Reflex Human Immunodeficiency Virus (HIV) Type 1 RNA Testing Enables Timely Differentiation of False-Positive Results From Acute HIV Infection

Kaperak, Christopher; Eller, Dylan; Devlin, Samantha A; Hall, André; Schmitt, Jessica; Friedman, Eleanor E; Beavis, Kathleen G; Stanford, Kimberly A; Pitrak, David; McNulty, Moira C

doi:10.1093/ofid/ofad629

Open Forum Infectious Diseases

2023

DOI: 10.1093/ofid/ofad629

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Reflex Human Immunodeficiency Virus (HIV) Type 1 RNA Testing Enables Timely Differentiation of False-Positive Results From Acute HIV Infection

Christopher Kaperak,

Dylan Eller,

Samantha A Devlin

et al.

Abstract: Accurate, timely HIV diagnosis is critical. Routine HIV screening program data were examined before and after reflex HIV-1 RNA testing. Reflex testing facilitated confirmation of reactive HIV screening assays (as true or false positives) (OR 23.7 [6.7 to 83.4]; p<.0001), improving detection of acute HIV and reducing unconfirmed discordant results.

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2024

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Cited by 2 publications

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Validating, Implementing, and Monitoring Machine Learning Solutions in the Clinical Laboratory Safely and Effectively

Spies,

Farnsworth,

Wheeler

et al. 2024

Clinical Chemistry

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Background Machine learning solutions offer tremendous promise for improving clinical and laboratory operations in pathology. Proof-of-concept descriptions of these approaches have become commonplace in laboratory medicine literature, but only a scant few of these have been implemented within clinical laboratories, owing to the often substantial barriers in validating, implementing, and monitoring these applications in practice. This mini-review aims to highlight the key considerations in each of these steps. Content Effective and responsible applications of machine learning in clinical laboratories require robust validation prior to implementation. A comprehensive validation study involves a critical evaluation of study design, data engineering and interoperability, target label definition, metric selection, generalizability and applicability assessment, algorithmic fairness, and explainability. While the main text highlights these concepts in broad strokes, a supplementary code walk-through is also provided to facilitate a more practical understanding of these topics using a real-world classification task example, the detection of saline-contaminated chemistry panels. Following validation, the laboratorian's role is far from over. Implementing machine learning solutions requires an interdisciplinary effort across several roles in an organization. We highlight the key roles, responsibilities, and terminologies for successfully deploying a validated solution into a live production environment. Finally, the implemented solution must be routinely monitored for signs of performance degradation and updated if necessary. Summary This mini-review aims to bridge the gap between theory and practice by highlighting key concepts in validation, implementation, and monitoring machine learning solutions effectively and responsibly in the clinical laboratory.

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Validating, Implementing, and Monitoring Machine Learning Solutions in the Clinical Laboratory Safely and Effectively

Spies,

Farnsworth,

Wheeler

et al. 2024

Clinical Chemistry

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Outcomes of discordant HIV screening test results at a southern academic medical center

Colbrunn,

Jacks,

Curry

et al. 2024

Aids

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Objective: The aim of this study was to examine outcomes of follow-up for persons with discordant fourth-generation HIV screening test results. Design: A retrospective chart review. Methods: We analyzed the electronic health record at the Medical University of South Carolina for a 10-year period spanning 2012–2022 to identify instances of discordant HIV screening test results, wherein initial antigen/antibody screening was positive, but reflex confirmatory testing for HIV-1 and HIV-2 antibodies was negative. We reviewed individual records to evaluate clinical follow-up and determine if the discordant test represented an acute HIV infection, a false-positive result, or was unresolved. Results: We identified 199 testing instances with discordant results. Most discordant results (n = 115) were subsequently determined to reflect a false-positive test, while 56 were unresolved without documented follow-up testing. Twenty-eight cases of acute HIV infection were identified of which 26 were linked to care within a month of initial testing. Two acute HIV cases were not identified in real time leading to delay in diagnosis and care. Testing done in the context of infectious symptoms and testing performed in the emergency department were associated with increased odds of a discordant test ultimately reflecting acute HIV infection. Conclusion: These results demonstrate the importance of appropriate and timely follow-up for discordant HIV screening test results.

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Reflex Human Immunodeficiency Virus (HIV) Type 1 RNA Testing Enables Timely Differentiation of False-Positive Results From Acute HIV Infection

Cited by 2 publications

References 15 publications

Validating, Implementing, and Monitoring Machine Learning Solutions in the Clinical Laboratory Safely and Effectively

Validating, Implementing, and Monitoring Machine Learning Solutions in the Clinical Laboratory Safely and Effectively

Outcomes of discordant HIV screening test results at a southern academic medical center

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