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INTRODUCTION Globally, an estimated 1.3 million pregnant women were living with HIV in 2021 [1]. Given the widespread scale‐up of universal antiretroviral therapy (ART) initiation for all individuals, an estimated [...], : Introduction: Lack of viral suppression (VS) among pregnant and breastfeeding women living with HIV poses challenges for maternal and infant health, and viral load (VL) monitoring via centralized laboratory systems faces many barriers. We aimed to determine the impact of point‐of‐care (POC) VL and targeted drug resistance mutation (DRM) testing in improving VS among pregnant and postpartum women on antiretroviral therapy. Methods: We conducted a pre/post‐intervention prospective cohort study among 820 pregnant women accessing HIV care at five public‐sector facilities in western Kenya from 2019 to 2022. The pre‐intervention or “control” group consisted of standard‐of‐care (SOC) centralized VL testing every 6 months and the post‐intervention or “intervention” group consisted of a combined strategy of POC VL every 3 months, targeted DRM testing, and clinical management support. The primary outcome was VS (VL ≤1000 copies/ml) at 6 months postpartum; secondary outcomes included uptake and turnaround times for VL testing and sustained VS. Results: At 6 months postpartum, 321/328 (98%) of participants in the intervention group and 339/347 (98%) in the control group achieved VS (aRR 1.00, 95% confidence interval [CI] 0.98, 1.02). When assessing VS using a threshold of 1000 copies/ml) who underwent successful DRM testing in the intervention group, all (46/46, 100%) had some DRMs and 20 (43%) had major DRMs (of which 80% were nucleos(t)ide reverse transcriptase inhibitor mutations). POC VL testing uptake was high (>89%) throughout pregnancy, delivery, and postpartum periods, with a median turnaround time of 1 day (IQR 1, 4) for POC VL in the intervention group and 7 days (IQR 5, 9) for SOC VL in the control group. Sustained VS throughout follow‐up was similar between groups with either POC or SOC VL testing (90−91% for Conclusions: Our combined strategy markedly decreased turnaround time but did not increase VS rates, which were already very high, or sustained VS among pregnant and postpartum women living with HIV. Further research on how best to utilize POC VL and DRM testing is needed to optimize sustained VS among this population.

Point-of-care (POC) technologies—including HIV viral load (VL) monitoring—are expanding globally, including in resource-limited settings. Modelling could allow decision-makers to consider the optimal strategy(ies) to maximize coverage and access, minimize turnaround time (TAT) and minimize cost with limited machines. Informed by formative qualitative focus group discussions with stakeholders focused on model inputs, outputs and format, we created an optimization model incorporating queueing theory and solved it using integer programming methods to reflect HIV VL monitoring in Kisumu County, Kenya. We modelled three scenarios for sample processing: (1) centralized laboratories only, (2) centralized labs with 7 existing POC 'hub' facilities and (3) centralized labs with 7 existing and 1–7 new 'hub' facilities. We calculated total TAT using the existing referral network for scenario 1 and solved for the optimal referral network by minimizing TAT for scenarios 2 and 3. We conducted one-way sensitivity analyses, including distributional fairness in each sub-county. Through two focus groups, stakeholders endorsed the provisionally selected model inputs, outputs and format with modifications incorporated during model-building. In all three scenarios, the largest component of TAT was time spent at a facility awaiting sample batching and transport (scenarios 1–3: 78.7%, 89.9%, 91.8%) and waiting time at the testing site (18.7%, 8.7%, 7.5%); transportation time contributed minimally to overall time (2.6%, 1.3%, 0.7%). In scenario 1, the average TAT was 39.8 h (SD: 2.9), with 1077 h that samples spent cumulatively in the VL processing system. In scenario 2, the average TAT decreased to 33.8 h (SD: 4.8), totalling 430 h. In scenario 3, the average TAT decreased nearly monotonically with each new machine to 31.1 h (SD: 8.4) and 346 total hours. Frequency of sample batching and processing rate most impacted TAT, and inclusion of distributional fairness minimally impacted TAT. In conclusion, a stakeholder-informed resource allocation model identified optimal POC VL hub allocations and referral networks. Using existing—and adding new—POC machines could markedly decrease TAT, as could operational changes. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Giang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)