Optimising HIV drug resistance testing laboratory networks in Kenya: insights from systems engineering modelling.

BACKGROUND: HIV drug resistance (DR) is a growing threat to the durability of current and future HIV treatment success. DR testing (DRT) technologies are very expensive and specialised, relying on centralised laboratories in most low and middle-income countries. Modelling for laboratory network with point-of-care (POC) DRT assays to minimise turnaround time (TAT), is urgently needed to meet the growing demand. METHODS: We developed a model with user-friendly interface using integer programming and queueing theory to improve the DRT system in Kisumu County, Kenya. We estimated DRT demand based on both current and idealised scenarios and evaluated a centralised laboratory-only network and an optimised POC DRT network. A one-way sensitivity analysis of key user inputs was conducted. RESULTS: In a centralised laboratory-only network, the mean TAT ranged from 8.52 to 8.55 working days, and the system could not handle a demand proportion exceeding 1.6%. In contrast, the mean TAT for POC DRT network ranged from 1.13 to 2.11 working days, with demand proportion up to 4.8%. Sensitivity analyses showed that expanding DRT hubs reduces mean TAT substantially while increasing the processing rate at national labs had minimal effect. For instance, doubling the current service rate at national labs reduced the mean TAT by only 0.0%-1.9% in various tested scenarios, whereas doubling the current service rate at DRT hubs reduced the mean TAT by 37.5%-49.8%. In addition, faster batching modes and transportation were important factors influencing the mean TAT. CONCLUSIONS: Our model offers decision-makers an informed framework for improving the DRT system using POC in Kenya. POC DRT networks substantially reduce mean TAT and can handle a higher demand proportion than a centralised laboratory-only network, especially for children and pregnant women living with HIV, where there is an immediate push to use DRT results for patient case management.

Real-world performance of point-of-care vs. standard-of-care HIV viral load testing in western Kenya: Secondary analysis of Opt4Kids and Opt4Mamas studies.

Routine HIV viral load testing is important for evaluating HIV treatment outcomes, but conventional viral load testing has many barriers including expensive laboratory equipment and lengthy results return times to patients. A point-of-care viral load testing technology, such as GeneXpert HIV-1 quantification assay, could reduce these barriers by decreasing cost and turnaround time, however real-world performance is limited. We conducted a secondary analysis using 900 samples collected from participants in two studies to examine the performance of GeneXpert as point-of-care viral load compared to standard-of-care testing (which was conducted with two centralized laboratories using traditional HIV-1 RNA PCR quantification assays). The two studies, Opt4Kids (n = 704 participants) and Opt4Mamas (n = 820 participants), were conducted in western Kenya from 2019-2021 to evaluate the effectiveness of a combined intervention strategy, which included point-of-care viral load testing. Paired viral load results were compared using four different thresholds for virological non-suppression, namely ≥50, ≥200, ≥400, ≥1000 copies/ml. At a threshold of ≥1000 copies/mL, paired samples collected on the same day: demonstrated sensitivities of 90.0% (95% confidence interval [CI] 68.3, 98.8) and 66.7% (9.4, 99.2), specificities of 98.4% (95.5, 99.7) and 100% (96.5, 100), and percent agreements of 97.7% (94.6, 99.2) and 99.1% (95.0, 100) in Opt4Kids and Opt4Mamas studies, respectively. When lower viral load thresholds were used and the paired samples were collected an increasing number of days apart, sensitivity, specificity, and percent agreement generally decreased. While specificity and percent agreement were uniformly high, sensitivity was lower than expected. Non-specificity of the standard of care testing may have been responsible for the sensitivity values. Nonetheless, our results demonstrate that GeneXpert may be used reliably to monitor HIV treatment in low- and middle- income countries to attain UNAID's 95-95-95 HIV goals.