Association of an HIV-Prediction Model with Uptake of Preexposure Prophylaxis

Abstract

Background

Global efforts aimed at ending human immunodeficiency virus (HIV) incidence have adapted and evolved since the turn of the century. The utilization of machine learning incorporated into an electronic health record (EHR) can be refined into prediction models that identify when an individual is at greater HIV infection risk. This can create a novel and innovative approach to identifying patients eligible for preventative therapy.

Objectives

This study's aim was to evaluate the effectiveness of an HIV prediction model in clinical workflows. Outcomes included preexposure prophylaxis (PrEP) prescriptions generated and the model's ability to identify eligible patients.

Methods

A prediction model was developed and implemented at the safety-net hospital in Dallas County. Patients seen in primary care clinics were evaluated between July 2020 and June 2022. The prediction model was incorporated into an existing best practice advisory (BPA) used to identify potentially eligible PrEP patients. The prior, basic BPA (bBPA) displayed if a prior sexually transmitted infection was documented, and the enhanced BPA (eBPA) incorporated the HIV prediction model.

Results

A total of 3,218 unique patients received the BPA during the study time period, with 2,346 ultimately included for evaluation. There were 678 patients in the bBPA group and 1,666 in the eBPA group. PrEP prescriptions generated increased in the postimplementation group within the 90-day follow-up period (bBPA:1.48 vs. eBPA:3.67 prescriptions per month, p < 0.001). Patient demographics also differed between groups, resulting in a higher median age (bBPA: 36[interquartile range (IQR): 24] vs. eBPA: 52[QR: 19] years, p < 0.001) and an even distribution between birth sex in the postimplementation group (female sex at birth bBPA: 62.2% vs. eBPA:50.2%, p ≤ 0.001).

Conclusion

The implementation of an HIV prediction model yielded a higher number of PrEP prescriptions generated and was associated with the identification of twice the number of potentially eligible patients.

Keywords

machine learning - human immunodeficiency virus - preexposure prophylaxis - decision support systems

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