iScience (Oct 2023)

Development of a multiassay algorithm (MAA) to identify recent HIV infection in newly diagnosed individuals in Indonesia

  • Wahyu Nawang Wulan,
  • Evy Yunihastuti,
  • Dona Arlinda,
  • Tuti Parwati Merati,
  • Rudi Wisaksana,
  • Dewi Lokida,
  • Zehava Grossman,
  • Kristi Huik,
  • Chuen-Yen Lau,
  • Nugroho Harry Susanto,
  • Herman Kosasih,
  • Abu Tholib Aman,
  • Sunarto Ang,
  • Rita Evalina,
  • Anak Agung Ayu Yuli Gayatri,
  • Chakrawati Hayuningsih,
  • Agnes Rengga Indrati,
  • July Kumalawati,
  • Vivi Keumala Mutiawati,
  • Mario Bernardinus Realino Nara,
  • Asvin Nurulita,
  • Rahmawati Rahmawati,
  • Adria Rusli,
  • Musofa Rusli,
  • Dewi Yennita Sari,
  • Justina Sembiring,
  • Muchlis Achsan Udji Sofro,
  • Wiwi Endang Susanti,
  • Janice Tandraeliene,
  • Fransisca Lianiwati Tanzil,
  • Aaron Neal,
  • Muhammad Karyana,
  • Pratiwi Sudarmono,
  • Frank Maldarelli

Journal volume & issue
Vol. 26, no. 10
p. 107986

Abstract

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Summary: Ongoing HIV transmission is a public health priority in Indonesia. We developed a new multiassay algorithm (MAA) to identify recent HIV infection. The MAA is a sequential decision tree based on multiple biomarkers, starting with CD4+ T cells >200/μL, followed by plasma viral load (pVL) > 1,000 copies/ml, avidity index (AI) 12-month) and a test set (N = 80) of newly diagnosed (≤1-month) antiretroviral (ARV) drug naive individuals. Ten of eighty (12 · 5%) newly diagnosed individuals were classified as recent infections. Drug resistance mutations (DRMs) against reverse transcriptase inhibitors were identified in two individuals: one infected with HIV subtype C (K219Q, V179T) and the other with CRF01_AE (V179D). Ongoing HIV transmission, including infections with DRMs, is substantial in Indonesia.

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