AIDS Research and Therapy (Aug 2021)

Closing gaps in histoplasmosis: clinical characteristics and factors associated with probable/histoplasmosis in HIV/AIDS hospitalized patients, a retrospective cross-sectional study in two tertiary centers in Pereira, Colombia

  • Julián Andrés Hoyos Pulgarin,
  • John Alexander Alzate Piedrahita,
  • German Alberto Moreno Gómez,
  • Juan Felipe Sierra Palacio,
  • Karen Melissa Ordoñez,
  • Deving Arias Ramos

DOI
https://doi.org/10.1186/s12981-021-00377-5
Journal volume & issue
Vol. 18, no. 1
pp. 1 – 8

Abstract

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Abstract Background The HIV pandemic continues to cause a high burden of morbidity and mortality due to delayed diagnosis. Histoplasmosis is prevalent in Latin America and Colombia, is difficult to diagnose and has a high mortality. Here we determined the clinical characteristics and risk factors of histoplasmosis in people living with HIV (PLWH) in Pereira, Colombia. Materials and methods This was a retrospective cross-sectional study (2014–2019) involving two tertiary medical centers in Pereira, Colombia. People hospitalized with HIV were included. Histoplasma antigen detection was performed in urine samples. Probable histoplasmosis was defined according to European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group/National Institute of Allergy and Infectious Diseases Mycoses Study Group criteria. Results 172 HIV-infected patients were analyzed. Histoplasmosis was confirmed in 29% (n = 50/172) of patients. The logistic regression analysis showed that the risk factors for histoplasmosis were pancytopenia (OR 4.1, 95% CI 1.6–10.3, P = 0.002), 46 IU/L (OR 3.2, 95% CI 1.3–8, P = 0.010). Conclusions Histoplasmosis is highly prevalent in hospitalized patients with HIV in Pereira, Colombia. The clinical findings are nonspecific, but there are some clinical abnormalities that can lead to suspicion of the disease, early diagnosis and prompt treatment. Urine antigen detection is useful for diagnosis, but is not widely available. An algorithmic approach is proposed for low-resource clinical settings.

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