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Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection

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Abstract
Background: There is today no gold standard method to accurately define the time passed since infection at HIV diagnosis. Infection timing and incidence measurement is however essential to better monitor the dynamics of local epidemics and the effect of prevention initiatives. Methods: Three methods for infection timing were evaluated using 237 serial samples from documented seroconversions and 566 cross sectional samples from newly diagnosed patients: identification of antibodies against the HIV p31 protein in INNO-LIA, SediaTM BED CEIA and SediaTM LAg-Avidity EIA. A multi-assay decision tree for infection timing was developed. Results: Clear differences in recency window between BED CEIA, LAg-Avidity EIA and p31 antibody presence were observed with a switch from recent to long term infection a median of 169.5, 108.0 and 64.5 days after collection of the pre-seroconversion sample respectively. BED showed high reliability for identification of long term infections while LAg-Avidity is highly accurate for identification of recent infections. Using BED as initial assay to identify the long term infections and LAg-Avidity as a confirmatory assay for those classified as recent infection by BED, explores the strengths of both while reduces the workload. The short recency window of p31 antibodies allows to discriminate very early from early infections based on this marker. BED recent infection results not confirmed by LAg-Avidity are considered to reflect a period more distant from the infection time. False recency predictions in this group can be minimized by elimination of patients with a CD4 count of less than 100 cells/mm3 or without no p31 antibodies. For 566 cross sectional sample the outcome of the decision tree confirmed the infection timing based on the results of all 3 markers but reduced the overall cost from 13.2 USD to 5.2 USD per sample. Conclusions: A step-wise multi assay decision tree allows accurate timing of the HIV infection at diagnosis at affordable effort and cost and can be an important new tool in studies analyzing the dynamics of local epidemics or the effects of prevention strategies.
Keywords
HIV, Infection timing, Incidence measurement, INCIDENCE ASSAYS, ENZYME-IMMUNOASSAY, SUBTYPES, SEROCONVERSION, SPECIFICITY, COHORT, PERIOD

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MLA
Verhofstede, Chris, Katrien Fransen, Annelies Van Den Heuvel, et al. “Decision Tree for Accurate Infection Timing in Individuals Newly Diagnosed with HIV-1 Infection.” BMC INFECTIOUS DISEASES 17 (2017): n. pag. Print.
APA
Verhofstede, C., Fransen, K., Van Den Heuvel, A., Van Laethem, K., Ruelle, J., Vancutsem, E., Stoffels, K., et al. (2017). Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection. BMC INFECTIOUS DISEASES, 17.
Chicago author-date
Verhofstede, Chris, Katrien Fransen, Annelies Van Den Heuvel, Kristel Van Laethem, Jean Ruelle, Ellen Vancutsem, Karolien Stoffels, et al. 2017. “Decision Tree for Accurate Infection Timing in Individuals Newly Diagnosed with HIV-1 Infection.” Bmc Infectious Diseases 17.
Chicago author-date (all authors)
Verhofstede, Chris, Katrien Fransen, Annelies Van Den Heuvel, Kristel Van Laethem, Jean Ruelle, Ellen Vancutsem, Karolien Stoffels, Sigi Van den Wijngaert, Marie-Luce Delforge, Dolores Vaira, Laura Hebberecht, Marlies Schauvliege, Virginie Mortier, Kenny Dauwe, and Steven Callens. 2017. “Decision Tree for Accurate Infection Timing in Individuals Newly Diagnosed with HIV-1 Infection.” Bmc Infectious Diseases 17.
Vancouver
1.
Verhofstede C, Fransen K, Van Den Heuvel A, Van Laethem K, Ruelle J, Vancutsem E, et al. Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection. BMC INFECTIOUS DISEASES. 2017;17.
IEEE
[1]
C. Verhofstede et al., “Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection,” BMC INFECTIOUS DISEASES, vol. 17, 2017.
@article{8553019,
  abstract     = {Background: There is today no gold standard method to accurately define the time passed since infection at HIV diagnosis. Infection timing and incidence measurement is however essential to better monitor the dynamics of local epidemics and the effect of prevention initiatives. 
Methods: Three methods for infection timing were evaluated using 237 serial samples from documented seroconversions and 566 cross sectional samples from newly diagnosed patients: identification of antibodies against the HIV p31 protein in INNO-LIA, SediaTM BED CEIA and SediaTM LAg-Avidity EIA. A multi-assay decision tree for infection timing was developed. 
Results: Clear differences in recency window between BED CEIA, LAg-Avidity EIA and p31 antibody presence were observed with a switch from recent to long term infection a median of 169.5, 108.0 and 64.5 days after collection of the pre-seroconversion sample respectively. BED showed high reliability for identification of long term infections while LAg-Avidity is highly accurate for identification of recent infections. Using BED as initial assay to identify the long term infections and LAg-Avidity as a confirmatory assay for those classified as recent infection by BED, explores the strengths of both while reduces the workload. The short recency window of p31 antibodies allows to discriminate very early from early infections based on this marker. BED recent infection results not confirmed by LAg-Avidity are considered to reflect a period more distant from the infection time. False recency predictions in this group can be minimized by elimination of patients with a CD4 count of less than 100 cells/mm3 or without no p31 antibodies. For 566 cross sectional sample the outcome of the decision tree confirmed the infection timing based on the results of all 3 markers but reduced the overall cost from 13.2 USD to 5.2 USD per sample. 
Conclusions: A step-wise multi assay decision tree allows accurate timing of the HIV infection at diagnosis at affordable effort and cost and can be an important new tool in studies analyzing the dynamics of local epidemics or the effects of prevention strategies.},
  articleno    = {738},
  author       = {Verhofstede, Chris and Fransen, Katrien and Van Den Heuvel, Annelies and Van Laethem, Kristel and Ruelle, Jean and Vancutsem, Ellen and Stoffels, Karolien and Van den Wijngaert, Sigi and Delforge, Marie-Luce and Vaira, Dolores and Hebberecht, Laura and Schauvliege, Marlies and Mortier, Virginie and Dauwe, Kenny and Callens, Steven},
  issn         = {1471-2334},
  journal      = {BMC INFECTIOUS DISEASES},
  keywords     = {HIV,Infection timing,Incidence measurement,INCIDENCE ASSAYS,ENZYME-IMMUNOASSAY,SUBTYPES,SEROCONVERSION,SPECIFICITY,COHORT,PERIOD},
  language     = {eng},
  pages        = {11},
  title        = {Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection},
  url          = {http://dx.doi.org/10.1186/s12879-017-2850-6},
  volume       = {17},
  year         = {2017},
}

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