Utility of a Machine-Guided Tool for Assessing Risk Behaviour Associated with Contracting HIV in Three Sites in South Africa: A field evaluation (Preprint)

Abstract

BACKGROUND Automated machine guided tools could be a valuable complement to electronic health initiatives to screen for diseases as well as link patients to care. Using supervised learning where a machine is programmed with labelled data sets with a built-in desired output, such tools can be trained to predict how prone an individual is to being infected with a disease. This study explored the feasibility of using a machine guided tool to predict susceptibility to HIV infection. OBJECTIVE To evaluate the accuracy of a machine-guided risk assessment tool in assessing HIV risk in those believed to be negative or unaware of their HIV status. METHODS 593 participants were recruited from three different geographical locations. On enrolment, participants undertook their first visit where they answered a collection of questions on the machine-guided tool, before having two HIV rapid diagnostics tests performed on them. HIV negative participants were invited to a follow-up visit, where the process was repeated. The machine-guided tool evaluated the participants’ individual responses and generated risk-assessment scores or predictions which were used to assess the viability of machine-guided tool in the identification of HIV at-risk patients. RESULTS A great majority of participants were HIV negative and male (333/517, 64.4%). The median age of participants who were HIV negative was 26 years, whilst that of HIV positive participants was 32 years. KwaZulu-Natal yielded the largest number of patients (n=199). Only 36/517 (7.0%) HIV negative and 12/76 HIV (15.8%) positive patients had never been tested for HIV previously. The greatest predictors of HIV susceptibility were: age (P ≤ .001), Frequency of HIV testing (P ≤ .001), sex (P = .012), and vaginal insertive sexual activity (P = .002). Risk prediction models yielded AUROC values ranging from 77.78% to 81.72%. A boosted tree model with a cut-off value of 0.15 performed best with a sensitivity of 83% (95% CI 71 – 91), specificity of 71% (95% CI 67 – 76), and a negative predictive value of 97% (95% CI 94 – 98) in a hold-out dataset. CONCLUSIONS The machine guided tool risk prediction indicated a high degree of sero-positivity sensitivity. Therefore, it displays potential in identifying candidates at risk of contracting HIV or needing intervention. However, the extent of the machine-guided tool’s viability remains to be validated. CLINICALTRIAL South African National Clinical Trial Registry: DOH-27-042021-679