scholarly journals Interpreting HIV Diagnostic Histories into Infection Time Estimates: Analytical Framework and Online Tool

2018 ◽  
Author(s):  
Eduard Grebe ◽  
Shelley N. Facente ◽  
Jeremy Bingham ◽  
Christopher D. Pilcher ◽  
Andrew Powrie ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Diagnostic Testing ◽  
Diagnostic Delay ◽  
Analytical Framework ◽  
Online Tool ◽  
Infection Time ◽  
Test Sensitivity ◽  
Time Estimates ◽  
Hiv Test ◽  
Testing Data

AbstractBackgroundIt is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results, using tests of different sensitivity.ObjectiveTo develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is conceptualised as the probability that a specimen will produce a positive result, expressed as a function of time since infection. This can be summarised as a median ‘diagnostic delay’ parameter, together with a measure of inter-subject variability.MethodsThe present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs).ResultsIn many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of ‘diagnostic testing histories’ into ‘infection time estimates’, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic.ConclusionsThis tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

scholarly journals Interpreting HIV Diagnostic Histories into Infection Time Estimates: Analytical Framework and Online Tool

2019 ◽  
Author(s):  
Eduard Grebe ◽  
Shelley N. Facente ◽  
Jeremy Bingham ◽  
Christopher D. Pilcher ◽  
Andrew Powrie ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Diagnostic Testing ◽  
Diagnostic Delay ◽  
Analytical Framework ◽  
Online Tool ◽  
Infection Time ◽  
Test Sensitivity ◽  
Time Estimates ◽  
Hiv Test ◽  
Testing Data

Abstract Background It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results, using tests of different sensitivity. Objective To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is conceptualised as the probability that a specimen will produce a positive result, expressed as a function of time since infection. This can be summarised as a median ‘diagnostic delay’ parameter, together with a measure of inter-subject variability. Methods The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). Results In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of ‘diagnostic testing histories’ into ‘infection time estimates’, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions This tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

scholarly journals Interpreting HIV Diagnostic Histories into Infection Time Estimates: Analytical Framework and Online Tool (Preprint)

2018 ◽  
Author(s):  
Eduard Grebe ◽  
Shelley N. Facente ◽  
Jeremy Bingham ◽  
Christopher D. Pilcher ◽  
Andrew Powrie ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Diagnostic Testing ◽  
Diagnostic Delay ◽  
Analytical Framework ◽  
Online Tool ◽  
Infection Time ◽  
Test Sensitivity ◽  
Time Estimates ◽  
Hiv Test ◽  
Testing Data

BACKGROUND It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results, using tests of different sensitivity. OBJECTIVE To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is conceptualised as the probability that a specimen will produce a positive result, expressed as a function of time since infection. This can be summarised as a median ‘diagnostic delay’ parameter, together with a measure of inter-subject variability. METHODS The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). RESULTS In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of ‘diagnostic testing histories’ into ‘infection time estimates’, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. CONCLUSIONS This tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

scholarly journals Interpreting HIV Diagnostic Histories into Infection Time Estimates: Analytical Framework and Online Tool

2019 ◽  
Author(s):  
Eduard Grebe ◽  
Shelley N. Facente ◽  
Jeremy Bingham ◽  
Christopher D. Pilcher ◽  
Andrew Powrie ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Diagnostic Testing ◽  
Analytical Framework ◽  
Online Tool ◽  
Infection Time ◽  
Test Sensitivity ◽  
Time Estimates ◽  
Hiv Test ◽  
Testing Data ◽  
Research Studies

Abstract Background It is frequently of epidemiological or clinical interest to estimate the date of HIV infection or time-since-infection. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results, using tests of different sensitivity. Objective To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is defined as the probability that a specimen will produce a positive result, expressed as a function of time since infection. Methods The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). Results In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of ‘diagnostic testing histories’ into ‘infection time estimates’, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions This tool is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

scholarly journals Interpreting HIV diagnostic histories into infection time estimates: analytical framework and online tool

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Eduard Grebe ◽  
◽  
Shelley N. Facente ◽  
Jeremy Bingham ◽  
Christopher D. Pilcher ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Diagnostic Testing ◽  
Analytical Framework ◽  
Online Tool ◽  
Infection Time ◽  
Test Sensitivity ◽  
Time Estimates ◽  
Hiv Test ◽  
Testing Data ◽  
Research Studies

Abstract Background It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. Methods The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). Results In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions This tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

scholarly journals Interpreting HIV Diagnostic Histories into Infection Time Estimates: Analytical Framework and Online Tool

2019 ◽  
Author(s):  
Eduard Grebe ◽  
Shelley N. Facente ◽  
Jeremy Bingham ◽  
Christopher D. Pilcher ◽  
Andrew Powrie ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Diagnostic Testing ◽  
Analytical Framework ◽  
Online Tool ◽  
Infection Time ◽  
Test Sensitivity ◽  
Time Estimates ◽  
Hiv Test ◽  
Testing Data ◽  
Research Studies

Abstract Background It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results, using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. Methods The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). Results In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of ‘diagnostic testing histories’ into ‘infection time estimates’, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions This tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

scholarly journals Estimated dates of detectable infection (EDDIs) as an improvement upon Fiebig staging for HIV infection dating

2020 ◽  
Vol 148 ◽  
Author(s):  
S. N. Facente ◽  
E. Grebe ◽  
C. D. Pilcher ◽  
M. P. Busch ◽  
G. Murphy ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Acute Infection ◽  
Diagnostic Delay ◽  
Staging System ◽  
Point Estimate ◽  
Individual Level ◽  
Hiv Test ◽  
Testing Technology ◽  
The Individual ◽  
Test Result

Abstract Accurate methods for determining the duration of HIV infection at the individual level are valuable in many settings, including many critical research studies and in clinical practice (especially for acute infection). Since first published in 2003, the ‘Fiebig staging system’ has been used as the primary way of classifying early HIV infection into five sequential stages based on HIV test result patterns in newly diagnosed individuals. However, Fiebig stages can only be assigned to individuals who produce both a negative and a positive test result on the same day, on specific pairs of tests of varying ‘sensitivity’. Further, in the past 16 years HIV-testing technology has evolved substantially, and three of the five key assays used to define Fiebig stages are no longer widely used. To address these limitations, we developed an improved and more general framework for estimating the duration of HIV infection by interpreting any combination of diagnostic test results, whether obtained on single or multiple days, into an estimated date of detectable infection, or EDDI. A key advantage of the EDDI method over Fiebig staging is that it allows for the generation of a point estimate, as well as an associated credibility interval for the date of first detectable infection, for any person who has at least one positive and one negative HIV test of any kind. The tests do not have to be run on the same day; they do not have to be run during the acute phase of infection and the method does not rely on any special pairing of tests to define ‘stages’ of infection. The size of the interval surrounding the EDDI (and therefore the precision of the estimate itself) depends largely on the length of time between negative and positive tests. The EDDI approach is also flexible, seamlessly incorporating any assay for which there is a reasonable diagnostic delay estimate. An open-source, free online tool includes a user-updatable curated database of published diagnostic delays. HIV diagnostics have evolved tremendously since that original publication more than 15 years ago, and it is time to similarly evolve the methods used to estimate timing of infection. The EDDI method is a flexible and rigorous way to estimate the timing of HIV infection in a continuously evolving diagnostic landscape.

scholarly journals Differential Diagnosis of Chorea—HIV Infection Delays Diagnosis of Huntington’s Disease by Years

2021 ◽  
Vol 11 (6) ◽  
pp. 710
Author(s):  
Jannis Achenbach ◽  
Simon Faissner ◽  
Carsten Saft
Keyword(s):  
Hiv Infection ◽  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Disease Onset ◽  
Diagnostic Delay ◽  
Depression Scale ◽  
Cag Repeat ◽  
Psychiatric Disease ◽  
Disease Manifestation ◽  
Cross Sectional

Background: There is a broad range of potential differential diagnoses for chorea. Besides rare, inherited neurodegenerative diseases such as Huntington’s disease (HD) chorea can accompany basal ganglia disorders due to vasculitis or infections, e.g., with the human immunodeficiency virus (HIV). The clinical picture is complicated by the rare occurrence of HIV infection and HD. Methods: First, we present a case suffering simultaneously from HIV and HD (HIV/HD) focusing on clinical manifestation and disease onset. We investigated cross-sectional data regarding molecular genetic, motoric, cognitive, functional, and psychiatric disease manifestation of HIV/HD in comparison to motor-manifest HD patients without HIV infection (nonHIV/HD) in the largest cohort of HD patients worldwide using the registry study ENROLL-HD. Data were analyzed using ANCOVA analyses controlling for covariates of age and CAG repeat length between groups in IBM SPSS Statistics V.25. Results: The HD diagnosis in our case report was delayed by approximately nine years due to the false assumption that the HIV infection might have been the cause of chorea. Out of n = 21,116 participants in ENROLL-HD, we identified n = 10,125 motor-manifest HD patients. n = 23 male participants were classified as suffering from HIV infection as a comorbidity, compared to n = 4898 male non-HIV/HD patients. Except for age, with HIV/HD being significantly younger (p < 0.050), we observed no group differences regarding sociodemographic, genetic, educational, motoric, functional, and cognitive parameters. Male HIV/HD patients reported about a 5.3-year-earlier onset of HD symptoms noticed by themselves compared to non-HIV/HD (p < 0.050). Moreover, patients in the HIV/HD group had a longer diagnostic delay of 1.8 years between onset of symptoms and HD diagnosis and a longer time regarding assessment of first symptoms by the rater and judgement of the patient (all p < 0.050). Unexpectedly, HIV/HD patients showed less irritability in the Hospital Anxiety and Depression Scale (all p < 0.05). Conclusions: The HD diagnosis in HIV-infected male patients is secured with a diagnostic delay between first symptoms noticed by the patient and final diagnosis. Treating physicians therefore should be sensitized to think of potential alternative diagnoses in HIV-infected patients also afflicted by movement disorders, especially if there is evidence of subcortical atrophy and a history of hyperkinesia, even without a clear HD-family history. Those patients should be transferred for early genetic testing to avoid further unnecessary diagnostics and improve sociomedical care.

scholarly journals Do I Have HIV or Not? Lack of RNA Detection and the Case for Sensitive DNA Testing

2020 ◽  
Vol 7 (11) ◽  
Author(s):  
Sandra A Springer ◽  
Silvina Masciotra ◽  
Jeffrey A Johnson ◽  
Sheldon Campbell
Keyword(s):  
Antiretroviral Therapy ◽  
Hiv Infection ◽  
Viral Rna ◽  
Infection Status ◽  
Test Results ◽  
Dna Testing ◽  
Rna Detection ◽  
Hiv Test

Abstract We present a case of a 20-year-old male who had ambiguous HIV test results after entering new provider care and whose status was later complicated by undetectable viral RNA off antiretroviral therapy (ART). Verifying HIV infection status may occasionally require sensitive DNA testing that might need to be considered in diagnostic guidelines to resolve diagnosis and ensure appropriate ART management.

scholarly journals Predictors Of Provider-Initiated Hiv Testing In Heterosexuals At Increased Risk Of Hiv (Het) In Puerto Rico: Data From Nhbs-Het Cycle 2016

2020 ◽  
Author(s):  
Maureen Marie Canario de la Torre ◽  
Ivony Yireth Agudelo Salas ◽  
Sandra Miranda de León ◽  
Yadira Rolón Colón ◽  
María Pabón Martínez ◽  
...  
Keyword(s):  
Sexual Behavior ◽  
Puerto Rico ◽  
Hiv Infection ◽  
Hiv Testing ◽  
Perceived Risk ◽  
Healthcare Providers ◽  
World Health ◽  
Healthcare Settings ◽  
Hiv Test ◽  
Increased Risk

Abstract The Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) have established guidelines for HIV testing in healthcare settings. The aim of this study was to identify the sociodemographic, healthcare, and sexual-behavior predictors of provider-initiated HIV testing (PIHT) using data from the Puerto Rico National HIV Behavioral Surveillance (PR-NHBS) 2016 cycle directed towards heterosexuals at increased risk of HIV infection (HET). A total sample of 531 eligible participants were recruited through respondent-driven sampling (RDS). Logistic regression models assessed the associations between sociodemographic, healthcare, and sexual-behavior predictors, whilst adjusting for sex and age. The majority of the participants were women (66.1%), with 72.7% reporting having received healthcare services in the past year. Of them, 18.7% had received an HIV-test offer from their healthcare providers. More than half of the participants (65.2%) reported a low perceived risk of getting infected with HIV in the next 12 months. Results suggest an overall low prevalence of PIHT among HET in PR who exhibited a relatively high prevalence of low perceived risk of HIV infection. Furthermore, the assessed predictors show that individuals who engaged in high-risk sexual behaviors (AOR = 0.52; 95% CI: 0.30–0.90) were less likely to receive HIV-test offers from their providers. This study further emphasizes the need for healthcare providers to follow recommended guidelines for HIV testing in healthcare settings as a means of establishing preventive measures to further counteract the HIV epidemic in Puerto Rico, specifically among HET.

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