In:
JAIDS Journal of Acquired Immune Deficiency Syndromes, Ovid Technologies (Wolters Kluwer Health), Vol. 86, No. 5 ( 2021-04-15), p. 587-592
Abstract:
The diagnosis of paradoxical tuberculosis–associated immune reconstitution inflammatory syndrome (TB-IRIS) relies on characteristic clinical features synthesized as the International Network for the Study of HIV-associated IRIS (INSHI) case definition. There is no confirmatory laboratory test. Setting: Site B HIV-TB clinic in Khayelitsha, Cape Town, South Africa. Methods: Using data of participants with HIV-associated tuberculosis starting antiretroviral treatment from a prospective trial evaluating prednisone for TB-IRIS prevention, we applied latent class analysis to model a gold standard for TB-IRIS. The model-predicted probability of TB-IRIS for each participant was used to assess the performance of the INSHI case definition and compare its diagnostic accuracy with several adapted case definitions. Results: Data for this analysis were complete for 217 participants; 41% developed TB-IRIS. Our latent class model included the following parameters: respiratory symptoms; night sweats; INSHI major criteria 1, 2, and 4; maximum C-reactive protein 〉 90 mg/L; maximum heart rate 〉 120/min; maximum temperature 〉 37.7°C; and preantiretroviral therapy CD4 count 〈 50 cells/µL. The model estimated a TB-IRIS incidence of 43% and had optimal goodness of fit (χ 2 = 337, P = 1.0). The INSHI case definition displayed a sensitivity of 0.77 and a specificity of 0.86. Replacing all the minor INSHI criteria with objectives measures (C-reactive protein elevation, fever, and/or tachycardia) resulted in a definition with better diagnostic accuracy, with a sensitivity of 0.89 and a specificity of 0.88. Conclusion: The INSHI case definition identifies TB-IRIS with reasonable accuracy. Amending the case definition by replacing INSHI minor criteria with objective variables improved sensitivity without loss of specificity.
Type of Medium:
Online Resource
ISSN:
1525-4135
DOI:
10.1097/QAI.0000000000002606
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2021
detail.hit.zdb_id:
2038673-4
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