Implementing Rapid Initiation of Antiretroviral Therapy for Acute HIV Infection Within a Routine Testing and Linkage to Care Program in Chicago.
Moira C McNultyJessica SchmittEleanor FriedmanBijou HuntAudra TobinAnjana Bairavi MaheswaranJanet LinRichard NovakBeverly ShaNorma RolfsenArthur MoswinBreon RoseDavid PitrakNancy GlickPublished in: Journal of the International Association of Providers of AIDS Care (2021)
Growing evidence suggests that rapid initiation of antiretroviral therapy for HIV improves care continuum outcomes. We evaluated process and clinical outcomes for rapid initiation in acute HIV infection within a multisite health care-based HIV testing and linkage to care program in Chicago. Through retrospective analysis of HIV testing data (2016-2017), we assessed linkage to care, initiation of antiretroviral therapy, and viral suppression. Of 334 new HIV diagnoses, 33 (9.9%) individuals had acute HIV infection. Median time to linkage was 11 (interquartile range [IQR]: 5-19.5) days, with 15 days (IQR 5-27) to initiation of antiretroviral therapy. Clients achieved viral suppression at a median of 131 (IQR: 54-188) days. Of all, 69.7% were retained in care, all of whom were virally suppressed. Sites required few additional resources to incorporate rapid initiation into existing processes. Integration of rapid initiation of antiretroviral therapy into existing HIV screening programs is a promising strategy for scaling up this important intervention.
Keyphrases
- antiretroviral therapy
- hiv testing
- men who have sex with men
- human immunodeficiency virus
- hiv positive
- hiv infected
- healthcare
- hiv infected patients
- hiv aids
- quality improvement
- palliative care
- liver failure
- affordable care act
- respiratory failure
- randomized controlled trial
- genome wide
- pain management
- public health
- loop mediated isothermal amplification
- drug induced
- hepatitis c virus
- metabolic syndrome
- aortic dissection
- insulin resistance
- data analysis
- dna methylation
- skeletal muscle
- deep learning
- machine learning
- hepatitis b virus
- big data
- glycemic control