| Literature DB >> 30168275 |
Amy S Kravitz Del Solar1, Bharat Parekh2, Meaghan O'Keefe Douglas1, Dianna Edgil1, Joel Kuritsky1, John Nkengasong3.
Abstract
As part of the global response to the HIV/AIDS epidemic, the U.S. President's Emergency Plan for AIDS Relief (PEPFAR) is committed to the provision of high-quality services and ensuring testing accuracy. Two recently published papers focusing on HIV testing and misdiagnosis in sub-Saharan Africa by Kosack et al. report on evaluations of HIV rapid diagnostic tests (RDTs) and found lower than expected specificity and sensitivity on some tests when used in certain geographic locations. The magnitude of PEPFAR's global HIV response has been possible due to the extensive use of RDTs, which have made HIV diagnosis accessible all over the world. We take the opportunity to address concerns raised about the potential implications that these findings could have on real-world HIV testing accuracy. PEPFAR supported countries adhere to the normative guidance by World Health Organization (WHO) supporting algorithms which require sequential positive tests for diagnostic accuracy. An analysis of Médecins Sans Frontières (MSF) RDT site-specific data applied to PEPFAR in-country protocols demonstrate a variation in the diagnostic accuracy of the testing algorithms, but with a very small population-level effect. The data demonstrate, with the use of these algorithms, that the RDT outcomes found in the study by Kosack et al. would be largely mitigated and would not be expected to have a significant impact on diagnostic accuracy and overall programming in most countries. Avoiding any misdiagnosis is a priority for PEPFAR, and it remains vital to gain a deeper understanding of the causes and the extent of diagnostic errors and any misclassification. Extensive quality control mechanisms and continued research are essential. With a focus on epidemic control and ensuring diagnostic accuracy, PEPFAR recommends that all countries use WHO pre-qualified RDTs within the recommended strategies and algorithms for HIV testing. We also support validation of HIV testing algorithms using in-country specimens to determine optimal performance, and the reverification testing of all people diagnosed with HIV prior to starting treatment as an essential quality assurance measure. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of International AIDS Society.Entities:
Mesh:
Year: 2018 PMID: 30168275 PMCID: PMC6117497 DOI: 10.1002/jia2.25177
Source DB: PubMed Journal: J Int AIDS Soc ISSN: 1758-2652 Impact factor: 5.396
Kosack et al. data calculated to illustrate the algorithms’ positive predictive valuea
| Site | HIV prevalence | Algorithm | Sensitivity | Algorithm PPV | |||||
|---|---|---|---|---|---|---|---|---|---|
| Lowest bound of performance based on low confidence interval (MSF data) | Highest bound of performance based on high confidence interval (MSF data) | Point estimate of confidence interval (MSF data) | Manufacturer data | Worst case | Best case | Point estimate of confidence interval (MSF data) | |||
| Guinea, Conakry | 2.7% | Determine | 98.30% | 100% | 100% | 99.9% | 98.89% | 99.98% | 99.89% |
| SD Bioline | 98.30% | 100% | 100% | 100% | |||||
| Uganda, Kitgum | 8.3% | Determine | 98.3% | 100% | 100% | 99.9% | 97.3% | 100% | 100.0% |
| HIV STAT‐PAK | 77.9% | 99.5% | 96.2% | 99.7% | |||||
| Uni‐Gold | 77.9% | 99.5% | 96.2% | 100% | |||||
| Uganda, Arua | 4.9% | Determine | 98.3% | 100% | 100% | 99.9% | 99.1% | 100.0% | 99.9% |
| HIV STAT‐PAK | 98.3% | 100% | 100% | 99.7% | |||||
| Uni‐Gold | 98.3% | 100% | 100% | 100% | |||||
| Kenya, Homa Bay | 26% | Determine | 98.3% | 100% | 100% | 99.9% | 95.1% | 98.9% | 97.7% |
| First Response | 98.3% | 100% | 100% | 99.4% | |||||
| Uni‐Gold | 96.8% | 99.9% | 99.6% | 100% | |||||
| DRC, Baraka | 0.8% | Determine | 98.3% | 100% | 100% | 99.9% | 93.6% | 99.8% | 98.9% |
| Uni‐Gold | 96.8% | 99.9% | 99.6% | 100% | |||||
| Vikia | 96.8% | 99.9% | 99.6% | 99.95% | |||||
Estimates for the algorithm assume that test results at each step are independent of those in the prior step; worst case and best case performance estimates were calculated using the lower and upper 95% bounds for each test respectively.
Kosack et al. data calculated to illustrate the algorithms’ negative predictive valuea
| Site | HIV prevalence | Algorithm | Specificity | Algorithm NPV | |||||
|---|---|---|---|---|---|---|---|---|---|
| Lowest end of confidence interval (MSF data) | Highest end of confidence interval (MSF data) | Point estimate of confidence interval (MSF data) | Manufacturer data | Worst case | Best case | Point estimate of confidence interval (MSF data) | |||
| Guinea, Conakry | 2.7% | Determine | 97.70% | 99.6% | 99% | 98.2% | 99.9% | 100% | 100% |
| SD Bioline | 98.70% | 99.9% | 99.7% | 99.8% | |||||
| Uganda, Kitgum | 8.3% | Determine | 88.8% | 95.8% | 93.1% | 98.2% | 99.4% | 100% | 100.0% |
| HIV STAT‐PAK | 98.3% | 100.0% | 100.0% | 99.9% | |||||
| Uni‐Gold | 95.2% | 99.3% | 98.2% | 100% | |||||
| Uganda, Arua | 4.9% | Determine | 90.6% | 96.8% | 94.4% | 98.2% | 99.9% | 100.0% | 100.0% |
| HIV STAT‐PAK | 99.5% | 100.0% | 99.9% | 99.9% | |||||
| Uni‐Gold | 93.7% | 98.5% | 96.9% | 100% | |||||
| Kenya, Homa Bay | 26% | Determine | 91.0% | 96.5% | 94.4% | 98.2% | 99.4% | 100.0% | 100.0% |
| First Response | 80.6% | 89.0% | 85.3% | 99.4% | |||||
| Uni‐Gold | 96.9% | 99.7% | 99.0% | 99.8% | |||||
| DRC, Baraka | 0.8% | Determine | 87.8% | 94.7% | 91.9% | 98.2% | 100.0% | 100.0% | 100.0% |
| Uni‐Gold | 93.3% | 98.2% | 96.5% | 100% | |||||
| Vikia | 93.8% | 98.4% | 96.8% | 99.86% | |||||
Estimates for the algorithm assume that test results at each step are independent of those in the prior step; worst case and best case performance estimates were calculated using the lower and upper 95% bounds for each test respectively.