In:
JAIDS Journal of Acquired Immune Deficiency Syndromes, Ovid Technologies (Wolters Kluwer Health), Vol. 82, No. 2 ( 2019-10-1), p. 188-194
Abstract:
Multiple antiretroviral (ARV) regimens are effective at achieving HIV viral suppression, but differ in pill burden, side effects, barriers to resistance, and impact on comorbidities. Current guidelines advocate for an individualized approach to ARV regimen selection, but synthesizing these modifying factors is complex and time-consuming. Methods: We describe the development of HIV-ASSIST (https://www.hivassist.com), a free, online decision support tool for ARV selection and HIV education. HIV-ASSIST ranks potential ARV options for any given patient scenario using a composite objective of achieving viral suppression while maximizing tolerability and adherence. We used a multiple-criteria decision analysis framework to construct mathematical algorithms and synthesize various patient-specific (eg, comorbidities and treatment history) and virus-specific (eg, HIV mutations) attributes. We then conducted a validation study to evaluate HIV-ASSIST with prescribing practices of experienced HIV providers at 4 large academic centers. We report on concordance of provider ARV selections with the 5 top-ranked HIV-ASSIST regimens for 10 diverse hypothetical patient-case scenarios. Results: In the validation cohort of 17 experienced HIV providers, we found 99% concordance between HIV-ASSIST recommendations and provider ARV selections for 4 case-scenarios of ARV-naive patients. Among 6 cases of ARV-experienced patients (3 with and 3 without viremia), there was 84% and 88% concordance, respectively. Among 3 cases of ARV-experienced patients with viremia, providers reported 20 different ARV selections, suggesting substantial heterogeneity in ARV preferences in clinical practice. Conclusions: HIV-ASSIST is a novel patient-centric educational decision support tool that provides ARV recommendations concordant with experienced HIV providers for a diverse set of patient scenarios.
Type of Medium:
Online Resource
ISSN:
1525-4135
DOI:
10.1097/QAI.0000000000002118
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2019
detail.hit.zdb_id:
645053-2
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