A single pathway for HIV testing and therapy

Anne Paxton

June 2022—By revealing the value of a diagnostic algorithm using quantitative RNA as the second test to confirm reactive HIV screening results, Daniel Gromer, MD, and colleagues say their simulation modeling suggests clinical improvement over the standard-of-care algorithm, and at lower cost if HIV specimen positivity is high.

The current guideline-based standard of care for HIV detection, outlined in CDC recommendations, consists of a fourth-generation HIV p24 antigen/antibody test that, if reactive, is followed by an antibody differentiation immunoassay. If indeterminate or negative, a qualitative RNA test is done (QL-RNA). For therapy, testing starts with a baseline quantitative RNA (QT-RNA) test on a plasma specimen, followed by antiretroviral therapy initiation, and then by serial QT-RNA testing to monitor response.

“Our objective was to use simulation modeling to compare the clinical and cost implications of two HIV diagnostic algorithms,” Dr. Gromer says: an RNAplasma algorithm employing QT-RNA as the second test (Ag/Ab→QT-RNA→AbDiff) and the standard-of-care algorithm with antibody differentiation as the second test (Ag/Ab→AbDiff→QL-RNA).

Simulation studies aren’t perfect, admits Dr. Gromer, an infectious disease fellow at Emory University School of Medicine. “All models you use in a simulation setting are kind of a false construct. But you often need to model the truth as closely as you can to tell intrinsic truths about what’s actually happening in the world.”

Since late 2020, clinicians and laboratories have had the benefit of the FDA’s approval of a QT-RNA assay for diagnosis—Hologic’s Aptima HIV-1 Quant Dx. On plasma specimens, it’s approved for quantitative reporting as a numeric value. On serum specimens, it’s approved for reporting as “detectable” or “undetectable.”

QT-RNA offered much more, in the view of Dr. Gromer and infectious disease specialists from Yale School of Public Health, Harvard Medical School, and Massachusetts General Hospital. They hypothesized that the QT-RNA test’s quantitative results at diagnosis, used as part of a plasma algorithm, could also improve HIV clinical care and reduce costs.

This spring, at the virtual 2022 Advancing HIV, STI and Viral Hepatitis Testing Conference, sponsored by the CDC, Association of Public Health Laboratories, American Sexual Health Association, and American Sexually Transmitted Diseases Association, and in a recent interview, Dr. Gromer explained how the group’s study suggests that incorporating the QT-RNA test into the HIV testing algorithm—using it as the second test in place of AbDiff—could lead to faster confirmation of HIV diagnosis, which could expedite antiretroviral initiation and reduce the potential for new transmissions, while also reducing time to reassurance for patients with false-positive antigen/antibody test results.

“There’s been somewhat of a disconnect between diagnosis and therapy of HIV, which is one of the major challenges we face in controlling and limiting the spread of HIV,” Dr. Gromer tells CAP TODAY. “Diagnosis has a few steps and then therapy initiation has a few steps and there’s no single test in the guidelines that acts as a perfect bridge between them.” As RNA testing has become much more affordable, faster, and more facile, “I think those molecular techniques have become more widespread. And there’s been enthusiasm for trying to figure out a way of streamlining the HIV testing cascade for diagnosis in particular, and also for screening, that prioritizes RNA testing and deprioritizes antibody differentiation testing.”

Both algorithms were examined in a population of specimens tested for HIV, which included specimens with no HIV infection and with HIV infection. For specimens with HIV, they categorized by specific subpopulations: chronic HIV-1, acute HIV-1, elite control of HIV-1, and HIV-2, although the latter two are rare among specimens tested for HIV in the United States.

“We populated the model with data from three large U.S. laboratory systems with low (.25 percent), moderate (.51 percent), and high (1.98 percent) HIV specimen positivity, and we examined the algorithms in each laboratory setting,” Dr. Gromer says. Other model input parameters were the tests’ performance characteristics, turnaround times (from collection to result reported) based on experience for both reflex (AbDiff, 12 hours; RNA, 24 hours) and return (add 60 hours to reflex test time) testing, and test cost based on the CMS laboratory fee schedule, which lists AbDiff at $13.71, QL-RNA at $35.09, and QT-RNA at $85.10.

The comparison covered two time-based outcomes. The first, time to action, was defined as the time to when clinicians initiate antiretroviral therapy for people diagnosed with HIV or complete the diagnostic algorithm for a specimen without HIV. “This outcome is especially important in some presentations of acute HIV when the antigen/antibody is reactive but the differentiation assay will not be,” Dr. Gromer says. The second outcome was time to reassurance, or the time to when clinicians can inform a person with a false-positive antigen/antibody that they have a negative RNA test. Two other outcomes studied were the number of blood draws needed and testing cost.

The base case results when the two algorithms were compared were as follows:

• RNAplasma would reduce time to action compared with the standard of care for persons with HIV from 112 hours to 60 hours.
• RNAplasma would reduce time to reassurance compared with the standard of care for persons with a false-positive Ag/Ab from 132 hours to 60 hours.
• RNAplasma would result in similar costs for all specimens tested: $24.74 per run for RNAplasma versus $24.70 per run for the standard of care.
• RNAplasma would reduce visits for specimen collection before antiretroviral therapy initiation compared with the standard of care for persons with HIV from 2.05 visits/person for the standard of care to 1.01 visits/person for RNAplasma.

In terms of time to action, the only laboratories in which the standard of care is preferred is where antibody differentiation provides results quickly and QT-RNA takes much longer, Dr. Gromer says. For example, standard-of-care testing would result in faster time to action when AbDiff turnaround time is six hours and QT-RNA turnaround time is longer than 76 hours, assuming that ART is not initiated until a specimen for QT-RNA is obtained.

Testing costs would be lower for the RNAplasma algorithm when HIV specimen positivity in the laboratory is higher, the model shows. In low-positivity laboratories, QT-RNA test cost would need to be about $50 for RNAplasma to result in lower testing costs, while at the base cost of $85 per QT-RNA test, RNAplasma would result in lower testing costs if laboratory HIV positivity is high, at .8 percent or greater.