms for Acute HIV Infection Detection at a Sexually Transmitted Infections Clinic in Lilongwe, Malawi

J Acquir Immune Defic Syndr. 2024 Sep 9. doi: 10.1097/QAI.0000000000003519. Online ahead of print.

Authors

Griffin J Bell¹, Jane S Chen², Courtney N Maierhofer¹, Mitch Matoga³, Sarah E Rutstein⁴, Kathryn E Lancaster⁵, Maganizo B Chagomerana^{3

4}, Edward Jere³, Pearson Mmodzi³, Naomi Bonongwe³, Esther Mathiya³, Beatrice Ndalama³, Mina C Hosseinipour^{3

4}, Michael Emch^{1

6}, Ann M Dennis⁴, Myron S Cohen^{1

4

7}, Irving F Hoffman^{3

4}, William C Miller¹, Kimberly A Powers¹

Affiliations

¹ Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, 27599, USA.
² Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, 27599, USA.
³ University of North Carolina Project, Lilongwe, Malawi.
⁴ Division of Infectious Diseases, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA.
⁵ School of Medicine, Wake Forest University, Winston-Salem, NC, 27101, USA.
⁶ Department of Geography, University of North Carolina, Chapel Hill, NC, 27599, USA.
⁷ Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA.

PMID: 39250334
DOI: 10.1097/QAI.0000000000003519

Abstract

Background: Detection of acute (pre-seroconversion) HIV infection (AHI), the phase of highest transmission risk, requires resource-intensive RNA- or antigen-based detection methods that can be infeasible for routine use. Risk score algorithms can improve the efficiency of AHI detection by identifying persons at highest risk of AHI for prioritized RNA/antigen testing, but prior algorithms have not considered geospatial information, potential differences by sex, or current antibody testing paradigms.

Methods: We used elastic net models to develop sex-stratified risk score algorithms in a case-control study of persons (136 with AHI, 250 without HIV) attending a sexually transmitted infections (STI) clinic in Lilongwe, Malawi from 2015 to 2019. We designed algorithms for varying clinical contexts according to three levels of data availability: 1) routine demographic and clinical information, 2) behavioral and occupational data obtainable through patient interview, and 3) geospatial variables requiring external datasets or field data collection. We calculated sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to assess model performance and developed a web application to support implementation.

Results: The highest-performing AHI risk score algorithm for men (AUC=0.74) contained five variables (condom use, body aches, fever, rash, genital sores/ulcers) from the first two levels of data availability. The highest-performing algorithm for women (AUC=0.81) contained fifteen variables from all three levels of data availability. A risk score cut-point of 0.26 had an AHI detection sensitivity of 93% and specificity of 27% for males, and a cut-point of 0.15 had 97% sensitivity and 44% specificity for females. Additional models are available in the web application.

Conclusion: Risk score algorithms can facilitate efficient AHI detection in STI clinic settings, creating opportunities for HIV transmission prevention interventions during this critical period of elevated transmission risk.