Previously submitted to: JMIR AI (no longer under consideration since Sep 21, 2022)
Date Submitted: May 12, 2022
Open Peer Review Period: May 12, 2022 - Jul 7, 2022
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Assessing Risk Behaviour Associated with Contracting HIV in Three Sites in South Africa: Utility of a Machine-Guided Tool
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. Clinical Trial: South African National Clinical Trial Registry: DOH-27-042021-679
Citation
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